Intelligent marketing customer obtaining platform system based on AI

Abstract

The invention discloses an intelligent marketing customer obtaining platform system based on AI, and relates to the technical field of intelligent marketing, and the system comprises the steps: collecting basic attribute information data and browsing historical data of a user through a data source, and carrying out the preprocessing; constructing a personalized recommendation algorithm model according to the basic attribute information data and analyzing behavior characteristics of the user; generating a personalized recommendation list according to the behavior characteristics of the user and the browsing history data; marketing information is pointedly pushed to the user according to the personalized recommendation result; and evaluating the recommendation effect in real time according to an evaluation result. According to the invention, through accurate personalized recommendation and targeted marketing, resources are concentrated to carry out marketing on the target user, so that the utilization efficiency of marketing resources is improved, a better marketing effect is obtained at a lower cost, in a market environment with intense competition, user demands and market trends may change at any time, and the marketing efficiency is improved. The platform can capture the changes in time and adjust the marketing scheme.

Description

Technical Field

[0001] The present invention relates to the field of intelligent marketing technology, and in particular to an AI-based intelligent marketing customer acquisition platform system. Background Art

[0002] In recent years, the concept of data-driven marketing has gradually gained popularity. Enterprises have begun to realize that by collecting and analyzing user data, they can better understand customer needs and achieve precision marketing. Simple data collection is not enough to solve the problem. The key lies in how to transform this data into valuable marketing strategies and actions. Although many enterprises have accumulated a large amount of user data, they are unable to give full play to the potential of data due to the lack of effective data analysis tools and methods. The existing marketing tools and platforms on the market have certain limitations in personalized recommendations and strategy optimization, and it is difficult to meet the growing precision marketing needs of enterprises. The development of artificial intelligence technology has brought new opportunities for intelligent marketing. AI has powerful data analysis and processing capabilities, and can mine valuable information and patterns from massive data. Through machine learning algorithms, AI can build personalized recommendation models, provide users with accurate recommendation content based on their behavioral characteristics and preferences, and improve user participation and willingness to buy. AI can also monitor and evaluate the effectiveness of marketing activities in real time, discover problems in a timely manner and make optimization adjustments. Through the analysis of user feedback and behavioral data, AI can automatically adjust recommendation algorithms and marketing activity strategies to adapt to market changes and dynamic changes in user needs. Although AI has great potential in the field of intelligent marketing, its current application is still in its infancy.

[0003] However, the common solutions currently available have many shortcomings, including: most existing recommendation algorithms are based on simple rules or traditional machine learning models, and the analysis of user behavior characteristics is not in-depth enough, and cannot accurately capture users' personalized needs and changes in interests, resulting in low relevance and accuracy of recommendation results, poor user experience, and reduced user attention and participation in recommended content. When analyzing user behavior characteristics, existing technologies usually only consider a few dimensions or simple statistical indicators, and are unable to fully and deeply understand user behavior patterns and preferences. It is difficult to track dynamic changes in user behavior in real time, and it is impossible to adjust recommendation strategies in a timely manner to adapt to changes in user needs. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the problems existing in the above-mentioned existing system and method for constructing an AI-based intelligent marketing customer acquisition platform system, the present invention is proposed.

[0006] Therefore, the purpose of the present invention is to provide a system and method for constructing an AI-based intelligent marketing customer acquisition platform system, which is suitable for solving the problem that most existing recommendation algorithms are based on simple rules or traditional machine learning models, and the analysis of user behavior characteristics is not in-depth enough, and cannot accurately capture users' personalized needs and interest changes, resulting in low relevance and accuracy of recommendation results, poor user experience, and reduced user attention and participation in recommended content.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In the first aspect, an embodiment of the present invention provides an AI-based intelligent marketing customer acquisition platform system, which includes utilizing a data source to collect and preprocess users' basic attribute information data and browsing history data; constructing a personalized recommendation algorithm model based on the basic attribute information data and analyzing the user's behavioral characteristics; generating a personalized recommendation list based on the user's behavioral characteristics and browsing history data; generating a marketing activity strategy based on the personalized recommendation results to push marketing information to the user in a targeted manner; evaluating the recommendation effect in real time, and optimizing and adjusting the personalized recommendation algorithm and marketing activity strategy based on the evaluation results.

[0009] As a preferred solution of the AI-based intelligent marketing customer acquisition platform system described in the present invention, wherein: the data source includes the company's own website, APP, customer relationship management system and business database; the user's basic attribute information data includes personal identity data, user device-related information, user social information, and user consumption and financial information; the browsing history data includes website browsing data, in-application browsing data and content browsing data; the user's behavioral characteristics include registration information, geographic location information, browsing content data, click behavior data, sharing behavior data, transaction record data and search keyword data.

[0010] As a preferred solution of the AI-based intelligent marketing customer acquisition platform system described in the present invention, the construction of a personalized recommendation algorithm model includes the following steps: using a data source to obtain the user's basic attribute information data and browsing history data and perform preprocessing; building a personalized recommendation algorithm model based on a recurrent neural network and analyzing user behavior characteristics; using browsing history data to train the personalized recommendation algorithm model; deploying the trained personalized recommendation algorithm model to the actual intelligent marketing customer acquisition platform system; and regularly updating and optimizing the recommendation model as user behavior data continues to accumulate and the market environment changes.

[0011] As a preferred solution of the AI-based intelligent marketing customer acquisition platform system of the present invention, the specific formula of the user behavior data is as follows:

[0012]

[0013] Among them, U t is the user behavior feature; n is the number of basic attribute information data of the user; m is the number of browsing history data; b i is the basic attribute information data of the ith user; h j is the jth browsing history data.

[0014] As a preferred solution of the AI-based intelligent marketing customer acquisition platform system of the present invention, the result of the user behavior characteristics is specifically expressed as follows: t When the value of is less than the first threshold, it means that the basic attribute information of the user contributes little to the overall behavior characteristics, and the browsing history data is not outstanding, reflecting that the user's behavior characteristics are relatively unclear and the responsiveness to recommendations or marketing activities is low; when U t When the value of is greater than the first threshold, it means that the user has more significant characteristics in basic attributes and browsing behavior. When performing personalized recommendations and marketing, we can provide such users with content and services that are in line with their interests and behavior patterns in a more targeted manner.

[0015] As a preferred solution of the AI-based intelligent marketing customer acquisition platform system of the present invention, the specific formula for real-time updating of the marketing activity strategy is as follows:

[0016]

[0017] Among them, S is the value of the marketing activity strategy; S0 is the initial marketing activity strategy; G is the adjustment parameter; N is the number of recommended results; r k is the kth recommendation result; H is the adjustment amplitude of the regulation strategy.

[0018] As a preferred solution of the AI-based intelligent marketing customer acquisition platform system described in the present invention, the specific circumstances of updating the marketing campaign strategy are as follows: when the value S of the marketing campaign strategy is equal to the value S0 of the initial marketing campaign strategy, it indicates that the marketing campaign strategy maintains the initial strategy unchanged; when the value S of the marketing campaign strategy is greater than the value S0 of the initial marketing campaign strategy, it indicates that the marketing campaign strategy is adjusted in a positive direction.

[0019] In the second aspect, in order to further solve the above-mentioned technical problems, the embodiments of the present invention provide an AI-based intelligent marketing customer acquisition platform system, which includes: a data collection module, which is used to collect users' basic attribute information data and browsing history data through data sources; a model building module, which is used to build a personalized recommendation algorithm model based on the basic attribute information data and analyze the user's behavioral characteristics; a personalized recommendation module, which is used to generate a personalized recommendation list based on the user's behavioral characteristics and browsing history data; an optimization and adjustment module, which is used to evaluate the recommendation effect in real time, and optimize and adjust the personalized recommendation algorithm and marketing activity strategy according to the evaluation results.

[0020] In a third aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, it implements any step of an AI-based intelligent marketing customer acquisition platform system as described in the first aspect of the present invention.

[0021] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of an AI-based intelligent marketing customer acquisition platform system as described in the first aspect of the present invention.

[0022] The beneficial effects of the present invention are as follows: the present invention can greatly improve key indicators such as click-through rate and conversion rate of marketing activities through precise personalized recommendations and targeted marketing. Compared with traditional extensive marketing methods, it reduces invalid pushes to irrelevant users and concentrates resources on marketing to target users, thereby improving the utilization efficiency of marketing resources and achieving better marketing results at a lower cost. The mechanism of real-time evaluation and adjustment of strategies enables enterprises to respond quickly to market changes. In a highly competitive market environment, user needs and market trends may change at any time. The platform can capture these changes in a timely manner and adjust marketing plans to enable enterprises to always remain competitive. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. 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 these drawings without creative work. Among them:

[0024] Figure 1 This is a flow chart for implementing the present invention in Example 1. DETAILED DESCRIPTION

[0025] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0026] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0027] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0028] Example 1

[0029] Reference Figure 1 , which is the first embodiment of the present invention, and provides an AI-based intelligent marketing customer acquisition platform system, comprising the following steps:

[0030] S1: Use data sources to collect users’ basic attribute information and browsing history data and perform preprocessing.

[0031] Preferably, Figure 1 The figure shows the implementation process of the present invention. First, the user's basic attribute information data and browsing history data are collected and preprocessed by utilizing the data source. Then, a personalized recommendation algorithm model is constructed based on the basic attribute information data and the user's behavioral characteristics are analyzed. A personalized recommendation list is generated based on the user's behavioral characteristics and browsing history data. Then, a marketing activity strategy is generated based on the personalized recommendation results to push marketing information to the user in a targeted manner. Finally, the recommendation effect is evaluated in real time. According to the evaluation results, the personalized recommendation algorithm and marketing activity strategy are optimized and adjusted.

[0032] Furthermore, data sources include the company's own website, APP, customer relationship management system and business database.

[0033] Furthermore, the user's basic attribute information data includes personal identity data, user device related information, user social information, and user consumption and financial information.

[0034] Furthermore, the browsing history data includes website browsing data, in-app browsing data, and content browsing data.

[0035] Specifically, preprocessing ensures the quality and consistency of data by performing real-time preprocessing of basic attribute information data and browsing history data on data nodes, including data cleaning, noise reduction, and standardization.

[0036] S2: Build a personalized recommendation algorithm model based on basic attribute information data and analyze user behavior characteristics.

[0037] Preferably, the user's behavioral characteristics include registration information, geographic location information, browsing content data, click behavior data, sharing behavior data, transaction record data, and search keyword data.

[0038] Specifically, building a personalized recommendation algorithm model includes the following steps:

[0039] Utilize the data source to obtain the user's basic attribute information data and browsing history data and perform pre-processing;

[0040] Build a personalized recommendation algorithm model based on recurrent neural network and analyze user behavior characteristics;

[0041] Use browsing history data to train personalized recommendation algorithm models;

[0042] Deploy the trained personalized recommendation algorithm model to the actual intelligent marketing customer acquisition platform system;

[0043] As user behavior data continues to accumulate and the market environment changes, the recommendation model is regularly updated and optimized.

[0044] S3: Generate a personalized recommendation list based on the user's behavioral characteristics and browsing history data.

[0045] Preferably, the specific formula of user behavior characteristics is as follows:

[0046]

[0047] Among them, U t is the user behavior feature; n is the number of basic attribute information data of the user; m is the number of browsing history data; b i is the basic attribute information data of the ith user; h j is the jth browsing history data.

[0048] Furthermore, the results of user behavior characteristics are specifically expressed as follows:

[0049] WhenU t When the value of is less than the first threshold, it means that the basic attribute information of the user contributes less to the overall behavior characteristics, and the browsing history data is not outstanding, which reflects that the user's behavior characteristics are relatively not obvious and the responsiveness to recommendations or marketing activities is low;

[0050] WhenU t When the value of is greater than the first threshold, it means that the user has more significant characteristics in basic attributes and browsing behavior. When performing personalized recommendations and marketing, we can provide such users with content and services that are in line with their interests and behavior patterns in a more targeted manner.

[0051] Furthermore, when the weights of the user basic attribute information data and the browsing history data in the user behavior feature algorithm are equal, the user behavior feature U in this case is calculated. t This maximum value is the first threshold.

[0052] S4: Generate marketing campaign strategies based on personalized recommendation results and push marketing information to users in a targeted manner.

[0053] Preferably, marketing information is pushed to users based on personalized recommendation results, which improves the pertinence and effectiveness of marketing information. Through multi-channel push, the reach of information can be increased and user participation in marketing activities can be improved.

[0054] S5: Evaluate the recommendation effect in real time, and optimize and adjust the personalized recommendation algorithm and marketing campaign strategy based on the evaluation results.

[0055] Specifically, the specific formula for real-time updating of marketing campaign strategies is as follows:

[0056]

[0057] Among them, S is the value of the marketing activity strategy; S0 is the initial marketing activity strategy; G is the adjustment parameter; N is the number of recommended results; r k is the kth recommendation result; H is the adjustment amplitude of the regulation strategy.

[0058] Furthermore, the details of updating the marketing campaign strategy are as follows:

[0059] When the value S of the marketing activity strategy is equal to the value S0 of the initial marketing activity strategy, it means that the marketing activity strategy maintains the initial strategy unchanged;

[0060] When the value S of the marketing campaign strategy is greater than the value S0 of the initial marketing campaign strategy, it means that the marketing campaign strategy is adjusted in a positive direction.

[0061] Preferably, the recommendation effect is evaluated in real time, problems can be discovered and strategies can be adjusted in time, so that marketing activities are always kept in the best state. The personalized recommendation algorithm and marketing activity strategy are optimized according to the evaluation results, which improves the adaptability and flexibility of the system.

[0062] This embodiment also provides an AI-based intelligent marketing customer acquisition platform system, including: a data collection module, which is used to collect users' basic attribute information data and browsing history data through data sources; a model building module, which is used to build a personalized recommendation algorithm model based on the basic attribute information data and analyze the user's behavioral characteristics; a personalized recommendation module, which is used to generate a personalized recommendation list based on the user's behavioral characteristics and browsing history data; an optimization and adjustment module, which is used to evaluate the recommendation effect in real time, and optimize and adjust the personalized recommendation algorithm and marketing activity strategy according to the evaluation results.

[0063] This embodiment also provides a computer device, which is suitable for an AI-based intelligent marketing and customer acquisition platform system, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement an AI-based intelligent marketing and customer acquisition platform system as proposed in the above embodiment.

[0064] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0065] The present embodiment also provides a storage medium having a computer program stored thereon, which, when executed by a processor, implements an AI-based intelligent marketing customer acquisition platform system as proposed in the above embodiment; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0066] In summary, the present invention can greatly improve key indicators such as click-through rate and conversion rate of marketing activities through precise personalized recommendations and targeted marketing. Compared with traditional extensive marketing methods, it reduces invalid pushes to irrelevant users and concentrates resources on marketing to target users, thereby improving the utilization efficiency of marketing resources and achieving better marketing results at a lower cost. The mechanism of real-time evaluation and adjustment of strategies enables enterprises to respond quickly to market changes. In a highly competitive market environment, user needs and market trends may change at any time. The platform can capture these changes in a timely manner and adjust marketing plans to enable enterprises to always remain competitive.

[0067] Example 2

[0068] Referring to Tables 1 and 2, the second embodiment of the present invention is shown. This embodiment is different from the first embodiment in that, in order to verify its beneficial effects, operating data and related instructions of the present invention in an actual environment are provided.

[0069] Tables 1 and 2 show the comparison between the prior art and our invention in this example before and after the implementation of the activity marketing strategy.

[0070] Table 1 Recommended effect evaluation index table

[0071]

[0072] Table 2 Marketing activity strategy adjustment and effect table

[0073]

[0074] It can be seen from the above table that compared with the traditional extensive marketing method, the present invention reduces invalid pushes to irrelevant users and concentrates resources on marketing to target users, thereby improving the utilization efficiency of marketing resources and achieving better marketing results at a lower cost.

[0075] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. An AI-based intelligent marketing customer acquisition platform system, characterized by: include: Utilize data sources to collect users' basic attribute information and browsing history data and perform preprocessing; Constructing a personalized recommendation algorithm model based on the basic attribute information data and analyzing the user's behavioral characteristics; Generate a personalized recommendation list based on the user's behavioral characteristics and browsing history data; Generate marketing campaign strategies based on personalized recommendation results and push marketing information to users in a targeted manner; Evaluate the recommendation effects in real time, and optimize and adjust the personalized recommendation algorithm and marketing campaign strategies based on the evaluation results.

2. The AI-based intelligent marketing customer acquisition platform system according to claim 1, characterized in that: The data sources include the enterprise's own website, APP, customer relationship management system and business database; The user's basic attribute information data includes personal identity data, user device related information, user social information, and user consumption and financial information; The browsing history data includes website browsing data, in-application browsing data, and content browsing data; The user's behavioral characteristics include registration information, geographic location information, browsing content data, click behavior data, sharing behavior data, transaction record data, and search keyword data.

3. The AI-based intelligent marketing customer acquisition platform system according to claim 2, characterized in that: The construction of the personalized recommendation algorithm model includes the following steps: Utilize the data source to obtain the user's basic attribute information data and browsing history data and perform pre-processing; Build a personalized recommendation algorithm model based on recurrent neural network and analyze user behavior characteristics; Use browsing history data to train personalized recommendation algorithm models; Deploy the trained personalized recommendation algorithm model to the actual intelligent marketing customer acquisition platform system; As user behavior data continues to accumulate and the market environment changes, the recommendation model is regularly updated and optimized.

4. The AI-based intelligent marketing customer acquisition platform system according to claim 3, characterized in that: The specific formula of the user behavior characteristics is as follows: Among them, U t is the user behavior feature; n is the number of basic attribute information data of the user; m is the number of browsing history data; b i is the basic attribute information data of the ith user; h j is the jth browsing history data.

5. The AI-based intelligent marketing customer acquisition platform system according to claim 4, characterized in that: The result of the user behavior feature is specifically expressed as: When U t When the value of is less than the first threshold, it means that the basic attribute information of the user contributes little to the overall behavior characteristics, and the browsing history data is not outstanding, reflecting that the user's behavior characteristics are not obvious and the responsiveness to recommendations or marketing activities is low; When U t When the value of is greater than the first threshold, it indicates that the user has significant characteristics in basic attributes and browsing behavior. When performing personalized recommendations and marketing, targeted content and services based on interests and behavior patterns are provided to such users.

6. The AI-based intelligent marketing customer acquisition platform system according to claim 5, characterized in that: The specific formula for the real-time update marketing campaign strategy is as follows: Among them, S is the value of the marketing activity strategy; S0 is the initial marketing activity strategy; G is the adjustment parameter; N is the number of recommended results; r k is the kth recommendation result; H is the adjustment amplitude of the regulation strategy.

7. The AI-based intelligent marketing customer acquisition platform system according to claim 6, characterized in that: The details of the updated marketing campaign strategy are as follows: When the value S of the marketing activity strategy is equal to the value S0 of the initial marketing activity strategy, it means that the marketing activity strategy maintains the initial strategy unchanged; When the value S of the marketing campaign strategy is greater than the value S0 of the initial marketing campaign strategy, it means that the marketing campaign strategy is adjusted in a positive direction.

8. An AI-based intelligent marketing customer acquisition platform system according to any one of claims 1 to 7, characterized in that: include, The data collection module is used to collect the user's basic attribute information data and browsing history data through the data source; A model building module, used to build a personalized recommendation algorithm model based on the basic attribute information data and analyze the user's behavioral characteristics; A personalized recommendation module, used to generate a personalized recommendation list based on the user's behavioral characteristics and browsing history data; The optimization and adjustment module is used to evaluate the recommendation effect in real time, and optimize and adjust the personalized recommendation algorithm and marketing activity strategy based on the evaluation results.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of an AI-based intelligent marketing customer acquisition platform system as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of an AI-based intelligent marketing customer acquisition platform system as described in any one of claims 1 to 7 are implemented.

Images