Previously published
"Who Controls Ups and Downs?" Precision Marketing for Banks, Consumer Finance, and Internet Companies_Comprehensive Interpretation of Smart Marketing" introduces the purpose of smart marketing/precision marketing to reduce operating costs. But precision marketing can bring a lot of additional benefits, such as increasing sales profits, improving customer loyalty, reducing customer churn, and improving operational capabilities and strategic management capabilities. The article "RMF Model-Realizing Bank Credit Card User Classification_E-commerce VIP Customer Mining (Precise Marketing/Smart Marketing)" published last month
introduced how to operate the RFM model to mine VIP customers, thereby saving operating costs and achieving precision marketing.
RFM is a method for analyzing customer value and segmenting customers, often used in database marketing and direct marketing. It has received particular attention in the retail and professional services industries.
RFM is at best a marketing strategy. Today, Mr. Toby will introduce the e-commerce customer consumption prediction model based on the above two articles. The e-commerce customer consumption prediction model is based on artificial intelligence and machine learning models, which can accurately quantify and predict customers' future purchase behavior.
Business Background - E-commerce as a market is growing rapidly
Due to the numerous advantages and benefits, more and more people say that they prefer online shopping over traditional shopping these days. The buyer's decision-making process has changed dramatically in recent years. Buyers do extensive research online before speaking with a salesperson. Buyers are also making more direct purchases online and via smartphones, never setting foot in a traditional brick-and-mortar store. The Internet has made doing business easier and faster. It has led to a change in the way people do business and the worldwide trend of online shopping or e-commerce is growing rapidly.
E-commerce systems provide real-time data and analytics about customers. You can see how people interact with your site, what products they're interested in, what they leave in their shopping carts, and what the average purchase is. Valuable metrics that allow you to make adjustments to meet your client's needs.
E-commerce companies such as Amazon, JD.com, Vipshop, Taobao, Pinduoduo, and Meituan want to segment their customers and determine marketing strategies based on these segments. For example, you want to organize different campaigns to retain customers who are very profitable for your company, and arrange different marketing campaigns for new customers.
The benefits of e-commerce precision marketing/smart marketing :
1. Increase sales through better product availability.
2. Less spoilage and fresher, more appealing product through more accurate inventory allocation.
3. Increase inventory turnover by reducing the need for safety stock.
4. Gain higher profits through proactive and optimized price reduction promotions.
5. Improved capacity utilization and more reliable fulfillment through better understanding of capacity needs and proactive resolution of bottlenecks.
6. Reduce personnel costs through forecast-based store and distribution center shift optimization.
Machine Learning Model - E-commerce Customer Consumption Prediction
E-commerce companies are able to automatically leverage retail sales and assortment data to make more accurate short-term forecasts. Predictive models using machine learning can automatically calculate complex and variable consumer behavior data, enabling manufacturers to adjust forecasts correctly to adapt to changes in demand patterns. Through machine learning models, some e-commerce companies in Europe and the United States have achieved a weekly forecast accuracy of more than 90%, a 9% increase in peak season forecast accuracy, and a 10% increase in forecast accuracy when using retailer data.
Not only can machine learning improve the accuracy of demand forecasts, but it can also automate much of the work of planners and can handle vast data sets—far beyond the capabilities of any human planner.
In order to generate accurate demand forecasts, the system must be able to handle large volumes of data on various variables that can affect demand. With advances in large-scale data processing and in-memory computing, modern demand planning systems can perform millions of forecast calculations a minute, taking into account more variables than ever before.
Machine learning models can calculate hundreds of variables/factors of consumer shopping behavior, which is beyond the ability of human calculation.
solution
Chongqing Future Wisdom Information Technology Consulting Service Co., Ltd. can provide a machine learning-based e-commerce customer consumption prediction model to achieve customer classification, VIP customer mining, and accurate consumption prediction.
If you have custom service needs, such as enterprise modeling, patents, thesis, graduation design, assignments, please contact the author.
Practical case display
We take the data of a foreign online retail company as an example, which contains tens of millions of data sets.
Through the program, our company can realize consumer user portraits, data visualization analysis, including consumption statistics at different times for decision analysis.
Our company derives a large number of new variables based on a small number of variables in the e-commerce database, breaking through technical bottlenecks and using them for modeling.
After the model reads the data, it quickly parallelizes the training, and finally generates a machine learning model with automatic prediction capabilities.
The model realizes the probability prediction of all customers' shopping consumption in the future. The larger the probability value, the more likely the customer will consume in the future, and vice versa.
The model predicts whether the customer will continue to consume in the future, 0 means no consumption, 1 means consumption.
The model verification index is as shown in the figure below, and the accuracy of the model reaches 0.938.
The AUC of the e-commerce customer consumption prediction model reaches 0.94, and the smooth ROC curve shows that the model has excellent prediction ability.
The ks of the e-commerce customer consumption prediction model reaches 0.7537, and the prediction ability of the model is very strong.
The business background, significance, and demonstration cases of the e-commerce customer consumption forecasting model are introduced here. If you have cooperation needs, you can leave a message.
E-commerce customer consumption prediction model - based on tens of millions of real online retail data
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