Personalization Glossary

Personalization applied inside native apps—typically via embed widgets, the Personyze SDK, or API integrations (e.g., in-app recommendations or targeted UI).

Targeting based on on-site behavior (source, pages viewed, dwell time, sequences). Example: visitors who engaged with Product X see an exit popup offering a discount on Product X.

Algorithms that recommend articles, posts, case studies, etc., to grow engagement and time-on-site. Personyze includes dedicated wizards/algorithms for content use cases.

Using consistent personalization across multiple channels (site, email, app, push) with a unified profile informing each touchpoint.

One page that adapts to audience/attributes (industry, role, company size). Elements like headlines, CTAs, badges, and pricing can vary by segment.

Personalized emails using variables (name, company) plus AI recommendations; behavior-triggered workflows from on-site signals.

Embedding recommendation blocks in email (content or products) and triggering sends based on precise behaviors/events.

Replacing manual outreach with automated, rules-based experiences: targeting, recommendations, drip emails, and more—across channels.

Personalization across all channels (site, app, email, push), informed by the same profile so no touchpoint is generic.

Umbrella term for adapting digital experiences per visitor or segment—targeted content, recommendations, triggered comms, etc.

Algorithms that present products each shopper is likely to buy (cross-sell, upsell, new-in-stock, repeat purchase, etc.).

Algorithmic selection of items (content or products) likely to drive engagement or revenue, informed by behavior + item metadata.

Grouping visitors into cohorts based on shared attributes/behaviors to tailor what they see.

Rules that determine who should see an experience (e.g., new visitors from industry=Aviation, location=UK, interest=Loans).

All on-site adaptations—targeted edits, banners, popups, recommendations—driven by audience logic.

Test competing variants to find winners. Personyze can run A/B tests inside targeted segments (not just random to all traffic).

Preference data that users intentionally share (e.g., quiz answers, profile choices). High-signal input for targeting and recommendations.

Conditions define who qualifies (audience). Triggers define when to fire (e.g., exit intent, time on page, scroll depth).

Segment is a rule-based cohort. Audience is the actual, live set of users who currently match those rules.

Reduce abandonment with: abandoned-cart emails (include similar items), exit popups, targeted incentives, and overall on-site personalization.

Core elements: product recommendations, targeted promos, cart-save tactics, email remarketing, social proof, urgency, and more.

Capture leaving shoppers with targeted offers (coupon, save cart link) or high-appeal recommendations; great for bounce/abandon mitigation.

Product-focused algorithms consider browsing, cart, purchase, inventory, and price signals to rank what to show next.

Inventory/catalog data that powers interest tracking and recommendations. Feed onboarding is supported for all accounts.

Triggered by recent interest but no purchase (or complementary to a purchase). Helps recover sales and grow AOV.

Captures Viewed / Add-to-Cart / Wishlist / Purchased events to feed algorithms and measure lift; set during onboarding.

Low-stock, countdowns, or limited-time offers to motivate checkout—targeted so urgency stays credible.

Real-time or recent-trend cues (e.g., “54 shoppers from your city viewed this today”) to validate choices and increase AOV.

Offers shown to specific audiences (e.g., geo-based free shipping, first-purchase coupons, loyalty tiers) via banners or smart popups.

Cross-sell: complementary items (memory card with camera). Upsell: higher-tier alternative of the same product category.

Remind or auto-suggest repurchases on expected cycles (e.g., filters, supplements), via on-site prompts or email.

Combine items and show “frequently bought together” suggestions to raise AOV while keeping relevancy high.

On-page: real-time context (current PDP/cart). In-email: re-engagement and lifecycle prompts. Both share the same profile/signals.