Case Study

Increased Product Adoption

Increased Product Adoption

Analytical Framework

A leading retail bank in Africa serving a large and diverse customer base with a strong focus on revenue growth through deeper product adoption.

Type

Industry

Banking

Categories

AI GovernanceCollections & Recovery

Industry

Data AcceleratorModel Risk Management

The Challenge

Financial institutions are constantly seeking new revenue opportunities by increasing product adoption and utilization among existing customers.

In a highly competitive banking landscape, the challenge lies in: – Identifying customers who are likely to buy the next best logical product – Increasing utilization of products customers already own – Reaching the right customer at the right time with the right price and offer Without data-driven insights, banks struggle to improve product penetration and usage, leading to missed revenue opportunities.

The Solution

iTuring.ai enabled the bank to drive adoption using highly accurate propensity models built in just a few hours.

iTuring was used to: – Identify customers likely to buy additional products based on need and behavior – Detect customers underutilizing existing products but likely to increase usage if targeted correctly – Predict price sensitivity to recommend optimal pricing and incentives For this bank, iTuring.ai built: – Overdraft Limit Enhancement Propensity Models to identify customers most likely to accept higher overdraft limits – Overdraft Cross-Sell Propensity Models to increase adoption of overdraft protection products – Sales teams used these insights to target only the top 30% of customers with the highest probability of conversion.

IMPACT

Overdraft Utilization

32% ↑

Product Penetration

7% ↑

WHY ITURING.AI

iTuring goes beyond predicting who is likely to buy.

The platform:
Identifies drivers behind customers’ willingness to accept credit limit enhancements
Uncovers hidden behavioral patterns influencing product adoption
Enables focused, high-impact sales and marketing strategies

FAQs

01

What product adoption problem was the bank trying to solve?

+

The bank wanted to increase overdraft usage and cross-sell related products by targeting customers most likely to accept higher limits or additional offerings.

02

How did iTuring identify the right customers to target?

+

iTuring built propensity models based on customer behavior and need to predict which customers were most likely to convert.

03

Why was targeting only 30% of customers important?

+

Focusing on the top 30% high-propensity customers improved conversion rates while reducing unnecessary sales effort.

04

How did iTuring improve overdraft utilization?

+

By predicting acceptance of higher limits and identifying customers likely to increase usage, iTuring helped drive a 32% increase in utilization.

05

What additional insights did the bank gain from this solution?

+

The bank gained visibility into behavioral factors influencing customers’ ability and willingness to adopt overdraft products.

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