Personalization
What to know
Personalization questions cover delivering the right experience to the right visitor. Know Experience Targeting (XT), Automated Personalization (AP), Recommendations, and Auto-Target. Understand rule-based vs. algorithmic personalization, offer selection, profile attributes and real-time data feeding algorithms, and experience prioritization within XT. Also know Recommendations criteria (e.g., people who viewed this also viewed), exclusions, and fallback/default content.
Traps in questions
- Using XT when the requirement is algorithmic optimization across many offers (that is AP).
- Forgetting that AP needs enough traffic and data to learn; low-traffic scenarios favor rule-based XT.
- Overlooking the default/fallback experience - every visitor must receive valid content.
- Confusing Recommendations criteria types (item-based, user-based, popularity) and their data requirements.
Worked example / rule of thumb
A retailer wants each visitor to see the most relevant hero among 10 variants, optimized automatically. Use AP with the 10 offers and a conversion metric; ensure traffic supports learning. If the business rule is "show loyalty messaging to members, new-arrival messaging to everyone else," use XT with two experiences and a default. Rule of thumb: rules → XT; algorithmic optimization → AP/Auto-Target; product suggestions → Recommendations. Always define a sensible default.