Customer behavior prediction method based on big data analysis
Through big data analysis, customer data is integrated from multiple data sources, preprocessing and feature engineering, and machine learning models are built, which solves the problem that traditional methods cannot fully understand customer behavior, and achieves more accurate customer behavior prediction and precise marketing.
Patent Information
- Application Number
- CN202411441948.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-16
- Publication Date
- 2025-02-14
AI Technical Summary
Traditional customer behavior analysis methods rely on limited data sources within the enterprise, cannot fully understand the overall picture of customers, and lack the integration of external data, making it difficult to accurately predict customer behavior.
The customer behavior prediction method based on big data analysis is adopted, and data is collected from multiple data sources such as internal systems of the enterprise, social media, market research institutions, etc., data preprocessing and feature engineering are carried out, and a machine learning model is constructed to predict customer behavior.
It achieves more accurate predictions of customer behavior, helps enterprises formulate accurate marketing strategies, improve the effectiveness of marketing activities, reduce customer churn, and improve marketing ROI.
Smart Images
Figure CN119444300A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of big data analysis and customer behavior prediction, and specifically to a customer behavior prediction method based on big data analysis. Background Art
[0002] In today's globalized and digitalized business environment, market competition is becoming increasingly fierce. Enterprises are facing tremendous competitive pressure from their peers and emerging companies. In order to stand out from the competition, enterprises need to have a deep understanding of customer needs and behaviors in order to provide more accurate products and services and improve customer satisfaction and loyalty. Traditional customer behavior analysis methods mainly rely on limited data sources within the enterprise, such as customer transaction records. These data can only reflect partial information about customer purchasing behavior, cannot fully understand the overall picture of customers, and lack the integration of external data. Summary of the invention
[0003] The purpose of the present invention is to provide a customer behavior prediction method based on big data analysis to solve the problems raised in the above background technology.
[0004] By adopting the above technical solutions, customer behavior prediction is achieved, and companies can estimate product sales more accurately.
[0005] In view of the above problems, the technical solution proposed by the present invention is: A customer behavior prediction method based on big data analysis includes the following steps: Step 1: Data collection and storage: Collect data from the company's internal system to obtain basic customer information, transaction history, and customer service records. Analyze the impact of product supply on customer behavior through production, inventory, and supply chain data provided by the enterprise resource planning system. Obtain customer activity data on social media platforms. Understand the macro market environment and competitor situations through industry reports and consumer trend data provided by market research institutions, provide background information for customer behavior prediction, and establish a data warehouse to store the collected data. Step 2: Data preprocessing: Identify and process missing values in the data. For numerical data, use the mean, median or mode to fill in. For character data, you can fill in or mark it as missing based on business logic. Process outliers in the data, identify outliers through statistical methods, and modify or delete them directly according to the specific situation. Step 3: Feature Engineering: Based on business understanding and data analysis, select features related to customer behavior prediction from a large amount of preprocessed data, use statistical methods and machine learning algorithms to screen out features that have a significant impact on the target variable, remove redundant and irrelevant features, create new features based on business logic and data characteristics, and calculate the ratio of customers' likes and comments on social media as a new feature to measure customers' interaction style on social platforms; Step 4: Model construction and training: According to the specific goals of customer behavior prediction, such as classification problems, regression problems and data characteristics, select a machine learning model, divide the data set after feature engineering into a training set and a test set, with 70% to 80% of the data used as the training set and 20% to 30% of the data used as the test set. Use the training set to train the selected model to minimize the prediction error of the model on the training set. Step 5: Model evaluation and optimization: Evaluate the classification model and regression model. Based on the model evaluation results, if the model performance does not meet expectations, optimize the model. Step 6: Customer behavior prediction and application: Use the evaluated and optimized model to predict new customer data, and the company will formulate corresponding marketing strategies based on the prediction results.
[0006] As a preferred technical solution of the present invention, the internal enterprise system in step one includes a customer relationship management system, an enterprise resource planning system and a point of sale system.
[0007] As a preferred technical solution of the present invention, the statistical method in step 2 is the box plot method.
[0008] As a preferred technical solution of the present invention, the statistical method in step three is a correlation analysis method, and the machine learning algorithm is a random forest algorithm.
[0009] As a preferred technical solution of the present invention, the model of the classification problem in step 4 is one of logistic regression, decision tree, support vector machine, and naive Bayes, and the model of the regression problem is one of linear regression, ridge regression, Lasso regression, and decision tree regression.
[0010] As a preferred technical solution of the present invention, the evaluation index of the classification model in step five is one of accuracy, recall rate, and area under the ROC curve, and the evaluation index of the regression model is one of mean square error, mean absolute error, and determination coefficient.
[0011] As a preferred technical solution of the present invention, the model optimization in step five is an integrated learning method.
[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: the customer behavior prediction method based on big data analysis can, through comprehensive data collection, gain in-depth insights into customer preferences, needs and behavior patterns, and help enterprises formulate highly accurate marketing strategies for different customer groups, such as personalized product recommendations or advertising for customers with different preferences, thereby improving the effectiveness of marketing activities, increasing customer response rates to marketing content, and increasing sales opportunities. By predicting customer behavior, enterprises can avoid wasting marketing resources on low-potential customers or irrelevant marketing channels, and can concentrate resources on promoting customers who are most likely to generate purchasing behavior or are interested in specific products and services, thereby improving the marketing return on investment and reducing marketing costs. Moreover, by using big data to analyze and predict customer behavior, enterprises can provide customers with a more personalized service experience. Based on the continuous monitoring and prediction of customer behavior based on the model, enterprises can promptly identify customers at risk of churn and take targeted retention measures, thereby effectively reducing customer churn rate and maintaining the long-term stability of customer relationships. BRIEF DESCRIPTION OF THE DRAWINGS
[0013] Figure 1 A method block diagram of a customer behavior prediction method based on big data analysis disclosed in an embodiment of the present invention. DETAILED DESCRIPTION
[0014] 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.
[0015] See also Figure 1 The present invention provides a technical solution: a customer behavior prediction method based on big data analysis, comprising the following steps: Step 1: Data collection and storage: Collect data from the company's internal system to obtain basic customer information, transaction history, and customer service records. Analyze the impact of product supply on customer behavior through production, inventory, and supply chain data provided by the enterprise resource planning system. Obtain customer activity data on social media platforms. Understand the macro market environment and competitor situations through industry reports and consumer trend data provided by market research institutions, provide background information for customer behavior prediction, and establish a data warehouse to store the collected data. Step 2: Data preprocessing: Identify and process missing values in the data. For numerical data, use the mean, median or mode to fill in. For character data, you can fill in or mark it as missing based on business logic. Process outliers in the data, identify outliers through statistical methods, and modify or delete them directly according to the specific situation. Step 3: Feature Engineering: Based on business understanding and data analysis, select features related to customer behavior prediction from a large amount of preprocessed data, use statistical methods and machine learning algorithms to screen out features that have a significant impact on the target variable, remove redundant and irrelevant features, create new features based on business logic and data characteristics, and calculate the ratio of customers' likes and comments on social media as a new feature to measure customers' interaction style on social platforms; Step 4: Model construction and training: According to the specific goals of customer behavior prediction, such as classification problems, regression problems and data characteristics, select a machine learning model, divide the data set after feature engineering into a training set and a test set, with 70% to 80% of the data used as the training set and 20% to 30% of the data used as the test set. Use the training set to train the selected model to minimize the prediction error of the model on the training set. Step 5: Model evaluation and optimization: Evaluate the classification model and regression model. Based on the model evaluation results, if the model performance does not meet expectations, optimize the model. Step 6: Customer behavior prediction and application: Use the evaluated and optimized model to predict new customer data, and the company will formulate corresponding marketing strategies based on the prediction results.
[0016] 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.
[0017] See also Figure 1The internal enterprise systems in step one include a customer relationship management system, an enterprise resource planning system, and a point of sale system. The statistical method in step two is a box plot method. The statistical method in step three is a correlation analysis method. The machine learning algorithm is a random forest algorithm. The model for the classification problem in step four is one of logistic regression, decision tree, support vector machine, and naive Bayes. The model for the regression problem is one of linear regression, ridge regression, Lasso regression, and decision tree regression. The evaluation index of the classification model in step five is one of accuracy, recall rate, and area under the ROC curve. The evaluation index of the regression model is one of mean square error, mean absolute error, and determination coefficient. The model optimization in step five is an integrated learning method.
[0018] Specifically, the working principle of this customer behavior prediction method based on big data analysis is as follows: when using it, first collect and store data, obtain basic customer information, transaction history and customer service records from the internal enterprise system including customer relationship management system, enterprise resource planning system and point of sale system, and use the production, inventory and supply chain data of the enterprise resource planning system to analyze the impact on customer behavior from the perspective of product supply, and then obtain activity data from social media platforms, industry reports and consumer trend data from market research institutions, and then establish a data warehouse to store and integrate these multi-source data, and then perform data preprocessing, fill in missing values of numerical data with mean, median or mode, fill or mark missing values of character data according to business logic, identify and correct or delete outliers according to the situation through the box plot method, and then use the box plot method to identify outliers based on the situation. Business understanding and data analysis, using correlation analysis, statistics and random forest algorithm, select relevant features from the preprocessed data and remove redundant and irrelevant features, and create new features based on business logic and data characteristics, and then select a classification model or regression model based on whether customer behavior prediction is a classification problem or a regression problem and the characteristics of the data. The data set processed by feature engineering is divided into 70% to 80% as a training set and 20% to 30% as a test set, and the training set is used to train the model to minimize the prediction error. Then, the classification model and regression model are evaluated. If the model performance does not meet expectations, the hyperparameters are adjusted and the integrated learning method is used for optimization. Finally, the evaluated and optimized model is used to predict new customer data that has undergone the same preprocessing and feature engineering, and the company formulates a marketing strategy based on the prediction results.
Claims
1. A customer behavior prediction method based on big data analysis, characterized in that: The following steps are involved: Step 1: Data collection and storage: Collect data from the company's internal system to obtain basic customer information, transaction history, and customer service records. Analyze the impact of product supply on customer behavior through production, inventory, and supply chain data provided by the enterprise resource planning system. Obtain customer activity data on social media platforms. Understand the macro market environment and competitor situations through industry reports and consumer trend data provided by market research institutions, provide background information for customer behavior prediction, and establish a data warehouse to store the collected data. Step 2: Data preprocessing: Identify and process missing values in the data. For numerical data, use the mean, median or mode to fill in. For character data, you can fill in or mark it as missing based on business logic. Process outliers in the data, identify outliers through statistical methods, and modify or delete them directly according to the specific situation. Step 3: Feature Engineering: Based on business understanding and data analysis, select features related to customer behavior prediction from a large amount of preprocessed data, use statistical methods and machine learning algorithms to screen out features that have a significant impact on the target variable, remove redundant and irrelevant features, create new features based on business logic and data characteristics, and calculate the ratio of customers' likes and comments on social media as a new feature to measure customers' interaction style on social platforms; Step 4: Model construction and training: According to the specific goals of customer behavior prediction, such as classification problems, regression problems and data characteristics, select a machine learning model, divide the data set after feature engineering into a training set and a test set, with 70% to 80% of the data used as the training set and 20% to 30% of the data used as the test set. Use the training set to train the selected model to minimize the prediction error of the model on the training set. Step 5: Model evaluation and optimization: Evaluate the classification model and regression model. Based on the model evaluation results, if the model performance does not meet expectations, optimize the model. Step 6: Customer behavior prediction and application: Use the evaluated and optimized model to predict new customer data, and the company will formulate corresponding marketing strategies based on the prediction results.
2. The customer behavior prediction method based on big data analysis according to claim 1, characterized in that: The enterprise internal system in step 1 includes a customer relationship management system, an enterprise resource planning system and a point of sale system.
3. The customer behavior prediction method based on big data analysis according to claim 1 is characterized in that: The statistical method in step 2 is the box plot method.
4. The customer behavior prediction method based on big data analysis according to claim 1 is characterized in that: The statistical method in step three is the correlation analysis method, and the machine learning algorithm is the random forest algorithm.
5. The customer behavior prediction method based on big data analysis according to claim 1 is characterized in that: The model of the classification problem in step 4 is one of logistic regression, decision tree, support vector machine, and naive Bayes, and the model of the regression problem is one of linear regression, ridge regression, Lasso regression, and decision tree regression.
6. The customer behavior prediction method based on big data analysis according to claim 1 is characterized in that: The evaluation index of the classification model in step 5 is one of accuracy, recall rate, and area under the ROC curve, and the evaluation index of the regression model is one of mean square error, mean absolute error, and determination coefficient.
7. The customer behavior prediction method based on big data analysis according to claim 1 is characterized in that: The model optimization in step five is an integrated learning method.