Retail commodity precision marketing strategy making system

Abstract

The invention relates to the technical field of big data and retail management, in particular to a retail commodity precision marketing strategy making system which comprises a data recording module, a commodity life cycle intelligent management module, a preferential binding planning module and a strategy feedback adjustment module. The data recording module is used for recording the purchase time, the purchase quantity, the real-time stock quantity, the off-shelf duration, the sold-out time, the daily sale quantity and the total sales volume of the commodities by the user; the commodity life cycle intelligent management module is used for displaying strategy information of hot commodities and unsalable commodities; the preferential binding planning module is used for displaying binding strategy information of corresponding bound sales commodities; and the strategy feedback adjustment module is used for displaying adjustment strategy information of the corresponding bundled sales commodities, so that the risks of stock overstock and stock shortage are reduced, the stock turnover efficiency is improved, the sales opportunities of unsalable commodities are increased, and it is ensured that the bundling strategy can flexibly cope with market demand changes.

Description

Technical Field

[0001] The present invention relates to the technical field of automation and intelligent production, and particularly to a system for formulating precise marketing strategies for retail goods. Background Art

[0002] With the rapid development of modern retail, the diversification of consumption patterns and market demands has made enterprises face increasingly complex challenges in commodity management and marketing. The retail commodity management system has made certain progress in the past few decades, evolving from the initial manual inventory records to computer-based inventory management and sales tracking tools. Commodity management and marketing strategies also play a crucial role in improving sales efficiency, optimizing inventory management, and enhancing customer satisfaction. Traditional retail commodity management systems have achieved inventory monitoring and sales data recording to a certain extent. However, when facing the current complex market environment, traditional retail systems expose many problems and limitations, seriously restricting the operating efficiency and competitiveness of retail enterprises. The existing systems have relatively extensive classification and life cycle management of commodities, unable to accurately provide sales strategies for hot-selling and slow-selling commodities, thus increasing inventory backlogs or missing hot-selling opportunities. It is also difficult to formulate effective bundling strategies for slow-selling products, and unable to adjust the bundling strategies for bundled products, which not only reduces the effectiveness of promotional activities but also may lead to the loss of market opportunities.

[0003] Therefore, a system for formulating precise marketing strategies for retail goods is proposed. Summary of the Invention

[0004] The problem to be solved by the present invention is that the current retail commodity management system cannot accurately provide sales strategies for hot-selling and slow-selling commodities, thus increasing inventory backlogs or missing hot-selling opportunities. There is also a problem in formulating effective bundling strategies for slow-selling products, and at the same time, unable to adjust the bundling strategies for bundled products, reducing the effectiveness of promotional activities and possibly leading to the loss of market opportunities.

[0005] The technical solution adopted by the present invention to solve its technical problems is:

[0006] A system for formulating precise marketing strategies for retail goods, including a data recording module, an intelligent management module for the commodity life cycle, a preferential bundling planning module, and a strategy feedback adjustment module.

[0007] The data recording module is used for users to record the purchase time, purchase quantity, real-time inventory quantity, off-shelf duration, sold-out time, daily sales quantity, and total sales volume of commodities.

[0008] The intelligent management module for the commodity life cycle is used to display the strategy information of hot-selling and slow-selling commodities.

[0009] The preferential bundling planning module is used to display the bundling strategy information of the corresponding bundled products;

[0010] The strategy feedback and adjustment module is used to display the adjustment strategy information of the corresponding bundled products.

[0011] Preferably, the intelligent management module for product life cycle includes a product classification unit, a priority coefficient calculation unit, a product strategy adjustment unit and a mobile terminal.

[0012] The product classification unit is network-connected to the data recording module and is used to obtain the purchase time, purchase quantity, off-shelf duration, real-time inventory quantity, sold-out time data, and total sales volume of each product in the data recording module. Then, according to the purchase time, off-shelf duration, total sales volume, and sold-out time of the product, compare with the preset hot-selling duration, hot-selling sales volume threshold, and slow-selling sales volume threshold to classify the product into hot-selling products, slow-selling products, and normal products. Then, transmit the hot-selling product data, slow-selling product data, and the corresponding purchase time, purchase quantity, off-shelf duration, real-time inventory quantity, and sold-out time data to the priority coefficient calculation unit and the product strategy adjustment unit respectively;

[0013] The priority coefficient calculation unit is network-connected to the product classification unit and is used to calculate the product priority coefficient of each hot-selling product according to the hot-selling product data and the corresponding purchase time, purchase quantity, off-shelf duration, real-time inventory quantity, and sold-out time data after receiving the hot-selling product data and the corresponding purchase time, purchase quantity, off-shelf duration, real-time inventory quantity, and sold-out time data. Then, transmit the calculated product priority coefficient of each hot-selling product and the real-time inventory quantity to the product strategy adjustment unit;

[0014] The product strategy adjustment unit is network-connected to the priority coefficient calculation unit and is used to compare the product priority coefficient and the real-time inventory quantity of each hot-selling product with the preset two priority thresholds and the hot-selling inventory threshold after receiving the product priority coefficient and the real-time inventory quantity of each hot-selling product; and is used to generate the strategy of this hot-selling product to increase the purchase quantity of this product by 1 time when the product priority coefficient exceeds the preset first threshold and the real-time inventory quantity is lower than the preset hot-selling inventory threshold; and is used to generate the strategy of this hot-selling product to increase the purchase quantity of this product by 0.5 times when the product priority coefficient is between the preset first threshold and the second threshold and the real-time inventory quantity is lower than the preset hot-selling inventory threshold; and is used to compare the real-time inventory quantity of the slow-selling product with the preset slow-selling inventory threshold after receiving the slow-selling product data and the corresponding real-time inventory quantity data; and is used to generate the strategy of this slow-selling product to stop the purchase of this slow-selling product when the real-time inventory quantity of the slow-selling product exceeds the preset slow-selling inventory threshold and the off-shelf duration is less than two months. Then, transmit the generated different product strategy information to the mobile terminal;

[0015] The mobile terminal is connected to the commodity strategy adjustment unit network and is used to display the corresponding commodity strategy information after receiving the commodity strategy information.

[0016] Preferably, the method of classifying commodities into hot-selling commodities, slow-selling commodities, and normal commodities by comparing the purchase time, off-shelf duration, total sales volume, and out-of-stock time of the commodity with the preset hot-selling duration, slow-selling duration, hot-selling sales volume threshold, and slow-selling sales volume threshold is as follows:

[0017]

[0018] Among them, is the out-of-stock time of the commodity, is the purchase time of the commodity, is the off-shelf duration of the commodity, with the unit of days, T rs is the preset hot-selling duration, with the unit of days, is the total sales volume of the commodity, with the unit of pieces, S rs is the preset hot-selling sales volume threshold, with the unit of pieces, S zs is the preset slow-selling sales volume threshold, with the unit of pieces. When the output result is 0, the commodity is a hot-selling commodity; when the output result is 1, the commodity is a slow-selling commodity; when the output result is 2, the commodity is a normal commodity.

[0019] Preferably, the calculation formula for the commodity priority coefficient of each hot-selling commodity is:

[0020]

[0021] Among them, RPC n is the commodity priority coefficient of the hot-selling commodity, is the out-of-stock time of the hot-selling commodity, is the purchase time of the hot-selling commodity, is the purchase quantity of the hot-selling commodity, with the unit of pieces, is the real-time inventory quantity of the hot-selling commodity, with the unit of pieces, is the off-shelf duration of the hot-selling commodity, with the unit of days, and α1, α2, and α3 are weight coefficients, and α1 + α2 + α3 = 1.

[0022] Preferably, the method of comparing the commodity priority coefficient and real-time inventory quantity of each hot-selling commodity with the preset two priority thresholds and the hot-selling inventory threshold is as follows:

[0023]

[0024] Among them, RPC n is the commodity priority coefficient of the hot-selling commodity, RPC fir is the first priority threshold, RPC sec is the second priority threshold, is the real-time inventory quantity of the best-selling product, with the unit of piece, S re is the threshold value of the best-selling inventory, with the unit of piece, is the purchase quantity of the best-selling product, with the unit of piece.

[0025] Preferably, the preferential bundling planning module includes a preferential coefficient calculation unit and a bundling strategy formulation unit,

[0026] The product classification unit is used to classify products into best-selling products and unsalable products according to the purchase time, off-shelf duration and out-of-stock time of the products, and then transmit the best-selling product data, unsalable product data and the corresponding purchase quantity, off-shelf duration, and real-time inventory quantity to the preferential coefficient calculation unit and the bundled preferential strategy adjustment unit respectively;

[0027] The preferential coefficient calculation unit is connected to the product classification unit through the network. After receiving the best-selling product data, unsalable product data and the corresponding purchase quantity, off-shelf duration, and real-time inventory quantity, it calculates the preferential coefficient of the unsalable product according to the purchase quantity, off-shelf duration, and real-time inventory quantity of the unsalable product, and then transmits the preferential coefficient data of the unsalable product to the bundled product screening unit;

[0028] The bundled product screening unit is connected to the preferential coefficient calculation unit through the network. After receiving the preferential coefficient data of the unsalable product, it compares the preferential coefficient data of the unsalable product with the preset bundled preferential coefficient; when the preferential coefficient data of the unsalable product is greater than the preset bundled preferential coefficient, the unsalable product is used as the bundled sales product, and then the bundled sales product data and its preferential coefficient are transmitted to the bundled preferential strategy adjustment unit;

[0029] The bundling strategy formulation unit is respectively connected to the product classification unit and the bundled product screening unit through the network. After receiving the best-selling product data and the bundled sales product data and its preferential coefficient, it determines the discount ratio of the bundled sales product data according to the bundled sales product data and its preferential coefficient; and after determining the discount ratio of the bundled sales product data, it generates a bundling strategy that when a customer purchases three best-selling products, they can purchase one bundled sales product according to the discount ratio of the bundled sales product data, and then transmits the bundling strategy information of the corresponding bundled sales product to the mobile terminal;

[0030] The mobile terminal is connected to the bundling strategy formulation unit through the network. After receiving the bundling strategy information of the corresponding bundled sales product, it displays the bundling strategy information of the corresponding bundled sales product.

[0031] Preferably, the calculation formula for the preferential coefficient of unsalable products is;

[0032]

[0033] Among them, DCm is the preferential coefficient of slow-moving goods, is the real-time inventory quantity of slow-moving goods, with the unit of piece, is the purchase quantity of slow-moving goods, with the unit of piece, is the off-shelf duration of slow-moving goods, with the unit of day. ω1 and ω2 are de-weighting coefficients, and ω1 + ω2 = 1.

[0034] Preferably, the method for determining the discount ratio of bundled sales product data based on the bundled sales product data and its preferential coefficient is as follows:

[0035]

[0036] wherein, Rd k is the discount ratio of the bundled sales product, Rd k is at least 60%, is the discount coefficient, is an integer, DC m is the preferential coefficient of the bundled sales product.

[0037] Preferably, the policy feedback and adjustment module includes a bundled sales product monitoring unit and a policy adjustment unit.

[0038] The bundling strategy formulation unit is used to transmit the bundled sales product data to the bundled sales product monitoring unit after determining the discount ratio of the bundled sales product data;

[0039] The bundled sales product monitoring unit is respectively connected to the bundling strategy formulation unit and the data recording network, and is used to obtain the daily sold quantity data of the bundled sales product in the data recording module after receiving the bundled sales product data; and is used to record every five days as a stage, calculate the daily average sales volume according to the daily sold quantity data of the bundled sales product; and is used to compare the daily average sales volume with the preset average sales volume threshold. When the daily average sales volume is lower than the preset average sales volume threshold, a policy adjustment signal is generated, and then the corresponding bundled sales product data and the policy adjustment signal are transmitted to the policy adjustment unit;

[0040] The policy adjustment unit is connected to the bundled sales product monitoring unit through a network, and is used to generate the adjustment strategy information of the corresponding bundled sales product after receiving the corresponding bundled sales product data and the policy adjustment signal for different times, and then transmit the adjustment strategy information of the corresponding bundled sales product to the mobile terminal;

[0041] The mobile terminal is connected to the policy adjustment unit through a network, and is used to display the adjustment strategy information of the corresponding bundled sales product after receiving the adjustment strategy information of the corresponding bundled sales product.

[0042] Preferably, after receiving the corresponding bundled sales product data and strategy adjustment signals for different numbers of times, the method for generating the corresponding adjustment strategy information is as follows:

[0043]

[0044] Among them, sign k is the number of times of receiving the corresponding bundled sales product data and strategy adjustment signals, Rd k is the discount ratio of the bundled sales product, and Rd k is at least 60%, is the discount ratio of the bundled sales product after strategy adjustment. After receiving the first corresponding bundled sales product data and strategy adjustment signal, the adjustment strategy is that when a customer buys two popular products each time, they can buy one bundled sales product according to the discount ratio of the bundled sales product data; after receiving the second corresponding bundled sales product data and strategy adjustment signal, the adjustment strategy is to reduce by 10% on the discount ratio of the bundled sales product data; and for receiving three or more corresponding bundled sales product data and strategy adjustment signals, the adjustment strategy is to reduce by 10% again on the discount ratio of the bundled sales product data, with a maximum reduction of 30%.

[0045] Advantages of the present invention:

[0046] 1. By comprehensively analyzing multi-dimensional data of products, the products are accurately classified into popular products, slow-moving products, and normal products. The system automatically generates a replenishment strategy for popular products and a strategy to stop purchasing for slow-moving products according to the classification results and real-time inventory data, reducing the risks of inventory backlog and out-of-stock, improving the inventory turnover efficiency, and significantly reducing the enterprise operation cost.

[0047] 2. Through the preferential coefficient calculation unit and the bundling strategy formulation unit, the preferential coefficient of slow-moving products is calculated, and slow-moving products suitable for bundling sales are selected based on the preferential coefficient. By clarifying the discount ratio and bundling slow-moving products with popular products, the sales opportunities of slow-moving products are increased, and the purchase intention of customers and the overall shopping experience are improved.

[0048] 3. The strategy feedback adjustment module compares the daily average sales volume of the bundled sales product monitored in real time with the preset average sales volume threshold. When the promotion effect does not meet the standard, the system generates a strategy adjustment signal and dynamically adjusts the bundling strategy according to the number of signals, ensuring that the bundling strategy can flexibly respond to market demand changes, maximizing the promotion effect, and avoiding the loss of market opportunities at the same time. Description of the Drawings

[0049] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0050] In the accompanying drawings:

[0051] Figure 1 It is a schematic diagram of the system module composition of the present invention. Specific embodiments

[0052] The following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0053] Please refer to Figure 1 ,

[0054] The precise marketing strategy formulation system for retail goods includes a data recording module, an intelligent management module for the product life cycle, a preferential bundling planning module, and a strategy feedback adjustment module.

[0055] The data recording module is used for users to record the purchase time, purchase quantity, real-time inventory quantity, off-shelf duration, sold-out time, daily sales quantity, and total sales volume of goods. The data recording module is a database.

[0056] The intelligent management module for the product life cycle is used to display the strategy information of hot-selling and slow-selling goods.

[0057] The preferential bundling planning module is used to display the bundling strategy information of corresponding bundled sales goods.

[0058] The strategy feedback adjustment module is used to display the adjustment strategy information of corresponding bundled sales goods.

[0059] Preferably, the intelligent management module for the product life cycle includes a product classification unit, a priority coefficient calculation unit, a product strategy adjustment unit, and a mobile terminal.

[0060] The merchandise classification unit is network - connected to the data recording module and is used to obtain the purchase time, purchase quantity, off - shelf duration, real - time inventory quantity, sold - out time data, and total sales volume of each commodity in the data recording module. Then, according to the purchase time, off - shelf duration, total sales volume, and sold - out time of the commodity, compare with the preset hot - selling duration, hot - selling sales volume threshold, and slow - selling sales volume threshold to classify the commodity into hot - selling commodities, slow - selling commodities, and normal commodities. Then, transmit the hot - selling commodity data, slow - selling commodity data, and the corresponding purchase time, purchase quantity, off - shelf duration, real - time inventory quantity, and sold - out time data to the priority coefficient calculation unit and the commodity strategy adjustment unit respectively. The merchandise classification unit is a cloud server;

[0061] The priority coefficient calculation unit is network - connected to the merchandise classification unit and is used to calculate the commodity priority coefficient of each hot - selling commodity according to the hot - selling commodity data and the corresponding purchase time, purchase quantity, off - shelf duration, real - time inventory quantity, and sold - out time data after receiving the hot - selling commodity data and the corresponding purchase time, purchase quantity, off - shelf duration, real - time inventory quantity, and sold - out time data. Then, transmit the calculated commodity priority coefficient of each hot - selling commodity and the real - time inventory quantity to the commodity strategy adjustment unit. The priority coefficient calculation unit is a cloud server;

[0062] The commodity strategy adjustment unit is network - connected to the priority coefficient calculation unit and is used to compare the commodity priority coefficient and real - time inventory quantity of each hot - selling commodity with two preset priority thresholds and the hot - selling inventory threshold after receiving the commodity priority coefficient and real - time inventory quantity of each hot - selling commodity. And it is used to generate the strategy for the hot - selling commodity to increase the purchase quantity of the commodity by 1 time when the commodity priority coefficient exceeds the preset first threshold and the real - time inventory quantity is lower than the preset hot - selling inventory threshold. And it is used to generate the strategy for the hot - selling commodity to increase the purchase quantity of the commodity by 0.5 times when the commodity priority coefficient is between the preset first threshold and the second threshold and the real - time inventory quantity is lower than the preset hot - selling inventory threshold. And it is used to compare the real - time inventory quantity of the slow - selling commodity with the preset slow - selling inventory threshold after receiving the slow - selling commodity data and the corresponding real - time inventory quantity data. And it is used to generate the strategy for the slow - selling commodity to stop purchasing the slow - selling commodity when the real - time inventory quantity of the slow - selling commodity exceeds the preset slow - selling inventory threshold and the off - shelf duration is less than two months. Then, transmit the generated different commodity strategy information to the mobile terminal. The commodity strategy adjustment unit is a cloud server;

[0063] The mobile terminal is network - connected to the commodity strategy adjustment unit and is used to display the corresponding commodity strategy information after receiving the commodity strategy information.

[0064] Preferably, the method of classifying commodities into hot - selling commodities, slow - selling commodities, and normal commodities according to the purchase time, off - shelf duration, total sales volume, and sold - out time of the commodity by comparing with the preset hot - selling duration, slow - selling duration, hot - selling sales volume threshold, and slow - selling sales volume threshold is as follows:

[0065]

[0066] Among them, is the out-of-stock time of the product, is the restocking time of the product, is the out-of-shelf duration of the product, with the unit of days, T rs is the preset popular sales duration, with the unit of days, is the total sales volume of the product, with the unit of pieces, S rs is the preset popular sales volume threshold, with the unit of pieces, S zs is the preset slow-selling sales volume threshold, with the unit of pieces. The above time is a certain day of 365 days in a year, and 365 days will be added in the next year. When the output result is 0, the product is a popular product; when the output result is 1, the product is a slow-selling product; when the output result is 2, the product is a normal product.

[0067] Preferably, the calculation formula for the product priority coefficient of each popular product is:

[0068]

[0069] Among them, RPC n is the product priority coefficient of the popular product, is the out-of-stock time of the popular product, is the restocking time of the popular product, is the restocking quantity of the popular product, with the unit of pieces, is the real-time inventory quantity of the popular product, with the unit of pieces, is the out-of-shelf duration of the popular product, with the unit of days. α1, α2 and α3 are weight coefficients, and α1 + α2 + α3 = 1.

[0070] For example, when pieces, pieces, days, α1 = 0.4, α2 = 0.4, α3 = 0.2. At this time, the calculated product priority coefficient of this popular product is 0.96.

[0071] Preferably, the method for comparing the product priority coefficient and the real-time inventory quantity of each popular product with two preset priority thresholds and a popular inventory threshold is:

[0072]

[0073] Among them, RPC n is the product priority coefficient of the popular product, RPC fir is the first priority threshold, RPC sec is the second priority threshold, is the real-time inventory quantity of the best-selling product, with the unit of piece, S re is the threshold of the best-selling inventory, with the unit of piece, is the purchase quantity of the best-selling product, with the unit of piece. By combining the purchase time, off-shelf duration, total sales volume and out-of-stock time of the product with the preset best-selling duration, slow-selling duration, best-selling sales volume threshold and slow-selling sales volume threshold, the product is accurately classified into best-selling products, slow-selling products and normal products. According to the classification results and real-time inventory data, a replenishment strategy for best-selling products and a stop-purchase strategy for slow-selling products are generated, reducing the risks of inventory backlog and out-of-stock, and improving the inventory turnover efficiency.

[0074] Preferably, the preferential bundling planning module includes a preferential coefficient calculation unit and a bundling strategy formulation unit,

[0075] The product classification unit is used to transmit the best-selling product data, slow-selling product data, and the corresponding purchase quantity, off-shelf duration, and real-time inventory quantity to the preferential coefficient calculation unit and the bundled preferential strategy adjustment unit respectively after classifying the products into best-selling products and slow-selling products according to the purchase time, off-shelf duration, and out-of-stock time of the products;

[0076] The preferential coefficient calculation unit is connected to the product classification unit through a network. After receiving the best-selling product data, slow-selling product data, and the corresponding purchase quantity, off-shelf duration, and real-time inventory quantity, it calculates the preferential coefficient of the slow-selling product based on the purchase quantity, off-shelf duration, and real-time inventory quantity of the slow-selling product, and then transmits the preferential coefficient data of the slow-selling product to the bundled product screening unit. The preferential coefficient calculation unit is an edge computing platform;

[0077] The bundled product screening unit is connected to the preferential coefficient calculation unit through a network. After receiving the preferential coefficient data of the slow-selling product, it compares the preferential coefficient data of the slow-selling product with the preset bundled preferential coefficient; when the preferential coefficient data of the slow-selling product is greater than the preset bundled preferential coefficient, it takes the slow-selling product as a bundled sales product, and then transmits the bundled sales product data and its preferential coefficient to the bundled preferential strategy adjustment unit. The bundled product screening unit is an edge computing platform;

[0078] The bundling strategy formulation unit is connected to the product classification unit and the bundled product screening unit through a network. After receiving the best-selling product data and the bundled sales product data and its preferential coefficient, it determines the discount ratio of the bundled sales product data according to the bundled sales product data and its preferential coefficient; and after determining the discount ratio of the bundled sales product data, it generates a bundling strategy that when a customer purchases three best-selling products, they can purchase one bundled sales product according to the discount ratio of the bundled sales product data, and then transmits the bundling strategy information of the corresponding bundled sales product to the mobile terminal. The bundling strategy formulation unit is an edge computing platform;

[0079] The mobile terminal is connected to the bundling strategy formulation unit network, and is configured to display the bundling strategy information of the corresponding bundled goods after receiving the bundling strategy information of the corresponding bundled goods.

[0080] Preferably, the calculation formula for the preferential coefficient of slow-moving goods is;

[0081]

[0082] Among them, DC m is the preferential coefficient of slow-moving goods, is the real-time inventory quantity of slow-moving goods, and its unit is pieces, is the purchase quantity of slow-moving goods, and its unit is pieces, is the off-shelf duration of slow-moving goods, and its unit is days. ω1 and ω2 are de-weighting coefficients, and ω1 + ω2 = 1.

[0083] For example, when pieces, pieces, days, ω1 = 0.4, ω2 = 0.6, at this time, the preferential coefficient of this slow-moving good is calculated to be 1.22.

[0084] Preferably, the method for determining the discount ratio of the bundled goods data according to the bundled goods data and its preferential coefficient is:

[0085]

[0086] Among them, Rd k is the discount ratio of the bundled goods, and Rd k is at least 60%, is the discount coefficient, is an integer, and DC m is the preferential coefficient of the bundled goods.

[0087] For example, when DC m = 1.22, At this time, the discount ratio of the bundled goods is calculated to be 75%. By calculating the preferential coefficient of slow-moving goods based on the real-time inventory quantity, purchase quantity, and off-shelf duration of slow-moving goods, and screening slow-moving goods suitable for bundling sales based on the preferential coefficient, formulating a clear discount ratio and bundling slow-moving goods with hot-selling goods increases the sales opportunities of slow-moving goods, and at the same time enhances the customer's purchase intention and overall shopping experience.

[0088] Preferably, the strategy feedback adjustment module includes a bundled goods monitoring unit and a strategy adjustment unit,

[0089] The bundling strategy formulation unit is used to transmit the bundled sales product data to the bundled sales product monitoring unit after determining the discount ratio of the bundled sales product data;

[0090] The bundled sales product monitoring unit is respectively connected to the bundling strategy formulation unit and the data recording network. It is used to obtain the daily sold quantity data of the bundled sales products in the data recording module after receiving the bundled sales product data; and it is used to record a stage of every five days, calculate the daily average sales volume according to the daily sold quantity data of the bundled sales products; and it is used to compare the daily average sales volume with the preset average sales volume threshold. When the daily average sales volume is lower than the preset average sales volume threshold, it generates a strategy adjustment signal, and then transmits the corresponding bundled sales product data and the strategy adjustment signal to the strategy adjustment unit. The bundled sales product monitoring unit is a processor;

[0091] The strategy adjustment unit is connected to the bundled sales product monitoring unit through the network. It is used to generate the adjustment strategy information of the corresponding bundled sales product after receiving the corresponding bundled sales product data and the strategy adjustment signal for different times, and then transmit the adjustment strategy information of the corresponding bundled sales product to the mobile terminal. The strategy adjustment unit is a processor;

[0092] The mobile terminal is connected to the strategy adjustment unit through the network. It is used to display the adjustment strategy information of the corresponding bundled sales product after receiving the adjustment strategy information of the corresponding bundled sales product.

[0093] Preferably, the method of generating the corresponding adjustment strategy information after receiving the corresponding bundled sales product data and the strategy adjustment signal for different times is as follows:

[0094]

[0095] Among them, sign k is the number of times of the received corresponding bundled sales product data and the strategy adjustment signal, Rd k is the discount ratio of the bundled sales product, Rd k is at least 60%; It is the discount ratio of the bundled goods after the strategy adjustment. After receiving the first set of data on the bundled goods and the strategy adjustment signal, the strategy is adjusted so that when a customer buys two popular goods, they can buy one bundled good at the discount ratio of the bundled goods data. After receiving the second set of data on the bundled goods and the strategy adjustment signal, the strategy is adjusted to reduce the discount ratio of the bundled goods data by 10%. And for three or more times of receiving the corresponding data on the bundled goods and the strategy adjustment signal, the strategy is adjusted to reduce the discount ratio of the bundled goods data by another 10%, with a maximum reduction of 30%. By real-time monitoring the daily average sales volume of the bundled goods and comparing it with the preset average sales volume threshold, when the promotion effect does not meet the standard, the system will generate a strategy adjustment signal and dynamically adjust the bundling strategy according to the number of signals. For example, in the first strategy adjustment, attract customers by lowering the bundling condition requirements, and in subsequent adjustments, further increase the discount ratio according to the sales volume of the bundled goods, achieving a closed-loop management of the promotion activity, ensuring that the bundling strategy can flexibly respond to market demand changes and avoid the loss of market opportunities.

[0096] The above content is only an example and illustration of the concept of this application. Those skilled in the art of this technology can make various modifications or supplements to the described specific embodiments or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the protection scope of this application.

Claims

1. Retail product precision marketing strategy formulation system, characterized by: It includes data recording module, product life cycle intelligent management module, discount bundling planning module and strategy feedback adjustment module. The data recording module is used for users to record the purchase time, purchase quantity, real-time inventory quantity, shelf life, sold-out time, daily sales quantity, and total sales of goods; The product life cycle intelligent management module is used to display the strategic information of hot-selling products and slow-selling products; The preferential bundling planning module is used to display the bundling strategy information of the corresponding bundled sales products; The strategy feedback adjustment module is used to display the adjustment strategy information of the corresponding bundled sales products.

2. The retail commodity precision marketing strategy formulation system according to claim 1, characterized in that: The commodity life cycle intelligent management module includes a commodity classification unit, a priority coefficient calculation unit, a commodity strategy adjustment unit and a mobile terminal. The commodity classification unit is connected to the data recording module network, and is used to obtain the purchase time, purchase quantity, shelf removal time, real-time inventory quantity, sold-out time data, and total sales of each commodity in the data recording module, and then classify the commodities into hot-selling commodities, slow-selling commodities, and normal commodities according to the preset hot-selling time, hot-selling sales threshold, and slow-selling sales threshold according to the purchase time, shelf removal time, total sales, and sold-out time of the commodities, and then transmit the hot-selling commodity data and slow-selling commodity data and the corresponding purchase time, purchase quantity, shelf removal time, real-time inventory quantity, and sold-out time data to the priority coefficient calculation unit and the commodity strategy adjustment unit respectively; The priority coefficient calculation unit is connected to the commodity classification unit network, and is used to calculate the commodity priority coefficient of each hot-selling commodity according to the hot-selling commodity data and the corresponding purchase time, purchase quantity, shelf-off time, real-time inventory quantity, and sold-out time data after receiving the hot-selling commodity data and the corresponding purchase time, purchase quantity, shelf-off time, real-time inventory quantity, and sold-out time data, and then transmit the calculated commodity priority coefficient and real-time inventory quantity of each hot-selling commodity to the commodity strategy adjustment unit; The commodity strategy adjustment unit is connected to the priority coefficient calculation unit through a network, and is used to compare the commodity priority coefficient and the real-time inventory quantity of each hot-selling commodity with the two preset priority thresholds and the hot-selling inventory threshold after receiving the commodity priority coefficient and the real-time inventory quantity of each hot-selling commodity; And when the commodity priority coefficient exceeds a preset first threshold and the real-time inventory quantity is lower than a preset hot-selling inventory threshold, the hot-selling commodity strategy is generated to increase the purchase quantity of the commodity by 1 times; and when the commodity priority coefficient is between the preset first threshold and the second threshold and the real-time inventory quantity is lower than the preset hot-selling inventory threshold, the hot-selling commodity strategy is generated to increase the purchase quantity of the commodity by 0.5 times; and used to compare the real-time inventory quantity of the slow-moving goods with a preset slow-moving inventory threshold after receiving the slow-moving goods data and the corresponding real-time inventory quantity data; And when the real-time inventory quantity of the slow-moving commodity exceeds a preset slow-moving inventory threshold and the shelf-shelf duration is less than two months, the slow-moving commodity strategy is generated to stop the purchase of the slow-moving commodity, and then the generated different commodity strategy information is transmitted to the mobile terminal; The mobile terminal is connected to the commodity policy adjustment unit through a network, and is used for displaying corresponding commodity policy information after receiving the commodity policy information.

3. The retail commodity precision marketing strategy formulation system according to claim 2, characterized in that: The method of classifying products into hot-selling products, slow-selling products and normal products is as follows: in, The time when the product is sold out, The time for receiving goods. The time it takes for a product to be removed from the shelves, in days, T rs is the preset hot-selling duration, in days. is the total sales volume of the product, in units of pieces, S rs is the preset hot-selling sales threshold, in units of pieces, S zs is the preset slow-moving sales threshold, the unit is piece. When the output result is 0, the product is a hot-selling product. When the output result is 1, the product is a slow-moving product. When the output result is 2, the product is a normal product.

4. The retail commodity precision marketing strategy formulation system according to claim 3, characterized in that: The calculation formula for the product priority coefficient of each hot-selling product is: Among them, RPC n is the product priority coefficient for hot-selling products. For hot-selling products sold out time, It is the time to purchase hot-selling products. The quantity of hot-selling products purchased, in units of pieces. It is the real-time inventory quantity of hot-selling products, in units of pieces. is the time it takes for a hot-selling product to be taken off the shelves, in days. α1, α2 and α3 are weight coefficients, and α1+α2+α3=1.

5. The retail commodity precision marketing strategy formulation system according to claim 4, characterized in that: The method for comparing the product priority coefficient and real-time inventory quantity of each hot-selling product with the two preset priority thresholds and the hot-selling inventory threshold is as follows: Among them, RPC n is the product priority coefficient of hot-selling products, RPC fir is the first priority threshold, RPC sec is the second priority threshold, is the real-time inventory quantity of hot-selling products, in units of pieces, S re is the hot-selling inventory threshold, in units of pieces. It is the purchase quantity of hot-selling products, and the unit is piece.

6. The retail commodity precision marketing strategy formulation system according to claim 2, characterized in that: The preferential bundling planning module includes a preferential coefficient calculation unit and a bundling strategy formulation unit. The commodity classification unit is used to classify commodities into hot-selling commodities and slow-selling commodities according to the purchase time, shelf removal time and sold-out time of commodities, and transmit the hot-selling commodity data and slow-selling commodity data as well as the corresponding purchase quantity, shelf removal time and real-time inventory quantity to the discount coefficient calculation unit and the bundling discount strategy adjustment unit respectively; The preferential coefficient calculation unit is connected to the commodity classification unit network, and is used to calculate the preferential coefficient of the slow-moving commodity according to the preferential coefficient of the slow-moving commodity, after receiving the hot-selling commodity data and the slow-moving commodity data and the corresponding purchase quantity, shelf-off time, and real-time inventory quantity, and then transmit the preferential coefficient data of the slow-moving commodity to the bundled commodity screening unit; The bundled product screening unit is connected to the discount coefficient calculation unit through a network, and is used to compare the discount coefficient data of the slow-moving product with the preset bundled discount coefficient after receiving the discount coefficient data of the slow-moving product; when the discount coefficient data of the slow-moving product is greater than the preset bundled discount coefficient, the slow-moving product is used as a bundled sale product, and then the bundled sale product data and its discount coefficient are transmitted to the bundled discount strategy adjustment unit; The bundling strategy formulation unit is respectively connected to the commodity classification unit and the bundled commodity screening unit through a network, and is used to determine the discount ratio of the bundled commodity data according to the bundled commodity data and its discount coefficient after receiving the hot-selling commodity data and the bundled commodity data and their discount coefficients; and is used to generate a bundling strategy after determining the discount ratio of the bundled commodity data, that is, when a customer purchases three hot-selling commodities, he or she can purchase one bundled commodity according to the discount ratio of the bundled commodity data, and then transmit the bundling strategy information of the corresponding bundled commodity to the mobile terminal; The mobile terminal is connected to the bundling strategy formulation unit through a network, and is used to display the bundling strategy information of the corresponding bundled sales products after receiving the bundling strategy information of the corresponding bundled sales products.

7. The retail commodity precision marketing strategy formulation system according to claim 6, characterized in that: The calculation formula for the discount coefficient of slow-moving goods is: Among them, DC m is the discount coefficient for slow-moving goods. It is the real-time inventory quantity of slow-moving goods, in units of pieces. is the purchase quantity of slow-moving goods, in units of pieces. It is the time it takes for unsaleable goods to be removed from the shelves, and its unit is day. ω1 and ω2 are the weighting coefficients, and ω1+ω2=1.

8. The retail commodity precision marketing strategy formulation system according to claim 7, characterized in that: The method for determining the discount ratio of bundled sales product data based on the bundled sales product data and its discount coefficient is as follows: Among them, Rd k is the discount ratio of the bundled product, Rd k The minimum is 60%, is the discount factor, is an integer, DC m It is the discount coefficient of bundled sales products.

9. The retail commodity precision marketing strategy formulation system according to claim 6, characterized in that: The strategy feedback adjustment module includes a bundled sales product monitoring unit and a strategy adjustment unit. The bundling strategy formulation unit is used to transmit the bundled sale product data to the bundled sale product monitoring unit after determining the discount ratio of the bundled sale product data; The bundled sales commodity monitoring unit is connected to the bundled sales strategy formulation unit and the data recording network respectively, and is used to obtain the daily sales quantity data of the bundled sales commodity in the data recording module after receiving the bundled sales commodity data; and is used to record every five days as a stage, and calculate the daily average sales volume according to the daily sales quantity data of the bundled sales commodity; and is used to compare the daily average sales volume with a preset average sales volume threshold, and when the daily average sales volume is lower than the preset average sales volume threshold, generate a strategy adjustment signal, and then transmit the corresponding bundled sales commodity data and the strategy adjustment signal to the strategy adjustment unit; The strategy adjustment unit is connected to the bundled sales commodity monitoring unit through a network, and is used to generate adjustment strategy information of the corresponding bundled sales commodity after receiving the corresponding bundled sales commodity data and strategy adjustment signals at different times, and then transmit the adjustment strategy information of the corresponding bundled sales commodity to the mobile terminal; The mobile terminal is connected to the policy adjustment unit through a network, and is used to display the adjustment policy information of the corresponding bundled sales products after receiving the adjustment policy information of the corresponding bundled sales products.

10. The retail commodity precision marketing strategy formulation system according to claim 9, characterized in that: After receiving the corresponding bundled sales product data and strategy adjustment signals at different times, the corresponding adjustment strategy information is generated in the following manner: Among them, sign k Rd is the number of received corresponding bundled sales product data and strategy adjustment signals. k is the discount ratio of the bundled product, Rd k The minimum is 60%, It is the discount ratio of the bundled sales goods after the strategy adjustment. After receiving the first corresponding bundled sales goods data and the strategy adjustment signal, the adjustment strategy is that when a customer purchases two hot-selling goods, he can purchase one bundled sales goods according to the discount ratio of the bundled sales goods data; after receiving the second corresponding bundled sales goods data and the strategy adjustment signal, the adjustment strategy is to reduce the discount ratio of the bundled sales goods data by 10%; and after receiving the corresponding bundled sales goods data and the strategy adjustment signal three times or more, the adjustment strategy is to reduce the discount ratio of the bundled sales goods data by 10% again, with a maximum reduction of 30%.

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