What are Advocate Scores?
Advocate Scores are AI-generated scores assigned to each customer in your advocate pool. They give your team a fast, reliable signal for who is ready to be activated — and who needs a break.
Instead of relying on gut instinct, spreadsheets, or whoever raised their hand last, Advocate Scores surface your strongest potential advocates automatically, including customers you might have otherwise missed.
How scores are calculated
UserEvidence AI runs continuously in the background, analyzing multiple data sources to calculate and update each advocate's score in real time. The score reflects how likely a customer is to be a great advocate right now, based on:
Survey responses — sentiment, NPS, and the quality and specificity of their feedback
CRM data — account health, deal stage, contract value, and customer lifecycle information
Product usage — how actively and deeply the customer is using the product
Engagement activity — past advocacy participation, mission completions, community involvement, and responsiveness
Call data — signals from Gong call transcripts, such as positive sentiment, competitive comparisons, or unprompted praise
Scores are recalculated automatically as new data comes in — so your advocate list stays fresh without any manual work.
Where to find Advocate Scores
Advocate Scores appear directly on each 360° Advocate Profile in the Advocacy module. From the profile, you can see:
The advocate's current score
A full history of their advocacy activity across surveys, references, and community
Engagement signals that contributed to their score
You can also use scores to filter and sort your advocate list, making it easy to prioritize outreach or build targeted segments.
What are scores used for
Finding advocates you didn't know you had
Advocate Scores surface customers who are already strong advocates based on their behavior — not just the ones who explicitly opted in or raised their hand. This helps you grow your advocate pool beyond your usual go-to contacts.
Protecting your best advocates from burnout
The AI automatically deprioritizes customers who have been over-asked or are showing signs of fatigue. Advocates with lower health signals are hidden from recommendation results so your team doesn't accidentally over-tap the same few customers.
Running more targeted campaigns
Use score thresholds to create dynamic segments for your advocacy campaigns. For example, you can target highly engaged advocates for high-effort asks (like reference calls or video testimonials) and less-engaged customers for lower-lift activities (like reviews or social shares).
Frequently asked questions
Do I need to set up Advocate Scores manually?
Do I need to set up Advocate Scores manually?
No setup is required. Advocate Scores are calculated automatically from the data already in your UserEvidence account. As your advocacy program generates more data — surveys, references, community activity — scores will become more accurate over time.
Can I see why a customer received a particular score?
Can I see why a customer received a particular score?
Yes. Each 360° Advocate Profile shows the engagement history and activity signals that inform the score, so you can understand what's driving it. You can also use the built-in AI chat on the reference matching screen to ask specific questions about an advocate's background and suitability.
Can I override or adjust a score manually?
Can I override or adjust a score manually?
You can add custom notes and fields to any advocate profile, which the AI can use as additional context for matching and segmentation. You can also use custom fields to flag advocates as do-not-contact or mark reference opt-in status, which will influence how they appear in matching results.
How does the AI identify advocates from Gong calls?
How does the AI identify advocates from Gong calls?
When the Gong integration is connected, UserEvidence AI scores call transcript data to automatically identify customers who show strong advocate signals — such as unprompted positive feedback, competitive win stories, or willingness to refer. These customers are surfaced in the Advocate CRM.
What happens to advocates who are burned out or overused?
What happens to advocates who are burned out or overused?
UserEvidence AI automatically hides burned-out advocates from recommendation results.