In 2014, Unilever wanted to increase sales of its hair care products in India. The company used affinity analysis to identify which products were frequently purchased together by customers. The analysis revealed that customers who purchased hair color were also likely to purchase hair conditioner. Armed with this insight, Unilever implemented a promotion that offered a discount to customers who purchased both hair color and hair conditioner together. As a result, the sales of both products increased significantly, and Unilever was able to capture a larger share of the hair care market in India.
Similarly, in 2008, P&G used affinity analysis to optimize its product placement in stores. The company analyzed customer transaction data to identify which products were commonly purchased together, and used that information to strategically place products on store shelves. For example, P&G discovered that customers who purchased diapers were also likely to purchase baby wipes and diaper rash cream. To capitalize on this insight, the company placed these products together on store shelves, which led to increased sales of all three products.
Overall, these case studies demonstrate how affinity analysis can help FMCG companies like Unilever and P&G make informed decisions about product placement, pricing, and promotions. By leveraging data to identify product relationships, these companies were able to increase sales and improve their market share.
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