Deep learning recommendations –– Personalization use cases
2026-02-28 19:39 Diff

Recommend the next best product per user with deep learning

Who:

A leading Spanish electronics retailer

Strategy:

To maximize its product recommendations and drive users further down the funnel to a sale event, this retailer experimented with a state of the art deep learning recommendation algorithm, which it used to automatically predict the next product(s) each user was most likely to engage with.

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Hypothesis:

The key to unlocking exponential revenue gains from product recommendations largely depends on a brand’s ability to immediately identify shopper intent and dynamically recommend products based on their needs in that moment and over time. Unable to accomplish this with traditional machine learning models, this retailer decided to run a 50/50 split test comparing an advanced deep learning algorithm against its existing recommendation strategy on the homepage. In just 16 days, the deep learning-based recommendations produced a 252% increase in purchases and over €290,000 in incremental revenue.

What real growth looks like with personalization

  • How Valamar converts travel intent into bookings with real-time context

  • What makes Saks’ personalization engine a game-changer for driving 10% more conversions?

  • Bringing the in-store expert online: How Bergzeit reinvented gear discovery with conversational commerce

  • 32% of Total Purchases from AdaptML Recommendations

  • +88% Conversion Uplift with Mastercard Predictive Spend Insights

  • Making Personalization a Breeze for Leading Financial Institutions

  • +10.3% uplift in add-to-cart rate

  • 39% decrease in same-month cancellations

  • 16% contribution to D2C revenue from Dynamic Yield

  • 16% of revenue from recommendations