Challenge
Loyal customers of a well-known FMCG brand kept buying the same few products over and over again, completely ignoring the rest of the range. The company was looking for a way to show them other products and encourage them to try them.
The company decided to implement an automatic product recommendation system based on previous purchases to increase the value of the shopping cart.
Action
- Data collection
The iPresso system collected and analyzed data on the purchases of each loyalty program participant. The focus was on, among other things, the products purchased and the frequency of purchases. - Consumer segmentation and preference analysis
Consumers were dynamically segmented based on unique purchasing patterns and preferences. At the same time, the system identified complementary products. If a new product appeared in the offer, the system recommended it to consumers who were likely to be interested in it. - Sending personalized recommendations
The company launched a series of automated recommendations delivered through various communication channels:
- After logging into the mobile app, consumers saw a mobile push notification with products tailored to their preferences.
- Consumers received regular emails with a list of products that might interest them. The email could contain, for example, “If you like [Product A], try [Product B] too!”.
Results
- The average order value increased by 22%.
- Consumer satisfaction increased by 15%, as confirmed by NPS research. Consumers felt better understood and served, and shopping became more intuitive and efficient for them.
- Consumers were more likely to read emails with personalized recommendations. The CTR increased by 19%.
