Using Kayse AI Data to Improve Ad Platform Performance
If your leads come from Meta, Google Ads, or another ad platform, you can use Kayse AI outcomes to help those platforms learn which leads are worth finding again.
The key is to send lead outcomes back through your CRM so your ad platform gets better feedback over time.
Why This Helps
When your ad platform only knows that a lead filled out a form, it has very little context. It does not know whether that lead was a good fit, a bad fit, or someone who never engaged.
Kayse AI adds that missing layer by tracking what happened after outreach:
- Was the lead disqualified?
- Did the lead convert?
- Was the interaction successful?
- Did the case move to a meaningful status?
That feedback can then be sent back to your ad platform so it can optimize for better leads.
The Supported Workflow
The correct setup is:
Ad Platform → CRM → Kayse AI → CRM → Ad Platform
Here is what that means in practice:
- A lead comes in from Meta, Google Ads, or another ad platform.
- The lead is stored in your CRM, such as GoHighLevel, Law Ruler, SmartAdvocate, or another system.
- Your CRM sends that lead to Kayse AI for calls, messages, or both.
- Kayse AI records the outcome and sends it back through webhooks or synced status changes.
- Your CRM, Zapier, or another integration sends the final conversion signal back to the ad platform.
Best Practice
Keep your CRM in the middle. Kayse AI is the outreach layer, not the system of record for lead management.
What Not to Do
Unsupported setup
Do not send ad leads directly into Kayse AI and try to use Kayse as your CRM. Kayse is designed to communicate with leads, not to replace your CRM.
Which Kayse Events Are Useful for Ad Optimization
The most common signals are webhook events and case status updates.
| Kayse signal | What it means | Typical ad feedback use |
|---|---|---|
| DQ Message Intent | A message reply showed the lead is not a fit | Send a negative signal |
| Disqualified Call Outcome | A voice call ended with the lead marked disqualified | Send a negative signal |
| Converted | The lead completed the action you wanted | Send a positive signal |
| Successful | The call or message flow completed successfully | Send a positive signal |
| Case Status Changed | A case moved to a new stage | Map specific statuses to positive or negative signals |
| Call Completed | A voice interaction finished | Use the call result to decide what to send |
| Message Received | The lead replied or engaged | Track engagement or trigger a follow-up workflow |
Negative vs. Positive Feedback
For disqualified leads
Use these when you want the ad platform to stop finding similar leads:
- DQ Message Intent
- Disqualified Call Outcome
These can be sent to systems like Meta Conversions API or Google Ads as negative or low-value conversion signals, depending on how your tracking is configured.
For qualified or converted leads
Use these when you want the ad platform to find more leads like the ones that worked:
- Converted
- Successful
These are your strongest positive signals because they tell the ad platform what a good lead looks like after Kayse AI outreach.
Using Status Changes Instead of Webhooks
If your team does not want to build around webhooks right away, you can also base feedback on case status changes.
For example:
WonorQualifiedcan map to a positive signalLostorDisqualifiedcan map to a negative signal
This is simpler, but it is usually less precise than webhooks because it depends on your CRM sync and your internal status process.
Step-by-Step Setup
1. Set up webhooks in Kayse AI
- Go to Settings → Webhooks in Kayse AI.
- Click Add Webhook.
- Enter the destination URL for your CRM, Zapier, or custom endpoint.
- Choose the events you want to send, such as:
DQ Message IntentDisqualifiedConvertedSuccessful
- Add security if needed, such as API key, bearer token, or HMAC.
- Save and test the webhook.
Good to know
Kayse webhooks are set up at the company level, so you can reuse them across multiple campaigns.
2. Map Kayse events inside your CRM or Zapier
When a webhook arrives, your receiving system should:
- Store the event type
- Store the timestamp
- Update the lead or case record
- Decide whether the event is a positive or negative signal
Examples:
DQ Message Intent→ mark lead as disqualifiedConverted→ mark lead as qualified or convertedSuccessful→ mark lead as engaged or successful
3. Send the final signal to your ad platform
Once your CRM has the Kayse outcome, send that feedback to your ad platform:
- Meta: usually through Conversions API (CAPI)
- Google Ads: usually through offline conversion imports or the Google Ads API
At that point, your ad platform can use the signal to improve future ad delivery.
4. Test the full loop
Before going live, test the complete path:
- Ad click
- Lead enters CRM
- Lead is sent to Kayse AI
- Kayse AI triggers the webhook or status change
- CRM or Zapier receives it
- CRM sends the right signal to Meta or Google
If one step is missing, the optimization loop breaks.
How Kayse AI Fits Into This Workflow
Kayse AI supports this loop in a few different ways:
Voice AI
Voice AI can:
- Call leads from a list
- Answer inbound calls
- Collect information during calls
- Transfer calls when needed
- Run post-call analysis
- Record outcomes like successful, disqualified, or converted
AI Messaging
AI Messaging can:
- Reply to leads across SMS, email, and portal messaging
- Track message engagement
- Trigger webhook events based on intent
- Flag disqualifying responses that should feed back into ad optimization
Webhooks
Webhooks make the connection possible by sending Kayse events to your CRM or middleware in real time.
Key Reminders
- Keep the workflow as Ad Platform → CRM → Kayse AI → CRM → Ad Platform
- Do not use Kayse AI as a replacement for your CRM
- Use DQ outcomes as negative feedback
- Use Converted and Successful outcomes as positive feedback
- Status changes can work, but webhooks are usually more precise
- Test the full loop before relying on it for campaign optimization
Related Docs
If you need help choosing which events to send first, start with Disqualified, Converted, and Successful. Those usually give the clearest feedback loop with the least setup complexity.