Improve portfolio performance in the dining category
The bank’s dining portfolio saw low engagement. They needed to improve identify their customers’ dining tastes to improve recommendations.
maya.ai’s proprietary algorithms predicted customer dining tastes based on several parameters and tags.
- Type of restaurants the customer visits, i.e. premiumvs.fast food
- Preferred cuisine type,i.e. North Indian, Chineseor Italian
- Frequency of transactions on dining, i.e.weekly,bi-weekly, monthly, occasionally
- Time, i.e.weekdays,weekends or holidays
- Location, i.e. online, dine out, city
For a leading bank in India
identified 1.1 Mn
customers with tastes of premium diners but use their cards only occasionally for dining
sized an opportunity for ~130Mn USD
in incremental revenue and 4% to 8% spike in dining spends if these customers were incentivized