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CoreXAlpha
E-Commerce

E-Commerce Recommendation System

A behaviour-driven personalisation engine with a real-time merchandising dashboard.

Client
Vektör Perakende
Sector
E-Commerce
Duration
4 months
Year
2025
+27%
Basket value
+41%
Click-through rate
−90%
Curation time

Starting point

Product recommendations relied on hand-picked lists; the merchandising team spent hours each week curating them without being able to measure the outcome.

Our approach

  1. 1Designed an event schema that captures session-level behaviour.
  2. 2Built a hybrid engine combining collaborative filtering with content-based models.
  3. 3Wired every recommendation slot into an A/B testing framework.
  4. 4Shipped a console letting the merchandising team tune the rule/model blend.

Similar work

−58%

Urgent case wait time

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