Behavioural state
Also called: Fogg state, audience mindset, buyer mindset.
Behavioural state is the headspace a buyer is in at the moment they encounter your page. distracted, sceptical, or actively shopping. It comes from the Fogg Behaviour Model, which holds that an action occurs only when motivation, ability and a prompt converge. The same person, reading the same page, reaches a different verdict in each state, so testing with only one state tells you almost nothing about how the page will actually perform.
The three states
- Distracted. Multitasking, scrolling, low attention. Reads headlines only and will not scroll to find what they need. Everything below the fold is close to invisible.
- Sceptical. Interested, but burned by vendors before. Wants numbers, named customers, and mechanism. Discounts adjectives entirely.
- Ideal-Desperate. Actively shopping for a solution to this exact problem right now. Reads properly, forgives a weak hook, converts on a relevant page.
Everyone recognises the third one because it is who we picture when we write. It is also the rarest.
Why the mix matters more than the states
The states are only useful with a population share attached, and that share is a property of the channel, not of your product. Derived from published CTR benchmarks and the Ehrenberg-Bass 95:5 rule, which caps buyers who are actively in-market at any moment at roughly 5%. The distribution looks approximately like this:
| Traffic source | Distracted | Sceptical | Actively shopping |
|---|---|---|---|
| Google Search, retargeted | 15% | 20% | 65% |
| Google Search, cold | 30% | 25% | 45% |
| LinkedIn lead-gen form, warm | 45% | 20% | 35% |
| Email, warm list | 40% | 25% | 35% |
| Email, cold | 78% | 17% | 5% |
| LinkedIn sponsored, cold | 82% | 15% | 3% |
| Display, cold | 85% | 12% | 3% |
The right-hand column is the single most predictive variable for how a page performs, and it swings by more than twenty-fold across channels. A page that converts search traffic can fail completely on cold social without a single word changing.
The reviewer problem
Now sort the people who approve your page. Motivation: maximal, their name is on it. Familiarity: maximal, they know the category and the roadmap. Attention: maximal, it is a calendar event with the page on a screen.
Every reviewer is in the Ideal state. On cold social, roughly 3% of the audience is. Internal review tests with the 3% and ships to the 97%, which is a coverage problem rather than a competence one and cannot be fixed by reviewing more carefully.
The same applies to a general AI assistant. Paste a page into ChatGPT and it reads carefully, patiently, all the way down, cooperatively. The most attentive reader available. Useful for drafting. Not representative of anyone you are buying traffic from.
Distraction is not a small penalty
It is tempting to treat a distracted reader as an ideal reader with less patience. The arithmetic is harsher. In WhyUser’s engine, a Distracted persona gains only 0.7× on anything that resonates, while friction penalties are amplified by dividing by the same factor. Roughly twice as hard to convince and twice as easy to lose.
Two consequences follow, and both change what you build:
- Above the fold has to close on its own. Not tease, not set up. close. For 82% of cold social traffic the fold is the entire page.
- Proof beats polish. A sceptical reader discounts adjectives to zero. A number, a named customer, or a mechanism survives the discount.
How WhyUser uses it
Every committee role is run across all three states, and the seeds are allocated by the measured distribution for the channel you are actually buying, not evenly. Running the three states one-third each would over-represent the shopping buyer by more than tenfold on cold social and produce simulated click rates in the 20–30% range, which is fiction.
Weighting to the real mix brings simulated rates into the range published benchmarks actually show. The distribution used is stamped onto every run, so you can check which one produced your result, and you can override it with your own measured numbers once you have them.
Questions people ask about behavioural state
What is a behavioural state in marketing?
It is the headspace a buyer is in when they meet your page: distracted, sceptical, or actively shopping. It comes from the Fogg Behaviour Model, and the same person reaches a different verdict about the same page in each state.
What share of B2B traffic is actively in-market?
Roughly 5% at any moment, per the Ehrenberg-Bass 95:5 rule. On cold LinkedIn or display it is nearer 3%; on retargeted search it can reach 65%. That single share is the most predictive variable for how a page will perform.
Why do internal reviewers not represent real buyers?
Because they sit at maximum motivation, maximum familiarity and maximum attention, which is the ideal behavioural state. On cold social traffic roughly 3% of the audience is in that state, so the review samples the 3% and the campaign ships to the 97%.
How much harder is a distracted buyer to convert?
Roughly twice as hard. In WhyUser’s engine a distracted persona gains 0.7 times on anything that resonates while friction penalties are amplified by the same factor, so above-the-fold content has to close on its own.
Should I test my page against every behavioural state equally?
No. Weighting the three states evenly over-represents the shopping buyer by more than tenfold on cold social and produces unrealistic conversion estimates. The distribution should match the channel you are buying.