Case Study

Next Best Product Recommendation

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, with 15 years of operational experience.

Type

Industry

Retail

Categories

AI GovernanceCollections & Recovery

Industry

Data AcceleratorModel Risk Management

The Challenge

Retail companies are consistently challenged to anticipate customers’ ever-changing needs and offer relevant products at the right time, place, and price.

These decisions must be made in real time to improve customer experience, reduce costs, and increase supply-chain efficiency. Traditionally, this challenge is addressed using rules-based recommendation approaches such as: “Products frequently bought together” “Customers who bought X also bought Y” While effective at a segment or cluster level, these approaches fail to capture individual customer intent, limiting personalization, engagement, and revenue growth.

The Solution

iTuring.ai addressed this challenge using its AutoML/AI platform by analyzing large volumes of data across:

Browsing and shopping behavior Demographics Historical purchases Multi-channel customer interactions The platform identified customer need, intent, and purchase propensity at the individual level, enabling personalized Next Best Logical Product recommendations. In addition, iTuring’s models predicted customer price sensitivity, ensuring that each recommendation was delivered at a price customers were most likely to accept. The education media retailer used this approach to build a Next Best Product Recommendation engine, integrated with Dynamics 360 CRM, enabling personalized recommendations across: Website experiences Email communications Direct mail campaigns Business teams directly consumed these recommendations at the operational level.

IMPACT

Revenue

18%+

Model Development Time

2.8hrs

Product Penetration

9%+

WHY ITURING.AI

iTuring goes beyond predicting which customers are likely to buy.

By identifying customer need and intent, and combining it with propensity and price sensitivity, iTuring enables businesses to:
Deliver the next best logical product for each individual
Apply the right discount and pricing strategy
Better understand customer buying behavior and patterns
Drive sales without relying on generic recommendations

FAQs

01

What business problem was the retailer trying to solve?

+

The retailer wanted to move beyond generic, segment-based recommendations and instead understand individual customer intent to deliver the right product at the right time and price, in real time.

02

Why were traditional recommendation methods not sufficient?

+

Methods like “frequently bought together” worked for customer clusters but failed to personalize recommendations for each unique customer, limiting engagement and revenue growth.

03

How did iTuring improve product recommendations?

+

iTuring analyzed customer behavior, demographics, and multi-channel data to identify individual customer need and intent, enabling personalized “next best product” recommendations for each customer.

04

How were these recommendations used by the business?

+

The recommendations were integrated into Dynamics 360 CRM and activated across the website, email campaigns, and direct mail, allowing business teams to act on insights in real time.

05

What outcomes did the business achieve beyond revenue growth?

+

In addition to an 18% revenue increase and 9% higher product penetration, the retailer saw better campaign engagement, improved customer loyalty, and increased engagement from lower-tier customers.

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