Commodity price adjustment method, device and server

Abstract

This specification provides a method, device and server for adjusting the price of a commodity. In one embodiment, the method collects the first type of operation of the first object browsing the target commodity on the platform within the target time period, the second type of operation of adding the target commodity to the shopping cart, the third type of operation of purchasing the target commodity, and the fourth type of operation of repurchasing the target commodity; and establishes the relationship parameters between the first object and the commodity based on the above operation data, which can reflect the transaction tendency of the first object for the target commodity within the target time period from multiple operation stages and multiple transaction levels; and then uses the above relationship parameters between the first object and the commodity to determine whether the price of the target commodity needs to be adjusted. In this way, the time point when the price adjustment is required can be accurately identified, and the platform sales volume of the target commodity can be increased in a timely manner through price adjustment, the loss of the first object can be reduced, and the loss caused by the lag in price adjustment can be reduced.

Description

Technical Field

[0001] This specification belongs to the field of Internet technology, and in particular to a method, device and server for adjusting commodity prices. Background Art

[0002] In the Internet transaction scenario, many store sellers (for example, retail terminals, offline stores, etc., which can be recorded as the first object) often purchase goods from commodity suppliers on the platform (which can be recorded as the second object) through some trading platforms (for example, XX channel, or YY showcase APP, etc.), and then put the above goods into their own stores for retail. The commodity prices set by commodity suppliers on the platform will affect the purchasing intention of store sellers, and then affect the sales volume of target commodities on the platform, as well as the stickiness to store sellers. If the opportunity for commodity price adjustment can be discovered in time and timely intervention can be made, the sales volume of target commodities on the platform can be effectively maintained to avoid the loss of store sellers.

[0003] Therefore, there is an urgent need for a method that can timely and accurately identify the time point when price adjustments are required. Summary of the invention

[0004] The present specification provides a method, device and server for adjusting commodity prices, so as to timely and accurately identify the time point when commodity prices need to be adjusted, thereby timely adjusting commodity prices to increase the sales volume of target commodities on the platform and reduce the loss of the first object.

[0005] A commodity price adjustment method, device and server provided in this specification are implemented as follows:

[0006] A method for adjusting the price of a commodity, comprising: obtaining operation data related to a target commodity within a target time period; wherein the operation data comprises at least one of the following: a first type of operation of browsing the target commodity, a second type of operation of adding the target commodity to a shopping cart, a third type of operation of purchasing the target commodity, and a fourth type of operation of repurchasing the target commodity; establishing a relationship parameter between a first object and the target commodity in the target time period according to the operation data related to the target commodity in the target time period; wherein the relationship parameter between the first object and the target commodity is used to characterize the transaction tendency of the first object for the target commodity in the target time period; and determining whether to adjust the price of the target commodity according to the relationship parameter between the first object and the target commodity in the target time period.

[0007] A device for adjusting the price of goods, comprising: an acquisition module, used to acquire operation data related to a target commodity within a target time period; wherein the operation data includes at least one of the following: a first type of operation of browsing the target commodity, a second type of operation of adding the target commodity to a shopping cart, a third type of operation of purchasing the target commodity, and a fourth type of operation of repurchasing the target commodity; an establishment module, used to establish a relationship parameter between a first object and the target commodity in the target time period according to the operation data related to the target commodity in the target time period; wherein the relationship parameter between the first object and the target commodity is used to characterize the transaction tendency of the first object in the target time period for the target commodity; and a determination module, used to determine whether to adjust the price of the target commodity according to the relationship parameter between the first object and the target commodity in the target time period.

[0008] A method for adjusting the price of a commodity, comprising: obtaining operation data and price data related to a target time period and a target commodity; wherein the operation data comprises at least one of the following: a first type of operation of browsing the target commodity, a second type of operation of adding the target commodity to a shopping cart, a third type of operation of purchasing the target commodity, and a fourth type of operation of repurchasing the target commodity; the price data comprises at least one of the following: an exposure price displayed on a page when a first object performs a first type of operation, an add-to-cart price displayed on a page when the first object performs a second type of operation, an order transaction price displayed on a page when the first object performs a third type of operation, and a repurchasing transaction price displayed on a page when the first object performs a fourth type of operation; constructing a relationship matrix between the first object and the target commodity based on the price data and the operation data; determining the first object's intended price for the target commodity in the target time period based on the relationship matrix between the first object and the target commodity; wherein the intended price is used to adjust the price of the target commodity to increase the sales volume of the target commodity and strengthen the stickiness to the first object.

[0009] A method for adjusting commodity attributes, comprising: obtaining operation data related to a target commodity within a target time period; wherein the operation data comprises at least one of the following: a first type of operation of browsing the target commodity, a second type of operation of adding the target commodity to a shopping cart, a third type of operation of purchasing the target commodity, and a fourth type of operation of repurchasing the target commodity; establishing a relationship parameter between a first object in the target time period and the target commodity according to the operation data related to the target commodity within the target time period; wherein the relationship parameter between the first object and the target commodity is used to characterize the transaction tendency of the first object in the target time period for the target commodity; and determining whether to adjust the attribute data of the target commodity according to the relationship parameter between the first object in the target time period and the target commodity.

[0010] A method for adjusting commodity prices, comprising: generating a price detection request for a target commodity; sending the price detection request to a server; wherein the server is used to respond to the price detection request, establish and determine whether to adjust the price of the target commodity based on the relationship parameters between a first object and the target commodity in a target time period; and in the case of determining to adjust the price of the target commodity, determine the first object's intended price for the target commodity in the target time period.

[0011] A server comprises a processor and a memory for storing instructions executable by the processor, wherein when the processor executes the instructions, operation data related to a target commodity within a target time period is obtained; wherein the operation data comprises at least one of the following: a first type of operation of browsing the target commodity, a second type of operation of adding the target commodity to a shopping cart, a third type of operation of purchasing the target commodity, and a fourth type of operation of repurchasing the target commodity; according to the operation data related to the target commodity within the target time period, a relationship parameter between a first object and the target commodity within the target time period is established; wherein the relationship parameter between the first object and the target commodity is used to characterize the transaction tendency of the first object for the target commodity within the target time period; according to the relationship parameter between the first object and the target commodity within the target time period, it is determined whether to adjust the price of the target commodity.

[0012] A computer-readable storage medium stores computer instructions, which, when executed, are used to obtain operation data related to a target product within a target time period; wherein the operation data includes at least one of the following: a first type of operation of browsing the target product, a second type of operation of adding the target product to a shopping cart, a third type of operation of purchasing the target product, and a fourth type of operation of repurchasing the target product; based on the operation data related to the target product within the target time period, establish a relationship parameter between a first object and the target product within the target time period; wherein the relationship parameter between the first object and the target product is used to characterize the transaction tendency of the first object for the target product within the target time period; and determine whether to adjust the price of the target product based on the relationship parameter between the first object and the target product within the target time period.

[0013] This specification provides a method, device and server for adjusting the price of a commodity, which collects various operation data such as the first type of operation of the first object browsing the target commodity on the platform within the target time period, the second type of operation of adding the target commodity to the shopping cart, the third type of operation of purchasing the target commodity, and the fourth type of operation of repurchasing the target commodity; and establishes the relationship parameters between the first object and the target commodity based on the above operation data, which can reflect the transaction tendency of the first object for the target commodity within the target time period from multiple operation stages and multiple transaction levels; and then uses the relationship parameters between the first object and the target commodity to determine whether the price of the target commodity needs to be adjusted. In this way, the time point when the commodity price needs to be adjusted can be efficiently and accurately identified, and then the platform sales volume of the target commodity can be increased by adjusting the commodity price in a timely manner, the loss of the first object can be reduced, and the loss caused by the lag in price adjustment can be reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of this specification, the drawings required for use in the embodiments will be briefly introduced below. The drawings described below are only some embodiments recorded in this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0015] Figure 1 It is a schematic diagram of an embodiment of the structural composition of a system applying the commodity price adjustment method provided in the embodiments of this specification;

[0016] Figure 2 It is a schematic diagram of an embodiment of a method for adjusting the price of a commodity provided by an embodiment of this specification, in a scenario example;

[0017] Figure 3 It is a schematic diagram of an embodiment of a method for adjusting the price of a commodity provided by an embodiment of this specification, in a scenario example;

[0018] Figure 4 is a flowchart of a method for adjusting commodity prices provided by an embodiment of this specification;

[0019] Figure 5 It is a schematic diagram of an embodiment of a method for adjusting the price of a commodity provided by an embodiment of this specification, in a scenario example;

[0020] Figure 6 It is a schematic diagram of an embodiment of a method for adjusting the price of a commodity provided by an embodiment of this specification, in a scenario example;

[0021] Figure 7 It is a schematic diagram of an embodiment of a method for adjusting the price of a commodity provided by an embodiment of this specification, in a scenario example;

[0022] Figure 8 is a flowchart of a method for adjusting commodity prices provided by an embodiment of this specification;

[0023] Fig. 9 It is a schematic diagram of the structure of a server provided by an embodiment of this specification;

[0024] Fig.10 It is a schematic diagram of the structural composition of a commodity price adjustment device provided in one embodiment of this specification. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this specification.

[0026] The embodiment of this specification provides a method for adjusting commodity prices, which can be specifically applied to a system including a server and a collection terminal. Figure 1 As shown, the server can be connected to the acquisition terminal via wired or wireless means to perform data interaction.

[0027] In this embodiment, the server may specifically include a server that is applied to one side of the trading platform and is responsible for data processing in the background that can realize functions such as data transmission and data processing. Specifically, the server may be, for example, an electronic device with data calculation, storage and network interaction functions. Alternatively, the server may also be a software program running in the electronic device that provides support for data processing, storage and network interaction. In this embodiment, the number of servers included in the server is not specifically limited. The server may specifically be one server, or several servers, or a server cluster formed by several servers.

[0028] In this embodiment, the acquisition terminal may specifically include an electronic device that is arranged on the side of each first object (for example, store seller 1, store seller 2...store seller N, etc., which purchases goods on the trading platform) and can realize functions such as data collection and data transmission. Specifically, the first object can be an offline physical retail store, such as a QJ convenience store; it can also be an online store, etc. The acquisition terminal can be, for example, a desktop computer, a tablet computer, a laptop computer, a smart phone, etc. Alternatively, the acquisition terminal can also be a software application that can run in the above-mentioned electronic devices. For example, it can be an APP running on a mobile phone or a plug-in program embedded in the trading platform.

[0029] In this embodiment, the store seller can log in to the trading platform, browse the platform page, find the goods that he is interested in and wants to put in his own store for retail, and place an order to purchase the goods from the commodity supplier on the platform (which can be recorded as the second object). The collection terminal can monitor the behavior data of the store seller in the trading platform, and collect the operation data related to the target commodity in the target time period in real time. Among them, the above operation data can specifically include at least one of the following: the first type of operation of browsing the target commodity, the second type of operation of adding the target commodity to the shopping cart, the third type of operation of purchasing the target commodity, and the fourth type of operation of repurchasing the target commodity. And send the above operation data to the server.

[0030] The server can obtain the operation data related to the target product in the target time period through the acquisition terminal. And according to the operation data related to the target product in the target time period, establish the relationship parameters between the first object and the target product in the target time period; wherein, the relationship parameters between the first object and the target product are used to characterize the transaction tendency of the first object for the target product in the target time period. Then, according to the relationship parameters between the first object and the target product in the target time period, determine whether to adjust the price of the target product. In this way, the server can timely and accurately identify the time point when the price adjustment is required, and timely increase the sales volume of the target product on the platform through price adjustment, reduce the loss of the first object, and avoid the adjustment lag caused by relying on market prices for adjustment.

[0031] In addition, while collecting the above operation data, the collection terminal also collects price data related to the target product in the target time period; wherein the price data includes at least one of the following: the exposure price displayed on the page when the first object performs the first type of operation, the additional purchase price displayed on the page when the first object performs the second type of operation, the order transaction price displayed on the page when the first object performs the third type of operation, and the repurchase transaction price displayed on the page when the first object performs the fourth type of operation. The above price data is sent to the server.

[0032] In the case where the server determines to adjust the price of the target product, the server can further obtain price data related to the target product within the target time period. Then, based on the price data and the operation data, determine the intended price of the target product for the first object in the target time period; wherein the intended price is used to adjust the price of the target product to increase the sales volume of the target product and strengthen the stickiness to the first object. Furthermore, based on the above-mentioned intended price, the page display price of the target product on the platform at different operation stages can be adjusted in a targeted manner, which can more effectively achieve the technical effect of increasing the sales volume of the target product on the platform, strengthening the stickiness of the target product on the platform to the first object, and improving the relationship parameters between the first object and the target product by adjusting the price of the target product.

[0033] For an example scenario, see Figure 2 As shown, the commodity price adjustment method provided in the embodiments of this specification can be applied to monitor the page prices of commodities sold by commodity suppliers to sellers' stores on the XX trading platform, and when the time point when price adjustment is required is detected, the supplier can be promptly prompted and assisted to adjust the page price of the commodity.

[0034] In this scenario embodiment, the server can select commodities that meet preset requirements from the commodities currently on sale on the XX trading platform as target commodities. For example, based on the historical sales data of the last 90 days, commodities that have been sold every day in the last 90 days and whose price elasticity parameters are greater than 1 can be determined as target commodities that are worth monitoring and whose sales can be increased by adjusting prices later.

[0035] Furthermore, the server can collect target-related operation data in real time through the collection terminal. Among them, the above operation data can specifically include the first type of operation of browsing the target product in the seller's store, the second type of operation of adding the target product to the shopping cart, the third type of operation of purchasing the target product, and the fourth type of operation of repurchasing the target product. Among them, the above different types of operations can respectively correspond to an operation stage in the store seller's behavior operation process. The above behavior operation process can include: the process from browsing the target product to leaving the seller's store, the process from browsing the target product to adding the target product to the shopping cart, the process from browsing the target product to placing an order for the seller's store, and the process from browsing the target product to repurchasing the target product.

[0036] Specifically, the first type of operation may correspond to the first operation stage (e.g., the browsing stage), the second type of operation may correspond to the second operation stage (e.g., the add-to-cart stage), the third type of operation may correspond to the third operation stage (e.g., the ordering stage), and the fourth type of operation may correspond to the fourth operation stage (e.g., the repeat purchase stage).

[0037] The server may analyze and process the operation data of different operation stages related to the target product in the current time period at intervals (for example, one week) to determine whether it is necessary to adjust the price of the target product sold by the supplier on the platform.

[0038] In specific implementation, the server can establish the relationship parameters between the first object and the target product in the current time period based on the operation data related to the target product in the current time period. The relationship parameters between the first object and the target product are used to characterize the transaction tendency of the store sellers on the platform for the target product in the current time period. The relationship parameters between the first object and the target product can also be referred to as store-product relationship parameters, where "store-product" indicates the store seller (i.e., the first object) and the target product respectively.

[0039] When constructing the relationship parameters between the first object and the target product, you can refer to Figure 3 As shown. Based on the operation data related to the target product in the current time period, the server counts the number of store sellers who perform the first type of operation in the target time period as the first relationship data (i.e., A), counts the number of store sellers who perform the second type of operation in the target time period as the second relationship data (i.e., I), counts the number of store sellers who perform the third type of operation in the target time period as the third relationship data (i.e., P), and counts the number of store sellers who perform the fourth type of operation in the target time period as the fourth relationship data (i.e., L), thereby obtaining multiple relationship data.

[0040] Furthermore, the server can set weight coefficients X1, X2, X3, and X4 corresponding to the above four types of relationship data A, I, P, and L, respectively, according to the product type characteristics of the target product and the marketing strategy for the seller's store. Then, a weighted sum is performed based on the above four types of relationship data and the corresponding weight coefficients to obtain the relationship parameters between the first object and the target product in the current time period.

[0041] Among them, the relationship parameter between the first object and the target product (which can be recorded as value, or can also be called total store-product relationship assets or store-product relationship parameters) can specifically include a parameter data used to characterize the transaction tendency of store sellers for the target product in the current time period. Generally, if the value of the store-product relationship parameter of a product in the current time period is larger, it means that the number of store sellers reached by the product in the current time period is larger, and the coverage is higher; at the same time, it also means that the store sellers are more receptive to the price of the product sold by the product supplier on the platform, and are more inclined to purchase the product, and the stickiness to the product on the platform is higher, and it is less likely to churn.

[0042] Since the relationship parameters between the first object and the target product can reflect the price acceptance of the target product by the store sellers in the current time period from multiple operation stages and multiple transaction levels, the relationship parameters between the first object and the target product can be used to monitor and analyze the specific sales situation of the target product in the current time period, for example, the sales volume of the target product, the coverage of the target product to the store sellers, the stickiness of the store sellers to the target product, etc.

[0043] Specifically, the service may obtain the relationship parameters between the first object and the target product in the previous time period of the current time period, and then calculate the month-on-month growth rate of the relationship parameters between the first object and the target product in the current time period based on the relationship parameters between the first object and the target product in the previous time period of the current time period.

[0044] Then compare the month-on-month growth rate of the relationship parameter between the first object and the target product in the current time period with a preset growth rate threshold. The preset growth rate threshold includes a month-on-month growth rate at the lowest point of the relationship parameter between the first object and the product in the historical time period determined based on historical sales data. By determining whether the month-on-month growth rate of the relationship parameter between the first object and the target product in the current time period is greater than the preset growth rate threshold, it is determined whether the target product price needs to be adjusted.

[0045] For example, see Figure 3 In the fifth week (i.e., week 5), it is determined that the month-on-month growth rate of the relationship parameter between the first object and the target product in the current time period is less than or equal to the preset growth rate threshold. At this time, it can be judged that the relationship parameter between the first object and the target product in the target time period has experienced a serious attenuation, the sales volume has dropped significantly, and the store sellers on the platform have also lost. Figure 3 It can be seen that the value of the relationship parameter between the first object and the target product in week 5 is also the smallest. Therefore, the server can determine that the page price of the target product in the week 5 period is not reasonable and needs to intervene by adjusting the price of the target product. Further, the server can obtain the price data related to the target product in the current period (i.e. week 5). Figure 3As shown. The price data may include at least one of the following: the exposure price displayed on the page when the first object performs the first type of operation (also known as the exposure page price), the purchase price displayed on the page when the first object performs the second type of operation (also known as the purchase page price), the order transaction price displayed on the page when the first object performs the third type of operation, and the repurchase transaction price displayed on the page when the first object performs the fourth type of operation (also known as the re-order transaction price), etc. The above four types of price data are the page prices displayed by the target product in different operation stages. It should be noted that the above four prices may contain different discounts because they are in different operation stages. Therefore, the values ​​of the above four prices may be different, and the impact on the first object at different operation stages will also be different.

[0046] The server can construct a corresponding relationship matrix between the first object and the target product by combining the operation data and the price data. Then, the server can determine how to adjust the price of the target product according to the relationship matrix between the first object and the target product to achieve a better sales effect. The relationship matrix between the first object and the target product can specifically include a matrix that can reflect the relationship between the operation behavior of each seller store and the page price in the current time period by comprehensively utilizing the operation data and the price data.

[0047] During specific implementation, the server can count the number of times the target product is purchased, the number of times the target product is repurchased, the average price of the target product, and the average price of the repurchased target product by each store seller in the current time period based on the price data and the operation data; and then construct a relationship matrix between the first object and the target product based on the number of times the target product is purchased, the number of times the target product is repurchased, the average price of the target product, and the average price of the repurchased target product by each store seller in the current time period.

[0048] Furthermore, the server may call a preset prediction model to determine the store seller's intended price for the target product in the current time period according to the relationship matrix between the first object and the target product.

[0049] The above-mentioned intention price can be specifically understood as a price data that is determined by integrating the operation data of different operation stages in the current time period and the price data of different operation stages, which has a high acceptance rate among store sellers in the current time period, makes the store sellers more inclined to purchase the target product, and can increase the value of the relationship parameter between the first object and the target product. The above-mentioned preset prediction model can specifically include a model that is pre-trained using historical sales data to predict the intention price based on the relationship matrix between the first object and the target product.

[0050] According to the price data and the operation data, the number of times the target product is purchased, the number of times the target product is repurchased, the average price of the target product, and the average price of the repurchased target product by each store seller in the current time period are counted; according to the number of times the target product is purchased, the number of times the target product is repurchased, the average price of the target product, and the average price of the repurchased target product by each store seller in the current time period, a relationship matrix between the first object and the target product is constructed; and the preset prediction model is called to determine the store seller's intended price for the target product in the current time period according to the relationship matrix between the first object and the target product.

[0051] In this scenario example, since the platform server has signed an authorized price adjustment agreement with the product supplier in advance, the server can directly adjust the price of the target product on the platform in the next time period (i.e. week 6) according to the desired price within the price adjustment range specified in the agreement.

[0052] Specifically, the server can determine which operation stage has a problem, which affects the sales of the target product and its stickiness to the platform store sellers, based on the operation data and price data of multiple operation stages in the current time period, as well as the relationship parameters between the first object and the target product. Then, the page price in the operation stage with the problem can be adjusted in a targeted manner based on the desired price, so that in the next time period, the adjusted price of the target product can increase the sales of the target product and strengthen the stickiness to the store sellers (for example, increase the repurchase rate), thereby improving the relationship parameters between the first object and the target product.

[0053] See also Figure 3 It can be seen that, since it is discovered in time in week 5 that the price adjustment is required, and the price is determined and adjusted in a targeted manner according to the corresponding desired price, the sales volume and repurchase rate of the target product in the next time period (i.e., week 6) are improved, which not only attracts more store sellers, but also strengthens the stickiness to the store sellers, thereby improving the relationship parameters between the first object and the target product.

[0054] In this scenario example, relatively speaking, if the server determines that the month-on-month growth rate of the relationship parameter between the first object and the target product in the target time period is greater than the above-mentioned preset growth rate threshold, it can be determined that there is no need to adjust the page price of the target product at present, and the operation data of the target product in the next time period can continue to be monitored and analyzed.

[0055] As can be seen from the above scenario examples, the commodity price adjustment method provided in this manual can accurately identify the time point when price adjustment is required, and then can timely help commodity suppliers on the platform increase sales of target commodities on the platform through price adjustment, reduce the loss of store sellers, and avoid losses caused by delayed price adjustment.

[0056] See also Figure 4 As shown, the embodiment of this specification provides a method for adjusting the price of a commodity. The method can be specifically applied to a server side. When implemented specifically, the method can include the following contents.

[0057] S401: Acquire operation data related to the target product within a target time period; wherein the operation data includes at least one of the following: a first type of operation of browsing the target product, a second type of operation of adding the target product to a shopping cart, a third type of operation of purchasing the target product, and a fourth type of operation of repurchasing the target product.

[0058] In one embodiment, the above-mentioned method for adjusting the price of a commodity can be specifically applied to an application scenario in which a second object on a platform (or website) sells a commodity to a first object.

[0059] In one embodiment, the second object may specifically include a commodity supplier on the platform. Specifically, for example, the second object may be a manufacturer of a certain commodity, or a general agent of a certain commodity, etc. In addition, in some scenarios where commodities are sold directly by the platform, the second object may also be the platform, etc.

[0060] Accordingly, the first object may specifically include a store seller who purchases goods on the platform. Specifically, for example, the first object may be an online store on a shopping website, or an offline retail store, etc.

[0061] Of course, the first object and the second object listed above are only schematic illustrations. In specific implementation, according to specific application scenarios, the first object and the second object may also be other types of objects. For example, the first object may also be a customer who purchases goods on a shopping website, and correspondingly, the second object may be a store seller of retail goods on the shopping website.

[0062] In one embodiment, the target commodities may specifically include commodities whose sales volume is likely to be increased through price adjustment or commodities that are worthy of price adjustment.

[0063] In one embodiment, before collecting operational data related to the target product within the target time period, the method may also include the following contents when implemented: obtaining historical sales data of multiple products on the platform within a preset time period; determining the dynamic sales parameters of the product and the price elasticity parameters of the product based on the historical sales data within the preset time period; and screening out products that meet preset requirements from multiple products as target products based on the dynamic sales parameters of the product and the price elasticity parameters of the product.

[0064] In one embodiment, during specific implementation, the commodity pool of the platform may be queried to obtain commodities that are on sale on the platform and / or have been put into storage, and the commodities may be used as a plurality of commodities on the platform for screening.

[0065] In one embodiment, the preset time period may specifically include the latest 90 days, or the latest two months, etc. The historical sales data within the preset time period may specifically include price data, sales volume data, and other related data of the commodity within the historical preset time period.

[0066] In one embodiment, the dynamic sales parameters of the commodity may specifically include parameter data used to reflect the sales situation of the commodity in a preset time period. Specifically, the dynamic sales parameters may include the daily sales volume of the commodity in the preset time period.

[0067] In one embodiment, the price elasticity parameter of the commodity may specifically include parameter data for characterizing the impact of the commodity price on the sales of the commodity.

[0068] In one embodiment, the above-mentioned determination of the sales parameters of the product based on the historical sales data within the preset time period may specifically include: determining the daily sales volume or weekly sales volume of the product within the preset time period based on the historical sales data of the product within the preset time period.

[0069] Usually, if the daily sales volume of a certain product is often 0, it is likely that few people pay attention to the product. For such products, even if the price is adjusted later, it is often impossible to effectively increase sales.

[0070] In one embodiment, the above-mentioned determination of the price elasticity parameter of the commodity based on the historical sales data within the preset time period may include: dividing the preset time period into a plurality of equal time periods along the time axis; calculating the sales volume and the average sales price of each of the plurality of time periods based on the historical sales data within the preset time period; calculating the sales volume difference and the average sales price difference between two adjacent time periods in turn based on the sales volume and the average sales price of each of the plurality of time periods; determining the ratio of the sales volume difference and the average sales price difference between two adjacent time periods as the price elasticity parameter between the two adjacent time periods, and obtaining a plurality of price elasticity parameters; and averaging the above price elasticity parameters to obtain the price elasticity parameter of the commodity for the preset time period.

[0071] Generally, if the daily price elasticity parameter of a product is small, for example, less than 1, it means that the change in the price of the product has a relatively small impact on sales. In other words, it is difficult to influence the sales of the product by adjusting the price.

[0072] In one embodiment, the above-mentioned method of selecting commodities that meet preset requirements from multiple commodities as target commodities based on the dynamic sales parameters and price elasticity parameters of the commodities may include the following contents during implementation: based on the dynamic sales parameters and price elasticity parameters of each commodity, selecting commodities whose dynamic sales parameters are greater than a first threshold value (for example, a value of 0) and whose price elasticity parameters are greater than a second threshold value (for example, a value of 1) from multiple commodities as target commodities.

[0073] Through the above method, it is possible to effectively screen out the products that meet the preset requirements and are suitable for price adjustment from multiple products on the platform as target products for subsequent monitoring and corresponding processing.

[0074] Therefore, unsuitable products that do not meet the preset requirements can be removed from the platform first, for example, some products with low sales due to non-price factors such as poor quality or poor service. In this way, there is no need to waste processing time and resources to monitor and adjust the prices of such products in the future.

[0075] In one embodiment, the above operation data may specifically include at least one of the following: a first type of operation of browsing the target product, a second type of operation of adding the target product to the shopping cart, a third type of operation of purchasing the target product, a fourth type of operation of repurchasing the target product, and the like.

[0076] In one embodiment, the first type of operation can be specifically understood as the operation of the first object browsing the target product, corresponding to the first operation stage. The second type of operation can be specifically understood as the operation of the first object adding the target product to the shopping cart, corresponding to the second operation stage. The third type of operation can be specifically understood as the operation of the first object placing an order to purchase the target product, corresponding to the third operation stage. The fourth type of operation can be specifically understood as the operation of the first object covering the target product, corresponding to the fourth operation stage.

[0077] In one embodiment, the process from randomly browsing products on the platform to finally deciding to purchase products, or even repeatedly purchasing products, may often include multiple different operation stages and involve different operations. In addition, at different operation stages, the first subject's feelings and psychological state about the prices on the displayed pages often vary.

[0078] Specifically, when the first subject browses a list page containing multiple different products on the platform, if he is attracted by the page price (which can be recorded as the exposure price) displayed on the list page of a certain product, Figure 5 As shown, it is very likely that the icon link corresponding to the product will be selected separately in the list page to enter the product page containing only the product. At this time, the above operation of the first object can be recorded as the first type of operation, corresponding to the first operation stage. The page price (i.e., exposure price) of the target product displayed on the platform is 229 yuan. In this operation stage, the first object may have a preliminary interest in the product because of this page price, and then want to learn more about the product. Among them, the above exposure price can specifically be a price that does not include any discounts.

[0079] When the first object further browses the detailed information of the product in the product page containing only the product, if the first object is further attracted and particularly wants to purchase the product, the product can be added to the shopping cart, which is recorded as the second type of operation, corresponding to the second operation stage. Figure 6 As shown in the figure, after the product is added to the shopping cart, the page price of the product in the shopping cart (which can be simply referred to as the additional purchase price) will be displayed as RMB 229. In this operation stage, the first subject has already had a desire to buy, and the additional purchase price may further strengthen the first subject's desire to buy. The additional purchase price can be the same as the exposure price, or a price that is lower than the exposure price after including some discounts from the platform.

[0080] When the first party confirms to purchase the product, he can click to confirm the order on the order page, which is recorded as the third type of operation, corresponding to the third operation stage. Figure 7As shown, on the order page, the page price of the product when placing an order (which can be recorded as the order transaction price) is 219 yuan. In this operation stage, the first object has confirmed to purchase the product. Among them, the above-mentioned order transaction price can be the same as the exposure price, or the same price as the purchase price, or it can be a price lower than the purchase price after including platform discounts and discounts provided by the second object to the first object (for example, new customer discount: minus 10 yuan).

[0081] When the first object approves the product after purchasing it, he can purchase the same product again, which can be recorded as the fourth type of operation, corresponding to the fourth operation stage. In this operation stage, the product has already generated a certain viscosity for the first object. Specifically, the first object is more willing to accept the page price of the product, and is likely to continue to purchase the product through the second object on the platform, rather than losing it to the second object on other platforms. Among them, the page price displayed in this operation stage can be recorded as the repurchase transaction price. The repurchase transaction price can be the same price as the order transaction price when the product was purchased for the first time, or it can be a price different from the order transaction price when the product was purchased for the first time. For example, the above repurchase transaction price may no longer include new customer discounts, but include old customer discounts, etc.

[0082] In one embodiment, different operation stages can be finely distinguished in the above manner, and different types of operations in different specific operation stages in the target time period can be obtained as operation data related to the target product, so that the sales of the target product in the target time period can be more finely monitored and analyzed from multiple different operation stages and transaction levels based on the above operation data.

[0083] The target time period may be the current time period or a certain time period in the past. For example, the target time period may be the current week, the current month, or the past six months.

[0084] In one embodiment, during specific implementation, different types of operation data of the first object at different stages on the platform for the target object recorded in real time by the collection terminal may be obtained as the above-mentioned operation data related to the target commodity.

[0085] S402: Establish relationship parameters between the first object and the target product in the target time period based on the operation data related to the target time period and the target product; wherein the relationship parameters between the first object and the target product are used to characterize the transaction tendency of the first object in the target time period for the target product; and the first object includes a store that purchases products on the platform.

[0086] In one embodiment, the relationship parameter between the first object and the target product may specifically include parameter data for characterizing the transaction tendency of the first object for the target product in the target time period.

[0087] Specifically, the relationship parameter between the first object and the target product may be parameter data determined based on different types of operations of the first object at different operation stages during the target time period.

[0088] Since the relationship parameter between the first object and the target product is a combination of data generated by a large number of different types of operations in different operation stages of the first object in the target time period, the relationship parameter between the first object and the target product can reflect the attractiveness of the page price displayed at different operation stages in the target time period to the first object from multiple different operation stages and multiple different transaction levels, as well as the coverage of the first object based on the above page price in the target time period.

[0089] Specifically, for example, the larger the value of the first object's transaction tendency for a target commodity in a target time period, the larger the number of first objects reached by the commodity in the target time period and the higher the coverage; at the same time, it also indicates that the first objects have a higher acceptance of the price of the commodity on the platform and are more inclined to purchase the commodity; and the higher the stickiness of the first objects to the commodity on the platform, the less likely they are to churn.

[0090] In one embodiment, the above-mentioned establishment of relationship parameters between the first object in the target time period and the target commodity based on the operation data related to the target time period and the target commodity may include the following contents during specific implementation: based on the operation data related to the target commodity in the target time period, counting the number of first objects that perform various types of operations in the target time period as corresponding relationship data to obtain multiple relationship data; and determining the relationship parameters between the first object in the target time period and the target commodity based on the multiple relationship data.

[0091] Specifically, for example, based on the operation data related to the target time period and the target product, the number of first objects that perform the first type of operation within the target time period can be counted as the first relationship data (can also be recorded as the first store-product relationship pair, recorded as A), the number of first objects that perform the second type of operation within the target time period can be counted as the second relationship data (can also be recorded as the second store-product relationship pair, recorded as I), the number of first objects that perform the third type of operation within the target time period can be counted as the third relationship data (can also be recorded as the third store-product relationship pair, recorded as P), and the number of first objects that perform the fourth type of operation within the target time period can be counted as the fourth relationship data (can also be recorded as the fourth store-product relationship pair, recorded as L); based on the first relationship data, the second relationship data, the third relationship and the fourth relationship data, the relationship parameters between the first object and the target product in the target time period are determined.

[0092] In one embodiment, the relationship parameters between the first object and the target product in the target time period are determined based on the multiple relationship data. When implemented specifically, the following contents may be included: obtaining weight coefficients corresponding to the multiple relationship data respectively; wherein the weight coefficients are determined based on the type of the target product and / or the audience characteristics of the target product; and determining the relationship parameters between the first object and the target product in the target time period by weighted summation based on the multiple relationship data and the weight coefficients.

[0093] Specifically, for example, weight coefficients corresponding to the first relationship data, the second relationship data, the third relationship and the fourth relationship data can be obtained; wherein the weight coefficient is determined according to the type of the target product and / or the characteristics of the audience of the target product; and according to the first relationship data, the second relationship data, the third relationship, the fourth relationship data and the weight coefficient, the relationship parameters between the first object and the target product in the target time period are determined by weighted summation.

[0094] In one embodiment, the weight coefficient can be determined according to the type of the target product. For example, some products may be purchased directly by the first object after browsing. In this case, the value of the weight coefficient corresponding to the first relationship data can be set to be relatively large according to the type characteristics of the target product.

[0095] In one embodiment, the weight coefficient can also be determined based on the audience characteristics of the target product. The audience characteristics can specifically include the number of audiences, and can also include the type of audiences, etc. For example, the audience of a certain type of product is often people involved in a specific field, and people in this field generally have a certain type of behavioral characteristics, such as paying more attention to the operation of adding to a shopping cart. At this time, the value of the weight coefficient corresponding to the second relationship data can be set to be relatively large based on the behavioral characteristics of the above type of audience.

[0096] In one embodiment, the weight coefficient may also be determined based on other factors, such as the historical processing experience of the platform, or the activity strategy of the platform during the target time period, or the sales experience of the second object selling the target product on the platform.

[0097] In one embodiment, the initial weight coefficient may be set first, and then the initial relationship parameter model between the first object and the target product may be constructed based on the initial weight coefficient. The initial weight coefficient may be determined based on the processing experience of the platform. For example, the following formula may be set as the initial relationship parameter model between the first object and the target product: X1*A+X2*I+X3*P+X4*L=value. X1, X2, X3, and X4 may be expressed as initial weight coefficients corresponding to A, I, P, and L, respectively, and the value may represent the relationship parameter between the first object and the target product.

[0098] Furthermore, the weight coefficient in the parameter model of the relationship parameter between the first object and the target product can be continuously tested and modified using the historical sales data of the product until the relationship parameter between the first object and the target product obtained based on the parameter model of the relationship parameter between the first object and the target product matches the historical sales data. The weight coefficient at this time is then determined as the weight coefficient ultimately used to construct the relationship parameter between the first object and the target product.

[0099] In one embodiment, after determining the appropriate weight coefficient in the above manner, the value of value can be further controlled within a normalized numerical range (for example, a range greater than or equal to 1 and less than or equal to 100) through magnitude attribution processing, so that the relationship parameters between the first object and the target product can be better processed later.

[0100] S403: Determine whether to adjust the price of the target product according to the relationship parameter between the first object and the target product in the target time period.

[0101] In one embodiment, since the relationship parameters between the first object and the target product can reflect the first object's acceptance of the price of the target product and the transaction tendency of the target product in the target time period from multiple operation stages and multiple transaction levels, the relationship parameters between the first object and the target product can be used to monitor and analyze the specific sales of the target product in the target time period, such as the sales volume of the target product, the coverage of the target product to the first object, the viscosity of the first object to the target product, etc. Then, based on the relationship parameters between the first object and the target product, without obtaining and based on the latest market price of the competitor or the processing experience of people, it is possible to accurately judge whether there is a large loss of the first object in the target time period due to unreasonable prices, affecting the sales of the target product, etc., by determining whether the relationship parameters between the first object and the target product have a large attenuation, and then accurately determine whether it is necessary to intervene by adjusting the price of the target product to reverse the loss of the first object and increase the sales of the target product on the platform.

[0102] In one embodiment, the determining whether to adjust the price of the target commodity based on the relationship parameters between the first object and the target commodity in the target time period may include the following contents during specific implementation: obtaining the relationship parameters between the first object and the target commodity in the previous time period of the target time period; calculating the month-on-month growth rate of the relationship parameters between the first object and the target commodity in the target time period based on the relationship parameters between the first object and the target commodity in the target time period and the relationship parameters between the first object and the target commodity in the previous time period of the target time period; determining whether to adjust the price of the target commodity based on the month-on-month growth rate of the relationship parameters between the first object and the target commodity in the target time period.

[0103] In one embodiment, the above-mentioned calculation of the month-on-month growth rate of the relationship parameter between the first object and the target product in the target time period may specifically include: dividing the difference between the relationship parameter between the first object and the target product in the target time period and the relationship parameter between the first object and the target product in the previous time period of the target time period by the relationship parameter between the first object and the target product in the previous time period of the target time period; and multiplying the obtained quotient by 100% to obtain the corresponding month-on-month growth rate.

[0104] In one embodiment, the determining whether to adjust the price of the target commodity based on the month-on-month growth rate of the relationship parameter between the first object and the target commodity in the target time period may include the following contents during specific implementation: comparing the month-on-month growth rate of the relationship parameter between the first object and the target commodity in the target time period with a preset growth rate threshold; wherein the preset growth rate threshold includes the month-on-month growth rate determined based on historical sales data to indicate the lowest point of the relationship parameter between the first object and the commodity in the historical time period; and determining to adjust the price of the target commodity when it is determined that the month-on-month growth rate of the relationship parameter between the first object and the target commodity in the target time period is less than or equal to the preset growth rate threshold.

[0105] In one embodiment, the above-mentioned preset growth rate threshold can specifically be the month-on-month growth rate between the relationship parameters between the first object and the target commodity in this time period and the relationship parameters between the first object and the target commodity in the previous time period of the target time period when the value of the relationship parameters between the first object and the commodity in history determined in advance based on historical sales data is the smallest.

[0106] In one embodiment, when it is determined that the month-on-month growth rate of the relationship parameter between the first object and the target product in the target time period is less than or equal to the preset growth rate threshold, it can be determined that the current attenuation of the relationship parameter between the first object and the target product is more serious, and the page price of the target product is not reasonable enough, and then it can be determined that the page price of the target product needs to be adjusted accordingly to improve the relationship parameter between the first object and the target product regarding the target product.

[0107] In contrast, when it is determined that the month-on-month growth rate of the relationship parameter between the first object and the target product in the target time period is greater than the preset growth rate threshold, it can be determined that the price of the target product does not need to be adjusted currently.

[0108] In one embodiment, the determining whether to adjust the price of the target product based on the relationship parameters between the first object and the target product in the target time period may also include the following contents during specific implementation: obtaining the relationship parameters between the first object and the target product in multiple previous time periods before the target time period; fitting a fluctuation change curve of the current relationship parameters between the first object and the target product based on the relationship parameters between the first object and the target product in the multiple previous time periods and the relationship parameters between the first object and the target product in the target time period; comparing the above-mentioned fluctuation change curve of the current relationship parameters between the first object and the target product with the fluctuation change curve of the preset reference relationship parameters; if it is found that the fluctuation change curve of the current relationship parameters between the first object and the target product has a fluctuation shape that is the same as or similar to the fluctuation shape at the minimum value position of the relationship parameters between the first object and the target product in the preset reference fluctuation change curve, it can be determined that the page price of the target product needs to be adjusted accordingly.

[0109] In this embodiment, by collecting the first type of operation of the first object browsing the target product on the platform within the target time period, the second type of operation of adding the target product to the shopping cart, the third type of operation of purchasing the target product, and the fourth type of operation of repurchasing the target product, etc.; and based on the above operation data, the relationship parameters between the first object and the target product are established, which can reflect the first object's acceptance of the price of the target product and the transaction tendency within the target time period from multiple operation stages and multiple transaction levels; and then use the relationship parameters between the first object and the target product to determine whether the price of the target product needs to be adjusted. In this way, the time point when the price adjustment is required can be efficiently and accurately identified, and then the sales volume of the target product on the platform can be increased in time through price adjustment, the loss of the first object can be reduced, and the loss caused by the lag in price adjustment can be reduced.

[0110] In one embodiment, the above-mentioned method for adjusting the price of goods may be specifically triggered automatically by the server of the platform. For example, the server may determine the target goods from the goods sold on the platform at a preset time interval, and then detect whether the price of the target goods needs to be adjusted according to the above-mentioned method.

[0111] In one embodiment, the above-mentioned method for adjusting the commodity price may also be performed by the platform server in response to the request data initiated by the second object.

[0112] For example, if the second object wants to determine whether the price of the target product that it sells during the target time period is appropriate and whether it needs to be adjusted, the second object can actively send a price detection request for the target product to the server through a computer or other device. The above price detection request can carry the identification information of the target product set by the second object. Correspondingly, the server can respond to the above detection request, determine the target product indicated by the second object, and detect whether the price of the target product needs to be adjusted according to the above product price adjustment method, and feed back the detection result to the second object.

[0113] For another example, the second object can also send a price tracking request for the target product to the platform server through a computer or other device. Correspondingly, the server can respond to the tracking request and perform multiple tests on the price of the target product over a long period of time according to the above product price adjustment method. When it is detected that the price of the target product needs to be adjusted, the server will then feed back the test results to the second object and prompt the second object to modify the price of the target product.

[0114] In one embodiment, in order to simplify the operation and improve the processing efficiency, the data value of each indicator parameter in the preset indicator parameter group related to the target product in the target time period can be determined first, wherein the above-mentioned preset indicator parameter group includes three indicator parameters: the number of newly added dynamic sales stores, the month-on-month dynamic sales stores, and the number of repeat purchase stores; then, according to the data value of each indicator parameter in the preset indicator parameter group related to the target product in the target time period, the data value of each indicator parameter in the preset indicator parameter group related to the target product in the previous time period of the target time period is compared with the data value of each indicator parameter in the preset indicator parameter group related to the target product in the previous time period of the target time period. When it is determined that at least two indicator parameters among the preset indicator parameters in the target time period have decayed first compared with the previous time period of the target time period, it can be determined that the price of the target product needs to be adjusted.

[0115] In this embodiment, the number of newly added dynamic sales stores may specifically include the number of first objects who purchased the target product for the first time during the target time period. The month-on-month growth rate of the dynamic sales stores may specifically include the number of first objects who purchased the target product during the target time period and the month-on-month growth rate of the number of first objects who purchased the target product during the previous time period of the target time period. The number of repeat purchase stores may specifically include the number of first objects who repeatedly purchased the target product during the target time period.

[0116] In this embodiment, the three indicator parameters of the number of newly added dynamic sales stores, the month-on-month dynamic sales stores, and the number of repeat purchase stores included in the above-mentioned preset indicator parameter group are respectively associated with the relationship parameters between the first object and the target product in the target time period, and are indicator parameters that can characterize the data characteristics of the relationship parameters between the current first object and the target product from different dimensions. Therefore, it is possible to indirectly judge whether the relationship parameters between the current first object and the target product have experienced a more serious attenuation through the attenuation changes of the above-mentioned three indicator parameters, and then determine whether a price adjustment is needed. In this way, there is no need to collect a variety of operation data in real time, and there is no need to calculate and monitor the relationship parameters between the corresponding first object and the target product. Instead, it is only necessary to collect and monitor the three indicator parameters included in the above-mentioned preset indicator parameters, so as to more efficiently and accurately determine whether a price adjustment is needed. This reduces the amount of data processing and improves data processing efficiency.

[0117] In one embodiment, it is also possible to further obtain and, based on the price data of the page prices at multiple different operation stages in the target time period, combine the operation data of different types of operations at multiple different operation stages in the target time period, to accurately determine a parameter that helps improve the relationship between the first object and the target product in the current scenario, and use the price with a high acceptance degree and a high transaction tendency of the first object as the desired price. Then, based on the desired price, the page price of the target product on the platform can be modified to increase the sales volume of the target product on the platform, strengthen the stickiness of the target product to the first object, and avoid the loss of the first object.

[0118] In one embodiment, after determining to adjust the price of the target product, the method may also include the following contents when implemented: obtaining price data related to the target product in the target time period; wherein the price data includes at least one of the following: an exposure price displayed on the page when the first object performs a first type of operation, an add-to-cart price displayed on the page when the first object performs a second type of operation, an order transaction price displayed on the page when the first object performs a third type of operation, and a repurchase transaction price displayed on the page when the first object performs a fourth type of operation; based on the price data and the operation data, determining the first object's intended price for the target product in the target time period; wherein the intended price is used to adjust the price of the target product to increase the sales volume of the target product and strengthen the stickiness to the first object.

[0119] In one embodiment, the above-mentioned exposure price can be specifically understood as the page price of the target product displayed on the platform page when the first object performs the first type of operation in the first operation stage. The above-mentioned purchase price can be specifically understood as the page price of the target product displayed on the platform page when the first object performs the second type of operation in the second operation stage. The above-mentioned order transaction price can be specifically understood as the page price of the target product displayed on the platform page when the first object performs the third type of operation in the third operation stage. The above-mentioned repurchase transaction price can be specifically understood as the page price of the target product displayed on the platform page when the first object performs the fourth type of operation in the fourth operation stage.

[0120] It should be noted that the above four prices may contain different discounts because they are in different operation stages. Therefore, the values ​​of the above four prices may be different, and the impacts on the first object in different operation stages may also be different.

[0121] In one embodiment, the above-mentioned intended price can be specifically understood as a price data which is determined by integrating the operation data of different operation stages within the target time period and the price data of different operation stages, and has a high acceptance rate for the first object within the target time period and can increase the numerical value of the relationship parameter between the first object and the target product.

[0122] In one embodiment, the method of determining the desired price of the target commodity for the target time period by the first object based on the price data and the operation data may include the following contents during specific implementation: counting the number of target commodity purchases, the number of target commodity repurchases, the average price of target commodity purchases, and the average price of repurchased target commodities by each first object within the target time period based on the price data and the operation data; constructing a relationship matrix between the first object and the target commodity based on the number of target commodity purchases, the number of target commodity repurchases, the average price of target commodity purchases, and the average price of repurchased target commodities by each first object within the target time period; calling a preset prediction model to determine the desired price of the target commodity for the target time period by the first object based on the relationship matrix between the first object and the target commodity.

[0123] In one embodiment, the relationship matrix between the first objects and the target products may specifically include a matrix that integrates the operation data and the price data to reflect the relationship between the operation behaviors of each first object and the page price within the target time period.

[0124] In one embodiment, during specific implementation, according to specific circumstances, you can choose to construct four relationship matrices between the first objects and the target products corresponding to the above four different operation stages respectively; you can also construct only the relationship matrices between the first objects and the target products of certain operation stages with high attention (or the greatest impact on the sales of the target products) according to the degree of attention paid to different operation stages; you can also construct a relationship matrix between the first objects and the target products that simultaneously includes multiple different operation stages.

[0125] In one embodiment, when it is determined that the attenuation of the relationship parameter between the first object and the target commodity in the target time period is serious and price intervention is required, a step can be performed to compare the change of the relationship data (i.e., the first relationship data, the second relationship data, the third relationship data, and the fourth relationship data) corresponding to different operation stages in the relationship parameter between the first object and the target commodity in the target time period relative to the relationship data of the previous time period of the target time period, and one or more relationship data with the largest attenuation (or the largest impact on the attenuation of the relationship parameter between the first object and the target commodity) can be determined as the target relationship data to be concerned. And the operation stage corresponding to the above target relationship data is determined as the target operation stage to be concerned. Then, only the relationship matrix between the first object and the target commodity in the above target operation stage can be constructed.

[0126] In one embodiment, when constructing a relationship matrix between the first object and the target product, for example, when the target operation phase to be concerned is the third operation phase and the fourth operation phase, the number of purchases of the target product (or the number of orders), the number of repurchases of the target product (or the number of repurchases), the average price of the target product, and the average price of the repurchased target product of each first object in the target time period can be counted according to the operation data and the price data.

[0127] Furthermore, a first-category triple data group corresponding to the first object can be constructed based on the number of times each first object purchases the target product, the average price of the target product, and the identity identifier of the first object. For example, the first-category triple data group corresponding to buyer 1 can be expressed as: {Buyer 1|Number of times the target product was purchased 3|Average payment price 19.5}. In the above manner, multiple first-category triple data groups corresponding to multiple first objects that have purchased the target product within the target time period can be constructed. Then, the above multiple first-category triple data groups are combined to obtain a relationship matrix between the first object and the target product corresponding to the third operation stage. It can be recorded as A=[a,b1,c1]. Among them, the dimension of the matrix is ​​equal to the total number of first objects that have purchased the target product within the target time period.

[0128] In a similar manner, a second type triple data group corresponding to each first object can be constructed based on the number of times each first object has repurchased the target product, the average price of the repurchased target product, and the identity of the first object. For example, the second type triple data group corresponding to buyer 1 can be expressed as: {buyer 1|number of times the target product has been repurchased 3|average payment price 19.5}. It can be recorded as A = [a, b2, c2]. The dimension of the matrix is ​​equal to the total number of first objects that have repurchased the target product within the target time period.

[0129] In addition, in order to more accurately reflect the relationship between the operation behavior of each first object within the target time period and the page price, when constructing the relationship matrix between the first object and the target product, other relevant information besides the number of products listed above, the number of repurchases of the target product, the average price of the target product, and the average price of repurchased target products can also be introduced and used.

[0130] Specifically, for example, when constructing the relationship matrix between the first object and the target product corresponding to the first operation stage, the date (date) when the first object performs the first type of operation, whether the target product on the platform is exposed (if_expo) when the first object performs the first type of operation, the accumulated number of views (PV), and other information can also be obtained based on the operation data. Then, a data group containing richer operation information can be constructed, and then a more refined and detailed relationship matrix between the first object and the target product can be established based on the above data group, so as to more comprehensively and meticulously characterize the relationship between operation and price.

[0131] In one embodiment, after determining the relationship matrix between the corresponding first object and the target product in the above manner, the relationship matrix between the first object and the target product can be used to determine the corresponding intended price; and then the price adjustment range can be determined based on the intended price to make targeted adjustments to the price of the target product.

[0132] In one embodiment, the method of determining the desired price of the target commodity for the target time period by the first object based on the price data and the operation data may include the following contents during specific implementation: counting the number of target commodity purchases, the number of target commodity repurchases, the average price of target commodity purchases, and the average price of repurchased target commodities by each first object within the target time period based on the price data and the operation data; constructing a relationship matrix between the first object and the target commodity based on the number of target commodity purchases, the number of target commodity repurchases, the average price of target commodity purchases, and the average price of repurchased target commodities by each first object within the target time period; calling a preset prediction model to determine the desired price of the target commodity for the target time period by the first object based on the relationship matrix between the first object and the target commodity.

[0133] In this embodiment, considering that for most target commodities, the third operation stage and the fourth operation stage often have a relatively large impact on whether the first object purchases the target commodity on the platform, and whether it will continue to repurchase the same target commodity on the platform, and the correlation between the third operation stage and the fourth operation stage is often the highest. Therefore, the above-mentioned third operation stage and the fourth operation stage can be used as the target operation stage to be paid attention to to determine the specific relationship matrix between the first object and the target commodity. Of course, during the specific implementation, according to the specific circumstances and the specific characteristics of the target commodity, other operation stages can also be selected as the target operation stage to establish the relationship matrix between the first object and the target commodity. This specification does not limit this.

[0134] In this embodiment, the above-mentioned construction of a relationship matrix between the first objects and the target commodities based on the number of times each first object purchases the target commodity, the number of times each first object repurchases the target commodity, the average price of the target commodity purchased, and the average price of the repurchased target commodity within the target time period may include: constructing a relationship matrix between the first objects and the target commodities corresponding to the third operation stage (which may be denoted as A) based on the number of times each first object purchases the target commodity, the average price of the target commodity purchased, and the identity of the first object; constructing a relationship matrix between the first objects and the target commodities corresponding to the fourth operation stage (which may be denoted as B) based on the number of times each first object repurchases the target commodity, the average price of the repurchased target commodity, and the identity of the first object.

[0135] In one embodiment, a preset prediction model is called to determine the desired price of the first object for the target product in the target time period according to the relationship matrix between the first object and the target product.

[0136] In one embodiment, during specific implementation, the relationship matrix between the first object and the target product can be used as a model input and input into a preset prediction model; and the preset prediction model is run to obtain the corresponding model output, and the model output is used as the desired price of the target product.

[0137] In one embodiment, before the specific implementation, the preset prediction model can also be established by learning and training the historical sales data. Specifically, it can include: establishing an initial model. Divide the historical sales data into a plurality of different historical time periods, wherein the time length of each historical time period is the same. According to the operation data and price data in each historical time period, establish the relationship matrices A and B of the first object and the target commodity corresponding to the third operation stage and the fourth operation extreme in each time period. Then arrange the relationship matrices of the first object and the target commodity in chronological order as multiple groups of training sample data, and mark the sales of the commodities in the time period corresponding to each group of training sample data. For example, indication labels such as poor sales and good sales can be marked on each group of training sample data. Further, each group of labeled training sample data can be input into the model in chronological order to continuously adjust the model parameters, so as to train and obtain a preset prediction model that can use the relationship matrix of the first object and the target commodity in the previous time period to predict the desired price suitable for the next time period.

[0138] In one embodiment, during specific implementation, other methods may be used to determine the intended price of the first object for the target product in the target time period based on the relationship matrix between the first object and the target product. Specifically, for example, the overall average price of the target product purchased by the first object in the target time period may be calculated based on the relationship matrix between the first object and the target product corresponding to the third operation stage; the overall average price of the target product repurchased by the first object in the target time period may be calculated based on the relationship matrix between the first object and the target product corresponding to the fourth operation stage. Further, the intended price may be determined based on the overall average price of the target product purchased by the first object in the above target time period and the overall average price of the target product repurchased by the first object in the target time period.

[0139] For example, the corresponding desired price can be determined by taking a weighted average based on the overall average price of the target product purchased by the first subject in the target time period and the overall average price of the target product repurchased by the first subject in the target time period. The weight coefficient used in the weighted average can be determined based on the conversion rate between the number of first subjects who purchased the target product for the first time and the number of first subjects who repurchased the target product in the target time period.

[0140] In one embodiment, after determining the first object's intended price for the target product in the target time period based on the price data and the operation data, the method may also include the following contents when implemented: sending a price adjustment prompt message to the second object; wherein the prompt message carries the intended price; the second object includes suppliers who sell the target product on the platform; the first object includes store sellers who purchase the target product on the platform; receiving price adjustment data fed back by the second object; and adjusting the exposure price, and / or the add-to-cart price, and / or the order transaction price, and / or the repurchase transaction price according to the price adjustment data.

[0141] In this embodiment, the above-mentioned price adjustment prompt information can be specifically understood as a prompt information for prompting the second object: the relationship parameter between the first object and the target product in the target time period has greatly decayed, the sales volume has dropped significantly, the first object is being lost, and it is recommended to adjust the price immediately.

[0142] In this embodiment, after determining the desired price, the server may send a price adjustment prompt message carrying the desired price to the second object, wherein the second object, as a commodity supplier of the platform, has the pricing power for the target commodity.

[0143] In this embodiment, the second object can promptly learn that the sales volume of the target product has dropped significantly, the first object is losing customers, and the target needs to immediately adjust the price to increase sales and strengthen the stickiness to the first object. Furthermore, the second object can use the desired price carried by the price adjustment prompt information as a reference price, combined with the cost price of the target product and the marketing plan of the second object, to flexibly set the price adjustment data, and feedback the above price adjustment data to the server. The server can adjust the page price of the target product at the corresponding operation stage on the platform according to the requirements of the second object based on the above price adjustment data.

[0144] In one embodiment, the price adjustment prompt information may further carry operation data, price data, and statistical data graphs of the relationship parameters between the first object and the target product in multiple operation stages within the target time period. In this way, the second object can clearly know which operation stage has a problem based on the statistical data graph, which affects the sales of the target product and the stickiness to the first object. Then, the page price in the operation stage with the problem can be adjusted in a targeted manner based on the desired price, so as to achieve the effect of increasing sales and strengthening stickiness to the first object at a relatively low cost.

[0145] In one embodiment, if the second object has signed an authorized price adjustment agreement with the platform in advance, the server can adjust the page price of the target product according to the desired price within the adjustment range allowed by the authorized price adjustment agreement.

[0146] In one embodiment, after receiving the price adjustment data fed back by the second object, the method may further include the following contents when implemented: adjusting the platform traffic related to the second object selling the target product on the platform according to the price adjustment data.

[0147] In one embodiment, during specific implementation, when it is determined based on the price adjustment data that the second object has agreed to adjust the price of the target product to the desired price, or even a value lower than the desired price, it can be judged that the second object has made up its mind to do its utmost to increase sales and strengthen stickiness to the first object, and then the server can cooperate with the determination of the second object to adapt the highest traffic for the target product sold by the second object on the platform. For example, the target product sold by the second object on the platform can be arranged to be displayed on the platform homepage to increase the number of views and assist the second object in increasing the sales of the target product on the platform and stickiness to the first object.

[0148] In one embodiment, the first object may specifically include customers who purchase goods on the platform, and correspondingly, the second object may specifically include store sellers who sell goods on the platform.

[0149] In this embodiment, the server may be a server of a shopping website. In specific implementation, the server may collect and monitor the sales data of the stores on the shopping website in real time. Specifically, the target products that are worth monitoring and adjusting prices may be first screened out from the products on sale in the store. Operation data related to the target product in the target time period is obtained; wherein the operation data includes a first type of operation of a customer browsing the target product, a second type of operation of a customer adding the target product to a shopping cart, a third type of operation of a consumer purchasing the target product, and a fourth operation of a customer repurchasing the target product. According to the operation data related to the target product in the target time period, a relationship parameter between the first object and the target product in the target time period is established; wherein the relationship parameter between the first object and the target product is used to characterize the transaction tendency of the customer for the target product sold in the store in the target time period. According to the relationship parameter between the first object and the target product in the target time period, it is determined whether to adjust the price of the target product. In the case of determining that the price of the target product needs to be adjusted, the price data related to the target product is further obtained, and a relationship matrix between the first object and the target product for the target product of the store is constructed in combination with the above operation data, and the customer's willingness price for the target product is determined according to the relationship matrix between the first object and the target product. The desired price will be given to the store manager so that the store manager can adjust the page price of the target product in a targeted manner based on the desired price, thereby improving the efficiency of the target product and increasing the popularity and attention of the store.

[0150] As can be seen from the above, the method for adjusting the price of goods provided in the embodiment of this specification collects the first type of operation of the first object browsing the target goods on the platform within the target time period, the second type of operation of adding the target goods to the shopping cart, the third type of operation of purchasing the target goods, and the fourth type of operation of repurchasing the target goods; and establishes the relationship parameters between the first object and the target goods that can reflect the transaction tendency of the first object for the target goods within the target time period from multiple operation stages and multiple transaction levels based on the above operation data; and then uses the relationship parameters between the first object and the target goods to determine whether the price of the target goods needs to be adjusted. In this way, the time point when the price adjustment is required can be accurately identified, and then the sales volume of the target goods on the platform can be increased in time through price adjustment, the loss of the first object can be reduced, and the loss caused by the lag in price adjustment can be reduced. By collecting price data such as the exposure price displayed on the page when the first object on the platform performs the first type of operation, the add-to-cart price displayed on the page when the second type of operation is performed, the order transaction price displayed on the page when the third type of operation is performed, and the repurchase transaction price displayed on the page when the fourth type of operation is performed; then, based on the above price data and the operation data, a relationship matrix between the first object and the target product can be constructed, which can reflect the impact characteristics of the page display price on the first object in the target time period from multiple operation stages and multiple transaction levels; then, based on the above relationship matrix between the first object and the target product, a desired price with strong targeting, high acceptance by the first object, and high transaction tendency can be accurately determined; then, based on the above desired price, the page display price of the target product on the platform at different operation stages can be adjusted, so that the price of the target product can be more effectively adjusted to achieve the technical effect of increasing the sales volume of the target product on the platform, strengthening the stickiness of the target product on the platform to the first object, and improving the relationship parameters between the first object and the target product.

[0151] See also Figure 8 As shown, the embodiment of this specification provides a method for adjusting the price of a commodity, so that when necessary, the desired price that can effectively increase sales can be accurately determined. Among them, when the method is specifically implemented, it can include the following contents.

[0152] S801: Obtain operation data and price data related to the target product in the target time period; wherein the operation data includes at least one of the following: a first type of operation of browsing the target product, a second type of operation of adding the target product to the shopping cart, a third type of operation of purchasing the target product, and a fourth type of operation of repurchasing the target product; the price data includes at least one of the following: an exposure price displayed on the page when the first object performs the first type of operation, an add-to-cart price displayed on the page when the first object performs the second type of operation, an order transaction price displayed on the page when the first object performs the third type of operation, and a repurchase transaction price displayed on the page when the first object performs the fourth type of operation.

[0153] S802: Constructing a relationship matrix between the first object and the target product according to the price data and the operation data.

[0154] S803: Determine the desired price of the target product of the first object for the target time period according to the relationship matrix between the first object and the target product; wherein the desired price is used to adjust the price of the target product to increase the sales volume of the target product and enhance the stickiness to the first object.

[0155] In one embodiment, before determining the desired price of the first object for the target product in the target time period, the method may also include: detecting whether a price adjustment trigger condition occurs; and if it is determined that the price adjustment trigger condition occurs, determining the desired price of the first object for the target product in the target time period in the above manner.

[0156] In this embodiment, the above-mentioned price adjustment trigger condition may specifically include at least one of the following: receiving price adjustment prompt information for the target product generated and sent by the server by monitoring the operation data of the target time period; and / or receiving a price adjustment request actively initiated by the second object, etc.

[0157] In one embodiment, after determining the desired price of the target product of the first subject for the target time period, the method may further include the following when implemented: adjusting the display price of the target product on the platform page according to the desired price of the target product.

[0158] As can be seen from the above, the method for adjusting the price of goods provided in the embodiments of this specification collects price data such as the exposure price displayed on the page when the first object on the platform performs the first type of operation, the add-to-cart price displayed on the page when the second type of operation is performed, the order transaction price displayed on the page when the third type of operation is performed, and the repurchase transaction price displayed on the page when the fourth type of operation is performed; and then, based on the above price data and the operation data, a relationship matrix between the first object and the target commodity can be constructed, which can reflect the impact characteristics of the page display price on the first object in the target time period from multiple operation stages and multiple transaction levels; and then, based on the above relationship matrix between the first object and the target commodity, a desired price with strong targeting and high acceptance by the first object can be accurately determined, and then, based on the above desired price, the page display price of the target commodity on the platform at different operation stages can be adjusted, so that the price of the target commodity can be more effectively adjusted to achieve the technical effect of increasing the sales volume of the target commodity on the platform, strengthening the stickiness of the target commodity on the platform to the first object, and improving the relationship parameters between the first object and the target commodity.

[0159] The embodiment of the present specification also provides a server, including a processor and a memory for storing processor executable instructions, and the processor can perform the following steps according to the instructions when it is implemented: obtaining operation data related to the target product within a target time period; wherein the operation data includes at least one of the following: a first type of operation of browsing the target product, a second type of operation of adding the target product to a shopping cart, a third type of operation of purchasing the target product, and a fourth type of operation of repurchasing the target product; according to the operation data related to the target time period and the target product, establishing a relationship parameter between a first object and the target product in the target time period; wherein the relationship parameter between the first object and the target product is used to characterize the transaction tendency of the first object in the target time period for the target product; according to the relationship parameter between the first object and the target product in the target time period, determining whether to adjust the price of the target product.

[0160] In order to complete the above instructions more accurately, refer to Fig. 9 As shown, the embodiment of this specification also provides another specific server, wherein the server includes a network communication port 901, a processor 902 and a memory 903, and the above structures are connected through internal cables so that each structure can perform specific data interaction.

[0161] Among them, the network communication port 901 can be specifically used to obtain operation data related to the target product within the target time period; wherein the operation data includes at least one of the following: a first type of operation of browsing the target product, a second type of operation of adding the target product to the shopping cart, a third type of operation of purchasing the target product, and a fourth type of operation of repurchasing the target product.

[0162] The processor 902 can be specifically used to establish a relationship parameter between the first object and the target product in the target time period based on the operation data related to the target time period and the target product; wherein the relationship parameter between the first object and the target product is used to characterize the transaction tendency of the first object in the target time period for the target product; and determine whether to adjust the price of the target product based on the relationship parameter between the first object and the target product in the target time period.

[0163] The memory 903 may be specifically used to store corresponding instruction programs.

[0164] In this embodiment, the network communication port 901 can be a virtual port that is bound to different communication protocols so that different data can be sent or received. For example, the network communication port can be a port responsible for web data communication, a port responsible for FTP data communication, or a port responsible for email data communication. In addition, the network communication port can also be a physical communication interface or communication chip. For example, it can be a wireless mobile network communication chip, such as GSM, CDMA, etc.; it can also be a Wifi chip; it can also be a Bluetooth chip.

[0165] In this embodiment, the processor 902 may be implemented in any appropriate manner. For example, the processor may take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) executable by the (micro)processor, a logic gate, a switch, an application specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller, etc. This specification does not limit this.

[0166] In this embodiment, the memory 903 may include multiple levels. In a digital system, anything that can store binary data can be a memory; in an integrated circuit, a circuit with a storage function that has no physical form is also called a memory, such as RAM, FIFO, etc.; in a system, a storage device with a physical form is also called a memory, such as a memory stick, TF card, etc.

[0167] The embodiment of the present specification also provides a computer storage medium based on the above-mentioned commodity price adjustment method, and the computer storage medium stores computer program instructions, which, when executed, implement: obtaining operation data related to the target commodity within a target time period; wherein the operation data includes at least one of the following: a first type of operation of browsing the target commodity, a second type of operation of adding the target commodity to a shopping cart, a third type of operation of purchasing the target commodity, and a fourth type of operation of repurchasing the target commodity; establishing a relationship parameter between a first object in the target time period and the target commodity according to the operation data related to the target time period and the target commodity; wherein the relationship parameter between the first object and the target commodity is used to characterize the transaction tendency of the first object in the target time period for the target commodity; and determining whether to adjust the price of the target commodity according to the relationship parameter between the first object in the target time period and the target commodity.

[0168] In this embodiment, the storage medium includes, but is not limited to, a random access memory (RAM), a read-only memory (ROM), a cache, a hard disk (HDD), or a memory card. The memory may be used to store computer program instructions. The network communication unit may be an interface for network connection communication set in accordance with the standard specified by the communication protocol.

[0169] In this embodiment, the functions and effects specifically implemented by the program instructions stored in the computer storage medium can be explained in comparison with other implementations and will not be described in detail here.

[0170] See also Fig.10 As shown, at the software level, the embodiments of this specification also provide a device for adjusting commodity prices, which may specifically include the following structural modules.

[0171] The acquisition module 1001 can be specifically used to obtain operation data related to the target product within the target time period; wherein the operation data includes at least one of the following: a first type of operation of browsing the target product, a second type of operation of adding the target product to the shopping cart, a third type of operation of purchasing the target product, and a fourth type of operation of repurchasing the target product.

[0172] Establish module 1002, which can be specifically used to establish relationship parameters between the first object and the target product in the target time period based on the operation data related to the target time period and the target product; wherein the relationship parameters between the first object and the target product are used to characterize the transaction tendency of the first object in the target time period for the target product; the first object includes a store that purchases goods on the platform.

[0173] The determination module 1003 may be specifically configured to determine whether to adjust the price of the target product according to the relationship parameter between the first object and the target product in the target time period.

[0174] In one embodiment, the device may further include a screening module, which can be used to obtain historical sales data of multiple commodities on the platform within a preset time period; determine the dynamic sales parameters of the commodities and the price elasticity parameters of the commodities based on the historical sales data within the preset time period; and screen out commodities that meet preset requirements from multiple commodities as target commodities based on the dynamic sales parameters of the commodities and the price elasticity parameters of the commodities.

[0175] In one embodiment, the establishing module 1002 may specifically include the following structural units.

[0176] The first statistical unit can be specifically used to count the number of first objects that perform various operations within the target time period as corresponding relationship data according to the operation data related to the target product within the target time period, so as to obtain multiple relationship data.

[0177] The first determining unit may specifically determine the relationship parameter between the first object and the target product in the target time period according to the plurality of relationship data.

[0178] In one embodiment, the determining module 1003 may specifically include the following structural units.

[0179] The comparison unit can be specifically used to compare the month-on-month growth rate of the relationship parameter between the first object and the target product in the target time period with a preset growth rate threshold; wherein the preset growth rate threshold includes the month-on-month growth rate determined based on historical sales data to indicate the lowest point of the relationship parameter between the first object and the product in the historical time period.

[0180] The second determining unit may be specifically configured to determine to adjust the target product price when it is determined that the month-on-month growth rate of the relationship parameter between the first object and the target product in the target time period is less than or equal to the preset growth rate threshold.

[0181] In one embodiment, the device may further include a price adjustment module for collecting price data related to the target product in the target time period; wherein the price data includes at least one of the following: an exposure price displayed on the page when the first object performs a first type of operation, an add-to-cart price displayed on the page when the first object performs a second type of operation, an order transaction price displayed on the page when the first object performs a third type of operation, and a repurchase transaction price displayed on the page when the first object performs a fourth type of operation; based on the price data and the operation data, determine the first object's intended price for the target product in the target time period; wherein the intended price is used to adjust the price of the target product to increase the sales volume of the target product and strengthen the stickiness to the first object.

[0182] The embodiments of this specification also provide a method for adjusting product attributes, which may include the following contents during specific implementation.

[0183] S1: Obtain operation data related to the target product within a target time period; wherein the operation data includes at least one of the following: a first type of operation of browsing the target product, a second type of operation of adding the target product to a shopping cart, a third type of operation of purchasing the target product, and a fourth type of operation of repurchasing the target product.

[0184] S2: establishing a relationship parameter between the first object and the target product in the target time period according to the operation data related to the target product in the target time period; wherein the relationship parameter between the first object and the target product is used to characterize the transaction tendency of the first object for the target product in the target time period.

[0185] S3: Determine whether to adjust the attribute data of the target product according to the relationship parameter between the first object in the target time period and the target product.

[0186] In one embodiment, the attribute data of the target product may specifically include at least one of the following: product description data of the target product, the number of exposures of the target product, promotion information of the target product, etc. Of course, the attribute data listed above is only a schematic illustration. In specific implementation, the attribute data may also include other types of data related to the target product. For example, the price of the product, etc.

[0187] The embodiment of this specification also provides a method for adjusting the price of a commodity. The method can be specifically applied to a terminal device, wherein the terminal device is arranged on one side of the second object. When the method is specifically implemented, it can include the following contents.

[0188] S1: Generate a price detection request for the target product;

[0189] S2: Send the price detection request to the server; wherein the server is used to respond to the price detection request, establish and determine whether to adjust the price of the target product based on the relationship parameters between the first object and the target product in the target time period; if it is determined to adjust the price of the target product, determine the first object's intended price for the target product in the target time period.

[0190] In one embodiment, when the method is implemented, it may further include: receiving the desired price of the target product of the first object for the target time period sent by the server; and updating the price of the target product according to the desired price.

[0191] In one embodiment, the target commodity may specifically be a commodity to be price checked as indicated by the second object in the price check request.

[0192] In one embodiment, when the above-mentioned server is implemented, it can obtain operation data related to the target product within the target time period; wherein, the operation data includes at least one of the following: a first type of operation of browsing the target product, a second type of operation of adding the target product to the shopping cart, a third type of operation of purchasing the target product, and a fourth type of operation of repurchasing the target product; based on the operation data related to the target product within the target time period, a relationship parameter between the first object and the target product in the target time period is established; wherein, the relationship parameter between the first object and the target product is used to characterize the transaction tendency of the first object for the target product in the target time period; based on the relationship parameter between the first object and the target product in the target time period, it is determined whether to adjust the price of the target product.

[0193] In the above manner, the second object can accurately identify the time point when price adjustment is required, and timely increase the sales volume of the target product through price adjustment, reduce the loss of the first object, and reduce the loss caused by delayed price adjustment.

[0194] It should be noted that the units, devices or modules described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. For the convenience of description, the above devices are described separately by functions divided into various modules. Of course, when implementing this specification, the functions of each module can be implemented in the same or more software and / or hardware, or the modules that implement the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0195] As can be seen from the above, the commodity price adjustment device provided in the embodiment of this specification can accurately identify the time point when price adjustment is required, and then can timely increase the sales volume of the target commodity on the platform through price adjustment, reduce the loss of the first object, and reduce the loss caused by delayed price adjustment.

[0196] Although the present specification provides method operation steps as described in the embodiments or flow charts, more or less operation steps may be included based on conventional or non-creative means. The order of steps listed in the embodiments is only one way of executing the order of many steps, and does not represent a unique execution order. When the device or client product in practice is executed, it can be executed in sequence or in parallel according to the method shown in the embodiments or the drawings (for example, a parallel processor or a multi-threaded processing environment, or even a distributed data processing environment). The term "include", "include" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, product or device including a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements inherent to such a process, method, product or device. In the absence of more restrictions, it is not excluded that there are other identical or equivalent elements in the process, method, product or device including the elements. The first, second, etc. words are used to represent the name, and do not represent any particular order.

[0197] Those skilled in the art also know that, in addition to implementing the controller in a purely computer-readable program code, the controller can be made to implement the same function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered as a hardware component, and the devices for implementing various functions included therein can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software modules for implementing the method and structures within the hardware component.

[0198] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, classes, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media including storage devices.

[0199] Through the description of the above embodiments, it can be known that those skilled in the art can clearly understand that the present specification can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present specification can essentially be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a mobile terminal, a server, or a network device, etc.) to execute the methods described in each embodiment of the present specification or some parts of the embodiments.

[0200] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. This specification can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc.

[0201] Although the present specification is described through embodiments, those skilled in the art will appreciate that there are many modifications and changes to the present specification without departing from the spirit of the present specification, and it is intended that the appended claims include these modifications and changes without departing from the spirit of the present specification.

Claims

1. A method for adjusting commodity prices, comprising: Acquire operation data related to the target product within the target time period; wherein the operation data includes at least two of the following: a first type of operation of browsing the target product, a second type of operation of adding the target product to the shopping cart, a third type of operation of purchasing the target product, and a fourth type of operation of repurchasing the target product; different types of operations correspond to different operation stages in the store seller's behavior operation process; the first type of operation corresponds to the browsing stage, the second type of operation corresponds to the add-to-cart stage, the third type of operation corresponds to the ordering stage, and the fourth type of operation corresponds to the repurchasing stage; Establishing a relationship parameter between the first object and the target product in the target time period according to the operation data related to the target product in the target time period; wherein the relationship parameter between the first object and the target product is used to reflect the attractiveness of the page price displayed at different operation stages in the target time period to the first object from multiple different operation stages and multiple different transaction levels, and the coverage of the first object based on the page price in the target time period; It is determined whether to adjust the price of the target product according to the relationship parameter between the first object and the target product in the target time period.

2. The method according to claim 1, before collecting the operation data related to the target commodity within the target time period, the method further comprises: Obtain historical sales data for multiple products on the platform within a preset time period; Determining the commodity's sales performance parameters and the commodity's price elasticity parameters based on the historical sales data within the preset time period; According to the sales dynamic parameters of the commodity and the price elasticity parameters of the commodity, commodities meeting the preset requirements are screened out from a plurality of commodities as target commodities.

3. The method according to claim 1, wherein the establishing the relationship parameter between the first object and the target product in the target time period according to the operation data related to the target product in the target time period comprises: According to the operation data related to the target product in the target time period, the number of first objects that perform various operations in the target time period is counted as corresponding relationship data to obtain multiple relationship data; According to the plurality of relationship data, relationship parameters between the first object and the target product in the target time period are determined.

4. The method according to claim 3, wherein determining the relationship parameters between the first object and the target product in the target time period according to the plurality of relationship data comprises: Obtaining weight coefficients corresponding to the plurality of relationship data respectively; wherein the weight coefficients are determined according to the type of the target product and / or the audience characteristics of the target product; The relationship parameter between the first object and the target product in the target time period is determined by weighted summation according to the plurality of relationship data and the weight coefficients.

5. The method according to claim 1, wherein determining whether to adjust the price of the target product according to the relationship parameter between the first object and the target product in the target time period comprises: Obtaining relationship parameters between the first object and the target product in the previous time period of the target time period; Calculate the month-on-month growth rate of the relationship parameter between the first object and the target product in the target time period according to the relationship parameter between the first object and the target product in the target time period and the relationship parameter between the first object and the target product in the previous time period of the target time period; Whether to adjust the price of the target product is determined according to a month-on-month growth rate of a relationship parameter between the first object and the target product during the target time period.

6. The method according to claim 5, wherein determining whether to adjust the price of the target product according to the month-on-month growth rate of the relationship parameter between the first object and the target product in the target time period comprises: Comparing the month-on-month growth rate of the relationship parameter between the first object and the target product in the target time period with a preset growth rate threshold; wherein the preset growth rate threshold includes a month-on-month growth rate determined based on historical sales data to indicate the lowest point of the relationship parameter between the first object and the product in the historical time period; When it is determined that the month-on-month growth rate of the relationship parameter between the first object and the target product in the target time period is less than or equal to the preset growth rate threshold, it is determined to adjust the price of the target product.

7. The method according to claim 1, after determining to adjust the price of the target commodity, the method further comprises: Obtain price data related to the target product within the target time period; wherein the price data includes at least one of the following: an exposure price displayed on the page when the first object performs a first type of operation, an additional purchase price displayed on the page when the first object performs a second type of operation, an order transaction price displayed on the page when the first object performs a third type of operation, and a repurchase transaction price displayed on the page when the first object performs a fourth type of operation; The desired price of the first object for the target product in the target time period is determined based on the price data and the operation data; wherein the desired price is used to adjust the price of the target product to increase the sales volume of the target product and strengthen the stickiness to the first object.

8. The method according to claim 7, wherein determining the first subject's desired price for the target product in the target time period based on the price data and the operation data comprises: According to the price data and the operation data, the number of times each first subject purchases the target product, the number of times the target product is repurchased, the average price of the target product, and the average price of the repurchased target product are counted within the target time period; Constructing a relationship matrix between the first subjects and the target products according to the number of times each first subject purchased the target product, the number of times each first subject repurchased the target product, the average price each first subject paid for the target product, and the average price each first subject paid for the target product; A preset prediction model is called to determine the desired price of the first object for the target product in the target time period according to the relationship matrix between the first object and the target product.

9. The method according to claim 7, after determining the first subject's desired price for the target product in the target time period according to the price data and the operation data, the method further comprises: Sending price adjustment prompt information to the second object; wherein the prompt information carries the desired price; the second object includes suppliers who sell the target product on the platform; the first object includes store sellers who purchase the target product on the platform; receiving price adjustment data fed back by the second object; According to the price adjustment data, the exposure price, and / or the additional purchase price, and / or the order transaction price, and / or the repurchase transaction price are adjusted.

10. The method according to claim 9, after receiving the price adjustment data fed back by the second object, the method further comprises: According to the price adjustment data, the platform traffic related to the second object selling the target product on the platform is adjusted.

11. According to the method of claim 9, the first object also includes customers who purchase goods on the platform, and the second object also includes store sellers who sell goods on the platform.

12. A commodity price adjustment device, comprising: An acquisition module is used to acquire operation data related to a target product within a target time period; wherein the operation data includes at least two of the following: a first type of operation of browsing the target product, a second type of operation of adding the target product to a shopping cart, a third type of operation of purchasing the target product, and a fourth type of operation of repurchasing the target product; different types of operations correspond to different operation stages in the operation process of the store seller's behavior; the first type of operation corresponds to the browsing stage, the second type of operation corresponds to the add-to-cart stage, the third type of operation corresponds to the ordering stage, and the fourth type of operation corresponds to the repurchasing stage; An establishment module is used to establish a relationship parameter between the first object and the target product in the target time period according to the operation data related to the target product in the target time period; wherein the relationship parameter between the first object and the target product is used to reflect the attractiveness of the page price displayed at different operation stages in the target time period to the first object from multiple different operation stages and multiple different transaction levels, and the coverage of the first object based on the page price in the target time period; The determination module is used to determine whether to adjust the price of the target product according to the relationship parameter between the first object and the target product in the target time period.

13. According to the device of claim 12, the device also includes a screening module for obtaining historical sales data of multiple commodities on the platform within a preset time period; determining the dynamic sales parameters of the commodities and the price elasticity parameters of the commodities based on the historical sales data within the preset time period; and screening out commodities that meet the preset requirements from multiple commodities as target commodities based on the dynamic sales parameters of the commodities and the price elasticity parameters of the commodities.

14. The apparatus according to claim 12, wherein the establishing module comprises: A first statistical unit is used to count the number of first objects that perform various operations in the target time period as corresponding relationship data according to the operation data related to the target product in the target time period, so as to obtain a plurality of relationship data; According to the plurality of relationship data, relationship parameters between the first object and the target product in the target time period are determined.

15. The apparatus according to claim 12, wherein the determining module comprises: A comparison unit, used to compare the month-on-month growth rate of the relationship parameter between the first object and the target product in the target time period with a preset growth rate threshold; wherein the preset growth rate threshold includes a month-on-month growth rate determined based on historical sales data to indicate the lowest point of the relationship parameter between the first object and the product in the historical time period; The second determining unit is configured to determine to adjust the price of the target product when it is determined that the month-on-month growth rate of the relationship parameter between the first object and the target product in the target time period is less than or equal to the preset growth rate threshold.

16. The device according to claim 12, further comprising a price adjustment module for collecting price data related to the target product in the target time period; wherein The price data includes at least one of the following: an exposure price displayed on the page when the first object performs a first type of operation, an add-to-cart price displayed on the page when the first object performs a second type of operation, an order price displayed on the page when the first object performs a third type of operation, and a repurchase price displayed on the page when the first object performs a fourth type of operation; based on the price data and the operation data, determine the intended price of the target product of the first object for the target time period; wherein the intended price is used to adjust the price of the target product to increase sales of the target product and strengthen stickiness to the first object.

17. A method for adjusting commodity prices, comprising: Obtaining operation data and price data related to the target product in the target time period; wherein the operation data includes at least one of the following: a first type of operation of browsing the target product, a second type of operation of adding the target product to the shopping cart, a third type of operation of purchasing the target product, and a fourth type of operation of repurchasing the target product; the price data includes at least two of the following: an exposure price displayed on the page when the first object performs the first type of operation, an add-to-cart price displayed on the page when the first object performs the second type of operation, an order transaction price displayed on the page when the first object performs the third type of operation, and a repurchasing transaction price displayed on the page when the first object performs the fourth type of operation; different types of operations correspond to different operation stages in the store seller's behavior operation process; the first type of operation corresponds to the browsing stage, the second type of operation corresponds to the add-to-cart stage, the third type of operation corresponds to the order stage, and the fourth type of operation corresponds to the repurchasing stage; Constructing a relationship matrix between the first object and the target commodity according to the price data and the operation data; According to the relationship matrix between the first object and the target product, the desired price of the first object for the target product in the target time period is determined; wherein the desired price is used to adjust the price of the target product to increase the sales volume of the target product and strengthen the stickiness to the first object.

18. The method according to claim 17, after determining the desired price of the first subject for the target product in the target time period, the method further comprises: According to the desired price of the target product, the display price of the target product on the platform page is adjusted.

19. A method for adjusting commodity attributes, comprising: Acquire operation data related to the target product within the target time period; wherein the operation data includes at least two of the following: a first type of operation of browsing the target product, a second type of operation of adding the target product to the shopping cart, a third type of operation of purchasing the target product, and a fourth type of operation of repurchasing the target product; different types of operations correspond to different operation stages in the store seller's behavior operation process; the first type of operation corresponds to the browsing stage, the second type of operation corresponds to the add-to-cart stage, the third type of operation corresponds to the ordering stage, and the fourth type of operation corresponds to the repurchasing stage; Establishing a relationship parameter between the first object and the target product in the target time period according to the operation data related to the target product in the target time period; wherein the relationship parameter between the first object and the target product is used to reflect the attractiveness of the page price displayed at different operation stages in the target time period to the first object from multiple different operation stages and multiple different transaction levels, and the coverage of the first object based on the page price in the target time period; It is determined whether to adjust the attribute data of the target product according to the relationship parameter between the first object and the target product in the target time period.

20. The method according to claim 19, wherein the attribute data of the target product comprises at least one of the following: product description data of the target product, exposure times of the target product, and promotion information of the target product.

21. A method for adjusting commodity prices, comprising: Generate a price detection request for the target product; The price detection request is sent to the server; wherein the server is used to respond to the price detection request, establish and determine whether to adjust the price of the target product according to the relationship parameters between the first object and the target product in the target time period; in the case of determining to adjust the price of the target product, determine the first object's desired price for the target product in the target time period; wherein the relationship parameters between the first object and the target product are used to reflect the attractiveness of the page price displayed at different operation stages in the target time period to the first object from multiple different operation stages and multiple different transaction levels, as well as the coverage of the first object based on the page price in the target time period; the relationship parameters between the first object and the target product are determined according to the operation data related to the target product in the target time period; the operation data includes at least two of the following: a first type of operation of browsing the target product, a second type of operation of adding the target product to the shopping cart, a third type of operation of purchasing the target product, and a fourth type of operation of repurchasing the target product; different types of operations correspond to different operation stages in the store seller's behavior operation process; the first type of operation corresponds to the browsing stage, the second type of operation corresponds to the add-to-cart stage, the third type of operation corresponds to the ordering stage, and the fourth type of operation corresponds to the repurchasing stage.

22. The method according to claim 21, further comprising: receiving a desired price of a target commodity of a first subject for a target time period sent by a server; Update the price of the target product according to the desired price.

23. A server, comprising a processor and a memory for storing processor-executable instructions, wherein the processor implements the steps of the method according to any one of claims 1 to 11 when executing the instructions.

24. A computer-readable storage medium having computer instructions stored thereon, wherein the instructions, when executed, implement the steps of the method according to any one of claims 1 to 11.

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