So, how does AI help with buyer conversions and personalization? Through dynamic Product Components that surface contextually relevant SKUs around what the customer is viewing, plus manually-curated merchandising controls like the Related Products Component on the PDP, plus the AI Content Generation Module for marketing copy, plus the optional AI / Machine Learning Add-On Module for behavioral recommendations. Per the Clarity Level 3 documentation: “Components are used to provide specific sets of data to the storefront depending on what the customer is viewing. Like the Related Products Component that is built into the Product Details Page, Clarity can build in custom components for viewing Featured Products, Frequently Purchased With, Recently Viewed, Alternative, Top Sellers, Up-Sell, etc. Some personalization components may require the AI / Machine Learning Add-On Module.” That language is the source-of-truth definition. Everything on this page is built on it.
Most every eCommerce platform has "Recommended Products" already. Why doesn't that work for B2B? The reason this layered architecture matters in B2B is that consumer-grade “you may also like” recommendation engines weren't built for accounts. They were built for anonymous shoppers. They don't know that the user is a distributor with a different SKU list than the end-user account next to them, or that this contract excludes certain product families, or that this account has a custom pricing tier that changes what counts as “Top Sellers” for them. Clarity's Product Components inherit the Account Hierarchy and Configurable Attributes from the rest of the platform. A Frequently Purchased With suggestion to a distributor account never surfaces an end-user-only SKU, and a Top Sellers list is filtered to the catalog the logged-in user is actually entitled to buy from.
So how does it actually work then? Personalization & AI works alongside Product Catalog (whose Configurable Attributes are the structured signal the recommendation engine uses), Advanced Search (which shares the same attribute model for faceted filtering), and Analytics & Reporting (which surfaces which recommendation strategies are actually lifting AOV and conversion). The AI / Machine Learning Add-On Module is the differentiator angle when buyers ask “does Clarity do AI?” The answer is yes on two fronts: as a content engine and as a behavioral recommendation engine. Both are productized add-ons that fit into the same platform you already run.
Generic recommendation engines were built for an anonymous shopper buying a hoodie. They don't respect entitlement. They don't understand contract SKU lists. They confuse a distributor with an end-user. They surface complementary products no one's allowed to buy. And they leave the merchandiser with no way to override when human judgment says the engine is wrong. B2B actually demands more from a recommendation layer than B2C does, not less.
A distributor account logs in and sees a “Frequently Purchased With” carousel full of end-user-only SKUs. A contract-restricted account sees Up-Sell items outside their contract. The friction is real and the merchandiser ends up turning recommendations off entirely.
When the engine's “Alternative Products” suggestion is technically right but commercially wrong, your category manager needs a manual override. Pure black-box recommendation engines don't expose that lever, so category managers learn to ignore the channel entirely.
You have 20,000 SKUs. Each needs marketing copy, a description, a feature list, and meta tags. Doing it by hand takes thousands of hours. Doing it badly hurts SEO, conversion, and search relevance. Most teams compromise on quality because volume is the bottleneck.
Bolt-on recommendation engines often run as a separate SaaS, with their own data pipeline, their own indexing schedule, and their own admin UI. The merchandiser has to context-switch, the data is always a day stale, and the recommendation surfaces don't share the storefront's entitlement rules.
If you can't A/B test “Top Sellers on the homepage” against “Recently Viewed for returning buyers,” you can't tell which strategy is actually moving the needle. Half the recommendations in production are there on hope, not data.
The classic SaaS trap: AI is in the brochure, but enabling it requires a separate procurement, a separate integration, a separate data feed, and a separate admin tool. Teams sign for it and never turn it on. AI that's actually used has to live inside the platform you already run.
The cost of generic recs is real, and it's twofold. First: revenue you don't capture, because cross-sell and up-sell placements are either turned off or surfacing wrong-tier SKUs. Second: merchandising hours you do spend, because category managers manually compensate for an engine that's wrong more often than it's right. The fix isn't “more AI.” The fix is recommendations that respect the same Account Hierarchy, Configurable Attributes, and entitlement rules your storefront already enforces, backed by a merchandiser override and an AI / ML Add-On for deep behavioral signal.
Six platform capabilities that turn personalization from a bolt-on guess into a configurable, account-aware, merchandiser-controllable system. Optional AI / ML adds behavioral depth. The AI Content Generation Module handles the content-volume problem.
Featured Products, Frequently Purchased With, Recently Viewed, Alternative, Top Sellers, Up-Sell. Per the platform: “Components are used to provide specific sets of data to the storefront depending on what the customer is viewing.” Each is placeable on the homepage, the PDP, category pages, cart, checkout, the customer portal, or content pages.
The Related Products Component is built into the Product Details Page and is intentionally merchandiser-controlled. It's the carousel for cross-sells and complements that your category manager knows belong together but the engine can't infer from data alone. Human judgment where it matters.
“The AI Module can intelligently generate product and category marketing content via the Admin Portal.” PDP copy, category-page intros, meta descriptions, feature lists: drafted by AI, then edited and approved by your merchandisers. The volume problem stops being a bottleneck.
The optional behavioral-recommendation engine: collaborative filtering, propensity scoring, look-alike Alternative recommendations, browse-and-purchase pattern matching. Per the platform: “Some personalization components may require the AI / Machine Learning Add-On Module.” Add when you're ready.
Every Product Component inherits the logged-in Account's pricing tier, contract SKU list, and entitlement rules. Distributors see distributor recommendations. End-users see end-user recommendations. Contract-restricted accounts only see what's in the contract. No more wrong-tier suggestions.
Run A/B tests on recommendation strategies and surface placements. Product Reviews and user-generated content feed back into the personalization model. Cross-sell and Up-Sell components at cart and checkout drive measurable AOV lift, with the reporting layer to prove it.
The AI / ML Add-On is the differentiator. When buyers ask “does Clarity do AI?” the answer is yes, twice over: the AI Content Generation Module that writes product and category marketing content from the Admin Portal, and the AI / Machine Learning Add-On Module that powers behavioral recommendations behind the dynamic Product Components. Both are productized, both are extensible (other AI customizations are available on request, estimated per scope), and both run inside the same Clarity platform that already enforces your Account Hierarchy, Configurable Attributes, and contract-driven entitlement. The bi-directional ERP sync through Clarity Connect keeps your AI-generated content and AI-driven recs aligned with SAP, NetSuite, D365, Sage, Acumatica, Epicor, Infor, SYSPRO, and 17-plus more ERP customer masters.
A walkthrough of the dynamic Product Components that drive personalization in the Clarity eCommerce Framework storefront. It covers Featured Products, Frequently Purchased With, Recently Viewed, Alternative, Top Sellers, and Up-Sell associations, plus the manually-curated Related Products carousel on the Product Details Page. This is how merchandising and machine recommendation work together inside one platform.
Seven steps from mapping your Configurable Attributes through enabling Product Components, curating Related Products, generating AI content, and layering in the AI / ML Add-On.
In the Admin Portal Catalog module, set up the Configurable Attributes that describe your products: size, color, material, voltage, gauge, finish, application, certification, whatever your category needs. These attributes are the structured signal that powers filters, faceted search, and recommendation matching across every personalization component.
Turn on the Product Components you want surfaced in the storefront: Featured Products, Frequently Purchased With, Recently Viewed, Alternative Products, Top Sellers, Up-Sell. Per the platform: “Components are used to provide specific sets of data to the storefront depending on what the customer is viewing.”
Use the manually-curated Related Products Component built into the Product Details Page to merchandise cross-sells and complements that you know belong together. These are the items the engine can't infer but your category manager can.
Because pricing tier and contract live on the B2B Account, recommendations are filtered to the SKUs that account is actually allowed to buy: distributor catalog, end-user catalog, or contract-restricted catalog. Different account tiers see different recommendations from the same storefront.
Use the AI Content Generation Module in the Admin Portal to intelligently generate product and category marketing content. The same engine can be extended with custom AI workflows on request.
For deeper, behavior-driven recommendations (collaborative filtering, browse-and-purchase pattern matching, propensity scoring), enable the AI / Machine Learning Add-On Module. Per the platform: “Some personalization components may require the AI / Machine Learning Add-On Module.”
Run A/B tests of recommendation strategies, surface UGC and reviews as additional personalization signal, and use cart / checkout placements (Frequently Purchased With, Up-Sell) to lift average order value. Every interaction feeds back into the model and into your reporting dashboards.
Personalization that respects entitlement, merchandising that exposes a human override, AI that lives inside the platform you already run. Together, they shift the recommendation channel from “turned off because it embarrasses us” to a measurable lever on AOV, conversion, and content velocity.
Frequently Purchased With at cart, Up-Sell at checkout, Alternative Products on out-of-stock SKUs. The placements that consistently lift AOV in B2C work in B2B too, as long as the recommendations are filtered to what the buyer is allowed to purchase.
20,000 SKUs no longer means 20,000 hand-written PDPs. The AI Content Generation Module drafts. Your merchandisers edit and approve. Category pages, meta descriptions, and feature lists move at platform speed instead of writer speed.
The Related Products Component is manually-curated for a reason: human judgment beats algorithmic guess on commercial cross-sells. Featured Products gives the editorial team a fixed slot. The dynamic components fill in around them. Best of both worlds.
No more “Frequently Purchased With” lists full of items the buyer can't purchase. The Account Hierarchy filters every Product Component to the catalog the logged-in user is entitled to see and buy from. Trust in the channel goes up, opt-outs go down.
Every Product Component placement, every strategy, every algorithm choice can be A/B tested and measured against AOV, conversion, attach rate, and bounce. Personalization stops being a faith-based exercise and starts being a tunable lever.
Because the AI Content Generation Module and the AI / ML Add-On Module live inside the same Clarity platform that runs your storefront and admin, they don't need a separate procurement, a separate integration, or a separate data feed. Your team turns them on, they work.
The people who benefit span every commerce stakeholder: merchandisers and category managers get a recommendation channel they can trust, with manual override where they need it, content teams stop being the bottleneck on PDP and category-page copy, marketing finally gets account-aware Featured Products and Top Sellers placements that respect B2B entitlement, buyers and procurement teams see recommendations that reflect their actual contract and catalog, not a generic guess, analytics teams get A/B-testable, measurable personalization signal feeding their dashboards, and your CRO and CMO get a productized AI / ML differentiator that actually ships, instead of a license that sits unused.
Schedule a 30-minute walkthrough and we'll show the dynamic Product Components on a storefront tuned to a sample of your catalog: Featured Products, Frequently Purchased With, Recently Viewed, Top Sellers, Up-Sell. You'll see account-aware entitlement filtering, a curated Related Products carousel, an AI-generated content draft, and the AI/ML Add-On surfacing behavioral recommendations. Whatever your B2B catalog reality, we'll show personalization running cleanly inside it.
