Query-specific relevance
Contextualize uses anonymised collective signals such as clicks, add-to-cart activity and purchases after a query to adjust product relevance for that query. Teams choose which signals and catalogue attributes contribute.
Composable commerce search that helps retail teams guide shoppers and control rankings.
Empathy.co's Empathy Platform is a composable search and discovery system for online commerce. It combines keyword search with AI features and merchandising controls, so shoppers can refine queries and find relevant products while commerce teams shape ranking behavior around their business rules. The platform includes tools for examining why products rank where they do and for monitoring search demand and outcomes. Deployment options include SaaS and Self-managed Components (SmC). Retail teams can evaluate it using their own queries and catalogue before choosing an integration.
Contextualize uses anonymised collective signals such as clicks, add-to-cart activity and purchases after a query to adjust product relevance for that query. Teams choose which signals and catalogue attributes contribute.
Equalize weights textual matches, business rules and numeric measures such as stock, margin or units sold. Configurations can differ by store, language or device, with previews and A/B testing.
Explain breaks down a product's ranking score, including contributions from ranking configuration, merchandising rules, collective shopper behavior and term position. Teams can compare products and identify relevant adjustments.
Merchandising provides attribute boosts and buries, product pins, top or bottom placement, and no-show controls for products or attribute groups. Rules can be scheduled and previewed, carry IDs for A/B tests in your own tool, and be reviewed in an activity stream.
AI Search features include Related Tags, Next Queries, Related Prompts, AI Spell Check and AI Carousels. Related Tags can offer more precise catalogue terms after a broad initial search.
Playboard insights report on live queries, clicks and zero-result searches, as well as performance measures such as CTR, findability, add-to-cart and checkout across queries and categories.
Use query-level collective behavior to distinguish meanings that share the same term, such as shoppers seeking whole oranges versus orange juice, and adjust result relevance accordingly.
A merchandising team can pin a campaign product, boost a range across related queries, hide a discontinued item and schedule the rules to stop after the campaign.
A merchandiser can inspect the ranking explanation for a query, compare two products and see which scoring factors or tiebreaker affected their positions.
Search teams can track emerging terms and zero-result queries, then address them through merchandising or ranking configuration before a seasonal demand peak.
Use AI Search guidance such as Related Tags to present more specific catalogue terms alongside initial results, letting shoppers launch a narrower search with one click.
It combines keyword search with AI features that guide shoppers, including Related Tags, Next Queries, Related Prompts, AI Spell Check and AI Carousels. The platform also provides ranking and merchandising controls.
Yes. Equalize configures relevance and business weighting, while Merchandising provides controls such as boosts, buries, pins and no-show rules. Explain shows factors behind individual product positions.
Deployment options include SaaS and Self-managed Components (SmC). Choose the operating and integration setup with Empathy for your store; a demo can use your real queries and catalogue.
Contextualize builds query-level relevance models from anonymised collective clicks, add-to-cart activity and purchases, without shopper IDs or profiles. This describes Contextualize, rather than a guarantee about every service or customer-data workflow.
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| jun. | 17545 |
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| jul. | 19005 |
| ago. | 15310 |
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