Recommendation widgets

Recommendation widgets is an implementation project: we connect recommendation blocks on the card/cart/home — by manual links, by rules (category, brand, price) or by behavioral data (views, purchases, 'often together'); we set up display spots and defaults. Honestly about quality, this is key: recommendations are as good as the DATA and the catalog — with poor data, wrong categories or low traffic the selections will be weak or random (garbage in, garbage out); it is not 'AI magic' but a consequence of data and logic. Honestly about cold start: for new products and new visitors there is little data, so recommendations initially rely on rules, not behavior — we account for this honestly. Honestly about the result: recommendations can raise browsing depth, the average check and findability, but we do NOT guarantee growth and do not promise '+X%' — it depends on the catalog, data, relevance; the effect should be checked with a test. Honestly about relevance: the selection must be useful, not 'push stale stock' — irrelevant recommendations annoy and do not work. Honestly about the essence: these are recommendation widgets (rules/behavior), NOT full AI personalization (659 — separate) and NOT a guarantee. An important boundary: these are recommendation widgets, not cross-sells at checkout (654 — narrow at checkout), not content personalization (644) and not a recommender engine from scratch for a huge catalog (discussed separately). If the catalog is tiny, widgets are overkill. Picture this: instead of a dead end on a card with no alternatives a visitor sees relevant 'similar' and 'bought with'. The base price starts from 20,000 ₽ per project; it depends on logic and data.

• The visitor does not find alternatives and related items, leaves.
• There are no 'similar'/'bought with'/'you may like' blocks.
• Browsing depth and the average check are below potential.
• Recommendations, if any, are irrelevant or random.

• Connecting recommendation blocks (card/cart/home)
• Logic: manual links / rules / behavioral data
• Cold-start handling (new products/visitors — by rules)
• Setting up display spots and defaults
• Relevant selections (not 'push stale stock')
• A recommendation to check the effect with a test
• Checking correctness and selection quality
• Reviewing with you

• The visitor sees relevant alternatives and related items
• Higher potential of browsing depth and the check
• Cold start handled for new products/visitors
• A hypothesis to validate: does it help you (a test)

• We clarify the catalog, data, display spots; choose the recommendation logic
• We implement widgets, set up relevance and defaults
• We check selection quality, recommend a test, review with you

• Do recommendations guarantee check growth?

  No. They can raise browsing depth, the check and findability, but growth depends on the catalog, data and relevance. The effect should be checked with a test; we do not promise '+X%'.

• Do recommendations need data and AI?

  Quality depends on data and the catalog (garbage in, garbage out). With poor data or low traffic rules work, not behavior; for new products/visitors — a cold start. It is not 'AI magic' but a consequence of data and logic.

• Can we push stale stock?

  Irrelevant selections for the sake of clearing stock annoy and do not work — that harms. Recommendations must be useful to the buyer; business priorities can be gently accounted for, but not at the expense of relevance.