Metrics from the live pilot.
live A/B test · vs existing third-party recommender · 70K+ sessions · 7-figure GMV fashion store
Read more →Where current engines fall short.
Same products for every guest.
80%+ of ecom traffic is anonymous — most visitors never log in. History-based engines need history they don't have, so first-time visitors all get the same default bestsellers.
Stale preferences shape new visits.
76% of consumers get frustrated when recommendations don't fit. Profile-based engines recommend from past preferences — so a shopper who bought a red coat in November returns in May and still sees winter coats.
Most of the catalog is never shown.
Most of the catalog never reaches a shopper. Engines trained on engagement reinforce what's already winning — 20% of products drive ~80% of sales, with impressions even more skewed. The long tail gets no air and never breaks through.
Recommendations stay in one category.
Engines that recommend within a single category miss the cross-sell — a 10–30% share of ecommerce revenue. A shopper browsing dresses gets more dresses recommended, not the matching shoes or bag.
New products start cold.
Historical recommenders have no behavioral signal for new SKUs — they stay invisible until enough engagement is accumulated, which won't happen if they're never shown. New launches and refreshed inventory all hit the same wall.
How ob.session works.
ob.session attaches to your storefront and personalizes every page load from how each visitor engages with your catalog in the moment — what they pause on, scroll past, return to.
Delivered as widgets, PLP (Product Listing Page) personalization, or both — across your storefront.
Like a good salesperson reads behavior — not names.
What catches attention.
How each visitor moves on the page — where they pause, hover, scroll past, etc. The micro-behavior shows which products are actually looked at, not just shown.
What shoppers look for.
The kind of product the visitor is moving toward — a category, a price band, a style. Inferred from the path through your store, not from a stored profile.
What kind of visit this is.
Where each visitor came from, their device, how the session has unfolded so far. A quick mobile visit looks different from a long desktop browse.
< 200ms · no warm-up · no identity
Read more →Where ob.session is different.
| Popularity-basedPopularity | Collaborative FilteringCollab Filtering | Session-basedSession-based | ob.sessionob.session | |
|---|---|---|---|---|
| Serves anonymous visitors | ✓ | — | ✓ | ✓ |
| Personalizes per visitor | — | ✓ | ✓ | ✓ |
| Real-time, in-session | — | — | ✓ | ✓ |
| Uses behavioral signals (hover, scroll, etc.) | — | — | — | ✓ |
| PLP (Product Listing Page) personalization | — | — | — | ✓ |
| Recommends from the long tail | — | — | — | ✓ |
Fully private by design.
ob.session sees what they do, anonymously — never linked to a name, email, or account.
No personal data
ob.session never sees names, emails, accounts, or payment information.
Yours alone
Your data trains only your model and serves only your visitors. Never pooled with other merchants.
Consent-gated, GDPR-aligned
Nothing tracked before consent. Pseudonymous data handled per GDPR. DPA available on request.