What is AI Reference Matching?
AI Reference Matching is a feature in UserEvidence's References module that helps sales reps find the right customer reference for any deal — fast. Instead of submitting a rigid form or manually combing through a spreadsheet, reps describe what they need in plain language, and UserEvidence AI searches your advocate pool to surface the best-fit customer based on deal context.
The result: fewer Slack threads, faster reference fulfillment, and better-fit conversations for prospects.
How it works
When a rep submits a reference request, they can describe what they're looking for
in natural language — for example:
"Mid-market fintech customer in EMEA who switched from Competitor X"
UserEvidence AI then searches your advocate pool using both structured data (industry, company size, deal stage, product usage) and contextual signals (survey responses, engagement history, past reference activity) to return a ranked list of best-fit matches.
Submitting a reference request
Reference requests can be submitted in several ways:
Via Salesforce — create a reference request from within a Salesforce opportunity (see Creating a Reference Request in Salesforce)
Via Slack — use the Slack workflow to request a reference without leaving your workspace (see Requesting a Reference in Slack)
When submitting, describe the deal context in as much detail as possible. The more context AI has — competitor, industry, use case, deal stage — the stronger the match.
Reviewing AI-suggested matches
Once a request is submitted, AI surfaces a ranked list of suggested advocates on the reference matching screen. For each suggested match, you can:
View the advocate's full profile — engagement history, past reference activity, survey responses, and advocacy scores
Ask the AI questions about a specific advocate's background and suitability using the built-in AI chat (e.g. "Has this customer talked about switching from Salesforce?" or "How recently have they done a reference call?")
Select a match to move forward with the request
The built-in advocate chatbot makes it easy to vet a match before committing — without having to dig through past activities manually.
Customizing AI reference matching rules
Admins can configure the AI matching behavior to align with your program's rules and priorities. Go to Settings → References → AI System Prompt to write custom instructions in plain English, such as:
Only surface advocates who have opted in to references
Exclude advocates who have completed more than X reference calls in the past 90 days
Prioritize advocates from specific segments (e.g. enterprise accounts, strategic tier)
Apply internal naming conventions or account categorization
You can also use dynamic field placeholders by typing / and inserting fields that will auto-populate (such as NPS score).
Changes to the AI system prompt take effect immediately and apply to all future reference requests.
Tracking reference activity
All reference requests and matches are tracked in the Reference Board — a central view that updates automatically as Zoom meetings are scheduled and completed. No more checking Slack, email, or a shared spreadsheet to see where a reference stands.
CC a Team Member
Admins and Customer Success Managers can be auto-CC'd on reference communications. This can be configured in Settings → References → Step 3.
Frequently asked questions
Can reps see the full advocate profile before selecting a match?
Can reps see the full advocate profile before selecting a match?
Yes. The reference matching screen shows each suggested advocate's profile, including their engagement history, past reference activity, and advocacy score. Reps can also use the built-in AI chat to ask specific questions about an advocate before selecting them.
How does UserEvidence prevent advocate burnout?
How does UserEvidence prevent advocate burnout?
AI matching automatically factors in advocate health signals — including how frequently a customer has been contacted and whether they've opted in to reference activity. Admins can also configure custom rules in the AI system prompt to set hard limits on reference frequency per customer.
Can I customize what criteria the AI uses to match references?
Can I customize what criteria the AI uses to match references?
Yes. Admins can write custom matching rules in plain English via Settings → References → AI System Prompt. This lets you prioritize certain segments, exclude overused advocates, and apply program-specific logic without any code changes.
What integrations are supported for reference requests?
What integrations are supported for reference requests?
Reference requests can be submitted via Salesforce and Slack. The Reference Board also integrates with Zoom to automatically update meeting status when reference calls are completed.