Digital marketing service system based on big data

Abstract

The invention relates to the technical field of marketing service, in particular to a big data-based digital marketing service system, which comprises the following components: a data collection and analysis module which is used for acquiring browsing records, purchasing behaviors and social media interaction data of users in real time through butt joint with various digital platforms, processing and analyzing the browsing records, the purchasing behaviors and the social media interaction data, and sending the data to a cloud server; and the quality and the integrity of the data are ensured. And the target market positioning module is used for dividing the users into different subdivided markets by utilizing a machine learning and data mining algorithm according to the user portraits and the behavior patterns, and determining a target user group with the most potential. And the personalized recommendation module is used for utilizing a personalized recommendation algorithm machine learning and data mining algorithm based on the target user group and the marketing target. By collecting, processing and analyzing massive user data and combining an artificial intelligence algorithm and a personalized recommendation technology, accurate target market positioning, a personalized marketing strategy and a whole-course tracking analysis function are provided for enterprises.

Description

Technical Field

[0001] This invention relates to the field of marketing service technology, and in particular to a digital marketing service system based on big data. Background Technology

[0002] Digital marketing refers to a marketing approach that leverages the internet, computer communication technology, and digital interactive media to achieve marketing goals. Digital marketing utilizes advanced computer network technology to the greatest extent possible to explore new markets and discover new consumers in the most effective and cost-efficient way. With the increasing development of big data on the internet, more and more industries are adopting digital marketing to promote their products, provide customers with diverse choices, meet their consumption needs, and thus achieve a good balance between marketing services and customers.

[0003] Current marketing service systems, when enabling communication between products and customers, are unable to establish targeted channels with product brands based on the personalized needs of different customers. This prevents product brands from providing customers with the best experience in a comprehensive and accurate manner, thus failing to increase customer recognition of the product brand and consequently failing to stimulate customers' desire to consume, which is detrimental to product marketing services.

[0004] Traditional marketing methods often face problems such as information asymmetry, ineffective advertising, and delayed user feedback. However, with the development of big data technology, digital marketing services based on user behavior analysis and personalized recommendations are gradually becoming a key to corporate competitiveness.

[0005] To address this, a digital marketing service system based on big data is proposed. Summary of the Invention

[0006] The purpose of this invention is to provide a big data-based digital marketing service system. By collecting, processing, and analyzing massive amounts of user data, and combining artificial intelligence algorithms and personalized recommendation technology, it provides enterprises with precise target market positioning, personalized marketing strategies, and full-process tracking and analysis capabilities. This system has broad application prospects and can help enterprises improve the effectiveness and competitiveness of their digital marketing.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a digital marketing service system based on big data, comprising the following components:

[0008] Data collection and analysis module: By connecting with various digital platforms, it acquires users' browsing history, purchasing behavior, and social media interaction data in real time, and processes and analyzes the data to ensure its quality and integrity.

[0009] Target Market Positioning Module: Based on user profiles and behavioral patterns, machine learning and data mining algorithms are used to segment users into different market segments and identify the most promising target user groups.

[0010] Personalized recommendation module: Based on the target user group and marketing objectives, it uses personalized recommendation algorithms, machine learning and data mining algorithms to provide users with customized product recommendations, promotional activities and advertising information, improve advertising effectiveness and user engagement, segment users into different market segments and identify the most promising target user groups;

[0011] Advertising Placement and Personalized Recommendation Module: Based on the needs of the target user group and advertisers, this module utilizes personalized recommendation technology and intelligent algorithms to provide precise advertising placement and personalized recommendation services, thereby improving advertising effectiveness and user engagement.

[0012] Marketing strategy optimization module: Based on user feedback and advertising performance, marketing strategies are adjusted and optimized in real time using intelligent algorithms and A / B testing methods.

[0013] Data Analysis and Reporting Module: The system comprehensively analyzes user behavior data, advertising effectiveness, and conversion rate metrics, and generates relevant data reports and visualizations.

[0014] Data tracking and analysis module: The system tracks user behavior throughout the entire process, including ad click-through rate, conversion rate, and purchase behavior, and provides corresponding data analysis and reports.

[0015] Real-time optimization and decision support module: The system can perform real-time optimization and decision support based on real-time data and user feedback through intelligent algorithms, helping enterprises to adjust their advertising strategies and optimize marketing results in a timely manner.

[0016] Preferably, in the data collection and analysis module, user data information is acquired, processed and analyzed, and then user profiles and behavioral patterns are constructed.

[0017] Preferably, the personalized recommendation technology and intelligent algorithm comprise the following steps;

[0018] S1. Data collection and preprocessing;

[0019] S2, Feature Extraction;

[0020] S3. User similarity calculation;

[0021] S4, Neighbor Selection;

[0022] S5, Interest Prediction;

[0023] S6, Recommended Generation;

[0024] S7, feedback learning and model update.

[0025] Preferably, S1, data collection and preprocessing, involves collecting users' browsing history, click behavior, and purchase record data, and performing preprocessing, including noise removal, missing value filling, and normalization.

[0026] Preferably, in step S2, feature extraction, relevant feature information is extracted for users and advertisements. For example, for users, basic features such as age, gender, and geographical location can be extracted; for advertisements, advertisement type, keywords, and advertiser features can be extracted.

[0027] Preferably, step S3, user similarity calculation, calculates the similarity between users based on user behavior data and feature information, including cosine similarity and Euclidean distance. The similarity calculation can be based on behavioral similarity or feature similarity between users.

[0028] Preferably, in step S4, neighbor selection, some neighbor users similar to the target user are selected based on user similarity. The method of Top-N similar users is used to select the N users with the highest similarity to the target user as neighbors.

[0029] Preferably, in step S5, interest prediction, the target user's level of interest in the advertisement is predicted based on the behavioral data and feature information of neighboring users, and the target user's rating or level of interest in the advertisement is inferred through a collaborative filtering algorithm.

[0030] Preferably, in step S6, recommendation generation, a personalized ad recommendation list is generated based on the target user's interest prediction results. Ads with higher prediction scores are sorted according to certain rules and recommended to the target user.

[0031] Preferably, in step S7, feedback learning and model updating, the model is continuously updated and the recommendation algorithm is optimized based on user feedback data, such as clicks and purchase behavior. Incremental learning can be used to update the model using new user behavior data to improve recommendation accuracy.

[0032] Compared with the prior art, the beneficial effects of the present invention are:

[0033] 1. Big data processing and analysis capabilities: The system can process massive amounts of user data and conduct in-depth analysis through machine learning and data mining algorithms to build user profiles and behavioral patterns.

[0034] 2. Target market positioning and personalized recommendation capabilities: The system can segment users into different market segments and provide personalized product recommendations and advertising information for different user groups.

[0035] 3. Real-time optimization and data tracking capabilities: The system can adjust marketing strategies in real time based on user feedback and advertising effectiveness, and provides full-process tracking and analysis functions to help businesses understand market trends and user behavior. Attached Figure Description

[0036] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0037] Figure 1 This is a block diagram of the big data-based digital marketing service system of the present invention;

[0038] Figure 2 This is a flowchart of the personalized recommendation algorithm of the present invention;

[0039] Figure 3 This is a diagram of the data collection and preprocessing module of the present invention;

[0040] Figure 4 This is a feature extraction information diagram of the present invention. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] Please see Figures 1 to 4 The present invention provides a technical solution:

[0043] A big data-based digital marketing service system comprises the following components:

[0044] Data collection and analysis module: By connecting with various digital platforms, it acquires users' browsing history, purchasing behavior, and social media interaction data in real time, and processes and analyzes the data to ensure its quality and integrity.

[0045] Target Market Positioning Module: Based on user profiles and behavioral patterns, machine learning and data mining algorithms are used to segment users into different market segments and identify the most promising target user groups.

[0046] Personalized recommendation module: Based on the target user group and marketing objectives, it uses personalized recommendation algorithms, machine learning and data mining algorithms to provide users with customized product recommendations, promotional activities and advertising information, improve advertising effectiveness and user engagement, segment users into different market segments and identify the most promising target user groups;

[0047] Advertising Placement and Personalized Recommendation Module: Based on the needs of the target user group and advertisers, this module utilizes personalized recommendation technology and intelligent algorithms to provide precise advertising placement and personalized recommendation services, thereby improving advertising effectiveness and user engagement.

[0048] Marketing strategy optimization module: Based on user feedback and advertising performance, marketing strategies are adjusted and optimized in real time using intelligent algorithms and A / B testing methods.

[0049] Data Analysis and Reporting Module: The system comprehensively analyzes user behavior data, advertising effectiveness, and conversion rate metrics, and generates relevant data reports and visualizations.

[0050] Data tracking and analysis module: The system tracks user behavior throughout the entire process, including ad click-through rate, conversion rate, and purchase behavior, and provides corresponding data analysis and reports.

[0051] Real-time optimization and decision support module: The system can perform real-time optimization and decision support based on real-time data and user feedback through intelligent algorithms, helping enterprises to adjust their advertising strategies and optimize marketing results in a timely manner.

[0052] Specifically, in the data collection and analysis module, user data information is acquired, processed, and analyzed to construct user profiles and behavioral patterns.

[0053] Specifically, the personalized recommendation technology and intelligent algorithm consist of the following steps;

[0054] S1. Data collection and preprocessing;

[0055] S2, Feature Extraction;

[0056] S3. User similarity calculation;

[0057] S4, Neighbor Selection;

[0058] S5, Interest Prediction;

[0059] S6, Recommended Generation;

[0060] S7, feedback learning and model update.

[0061] Specifically, S1, data collection and preprocessing, involves collecting users' browsing history, click behavior, and purchase record data, and performing preprocessing, including noise removal, missing value filling, and normalization.

[0062] Specifically, S2, feature extraction, involves extracting relevant feature information for users and advertisements. For example, for users, basic features such as age, gender, and geographical location can be extracted; for advertisements, advertisement type, keywords, and advertiser features can be extracted.

[0063] Specifically, S3, user similarity calculation, calculates the similarity between users based on user behavior data and feature information, including cosine similarity and Euclidean distance. Similarity calculation can be based on behavioral similarity or feature similarity between users.

[0064] Specifically, in step S4, neighbor selection, based on user similarity, selects some neighboring users similar to the target user, and uses the Top-N similar users method to select the N users with the highest similarity to the target user as neighbors.

[0065] Specifically, S5, interest prediction, predicts the target user's interest in the advertisement based on the behavioral data and feature information of neighboring users, and infers the target user's rating or interest in the advertisement through a collaborative filtering algorithm.

[0066] Specifically, in step S6, recommendation generation, a personalized ad recommendation list is generated based on the target user's interest prediction results. Ads with higher prediction scores are sorted according to certain rules and recommended to the target user.

[0067] Specifically, S7, feedback learning, and model updating, continuously update the model and optimize the recommendation algorithm based on user feedback data, such as clicks and purchase behavior. Incremental learning can be used to update the model using new user behavior data, thereby improving recommendation accuracy.

[0068] 1. Big data processing and analysis capabilities: The system can process massive amounts of user data and conduct in-depth analysis through machine learning and data mining algorithms to build user profiles and behavioral patterns.

[0069] 2. Target market positioning and personalized recommendation capabilities: The system can segment users into different market segments and provide personalized product recommendations and advertising information for different user groups.

[0070] 3. Real-time optimization and data tracking capabilities: The system can adjust marketing strategies in real time based on user feedback and advertising effectiveness, and provides full-process tracking and analysis functions to help businesses understand market trends and user behavior.

[0071] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A digital marketing service system based on big data, characterized in that: It consists of the following components: Data collection and analysis module: By connecting with various digital platforms, it acquires users' browsing history, purchasing behavior, and social media interaction data in real time, and processes and analyzes the data to ensure its quality and integrity; Target Market Positioning Module: Based on user profiles and behavioral patterns, machine learning and data mining algorithms are used to segment users into different market segments and identify the most promising target user groups; Personalized recommendation module: Based on the target user group and marketing objectives, it uses personalized recommendation algorithms, machine learning and data mining algorithms to provide users with customized product recommendations, promotional activities and advertising information, improve advertising effectiveness and user engagement, segment users into different market segments and identify the most promising target user groups; Advertising delivery and personalized recommendation module: Based on the needs of the target user group and advertisers, it uses personalized recommendation technology and intelligent algorithms to carry out precise advertising delivery and personalized recommendation services, thereby improving advertising effectiveness and user engagement; Marketing strategy optimization module: Based on user feedback and advertising performance, marketing strategies are adjusted and optimized in real time using intelligent algorithms and A / B testing methods; Data Analysis and Reporting Module: The system comprehensively analyzes user behavior data, advertising effectiveness, and conversion rate metrics, and generates relevant data reports and visualizations. Data tracking and analysis module: The system tracks user behavior throughout the entire process, including ad click-through rate, conversion rate, and purchase behavior, and provides corresponding data analysis and reports; Real-time optimization and decision support module: The system can perform real-time optimization and decision support based on real-time data and user feedback through intelligent algorithms, helping enterprises to adjust their advertising strategies and optimize marketing results in a timely manner.

2. The digital marketing service system based on big data according to claim 1, characterized in that: In the data collection and analysis module, user data information is acquired, processed, and analyzed to construct user profiles and behavioral patterns.

3. The digital marketing service system based on big data according to claim 1, characterized in that: The personalized recommendation technology and intelligent algorithm consist of the following steps; S1. Data collection and preprocessing; S2, Feature Extraction; S3. User similarity calculation; S4, Neighbor Selection; S5, Interest Prediction; S6, Recommended Generation; S7, feedback learning and model update.

4. The digital marketing service system based on big data according to claim 3, characterized in that: S1, data collection and preprocessing, involves collecting user browsing history, click behavior, and purchase record data, and performing preprocessing, including noise removal, missing value filling, and normalization.

5. The digital marketing service system based on big data according to claim 3, characterized in that: The S2 feature extraction step involves extracting relevant feature information for users and advertisements. For example, for users, basic features such as age, gender, and geographic location can be extracted; for advertisements, ad type, keywords, and advertiser features can be extracted.

6. The digital marketing service system based on big data according to claim 3, characterized in that: The S3, user similarity calculation, calculates the similarity between users based on user behavior data and feature information, including cosine similarity and Euclidean distance; the similarity calculation can be based on behavioral similarity or feature similarity between users.

7. A digital marketing service system based on big data according to claim 3, characterized in that: S4, Neighbor Selection, selects some neighboring users similar to the target user based on user similarity. Using the Top-N similarity user method, the N users with the highest similarity to the target user are selected as neighbors.

8. A digital marketing service system based on big data according to claim 3, characterized in that: S5, Interest Prediction, predicts the target user's interest in the advertisement based on the behavioral data and feature information of neighboring users, and infers the target user's rating or interest in the advertisement through a collaborative filtering algorithm.

9. A digital marketing service system based on big data according to claim 3, characterized in that: S6, Recommendation Generation, generates a personalized ad recommendation list based on the target user's interest prediction results; and sorts the ads with higher prediction scores according to certain rules and recommends them to the target user.

10. A digital marketing service system based on big data according to claim 3, characterized in that: The S7 step, feedback learning and model update, continuously updates the model and optimizes the recommendation algorithm based on user feedback data, such as clicks and purchase behavior. Incremental learning can be used to update the model using new user behavior data to improve recommendation accuracy.

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