Eyes Light Up
Track the moments faces change, then cut everything that doesn't produce them.
- Difficulty
- Moderate
- Time to result
- ~weeks to results
- Steps
- 5
- Confidence
- 90%
People are polite: they nod and say 'that's interesting' regardless of whether they care. But there is usually one moment where their eyes actually light up — a visceral reaction the face can't fake. Eyes Light Up says to treat those moments as hard data, log what you said, look for the pattern across people, and then rebuild your content, pitch or update around the triggers while cutting whatever makes people go dead in the eyes.
Origin
Wes Kao's framework, drawn from teaching, sales and pitching contexts.
Core principles
- 01Faces can't lie; words can. Politeness contaminates verbal feedback.
- 02'This person looks bored' is data — ignoring it is a choice to be delusional.
- 03Signals include the face changing, the demeanour changing, leaning forward, energy in the voice.
- 04The patterns are person-specific: different people light up at numbers, at upside, or at how little effort something takes.
How to run it
- 1
Stop taking words at face value
Treat a polite nod and 'oh okay, that's interesting' as non-signal. Assume that people will be polite by default and that the verbal channel is therefore low-information.
Watch out Refusing to read the room and then complaining you don't get enough data is delusion, not rigour.
- 2
Watch for the visceral tell
While explaining, presenting or pitching, watch for the moment the face changes, the demeanour changes, they lean forward, or the excitement enters their voice.
Pro tip Note the exact hot-key word, phrase or framing that immediately preceded the change.
- 3
Log the trigger
Immediately after, jot down what you were saying at that moment — the angle, the word, the way you explained it.
Pro tip Kao's own prompt: think back over recent weeks to when you explained something or gave a pitch and saw eyes light up, and write down what you were saying.
- 4
Find the per-person pattern
Across repeated conversations with the same person — a manager, a customer, a cross-functional partner — identify what consistently gets a reaction: numbers, upside, low effort to try, or some other angle.
- 5
Cut and lean in
Trim the context that person doesn't care about, and expand the material that produced the reaction. Use the logged triggers as fodder for content angles, sales pitches and future explanations.
In the wild
Kao argues the method works internally, not just in sales. Explaining something to your manager, you can tell where the energy is in their response. Over time a pattern emerges — this person reacts well to these kinds of things.
→ You trim the context they don't care about and lead with the angle that reliably gets engagement, making each update land.
A salesperson tracks which section of the pitch produces the eyes-light-up moment across prospects — it might be numbers, upside, or how little effort it is to try.
→ The pitch gets rebuilt around the trigger and the dead-in-the-eyes sections get cut.
Common mistakes
Claiming you don't have enough signal
Kao calls this delusional. If someone looks bored, that is data. Pretending the signal isn't there is easier than acting on it.
Keeping content that kills the room
Most people log what worked but never delete what didn't. The method requires both — save the parts that make eyes light up, cut the parts that make people go dead in the eyes.
Is it for you?
Best for
Founders pitching, salespeople refining a pitch, creators finding content angles, and anyone tuning how they brief a specific stakeholder
Not ideal for
Async, written-only, or camera-off contexts where you have no view of the face
From the transcript
“there's usually a moment in the conversation where their eyes light up because they are genuinely actually interested in what you are saying”
“really cut out all the parts that that you know make people go dead in the eyes and just save the parts that make their…”
“this person's looks bored they look bored that is data okay like don't ignore that data”
From the episode
Persuasive communication and managing up
Wes Kao (Maven, Seth Godin, Section4)