Market Basket Analysis
Analyze Customer Buying Habits with AI based Market Basket Analysis
The purpose of the product recommender system is to analyze customer buying habits by finding associations between the different items that customer’s purchase. The discovery of these associations helps organizations develop marketing strategies by gaining insight into which items are frequently purchased together.
Product Recommender System
A large manufacturer / distributor for home appliances wants to sell products across to their retailers who will stock these products based on customer demand. The purpose of the recommender system was to recommend new products based on the purchasing habits of similar customer groups and their purchase history. The model developed followed the below five-step process along with the development of Mobile App which was installed on the Phone / Tablet of the salesperson to make appropriate recommendations.
Solution Features
- Detailed store profiling across countries, regions & purchasing habits.
- Hierarchical grouping of products across multiple levels.
- Product recommendations starting with categories that drill down to products.
- Historical order placements data along with recommended count for each product.
- Similar product recommendations based on store profiling.
- Market basket analysis considering relevant associations and recommendations.
- Cold start handling for new stores.
Business Benefits
- Cross selling and upselling of products.
- Better recommendations based on detailed customer profiling.
- Product stacking recommendations based on seasonal variations.
- Recommendations based on frequent data refresh.
- Expected consumption and likelihood of repurchase recommendations.
