Predict customer next actions from a large volume of purchase and browsing logs, and enhance personalization strategies!
We would like to introduce a case of developing a "Customer Behavior Prediction System" at our company. During the development, we faced challenges such as time series modeling of user behavior and personalization. To overcome these, we implemented time series analysis models, stream processing infrastructure, and privacy protection methods. 【Technical Challenges】 ■ Time series modeling of user behavior and personalization ■ High-frequency real-time data processing and model updates ■ Design considerations for privacy and personal information protection *For more details, please download the PDF or feel free to contact us.
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basic information
【Main Achievements】 ■ Next Purchase Forecast Accuracy: Improved by 12% ■ Campaign Click-Through Rate: Improved by 25% ■ Cross-Sell Effect: Average increase of 15% ■ Dropout Rate: Reduced by 10% *For more details, please download the PDF or feel free to contact us.
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For more details, please download the PDF or feel free to contact us.
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Company information
Our company acts based on a sense of "mission" as "IT professionals," focusing on the "thoughts" of each individual customer to lead them to success. We contribute to stable energy supply through energy solutions utilizing AI and IoT. Additionally, we provide services globally across our entire company group, including overseas locations. Please feel free to contact us if you have any requests.
![[Data] Manufacturing DX and Predictive AI](https://image.mono.ipros.com/public/product/image/2108751/IPROS712566552147280703.png?w=280&h=280)
