A large Product company

The company served clients managing over 1,000 product types, making it difficult for sales representatives to effectively recommend products. The challenge was to develop a system where sales staff could input a customer’s name and receive a list of probable products to pitch, based on purchasing patterns. The company needed a personalized recommendation engine to suggest relevant products for each customer, optimizing sales interactions and improving product matching.

Kaara processed the company’s transactional data by cleaning and moving it into Hadoop. Using Apache Spark, the team applied the FPGrowth algorithm for Market Basket Analysis to identify products frequently bought together and the ALS algorithm to assess customer-product affinity. The results from both algorithms were integrated into a unified dashboard, allowing sales representatives to easily access tailored product recommendations. This enabled them to suggest the most relevant products to individual customers during sales interactions.

The solution allowed the company to understand product purchase patterns, improving sales targeting. Sales representatives could now pitch relevant products to customers based on past buying behavior, leading to more effective upselling and cross-selling. The system also provided insights into which customers were most likely to buy certain products, increasing sales revenue and enhancing customer relationships. This personalized approach significantly improved the overall sales strategy and customer satisfaction.