Case Study

Next Best Product Recommendation

Analytical Framework

A leading U.S.-based education media retail company serving over 2 million customers, generating USD 120 million in annual revenue, and backed by 15 years of operational experience.

Type

Industry

BankingInsurance

Managing Dealer Defaults at Scale

Retail companies are consistently challenged to anticipate their customers’ ever-changing needs and offer them relevant products at the right time, the right place and the right price. All this needs to be executed in real-time for the best customer experience, to decrease costs and increase supply chain efficiencies. This challenge is traditionally handled through recommendation on “products frequently bought together” or “customers who bought this item also bought Y”. These recommendations might work generally for a customer cluster but not for every unique customer.

The Solution

This conversation assumes you're past "should we use AI?" and onto "how do we do this right?"

iTuring’s Auto ML/AI addresses this complex problem by sifting through enormous amount of data across multiple channels, browsing/shopping behaviour, demographics, and other attributes to understand “customer intent”. Based on that it offers personalized next best logical product recommendation at the individual customer level. Additionally, iTuring’s models can also predict the customer’s sensitivity to price, thereby ensuring that you offer the right price at which they are willing to buy. A leading education media retail client used iTuring to build “Next Best Product Recommendation” algorithm to increase product sales and drive Customer Lifetime Value. The recommendation was integrated to their Dynamics 360 CRM to drive decisions at the operational level through personalized recommendations on the website, email communications and direct mail. Business consumed the model recommendations to realize an 18% increase in product sales, increased campaign engagement, higher loyalty (lower attrition) and therefore higher customer lifetime value. One of the unexpected benefits of this approach was the increase in engagement and value of lower-tier customers.

IMPACT

Revenue

18%+

Model Development Time

2.8hrs

Product Penetration

9%+

WHY ITURING.AI

Enterprise-Grade AI Built for Real-World Risk

Not only does iTuring predict customers who are highly likely to buy products but also helped identify need and intent of customers to buy a product. Further, iTuring’s Integrated AI helped implement the next best logical product offered based on the customer’s propensity, need and intent to buy a product. This allows business users to better understand their customers buying behaviour and pattern to apply right discount levels and rebates to drive sales.

Delivered high-accuracy risk prediction at scale

Enabled monthly dealer default prediction with ~95% accuracy, giving teams confidence to act early.

Drove outsized collections impact without increasing effort

Helped teams focus on the top 30% high-risk dealers while capturing 98% of actual defaults, resulting in a 332% improvement in collections effectiveness.

Turned complex data into clear, actionable decisions

Segmented dealers into 9 risk categories, making credit and collection strategies easier to prioritize and execute.

Embedded AI directly into business workflows

Integrated predictions into existing credit and collections processes, ensuring adoption by business users—not just analysts.

FAQs

What business challenge did this Dealer Default Management solution address?

iTuring’s platform developed machine learning models that accurately forecasted dealer defaults for the following month. These models assessed risk by product, region, and customer segment to support proactive credit and collection actions.

PLATFORM

INDUSTRIES

USE CASES

RESOURCES

COMPANY