A marketing strategy generation method and system based on big data

Abstract

The embodiment of the present application relates to the field of Internet technology, and discloses a marketing strategy generation method and system based on big data. The marketing strategy generation method based on big data of the present application first determines the target product of pre-marketing based on the current hot search data, then obtains the demand index of the target product based on the hot search data, and then determines whether the historical marketing strategy of the target product needs to be adjusted based on the demand index. If the historical marketing strategy of the target product needs to be adjusted, the historical marketing strategy is adjusted according to the hot search parameter change data to obtain the target marketing strategy. If the historical marketing strategy of the target product does not need to be adjusted, the historical marketing strategy continues to be used. In this way, not only can the generation efficiency of the marketing strategy be improved and labor costs be saved, but also the marketing management technology that is dynamically adjusted according to real-time social hot spots is introduced to improve sales efficiency and input-output ratio.

Description

Technical Field

[0001] The present invention relates to the field of Internet technology, and in particular to a marketing strategy generation method and system based on big data. Background Art

[0002] In the current wave of rapid development of the e-commerce industry, e-commerce platforms not only play the role of a bridge connecting consumers and goods, but also become an important force in promoting the digital transformation of the economy. With the continuous advancement of technology and the continuous expansion of the market, the number of goods on e-commerce platforms has shown explosive growth, covering various categories from daily necessities to high-end luxury goods, greatly enriching consumers' choice space. However, behind this prosperous scene, there are also huge challenges. The trend of diversification and personalization of consumer demand is becoming increasingly obvious. Their demand for goods is no longer just about basic functionality, but more about quality, design, brand story, shopping experience and other dimensions. This change requires e-commerce platforms to have a high degree of market sensitivity and flexible adaptability to quickly respond to and meet the diversified needs of consumers.

[0003] Traditional marketing strategies rely too much on sellers to invest in traffic themselves. Although they can increase product exposure to a certain extent, their limitations are becoming increasingly prominent. First, sellers' resource allocation capabilities vary. Some sellers may not be able to conduct effective traffic activities due to limitations in funds, technology, or talent, resulting in products being buried in a sea of ​​information. Second, the accuracy of market insights is crucial to the effectiveness of traffic investment, but sellers often find it difficult to fully grasp market dynamics and consumer behavior data, resulting in a lack of targeting and effectiveness in the launch strategy, making it difficult for the input-output ratio to reach the expected level. Summary of the invention

[0004] The main purpose of the present invention is to provide a marketing strategy generation method and system based on big data, aiming to solve the technical problem in the prior art that the marketing strategy that over-relies on the seller's own investment makes it difficult to achieve the expected level of input-output ratio.

[0005] To achieve the above objectives, in a first aspect, an embodiment of the present application provides a marketing strategy generation method based on big data, which is applied to a marketing platform system, and the method includes:

[0006] Acquire search popularity data within a current preset time period based on big data, wherein the search popularity data includes hot search keyword data and hot search parameter change data;

[0007] Determine the target products for pre-marketing based on the hot search keyword data;

[0008] Determining a potential demand index of the target product according to a pre-trained demand analysis model;

[0009] Correcting the potential demand index according to market environment factors affecting the target product to obtain a target demand index;

[0010] When the target demand index is greater than or equal to an index threshold, obtaining a historical marketing strategy for the target product;

[0011] The historical marketing strategy is adjusted according to the hot search parameter change data to obtain a target marketing strategy.

[0012] In a possible implementation, adjusting the historical marketing strategy according to the hot search parameter change data to obtain a target marketing strategy includes:

[0013] Obtaining the contribution of each sub-marketing strategy in the historical marketing strategy to the sales, and determining the sub-marketing strategy with the highest contribution to the sales as the adjustment object;

[0014] The adjustment object is adjusted according to the hot search parameter change data to obtain a target marketing strategy.

[0015] In a possible implementation, the historical exposure of the target product is adjusted according to the search volume change rate of the hot search keyword to obtain a first target exposure, and / or,

[0016] The historical exposure of the substitutes of the target product is adjusted according to the search volume change rate of the hot search keyword to obtain a second target exposure.

[0017] When the search volume growth rate of the hot search keyword is greater than the first change threshold and less than the second change threshold, increasing the historical exposure of the target product to obtain a first target exposure;

[0018] When the search volume growth rate of the hot search keyword is greater than or equal to the second change threshold, the historical exposure of the target product is increased to obtain a first target exposure, and the historical exposure of the substitute of the target product is increased to obtain a second target exposure;

[0019] The second change threshold is greater than the first change threshold.

[0020] In a possible implementation, the hot search parameter change data includes a change range of a target search population, the adjustment object includes a historical exposure of a target product associated with the target search population, and the adjusting the historical marketing strategy according to the hot search parameter change data to obtain a target marketing strategy includes:

[0021] According to the change range of the target search population, the historical exposure of the target product associated with the target search population is adjusted to obtain the target exposure.

[0022] When the growth rate of the target search population is greater than the amplitude threshold, the historical exposure of the target product associated with the target search population is increased to obtain the target exposure.

[0023] In a possible implementation, determining the potential demand index of the target product according to the pre-trained demand analysis model includes:

[0024] Obtain market demand data, consumer evaluation data, product characteristics data, and platform sales growth data for the target product;

[0025] The market demand data, consumer evaluation data, product characteristics data and platform sales growth data of the target product are input into a pre-trained demand analysis model to determine the potential demand index of the target product. The demand analysis model satisfies the following expression:

[0026]

[0027] Among them, PDI is the potential demand index, is the market demand data, Cus is the current sales of the target product, Mac is the total market sales, is the consumer evaluation data, Por is the number of positive reviews of the target product, Tor is the total number of reviews of the target product, is the product characteristic data, is the importance weight of each feature of the target product, is the satisfaction score of the corresponding characteristic, Ecg Ing is the platform sales growth data, Ecg is the platform total sales growth rate, Ing is the sales growth rate of the target product, W 1 , W 2 , W 3 , W 4 are the corresponding influence weights, 0 <W 1 , W 2 , W 3 , W 4 <1.

[0028] In a possible implementation, the market environment factors affecting the target product include the price of substitute products and the price of complementary products, and the step of correcting the potential demand index according to the market environment factors affecting the target product to obtain the target demand index includes:

[0029] Obtaining price change data of substitute products and complementary products of the target product;

[0030] The target demand index is obtained by correcting the potential demand index according to the price change data of the substitute products and the price change data of the complementary products of the target product.

[0031] In a possible implementation, the step of correcting the potential demand index according to the price changes of substitutes of the target product and the price changes of complementary products to obtain the target demand index includes:

[0032] Determine that the average price of the substitutes of the target product increases, and make a positive correction to the potential demand index to obtain the target demand index; conversely, make a negative correction to the potential demand index to obtain the target demand index;

[0033] When it is determined that the average price of the complementary products of the target product increases, the potential demand index is negatively corrected to obtain the target demand index. Conversely, the potential demand index is positively corrected to obtain the target demand index.

[0034] In a possible implementation, the market environment factors affecting the target product include environmental parameters, and the step of correcting the potential demand index according to the market environment factors affecting the target product to obtain the target demand index includes:

[0035] Acquire the environmental parameters that are most associated with the target product, wherein the environmental parameters include the ambient temperature and the duration of the target ambient temperature;

[0036] The potential demand index is corrected according to the ambient temperature and the duration of the high temperature environment to obtain a target demand index.

[0037] In a possible implementation, the correcting the potential demand index according to the ambient temperature and the duration of the target temperature environment to obtain the target demand index includes:

[0038] Determining that the ambient temperature and the duration of the target ambient temperature have a positive impact on the sales of the target product, and performing a positive correction on the potential demand index to obtain a target demand index;

[0039] It is determined that the duration of the ambient temperature and the target ambient temperature has a negative impact on the sales of the target product, and the potential demand index is negatively corrected to obtain a target demand index.

[0040] In a second aspect, an embodiment of the present application further provides a marketing platform system, comprising a memory and a processor, wherein the memory is used to store program code, and the processor is used to call the program code to execute the method described in the first aspect.

[0041] Different from the prior art, the embodiment of the present application provides a method for generating a marketing strategy based on big data, firstly, based on big data, the search heat data in the current preset time period is obtained, the search heat data includes hot search keyword data and hot search parameter change data; then, the target product for pre-marketing is determined according to the hot search keyword data; then, the potential demand index of the target product is determined according to the pre-trained demand analysis model; then, the potential demand index is corrected according to the market environment factors affecting the target product to obtain the target demand index; when the target demand index is greater than or equal to the index threshold, the historical marketing strategy for the target product is obtained; finally, the historical marketing strategy is adjusted according to the hot search parameter change data to obtain the target marketing strategy. That is, first, the target product for pre-marketing is determined according to the current hot search data, then the demand index of the target product is obtained according to the hot search data, and then, according to the demand index, it is determined whether the historical marketing strategy of the target product needs to be adjusted. If the historical marketing strategy of the target product needs to be adjusted, the historical marketing strategy is adjusted according to the hot search parameter change data to obtain the target marketing strategy. If the historical marketing strategy of the target product does not need to be adjusted, the historical marketing strategy continues to be used. In this way, not only can the efficiency of generating marketing strategies be improved and labor costs be saved, but also the introduction of marketing management technology that can be dynamically adjusted according to real-time social hot spots can improve sales efficiency and input-output ratio. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0043] Figure 1 A flowchart of a method for generating a marketing strategy based on big data in some embodiments of the present application;

[0044] Figure 2 This is a schematic diagram of the hot search page of the e-commerce platform in some embodiments of the present application;

[0045] Figure 3 This is a specific flow chart of step S400 in an embodiment of the present application;

[0046] Figure 4 This is a specific flow chart of step S400 in another embodiment of the present application;

[0047] Figure 5 This is a schematic diagram of the hardware structure of the marketing platform system in some embodiments of the present application.

[0048] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0051] In addition, the descriptions of "first", "second", etc. in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, "and / or" in the full text includes three solutions. Taking A and / or B as an example, it includes technical solution A, technical solution B, and technical solution that satisfies both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in this field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0052] In the current wave of rapid development of the e-commerce industry, e-commerce platforms not only play the role of a bridge connecting consumers and goods, but also become an important force in promoting the digital transformation of the economy. With the continuous advancement of technology and the continuous expansion of the market, the number of goods on e-commerce platforms has shown explosive growth, covering various categories from daily necessities to high-end luxury goods, greatly enriching consumers' choice space. However, behind this prosperous scene, there are also huge challenges. The trend of diversification and personalization of consumer demand is becoming increasingly obvious. Their demand for goods is no longer just about basic functionality, but more about quality, design, brand story, shopping experience and other dimensions. This change requires e-commerce platforms to have a high degree of market sensitivity and flexible adaptability to quickly respond to and meet the diversified needs of consumers.

[0053] Traditional marketing strategies rely too much on sellers to invest in traffic themselves. Although they can increase product exposure to a certain extent, their limitations are becoming increasingly prominent. First, sellers' resource allocation capabilities vary. Some sellers may not be able to conduct effective traffic activities due to limitations in funds, technology, or talent, resulting in products being buried in a sea of ​​information. Second, the accuracy of market insights is crucial to the effectiveness of traffic investment, but sellers often find it difficult to fully grasp market dynamics and consumer behavior data, resulting in a lack of targeting and effectiveness in the launch strategy, making it difficult for the input-output ratio to reach the expected level.

[0054] In view of the above problems, this application proposes a marketing strategy generation method based on big data, which is applied to the marketing platform system, such as Figure 1-Figure 4 As shown, the following takes the marketing platform system executing the marketing strategy generation method based on big data as an example. It should be noted that although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order from here. Figure 1 The method includes the following steps S100 to S600:

[0055] S100, obtaining search popularity data within a current preset time period based on big data, wherein the search popularity data includes hot search keyword data and hot search parameter change data;

[0056] For example, the e-commerce platform system collects search popularity data within a preset time period (such as within the current week) based on the platform's search data, and analyzes and processes the search popularity data to obtain hot search keyword data and hot search parameter change data, such as Figure 2 As shown, the hot search keyword data may include but is not limited to the name, ranking and corresponding search quantity of the hot search keyword, and the hot search parameter change data may include but is not limited to the search volume growth rate of the hot search keyword, the change rate of the search population, etc.

[0057] It can be understood that the hot search keyword data can illustrate the current social hot spots. Therefore, the system can lock in what type of products are currently pursued by the public based on the hot spots; the hot search parameter change data can indicate the changes in social hot spots, such as whether the popularity of the current product has decreased, whether the group of people pursuing the product has changed, etc.

[0058] S200, determining a target product for pre-marketing according to the hot search keyword data;

[0059] Specifically, when determining target products, the e-commerce platform system will give priority to products that are highly relevant to hot search keywords, have differentiated advantages and have strong market demand. Secondly, it will combine market demand, consumer preferences and market competition situation to screen out products that meet the strategic positioning of the e-commerce platform and the needs of the target audience, thereby obtaining the target products.

[0060] For example, Figure 2 As shown, when the top-ranked hot search keyword is "baby daily straw cup", the system filters out the target product as a children's water cup with a straw.

[0061] S300, determining the potential demand index of the target product according to a pre-trained demand analysis model;

[0062] It is understandable that after determining the target product, in order to formulate a suitable marketing strategy, the e-commerce platform needs to deeply analyze the demand intensity of the target product, that is, the potential demand index. The potential demand index is an important indicator to measure the potential demand intensity and possibility of the target market for the product. It helps the e-commerce platform understand the market acceptance, competition situation and future sales potential.

[0063] In one embodiment, the step S300: determining the potential demand index of the target product according to the pre-trained demand analysis model includes:

[0064] Obtain market demand data, consumer evaluation data, product characteristics data, and platform sales growth data for the target product;

[0065] The market demand data, consumer evaluation data, product characteristics data and platform sales growth data of the target product are input into a pre-trained demand analysis model to determine the potential demand index of the target product. The demand analysis model satisfies the following expression:

[0066]

[0067] Among them, PDI is the potential demand index, is the market demand data, Cus is the current sales of the target product, Mac is the total market sales, is the consumer evaluation data, Por is the number of positive reviews of the target product, Tor is the total number of reviews of the target product, is the product characteristic data, is the importance weight of each feature of the target product, is the satisfaction score of the corresponding characteristic, Ecg Ing is the platform sales growth data, Ecg is the platform total sales growth rate, Ing is the sales growth rate of the target product, W 1 , W2 , W 3 , W 4 are the corresponding influence weights, 0 <W 1 , W 2 , W 3 , W 4 <1.

[0068] Specifically, market demand data is measured by the ratio of the current sales volume of the target product Cus to the total market sales volume Mac, which reflects the penetration rate and growth potential of the target product in the existing market; consumer evaluation data is measured by the ratio of positive evaluations Por to total evaluations Tor. The higher this ratio is, the higher the consumer satisfaction and preference for the product is; assuming that a product has multiple key features (such as function, performance, design, etc.), each feature has an importance weight and satisfaction scores , using the weighted sum to comprehensively evaluate the impact of product characteristics on potential demand; the platform sales growth data is measured by the product of the platform's total sales growth rate Ecg and the target product's sales growth rate Ing, which represents the growth performance of the target product in the overall growth trend of the platform and is an indicator for measuring growth potential. Therefore, market demand data, consumer evaluation data, product characteristics data, and platform sales growth data all have a certain impact on the target product's potential demand index, and the degree of impact varies; and the weight W in the demand analysis model of the embodiment of the present application is 1 ,W 2 ,W 3 ,W 4 The weight W can be determined by statistical methods (such as regression analysis, machine learning, etc.) or by assigning values ​​based on economic theory to ensure that they can accurately reflect the impact of various factors on potential demand. 1 ,W 2 ,W 3 ,W 4 To facilitate the training of the model, when the target product is a necessity, the market demand data and platform sales growth data have little impact on the marketing of the target product. Therefore, the value W is assigned. 1 =0.2,W 2 =0.3,W 3 =0.3,W 4 =0.2; When the target product is a non-necessity, the market demand data and platform sales growth data have a greater impact on the marketing of the target product. Therefore, the value W is assigned 1 =0.3,W 2 =0.2,W 3 =0.2,W 4 =0.3.

[0069] S400, correcting the potential demand index according to market environment factors affecting the target product to obtain a target demand index;

[0070] It can be understood that the potential demand index measures the intensity of the target market's potential demand for the product. However, the demand for goods is not only related to the above-mentioned market demand data, consumer evaluation data, product feature data and platform sales growth data, but also affected by external market environmental factors. External market environmental factors can be the prices of complementary products or substitutes or external environmental conditions. For example, the demand for umbrellas surges on rainy days, and the demand for air conditioners and fans surges in hot weather.

[0071] In one embodiment, the market environment factors affecting the target product include the price of substitute products and the price of complementary products. The step S400: correcting the potential demand index according to the market environment factors affecting the target product to obtain the target demand index includes:

[0072] S410, obtaining price change data of substitute products and complementary products of the target product;

[0073] S420: Correct the potential demand index according to the price change data of substitutes of the target product and the price change data of complementary products to obtain a target demand index.

[0074] Specifically, the e-commerce platform system can analyze the substitutes of the target product based on the target product, and further analyze the price change data of the substitutes and the price change data of the complementary products. When the average price of the substitutes of the target product increases, it means that users tend to buy cheaper substitutes, which usually has a positive impact on the demand for the target product, that is, it increases the demand for the target product. At this time, the potential demand index is positively corrected to obtain the target demand index, that is, the benchmark index value is added to the potential demand index; when the average price of the substitutes of the target product decreases, it means that users tend to buy cheaper substitutes, which usually has a negative impact on the demand for the target product, that is, it reduces the demand for the target product. At this time, the potential demand index is negatively corrected to obtain the target demand index, that is, the benchmark index value is subtracted from the potential demand index. When the average price of the complementary products of the target product increases, due to the increase in the prices of the supporting products, it usually has a negative impact on the demand for the target product, that is, it reduces the demand for the target product. At this time, the potential demand index is negatively corrected to obtain the target demand index, that is, the benchmark index value is subtracted from the potential demand index; when the average price of the complementary products of the target product decreases, due to the decrease in the prices of the supporting products, it usually has a positive impact on the demand for the target product, that is, it increases the demand for the target product. At this time, the potential demand index is positively corrected to obtain the target demand index, that is, the benchmark index value is added to the potential demand index.

[0075] When calculating the actual demand index of a target product, the embodiment of the present application first inputs the market demand data, consumer evaluation data, product characteristic data and platform sales growth data of the target product into a pre-trained demand analysis model to determine the potential demand index of the target product. The potential demand index is then corrected according to the price change data of substitute products and complementary products to obtain the target demand index. In this way, the accuracy of the actual demand index of the target product is greatly improved, providing accurate data support for subsequent adjustments to marketing strategies.

[0076] In another embodiment, the market environment factors affecting the target product include environmental parameters, and the step S400: correcting the potential demand index according to the market environment factors affecting the target product to obtain the target demand index includes:

[0077] S430, acquiring the environmental parameter with the highest correlation with the target product, wherein the environmental parameter includes the ambient temperature and the duration of the target ambient temperature;

[0078] S440: Correct the potential demand index according to the ambient temperature and the duration of the high temperature environment to obtain a target demand index.

[0079] For example, when the target product is a sun hat, the most correlated environmental parameters of the sun hat can be obtained, such as temperature parameters and the duration of high temperature. Since high temperature environment and high temperature duration can have a positive impact on the sales of sun hats, the demand will increase to a certain extent; rainy low temperature environment and rain duration can have a negative impact on the sales of sun hats, and the demand will decrease to a certain extent. Therefore, if the ambient temperature and the duration of the target ambient temperature have a positive impact on the sales of the target product, the potential demand index is positively corrected to obtain the target demand index, that is, the benchmark index value is added to the potential demand index; if the ambient temperature and the duration of the target ambient temperature have a negative impact on the sales of the target product, the potential demand index is negatively corrected to obtain the target demand index, that is, the benchmark index value is subtracted from the potential demand index.

[0080] In the embodiment of the present application, when calculating the actual demand index of a target product, the market demand data, consumer evaluation data, product characteristic data and platform sales growth data of the target product are first input into a pre-trained demand analysis model to determine the potential demand index of the target product, and then the potential demand index is corrected according to the ambient temperature and the duration of the high temperature environment to obtain the target demand index. In this way, the accuracy of the actual demand index of the target product is greatly improved, providing accurate data support for subsequent adjustments to marketing strategies.

[0081] S500: when the target demand index is greater than or equal to the index threshold, obtaining a historical marketing strategy for the target product;

[0082] It can be understood that when the target demand index is greater than or equal to the index threshold, it means that the demand intensity of the target product is high. In order to improve the overall input-output ratio of the e-commerce platform, it is necessary to optimize and adjust the marketing strategy of the target product. For example, adjust the exposure, price, exposure method, etc. of the target product, so as to maximize the exposure of the target product by using natural traffic.

[0083] The historical marketing strategy refers to the previous marketing strategy for the target product, for example, the historical exposure, historical price, historical exposure method and other marketing strategies for the target product.

[0084] S600: Adjust the historical marketing strategy according to the hot search parameter change data to obtain a target marketing strategy.

[0085] The marketing strategy for the target product includes a variety of marketing methods, that is, multiple sub-marketing strategies, such as adjusting exposure, adjusting exposure methods, and adjusting product prices, etc. Each marketing method will have different degrees of impact on the sales volume of the final product. Therefore, accurate analysis of the contribution of each sub-marketing strategy to sales volume or sales volume is crucial to the formulation of marketing strategies.

[0086] In one embodiment, the step S600: adjusting the historical marketing strategy according to the hot search parameter change data to obtain a target marketing strategy includes:

[0087] S610, obtaining the contribution of each sub-marketing strategy in the historical marketing strategy to the sales, and determining the sub-marketing strategy with the highest contribution to the sales as the adjustment object;

[0088] S620: Adjust the adjustment object according to the hot search parameter change data to obtain a target marketing strategy.

[0089] Specifically, in order to obtain the contribution of each sub-marketing strategy to sales in the historical marketing strategy and determine the adjustment object accordingly, it can be achieved in the following ways: collect total sales data over a period of time in the past (such as one year, half a year or quarter), record detailed information such as the implementation time, scope, and input cost of each sub-marketing strategy, and decompose the total sales to each sub-marketing strategy as much as possible. This can be achieved through market research, customer feedback, sales records and other methods; then, sales percentage, ROI (return on investment), profit contribution and other indicators can be used to measure the contribution of sub-marketing strategies. For example, if the sales percentage is used to measure the contribution of a sub-marketing strategy, then contribution = (sales brought by the sub-strategy / total sales) 100%, and then sort all sub-marketing strategies from high to low according to contribution, and finally determine the sub-marketing strategy with the highest contribution as the adjustment object. After obtaining the adjustment object, the adjustment object can be adjusted according to the hot search parameter change data to obtain the target marketing strategy, so as to improve the accuracy and efficiency of marketing strategy generation, and improve sales efficiency and input-output ratio.

[0090] In one embodiment, the hot search parameter change data includes a search change rate of a hot search keyword, the adjustment object includes a historical exposure of the target product and / or a historical exposure of a substitute product, and the step S600: adjusting the historical marketing strategy according to the hot search parameter change data to obtain a target marketing strategy includes:

[0091] Adjusting the historical exposure of the target product according to the search volume change rate of the hot search keyword to obtain a first target exposure, and / or,

[0092] The historical exposure of the substitutes of the target product is adjusted according to the search volume change rate of the hot search keyword to obtain a second target exposure.

[0093] When the search volume growth rate of the hot search keyword is greater than the first change threshold and less than the second change threshold, increasing the historical exposure of the target product to obtain a first target exposure;

[0094] When the search volume growth rate of the hot search keyword is greater than or equal to the second change threshold, the historical exposure of the target product is increased to obtain the first target exposure, and the historical exposure of the substitutes of the target product is increased to obtain the second target exposure; wherein the second change threshold is greater than the first change threshold.

[0095] Specifically, when it is judged that increasing the exposure of a product can more effectively improve the input-output ratio of the product than other sub-marketing strategies, it means that the exposure of the product is the object of adjustment of the historical marketing strategy. At this time, the exposure of the product can be adjusted according to the change in the search volume of the hot search keyword. When the search volume growth rate of the hot search keyword is greater than the first change threshold and less than the second change threshold, it means that the popularity of the hot search keyword is relatively large, and the demand for the target product is relatively large. At this time, the historical exposure of the target product can be increased to obtain the first target exposure. The target product can be exposed and marketed with the first target exposure, which can improve the sales input-output ratio of the target product, thereby improving the sales efficiency of the e-commerce platform; when the search volume growth rate of the hot search keyword is greater than or equal to the second change threshold, it means that the popularity of the hot search keyword is very large. Since the platform stock of the target product is limited, it will affect the demand for substitutes of the target product to a certain extent. At this time, the historical exposure of the target product is increased to obtain the first target exposure, and the historical exposure of the substitute of the target product is increased to obtain the second target exposure. In this way, the sales efficiency and input-output ratio of the e-commerce platform can be further improved.

[0096] In another embodiment, the hot search parameter change data includes the change range of the target search population, the adjustment object includes the historical exposure of the target product associated with the target search population, and the step S600: adjusting the historical marketing strategy according to the hot search parameter change data to obtain the target marketing strategy includes:

[0097] According to the change range of the target search population, the historical exposure of the target product associated with the target search population is adjusted to obtain the target exposure.

[0098] When the growth rate of the target search population is greater than the amplitude threshold, the historical exposure of the target product associated with the target search population is increased to obtain the target exposure.

[0099] Specifically, when it is judged that increasing the exposure of the product can more effectively improve the input-output ratio of the product than other sub-marketing strategies, it means that the exposure of the product is the adjustment object of the historical marketing strategy. At this time, the exposure of the product can be adjusted according to the change range of the target search population. It should be noted that the target search population may be highly correlated with a part of the target product. For example, for young groups, they may pursue the appearance of the product more, while for middle-aged or elderly groups, they pursue the cost performance of the product more. Therefore, young people are highly correlated with target products with beautiful appearance, and non-young people are highly correlated with target products with high cost performance. Therefore, when the growth rate of the target search population is greater than the amplitude threshold, the historical exposure of the target product associated with the target search population is increased to obtain the target exposure. For example, when the young group in the umbrella hot search population is surging, the fashionable, lightweight and beautiful umbrellas can be appropriately added as the exposure adjustment object, that is, the historical exposure of such umbrellas is increased to obtain the target exposure. In this way, the sales input-output ratio of the target product can be improved, thereby improving the sales efficiency of the e-commerce platform.

[0100] Based on this, the marketing strategy generation method based on big data of the present application first determines the target product for pre-marketing based on the current hot search data, then obtains the demand index of the target product based on the hot search data, and then determines whether the historical marketing strategy of the target product needs to be adjusted based on the demand index. If the historical marketing strategy of the target product needs to be adjusted, the historical marketing strategy is adjusted according to the hot search parameter change data to obtain the target marketing strategy. If the historical marketing strategy of the target product does not need to be adjusted, the historical marketing strategy continues to be used. In this way, adjustments are made based on the historical marketing strategy according to the current hot search data without re-applying the marketing strategy. This can not only improve the efficiency of generating marketing strategies and save labor costs, but also introduce marketing management technology that is dynamically adjusted according to real-time social hot spots, which can greatly improve sales efficiency and input-output ratio.

[0101] The present application also provides a marketing platform system. Figure 5 , Figure 5 A schematic diagram of the hardware structure of a marketing platform system provided for some embodiments of the present application; comprising a memory 110 and a processor 120, wherein the memory 110 is used to store program code, and the processor 120 is used to call the program code to execute the method described above.

[0102] Among them, the processor 120 is used to provide computing and control capabilities to control the marketing platform system to perform corresponding tasks, for example, to control the marketing platform system to execute the marketing strategy generation method based on big data in any of the above method embodiments, the method comprising: obtaining search popularity data within a current preset time period based on big data, the search popularity data including hot search keyword data and hot search parameter change data; determining the target product for pre-marketing based on the hot search keyword data; determining the potential demand index of the target product based on a pre-trained demand analysis model; correcting the potential demand index according to market environment factors affecting the target product to obtain a target demand index; when the target demand index is greater than or equal to an index threshold, obtaining a historical marketing strategy for the target product; adjusting the historical marketing strategy according to the hot search parameter change data to obtain a target marketing strategy.

[0103] The processor 120 may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), a hardware chip, or any combination thereof; it may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The above-mentioned PLD may be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0104] The memory 110, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer executable programs and modules, such as program instructions / modules corresponding to the marketing strategy generation method based on big data in the embodiments of the present application. The processor 120 can implement the marketing strategy generation method based on big data in any of the above method embodiments by running the non-transitory software programs, instructions and modules stored in the memory 110.

[0105] Specifically, the memory 110 may include a volatile memory (VM), such as a random access memory (RAM); the memory 110 may also include a non-volatile memory (NVM), such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD) or other non-transitory solid-state storage device; the memory 110 may also include a combination of the above types of memory.

[0106] To sum up, the marketing platform system of the present application adopts the technical solution of any one of the above-mentioned embodiments of the marketing strategy generation method based on big data. Therefore, it has at least the beneficial effects brought by the technical solution of the above-mentioned embodiments, which will not be elaborated one by one here.

[0107] The embodiment of the present application also provides a computer-readable storage medium, such as a memory including a program code, and the program code can be executed by a processor to complete the marketing strategy generation method based on big data in the above embodiment. For example, the computer-readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CDROM), a magnetic tape, a floppy disk, and an optical data storage device.

[0108] The embodiment of the present application also provides a computer program product, which includes one or more program codes, and the program codes are stored in a computer-readable storage medium. The processor of the marketing platform system reads the program code from the computer-readable storage medium, and the processor executes the program code to complete the steps of the marketing strategy generation method based on big data provided in the above embodiment.

[0109] A person skilled in the art will appreciate that all or part of the steps for implementing the above embodiments may be accomplished by hardware or by hardware associated with a program code, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0110] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0111] Through the description of the above embodiments, it can be clearly understood by those skilled in the art that each embodiment can be implemented by means of software plus a general hardware platform, or by hardware. It can be understood by those skilled in the art that all or part of the processes in the above embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0112] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the inventive concept of the present invention, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A marketing strategy generation method based on big data, applied to a marketing platform system, characterized in that: The method comprises: Acquire search popularity data within a current preset time period based on big data, wherein the search popularity data includes hot search keyword data and hot search parameter change data; Determine the target products for pre-marketing based on the hot search keyword data; Determining a potential demand index of the target product according to a pre-trained demand analysis model; Correcting the potential demand index according to market environment factors affecting the target product to obtain a target demand index; When the target demand index is greater than or equal to an index threshold, obtaining a historical marketing strategy for the target product; Adjust the historical marketing strategy according to the hot search parameter change data to obtain a target marketing strategy; Wherein, determining the potential demand index of the target product according to the pre-trained demand analysis model includes: Obtain market demand data, consumer evaluation data, product characteristics data, and platform sales growth data for the target product; The market demand data, consumer evaluation data, product characteristics data and platform sales growth data of the target product are input into a pre-trained demand analysis model to determine the potential demand index of the target product. The demand analysis model satisfies the following expression: Among them, PDI is the potential demand index, is the market demand data, Cus is the current sales of the target product, Mac is the total market sales, is the consumer evaluation data, Por is the number of positive reviews of the target product, Tor is the total number of reviews of the target product, For product characteristic data, is the importance weight of each feature of the target product, is the satisfaction score of the corresponding characteristic, Ecg Ing is the platform sales growth data, Ecg is the platform total sales growth rate, Ing is the sales growth rate of the target product, W1, W2, W3, W4 are the corresponding influence weights, 0 <W1、W2、W3、W4<1。 2. The method for generating marketing strategies based on big data according to claim 1, characterized in that: The adjusting the historical marketing strategy according to the hot search parameter change data to obtain a target marketing strategy includes: Obtaining the contribution of each sub-marketing strategy in the historical marketing strategy to the sales, and determining the sub-marketing strategy with the highest contribution to the sales as the adjustment object; The adjustment object is adjusted according to the hot search parameter change data to obtain a target marketing strategy.

3. The method for generating marketing strategies based on big data according to claim 2, characterized in that: The hot search parameter change data includes the search change rate of the hot search keyword, the adjustment object includes the historical exposure of the target product and / or the historical exposure of the substitute, and the historical marketing strategy is adjusted according to the hot search parameter change data to obtain the target marketing strategy, including: Adjusting the historical exposure of the target product according to the search volume change rate of the hot search keyword to obtain a first target exposure, and / or, The historical exposure of the substitutes of the target product is adjusted according to the search volume change rate of the hot search keyword to obtain a second target exposure. When the search volume growth rate of the hot search keyword is greater than the first change threshold and less than the second change threshold, increasing the historical exposure of the target product to obtain a first target exposure; When the search volume growth rate of the hot search keyword is greater than or equal to the second change threshold, the historical exposure of the target product is increased to obtain a first target exposure, and the historical exposure of the substitute of the target product is increased to obtain a second target exposure; The second change threshold is greater than the first change threshold.

4. The method for generating marketing strategies based on big data according to claim 2, characterized in that: The hot search parameter change data includes the change range of the target search population, the adjustment object includes the historical exposure of the target product associated with the target search population, and the target marketing strategy is obtained by adjusting the historical marketing strategy according to the hot search parameter change data, including: According to the change range of the target search population, the historical exposure of the target product associated with the target search population is adjusted to obtain the target exposure. When the growth rate of the target search population is greater than the amplitude threshold, the historical exposure of the target product associated with the target search population is increased to obtain the target exposure.

5. The method for generating marketing strategies based on big data according to claim 1, characterized in that: The market environment factors affecting the target product include the price of substitute products and the price of complementary products. The target demand index is obtained by correcting the potential demand index according to the market environment factors affecting the target product, including: Obtaining price change data of substitute products and complementary products of the target product; The target demand index is obtained by correcting the potential demand index according to the price change data of the substitute products and the price change data of the complementary products of the target product.

6. The method for generating marketing strategies based on big data according to claim 5, characterized in that: The step of correcting the potential demand index according to the price changes of the substitute products of the target product and the price changes of the complementary products to obtain the target demand index includes: Determine that the average price of the substitutes of the target product increases, and make a positive correction to the potential demand index to obtain the target demand index; conversely, make a negative correction to the potential demand index to obtain the target demand index; When it is determined that the average price of the complementary products of the target product increases, the potential demand index is negatively corrected to obtain the target demand index. Conversely, the potential demand index is positively corrected to obtain the target demand index.

7. The method for generating marketing strategies based on big data according to claim 1, characterized in that: The market environment factors affecting the target product include environmental parameters, and the correction of the potential demand index according to the market environment factors affecting the target product to obtain the target demand index includes: Acquire the environmental parameters that are most associated with the target product, wherein the environmental parameters include the ambient temperature and the duration of the target ambient temperature; The potential demand index is corrected according to the ambient temperature and the duration of the high temperature environment to obtain a target demand index.

8. The method for generating marketing strategies based on big data according to claim 7, characterized in that: The step of correcting the potential demand index according to the ambient temperature and the duration of the target temperature environment to obtain a target demand index includes: Determining that the ambient temperature and the duration of the target ambient temperature have a positive impact on the sales of the target product, and performing a positive correction on the potential demand index to obtain a target demand index; It is determined that the duration of the ambient temperature and the target ambient temperature has a negative impact on the sales of the target product, and the potential demand index is negatively corrected to obtain a target demand index.

9. A marketing platform system, characterized in that: The method comprises a memory and a processor, wherein the memory is used to store program codes, and the processor is used to call the program codes to execute the method according to any one of claims 1 to 8.

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