Case Study

Use machine learning to enable personalized targeting for customers

Challenge

Hyper-personalization is the first among them. He demands convenience and flexibility, and he expects to order from anywhere or any channel. He needs the offer, which fits exactly with him.

Now Personalization isn’t a perk but a necessity; this is what is driving marketers to demand more from their recommendation systems. Smart marketers now expect personalization solution to solve for “What’s next” and this doesn’t always mean products but includes intelligence on individualized offer and delivered through the right channel.

 

In the example below, we helped our client create a hyper-relevance solution to not only recommend the next best product, associated product affinities and offer but also created an algorithm to optimize placement of products in the creatives for different channels.

Approach

For a leading off-price departmental store, Convergytics implemented a machine learning based recommendation engine. The recommendation engine predicted accurate recommendation on most likely products – brands – style based on customer preferences and provided insights on related product affinities to compliment the primary product and style. Additional intelligence such as personalized offers and channel created true hyper-relevance

Result

As a result of the engagement, our client saw an increase in response rates by nearly 37%; the long-term impact could be seen through increase in number of repeat customers and offer redemptions.  Today, for any event based campaign as many as 2500 different versions of DM’s are created with an average of 235 customers / creative 

Increase in response rates by nearly

37%

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