E-commerce was one of the early adopters of AI/ML models that used them to design data clustering algorithms and automate which products should be shown on a user’s homepage. AI is now present in nearly every action a user takes, which allows retail companies to collect user data and offer more personalised services.

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Use Cases

Customer churn model

Predicting and reducing user churn rate with targeted recommendations and offers is a common strategy of retail companies as retaining customers is a key concern for them. betterdata helps to balance and generate synthetic data that matches real user data and is free to use, share, and store across different business units within the same company as well as externally with third-parties.

Recommendation systems that actually work

Advanced clustering algorithms power recommendation systems and need huge volumes of user data for profiling. A recommendation system also requires AI models to be properly fine-tuned and capture complex correlations between different user profiles. With betterdata, real user data can be transformed into synthetic data that can be openly shared and used for product development to offer users more personalised services.

Predictive sale analytics

Retail businesses usually forecast their revenue goals by predicting when users are most likely to buy a certain product. Such predictive analytics can boost a business multifold such as stock optimization and resource management can be effectively done by predicting user sentiment for a certain period of time. betterdata adds to this capability with data synthesis that can amplify hidden data patterns, which are otherwise missed with traditional techniques.