How does this process relate to how you are currently using AI? There’s been a lot of interest lately in using AI to better understand rep behavior and drive more consistent sales outcomes. I want to share a simplified version of a heavier, structured behavior engineering process built for that space. The idea is to treat rep behavior (and any behavior) as a node in a behavioral network — and use AI as a cognition engine to extract underlying insights. This is a new way of working with human behavior, made possible by tools like GPT-4o. This version still gives you real signal — especially if you’re curious about what actually shifts client momentum in a sales call. Have fun with it! Here’s step-by-step how to can identify that pattern: Process (1-2 hours of your time): Successful Behavior Pattern Extraction from Sales Calls Goal: Identify rep behaviors that consistently trigger client alignment, agreement, or momentum in successful calls — and are missing or misfired in unsuccessful ones. What You Need
5–7 successful sales calls transcripts in a single vertical
5–7 unsuccessful (or low-conversion) sales calls in same vertical
All transcripts must have clear annotation who the rep is and who is the client (Otter AI does this very well)
A lightweight client profile sketch (3–5 sentence description of client type, business stage, and key challenges)
Note: I am assuming at least 15 min client calls. The shorter the calls are the more you need for accurate signal extraction. Step 1: Behavioral Insight Extraction per Call Load transcript and profile, prompt AI per transcript:
“This is a transcript of a [successful/unsuccessful] sales call for [client name]. Attached, you will also find a client behavioral profile for this vertical. Extract all high-salience behavioral moments from the rep side that clearly attempt to: a) Validate the client’s internal narrative b) Shift client from a passive to active frame c) Surface or reframe urgency/motivation d) Manage friction, resistance, or ambiguity Return as:
Client: (name)
Call type: (successful / failed)
Behavior ID: (two digit ID number in sequence)
Prompt: (what client behavior prompted rep response):
Behavior Description (1–2 sentences):
Client Reaction (verbatim or paraphrased): (how client reacted to rep behavior)
Rep Intent (inferred if not explicit): (what was rep intent behind behavior)
Outcome: (momentum / stall / objection / resolution / unclear):
Store all extracted insights in a list (in notion or structured doc), one set per call. Step 2: Behavioral Differential Mapping Once all calls are processed and saved to file, load that file to new thread, load client behavioral profile (optional but helps with accuracy) and run this contrast prompt:
“Here are structured behavior insights from multiple successful and unsuccessful sales calls in the same vertical together with a client behavioral profile. Identify patterns of rep behavior that:
Appear only or more frequently in successful calls
Are absent or mishandled in failed calls
Tend to produce visible client momentum or alignment
Return a list of 5–10 contrasting behavior patterns with:
Pattern ID:
Rep Behavior Summary:
Observed Client Response:
Absent or Failed Variant (in failed calls):
Why It Likely Matters (1–2 sentences):
Example insight: Pattern ID: PAT-01 Behavior: Rep mirrors client language about risk in first 3 minutes to lower defensiveness Observed Impact: Client shifts from vague answers to disclosing specific pain Absent In Failed Calls: Reps skip early reframing and jump to solution talk Why It Matters: This early mirror reduces resistance and accelerates trust curve in short-cycle sales. New Hires: Trait Alignment Screening From extracted high-performing patterns, ask AI to identify lead personality traits signaling pattern alignment. Next, ask AI to generate screening questions against identified lead personality traits that make interviewees more likely to learn or naturally display high-performing patterns:
“Generate 3 interview questions to identify if interviewees possess lead personality traits making him/her likely to naturally use or be trained in [behavior pattern X].”
You now have behavioral traits anchored in real call data. ⚠️ Words of Caution:
You need a structured approach to extract insights reliably - Dropping transcripts in bulk and asking an AI to “find patterns” will lead to inconsistent output—hit-or-miss insights at best, smart-sounding but harmful nonsense at worst.
Hallucinations will happen—and most won’t look like hallucinations - You may notice some hallucinations during extractions. They will smooth out at pattern recognition step, but in any real behavior engineering process, you need QA layers at nearly every step. Without them, the model will confidently invent correlations that don’t exist. Especially from bigger datasets.
If you don’t have a behavioral client profile, the AI lacks context - Without a clear reference for what value looks like to this type of client, the AI is working blind. It may pick up surface-level language signals but miss what actually moved the conversation forward.
Success vs. failure isn’t just about outcomes—it’s about behavioral contrast - If you’re comparing successful and failed calls without aligning for client type, intent, and process stage, your analysis will break. You need controlled contrast to isolate signal from noise.
Summary: This quick process should highlight a new space of possibilities when you merge behavioral psychology with AI. If it feels like it’s pointing at something deeper, that’s because it is. Once you’ve got a few of these patterns extracted, the real value starts to show up in the questions you ask next. Try exploring:
Which behaviors only show up after trust is established?
What responses consistently precede objections vs. momentum?
Which rep moves recover stalled energy — and which ones kill it?
You’ll be surprised how much signal is already sitting in your call transcripts once you start looking at them this way. Let me know if that was helpful to you, how this relates to what you are already doing? Do you do things differentlty? How? (Oisín O., this post was inspired by our yestedays convo. I hope it helps)
Super insightful - love the structured approach to mapping rep behavior. The contrast between successful and failed calls is where the real gold is. At Passionbits, we're applying similar thinking to performance content and UGC ads - this resonates a lot. Appreciate you sharing!
This is brilliant!
Thanks KC - yes, we take a structured approach. We analyze top-performing UGC ads by mapping creative patterns against metrics like engagement, watch time, and conversion. Then we isolate what consistently drives action - tone shifts, opening hooks, pacing, etc. Applying those insights to new content has led to clear lifts in CTR and retention. would love to share more info
Just sent you a DM
