Customer Lifecycle : Engage Customers
Improve portfolio performance in the dining category
The hurdle
Bank’s dining portfolios often see low engagement. They need to identify their customers’ dining tastes to improve recommendations
The maya.ai solution
Using proprietary algorithms, maya.ai can predict customer dining tastes based on several parameters and tags including
- Type of restaurants the customer visits: premium vs fast food
- Preferred cuisine type: North Indian, Chinese or Italian
- Frequency of transactions on dining: weekly, bi-weekly, monthly, occasionally
- Time: weekdays, weekends or holidays
- Location: online, dine out, city
Customer proof
For a leading bank in India
1.1 Mn customers
Identified as premium diners who only use their cards occasionally for dining
~130Mn USD opportunity sized
for incremental revenue
4% to 8% spike
in dining spends if these customers were incentivized