Personalisation
Recommendation engines scoped against actual AOV and repeat-rate uplift, not just CTR. Attribution framework agreed upfront.
Adjacent — consumer-scale instinct transfers directly
Direct-to-consumer is one of the places our background transfers most cleanly. The discipline that turned free-to-play into a $50 billion category — daily measurement, disciplined prioritisation, ruthless kill criteria — is the same discipline that separates AI-native D2C winners from everyone else.
We work with D2C brands on four recurring problem shapes: personalisation at scale (product recommendations, email, onsite, across cohorts rather than individuals), pricing and promotion intelligence (margin-aware discounting, elasticity modelling, cohort-based offers), content at scale (PDP copy, ad creative iteration, SKU proliferation without creative drag), and retention and winback (LTV modelling, churn prediction, dormant-user reactivation).
What makes D2C work different from SaaS AI work: the loops are short, the feedback is unambiguous, and the consumer is not forgiving. Exactly the environment we spent fifteen years shipping into.
Recommendation engines scoped against actual AOV and repeat-rate uplift, not just CTR. Attribution framework agreed upfront.
Elasticity modelling and margin-aware promotional engines, with kill criteria so you don't bleed margin to an overfit model.
Product copy, ad creative iteration, lifecycle email — with editorial standards on what AI is permitted to ship unsupervised.
Cohort-based LTV modelling, churn prediction, and reactivation programmes that pay for themselves in the first quarter.
If your profile doesn't match any of these, we'd rather tell you on the first call. We'll point you to someone better suited — and mean it.
We come prepared on your business. You come prepared on your problem. We both leave knowing whether there's a fit.
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