A companion case study to the Personalization Core platform redesign. Three connected surfaces that turn a deeply technical configuration problem — scoping a product or content catalog, defining filter rules, choosing a recommender strategy — into something a marketer can do with a conversation and a few clicks.
Salesforce Personalization is a real-time decisioning engine — it picks the right product, article, or piece of content for each customer in the moment. Recommenders are the configurable strategies that drive that decision: collaborative filtering, content-based scoring, behavioral signals, business rules, and combinations of all of the above.
Marketers don't think in terms of cosine similarity or graph traversal. They think: "I want first-time visitors to see our most-viewed seasonal items, but exclude anything we're running low on." The product had to bridge that gap — between marketer intent and a working ML strategy — without dumbing down what the engine could actually do.
Three problems sat on top of each other — each made the next one worse.
A visual filter builder that handles nested groups, mixed AND/OR logic, dynamic value pickers (resolved against the live catalog), inventory-aware operators, and reusable saved filter sets. Marketers can build the kind of scoping logic that previously required engineering — and read it back out as something that looks like a sentence, not a query plan.
A conversational layer that sits on top of the visual builder, not instead of it. The marketer types "show me men's running shoes under $120 that are in stock in the EU and have at least 50 reviews" — the agent proposes the filter graph, populates the visual builder, and shows the live match count. The marketer can then edit visually, refine in chat, or both. The agent is a starting point, not a black box.
The marketer states an outcome — "recommend products that pair well with what's in the cart" — and the agent leads a guided setup: it proposes the right strategy (cross-sell, complementary, behavioral), suggests scoping filters, picks reasonable defaults for cold-start and fallback, and previews the live output against real customer profiles. The marketer ends with a tunable recommender, not a decision tree they have to navigate alone.
The recommendations workstream took the most technical surfaces in the product and made them the most marketer-friendly. Filter rules that previously required Professional Services became something a marketer could draft in a chat box. Recommender setup went from a wall of strategy options to a guided conversation. And every AI action stayed editable — which is what made the AI shippable in an enterprise org that had every reason to be cautious.
More information about the public Salesforce Personalization product: salesforce.com/marketing/personalization