1How do you deploy AI recommendation engines?
Install Recommender for WooCommerce, LimeSpot, or Clerk.io. Connect product feeds, define events (views, adds, purchases), and enable real-time personalization blocks for PDP, cart, and email. Map fallback rules for low-data SKUs.
Use server-side tagging to keep event streams accurate even with browser tracking restrictions.
2How do you combine AI with rule-based logic?
Use AI for behavior-driven suggestions and overlay business rules: exclude out-of-stock items, enforce brand collections, or promote high-margin SKUs. In WooCommerce, use `woocommerce_product_query` filters to block certain IDs from recommendation widgets.
Create rule tiers (must include, nice-to-have, exclude) so merchandising retains control.
3How do you A/B test recommendation algorithms?
Split traffic between AI engine variants (best sellers vs personalized) and measure click-through, add-to-cart, and revenue per session. Use built-in experimentation (Clerk A/B) or GA4 audiences with Optimize alternatives. Keep tests running until confidence >90%.
Test placement as well: carousel under hero vs inline grid near reviews.
4How do you stay compliant with GDPR?
Only capture behavioral data after consent, anonymize identifiers, and document data processors. Offer opt-out toggles in account settings. Update privacy policies to list AI vendors and data retention periods.
Enable data-subject access endpoints so customers can request exported recommendation profiles.
5What proof points demonstrate AOV impact?
Track AOV and repeat purchase rate for cohorts exposed to personalized modules. Collect case studies (e.g., beauty brand doubling AOV) and share with leadership. Use Looker Studio to chart uplift per placement.
Combine AI recommendations with loyalty tiers to reinforce retention loops (earn extra points for recommended bundles).