E-commerce precision marketing dynamic strategy generation method based on big data

Abstract

The invention relates to the technical field of e-commerce and computers, in particular to an e-commerce precision marketing dynamic strategy generation method based on big data, and the method comprises the following steps: S1, data collection and integration; s2, constructing a user portrait; s3, modeling commodity characteristics; s4, performing real-time data analysis and prediction; s5, generating a marketing dynamic strategy; according to the method, the user portraits are constructed through big data analysis and a computer algorithm, the features, interests and demands of each user can be deeply known, advertisements and promotion information can be accurately pushed to target users based on the generated marketing dynamic strategy, the pertinence and effectiveness of marketing information are improved, and the marketing efficiency is improved. Therefore, the user click rate and the conversion rate are obviously improved.

Description

Technical Field

[0001] The present invention relates to the fields of e-commerce and computer technology, and in particular to a method for generating dynamic strategies for e-commerce precision marketing based on big data. Background Art

[0002] In today's digital age, the e-commerce industry is experiencing rapid growth and increasingly fierce market competition. E-commerce platforms have accumulated vast amounts of user data, including browsing histories, purchasing behaviors, and search preferences. Furthermore, data on product information and market trends is also growing. However, fully leveraging this data for targeted marketing has become a major challenge for e-commerce companies.

[0003] Traditional e-commerce marketing approaches rely primarily on experience and simple data analysis, lacking a deep understanding of user needs and real-time insights. Marketing campaigns often employ a one-size-fits-all approach, pushing identical advertising and promotional messages to all users. This results in poor marketing effectiveness, low user conversion rates, and significant waste of marketing resources. Furthermore, traditional marketing approaches struggle to adapt marketing strategies to market changes and real-time user behavior, making them unable to meet the rapidly changing needs of e-commerce businesses.

[0004] The development of computer technology has made it possible to address these issues. Big data technology enables efficient storage, processing, and analysis of massive amounts of e-commerce data, unlocking the potential value inherent in this data. Machine learning and artificial intelligence algorithms can identify user behavior patterns and preferences from this data, predicting their needs and purchasing intentions. However, there is currently a lack of an effective method that deeply integrates big data technology, computer algorithms, and e-commerce marketing operations to achieve the real-time generation and optimization of dynamic precision marketing strategies. To this end, this paper proposes a method for generating dynamic precision marketing strategies for e-commerce based on big data. Summary of the Invention

[0005] In view of this, the present invention provides a method for generating dynamic strategies for e-commerce precision marketing based on big data to solve or alleviate the technical problems existing in the prior art and at least provide a beneficial option.

[0006] The technical solution of the present invention is implemented as follows: a method for generating a dynamic strategy for e-commerce precision marketing based on big data, comprising the following steps:

[0007] S1, data collection and integration;

[0008] S2, user portrait construction;

[0009] S3, product feature modeling;

[0010] S4, real-time data analysis and prediction;

[0011] S5. Generation of dynamic marketing strategies.

[0012] Further preferably, in said S1, data is collected from various data sources of the e-commerce platform, including user registration information, user browsing behavior data, user purchasing behavior data, product information data, and market competition data. The collected data is cleaned to remove duplicate, erroneous, and missing data. At the same time, the data is standardized to convert data of different formats and ranges into a unified format for subsequent analysis and processing.

[0013] Further preferably, in said S2, user characteristics, including basic characteristics, behavioral characteristics and interest characteristics, are extracted from the cleaned and preprocessed data, and a clustering algorithm is used to cluster users, and users with similar characteristics are divided into different user groups. Each user group has similar behavioral patterns and demand characteristics, and a user portrait is generated for each user group, including the group's basic characteristics, behavioral characteristics, interest characteristics and consumption capacity, etc. The user portrait can intuitively reflect the characteristics and needs of each user group.

[0014] Further preferably, in said S3, various attributes of the goods are extracted, including the basic attributes, functional attributes and market attributes of the goods, the attributes of the goods are quantified, and the text information is converted into numerical features for subsequent similarity calculation and recommendation, and the quantified product features are combined into a product feature matrix to represent the similarities and differences between the products.

[0015] Further preferably, in said S4, the user's behavior data and market data are monitored in real time, including the user's real-time browsing behavior, purchasing behavior, market price fluctuations, competitor's promotional activities, etc., and time series analysis and machine learning algorithms are used to analyze and predict the real-time data to predict the user's future behavior and market trends.

[0016] Further preferably, in said S5, corresponding dynamic marketing strategies are matched for different user groups based on user portraits, product characteristics and real-time data analysis results. For high-value user groups, personalized discounts, exclusive product recommendations and priority services can be provided; for new user groups, registration discounts, novice gift packs, etc. can be provided. Based on real-time market data and user feedback, the generated dynamic marketing strategies are optimized in real time, and the optimized dynamic marketing strategies are pushed to the target user groups through various channels of the e-commerce platform.

[0017] Further preferably, according to the marketing effect evaluation results, the marketing dynamic strategy is adjusted in real time, and an adjustment coefficient is introduced. The adjustment coefficient formula is:

[0018] AdjustmentFactor=ω1×(CTRtarget-CTRactua l)+ω2×(CRtarget-CRactual)+ω3×(APUtarget-APUactua l)+ω4×(RPRtarget-RPRactua l);

[0019] Among them: AdjustmentFactor is the adjustment coefficient of the marketing dynamic strategy, CTRtarget, CRtarget, APUtarget, and RPRtarget are the target values of click-through rate, conversion rate, average order value, and repurchase rate, respectively; CTRactua l, CRactua l, APUactua l, and RPRactua l are the actual values of click-through rate, conversion rate, average order value, and repurchase rate, respectively; ω1, ω2, ω3, and ω4 are weight coefficients, and ω1+ω2+ω3+ω4=1, which is used to balance the impact of each indicator on the adjustment coefficient.

[0020] The embodiment of the present invention adopts the above technical solution, which has the following advantages:

[0021] 1. The present invention constructs user portraits through big data analysis and computer algorithms, which can provide an in-depth understanding of each user's characteristics, interests and needs. The marketing dynamic strategies generated based on this can accurately push advertising and promotional information to target users, improve the pertinence and effectiveness of marketing information, and thus significantly increase user click-through rates and conversion rates.

[0022] Second, with the help of computer technology, user behavior and market changes can be monitored in real time, and marketing dynamic strategies can be adjusted in a timely manner. When market prices fluctuate, competitors launch new promotions, or user behavior changes, the system can quickly analyze data and generate corresponding adjustment plans to ensure that marketing strategies always adapt to the market environment and user needs.

[0023] 3. The precise marketing dynamic strategy generation method based on big data can avoid the blindness of traditional marketing methods, concentrate limited marketing resources on the most potential user groups and marketing channels, and continuously optimize marketing strategies through real-time evaluation and feedback of marketing effects, improve the utilization efficiency of marketing resources, and reduce marketing costs.

[0024] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0026] Figure 1 Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0027] Hereinafter, only certain exemplary embodiments are briefly described. As will be appreciated by those skilled in the art, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and description are to be considered as illustrative in nature and not restrictive.

[0028] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0029] like Figure 1 As shown, the embodiment of the present invention provides a method for generating a dynamic strategy for e-commerce precision marketing based on big data, comprising the following steps:

[0030] S1, data collection and integration;

[0031] S2, user portrait construction;

[0032] S3, product feature modeling;

[0033] S4, real-time data analysis and prediction;

[0034] S5. Generation of dynamic marketing strategies.

[0035] In one embodiment, in S1, data is collected from various data sources of the e-commerce platform, including user registration information, user browsing behavior data, user purchasing behavior data, product information data, and market competition data. The collected data is cleaned to remove duplicate, erroneous, and missing data. At the same time, the data is standardized to convert data of different formats and ranges into a unified format for subsequent analysis and processing.

[0036] In one embodiment, in S2, user characteristics, including basic characteristics, behavioral characteristics, and interest characteristics, are extracted from the cleaned and preprocessed data, and a clustering algorithm is used to cluster users. Users with similar characteristics are divided into different user groups. Each user group has similar behavioral patterns and demand characteristics. A user portrait is generated for each user group, including the group's basic characteristics, behavioral characteristics, interest characteristics, and consumption capacity. The user portrait can intuitively reflect the characteristics and needs of each user group.

[0037] In one embodiment, in S3, various attributes of the product are extracted, including the basic attributes, functional attributes, and market attributes of the product, the attributes of the product are quantified, and the text information is converted into numerical features for subsequent similarity calculation and recommendation. The quantified product features are combined into a product feature matrix to represent the similarities and differences between products.

[0038] In one embodiment, in S4, user behavior data and market data are monitored in real time, including users' real-time browsing behavior, purchasing behavior, market price fluctuations, competitors' promotional activities, etc., and time series analysis and machine learning algorithms are used to analyze and predict real-time data to predict users' future behavior and market trends.

[0039] In one embodiment, in S5, corresponding dynamic marketing strategies are matched for different user groups based on user portraits, product features, and real-time data analysis results. For high-value user groups, personalized discounts, exclusive product recommendations, and priority services can be provided; for new user groups, registration discounts, newbie gift packs, etc. can be provided. Based on real-time market data and user feedback, the generated dynamic marketing strategies are optimized in real time and pushed to the target user groups through various channels of the e-commerce platform.

[0040] In one embodiment, the marketing dynamic strategy is adjusted in real time based on the marketing effect evaluation results, and an adjustment coefficient is introduced. The adjustment coefficient formula is:

[0041] AdjustmentFactor=ω1×(CTRtarget-CTRactua l)+ω2×(CRtarget-CRactual)+ω3×(APUtarget-APUactua l)+ω4×(RPRtarget-RPRactua l);

[0042] Among them: AdjustmentFactor is the adjustment coefficient of the marketing dynamic strategy, CTRtarget, CRtarget, APUtarget, and RPRtarget are the target values of click-through rate, conversion rate, average order value, and repurchase rate, respectively; CTRactua l, CRactua l, APUactua l, and RPRactua l are the actual values of click-through rate, conversion rate, average order value, and repurchase rate, respectively; ω1, ω2, ω3, and ω4 are weight coefficients, and ω1+ω2+ω3+ω4=1, which is used to balance the impact of each indicator on the adjustment coefficient.

[0043] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various modifications and substitutions within the technical scope disclosed in the present invention, and such modifications and substitutions are intended to be within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be subject to the scope of protection of the claims.

Claims

1. A method for generating dynamic strategies for e-commerce precision marketing based on big data, characterized by: The following steps are involved: S1, data collection and integration; S2, user portrait construction; S3, product feature modeling; S4, real-time data analysis and prediction; S5. Generation of dynamic marketing strategies.

2. The method for generating dynamic strategies for e-commerce precision marketing based on big data according to claim 1, characterized in that: In said S1, data is collected from various data sources of the e-commerce platform, including user registration information, user browsing behavior data, user purchasing behavior data, product information data, and market competition data. The collected data is cleaned to remove duplicate, erroneous, and missing data. At the same time, the data is standardized to convert data of different formats and ranges into a unified format for subsequent analysis and processing.

3. The method for generating dynamic strategies for e-commerce precision marketing based on big data according to claim 1, characterized in that: In S2, user characteristics, including basic characteristics, behavioral characteristics and interest characteristics, are extracted from the cleaned and preprocessed data. A clustering algorithm is used to cluster users, and users with similar characteristics are divided into different user groups. Each user group has similar behavioral patterns and demand characteristics. A user portrait is generated for each user group, including the group's basic characteristics, behavioral characteristics, interest characteristics and consumption capacity, etc. The user portrait can intuitively reflect the characteristics and needs of each user group.

4. The method for generating dynamic strategies for e-commerce precision marketing based on big data according to claim 1, characterized in that: In S3, various attributes of the product are extracted, including the basic attributes, functional attributes, and market attributes of the product. The attributes of the product are quantified, and the text information is converted into numerical features for subsequent similarity calculation and recommendation. The quantified product features are combined into a product feature matrix to represent the similarities and differences between products.

5. The method for generating dynamic strategies for e-commerce precision marketing based on big data according to claim 1, characterized in that: In the S4, user behavior data and market data are monitored in real time, including users' real-time browsing behavior, purchasing behavior, market price fluctuations, competitors' promotional activities, etc., and time series analysis and machine learning algorithms are used to analyze and predict real-time data to predict users' future behavior and market trends.

6. The method for generating dynamic strategies for e-commerce precision marketing based on big data according to claim 1, characterized in that: In S5, corresponding dynamic marketing strategies are matched for different user groups based on user portraits, product features and real-time data analysis results. For high-value user groups, personalized discounts, exclusive product recommendations and priority services can be provided; for new user groups, registration discounts, novice gift packs, etc. can be provided. Based on real-time market data and user feedback, the generated dynamic marketing strategies are optimized in real time and pushed to the target user groups through various channels of the e-commerce platform.

7. The method for generating dynamic strategies for e-commerce precision marketing based on big data according to claim 1, characterized in that: According to the marketing effect evaluation results, the marketing dynamic strategy is adjusted in real time, and the adjustment coefficient is introduced. The adjustment coefficient formula is: AdjustmentFactor=ω1×(CTRtarget-CTRactual)+ω2×(CRtarget-CRactual)+ω3×(APUtarget-APUactual)+ω4×(RPRtarget-RPRactual); Among them: AdjustmentFactor is the adjustment coefficient of the marketing dynamic strategy, CTRtarget, CRtarget, APUtarget, and RPRtarget are the target values of click-through rate, conversion rate, average order value, and repurchase rate, respectively; CTRactual, CRactual, APUactual, and RPRactual are the actual values of click-through rate, conversion rate, average order value, and repurchase rate, respectively; ω1, ω2, ω3, and ω4 are weight coefficients, and ω1+ω2+ω3+ω4=1, which is used to balance the impact of each indicator on the adjustment coefficient.

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