Use Case — Recommendations & Personalization
Recommendation engines are one of the highest-ROI investments in data and AI — when they're built right. Generic segment-level targeting is table stakes. The real value comes from individual-level recommendations that respond to behavior in real time, improve with every interaction, and integrate directly into your product and marketing surfaces.
We have built recommendation and personalization systems at platforms serving tens of millions of users — from deal recommendation engines that match users to the right offers at the right moment, to ML-driven personalization that replaced off-the-shelf tools with models trained on proprietary customer behavior data. The architecture, the customer segmentation, the feedback loops, and the integration patterns are work we know well.
The playbook applies across retail, e-commerce, media, marketplaces, and any platform where matching the right content or offer to the right customer creates measurable lift in engagement, conversion, and lifetime value.
What We Build
ML models that match users to products, deals, or offers based on behavioral signals, purchase history, browsing patterns, and real-time context — delivering individual-level recommendations at platform scale, not segment-level approximations.
Clustering algorithms that identify behavioral segments with shared characteristics and distinct responses to marketing — enabling precise campaign targeting across email, push, paid, and on-site channels. We've built models that have segmented 2M+ users into actionable groups.
ML models that resolve user identity across devices, sessions, and channels — connecting anonymous behavior to known customers and building unified profiles that power downstream personalization across every touchpoint.
Event-driven personalization that responds to in-session behavior — triggering offers, rewards, or recommendations at the moment of highest relevance rather than in the next day's batch email. Built on streaming architectures that process customer events in milliseconds.
Experimentation frameworks that continuously test recommendation strategies, rank models, and targeting logic — with automated analysis that surfaces winning variants and rolls them into production without manual review cycles.
Predictive models that score customers on engagement trajectory, purchase likelihood, and churn risk — enabling proactive retention campaigns and acquisition targeting that finds the users most likely to become high-value customers.
Proven at Scale
At a consumer platform with 12 million active users, we replaced generic deal feeds with ML-driven personalization that matched each user to deals based on their browsing history, purchase patterns, device behavior, and category affinity. The recommendation engine combined collaborative filtering with content-based signals, with a real-time layer that updated rankings in response to current session behavior.
Alongside the recommendation engine, we built a customer segmentation model using clustering algorithms that identified more than 2 million users with distinct behavioral characteristics — enabling the marketing team to run differentiated campaigns with measurably higher open rates, click-through, and conversion than the prior one-size-fits-most approach.
At a 10-million-customer grocery retailer, we replaced a legacy Teradata-based recommendation system with ML-driven personalization models — delivering tailored offers and promotions based on individual purchase history and product affinity scores. The shift from rules-based to model-driven personalization produced measurable improvements in promotional redemption and basket size.
Who This Is For
Let's Talk
Tell us about your customer data, your current targeting approach, and what individual-level personalization would mean for your business.