Challenge
A major FMCG brand faced the issue of limited basket size among its loyal clientele. Although these customers consistently bought their favorite products, they were neglecting the rest of the product range. The company aimed to expose them to the broader catalog and incentivize them to try new items.
Action
The company decided to implement an automated product recommendation system based on purchase history to boost the average shopping cart value.
- Data Analysis: The iPresso system gathered and analyzed detailed purchase data for every loyalty program participant, focusing on the types and frequency of products bought.
- Dynamic Segmentation: Consumers were dynamically segmented based on their unique purchasing patterns and preferences. The system also identified complementary products. New offerings were automatically recommended to consumers most likely to be interested.
- Multi-Channel Communication: A series of automated, personalized recommendations were launched across key channels:
- Mobile Push: Upon logging into the mobile app, consumers received push notifications featuring products tailored to their profile.
- Email Marketing: Regular emails were sent with product suggestions, often using phrases like: "If you like [Product A], try [Product B] too!"
Results
The system implementation yielded significant improvements:
- Average Order Value (AOV) Increase: +22%.
- Customer Satisfaction Increase: +15% (confirmed by NPS research). Customers felt better understood and served, making shopping more intuitive and efficient.
- Email Effectiveness: The Click-Through Rate (CTR) on emails containing personalized recommendations increased by 19%.
