The board approved a year-long growth plan that assumes personalisation contributes a measurable AOV or retention lift. Missing it forces the CDO to re-baseline the whole roadmap. Two regional competitors have publicly demonstrated personalisation features — a peer retailer's “For You” tab, a peer streaming service's continue-watching strip — and analyst reviews are starting to compare. A failed POC has burned cloud spend and engineering goodwill; another visible failure costs the personalisation programme its independent budget. For streaming, licensing economics depend on watch-time on long-tail catalogues — a recommender that only surfaces top 100 titles is a renewal-negotiation problem.
Personalisation is one of the highest-return uses of machine learning in any consumer-facing business, but only when the recommendations are relevant, timely, and explainable. We build for bilingual catalogues, privacy obligations, and the seasonal rhythms of the region as first-class concerns rather than afterthoughts. Catalogues in the UAE rarely sit cleanly in one language. Product names mix Arabic and English, brand spellings vary, and customer reviews land in both languages along with Romanised Arabic. Our recommendation pipelines treat this as the default rather than an edge case.
UAE and GCC data-protection regimes are tightening and customer expectations around privacy continue to rise. We build systems that operate on minimal personal data, honour consent flags by default, and keep audit trails of which signals were used for which recommendation. Sensitive attributes — nationality, religion, health — are excluded from features unless there is a clear, lawful and documented basis for inclusion. Click-through rates are easy to optimise and easy to game; we instrument the system against the metrics that actually matter to the business — incremental revenue, basket size, repeat purchase rate, retention, or product-level margin — using proper A/B testing with statistical guardrails.