Insight Ranking
Autobound’s Insight Relevance Algorithm ranks available insights based on their fit with the prospect’s context and the seller’s value proposition to ensure only the most impactful signals are returned.
🧠 How Autobound Ranks Insight Relevance
When you generate insights or personalized content via the Autobound API, our system doesn’t just return a raw list of available insights. It first evaluates which insights are most relevant for your specific prospect and sales motion.
This relevance ranking ensures that the insights returned are:
- Contextually important to the prospect
- Aligned with your company’s value proposition
⚙️ What Happens Under the Hood
Once prospect and user resolution is complete, Autobound runs all eligible insights through our internal Insight Relevance Algorithm.
This algorithm evaluates each insight using:
-
Prospect Fit
Does the insight relate to the contact’s job, seniority, industry, or known company strategy? -
Seller Fit
Does the insight match what your company helps with, based on data like your website, content hub inputs, or provided metadata?
Each insight is scored accordingly, and the highest-ranking ones are returned first. If multiple variations are requested (using our n parameter), Autobound ensures each variation includes a diverse but still relevant set of top-ranked insights.
🛠 Resolution Context
The algorithm works best when the following are provided:
- Prospect context (company domain or LinkedIn)
- User context (rep email or company domain)
User context is optional but improves relevance. At minimum, a resolvable prospect company is required for the ranking engine to run.
✅ Applied Automatically
This logic is applied automatically when:
- Calling the
/generate-insightsendpoint - Calling the
/generate-contentendpoint - Generating insights within AI Studio or the Chrome extension from our webapp
Updated about 16 hours ago