E-commerce & retail
NUT Botanicals
Product recommendation engine
Measured results
+25%
average order value
+40%
customer retention
−20%
acquisition cost
Source: Epsilon AI Success Stories & Project Highlights, Ver. 3 — 15 October 2025, page 7
The problem
Generic merchandising was limiting conversion and engagement. The objective was recommendations tied to each customer's actual behaviour.
What was built
A recommendation engine built on behavioural segmentation rather than catalogue similarity alone.
Technical approach
- RFM clustering
- Recency, frequency and monetary segmentation driving which products surface for whom.
- Data preparation
- Transaction and browsing history cleaned and joined into a single customer view.
Measurement framework
Deployment evidence is drawn from the published engagement. The delivery and measurement plans are case-specific recommendations grounded in the documented solution and results.
- Evidence status
- The published engagement documents personalised recommendations operating on customer-behaviour data, supported by reported commercial uplift.
- Indicative delivery plan
- Unify catalogue and behavioural data, establish the commercial baseline, launch a controlled recommendation pilot, evaluate cohort results, then scale to eligible traffic.
- Recommended baseline
- Recommended: eight to twelve weeks of average order value, repeat-purchase retention and acquisition cost for eligible traffic before personalisation, segmented by channel and customer cohort.
- Recommended measurement method
- Use a randomised A/B test comparing existing merchandising with personalised recommendations. Keep pricing and campaign exposure balanced, report lift with confidence intervals, and measure retention on the same cohort window.
Recommended items are not presented as the original study method. They should be replaced with the confirmed project methodology when the underlying records are approved for publication.