Your static persona is actually five different users.
A planning canvas for design and product teams. For each of five stages of change, write down what your user does, when they can and want to act, when the product should speak up or stay silent, and how it should respond.
Free for one project, AI included. No card.
A persona is a snapshot. Your user is in motion.
The same person is a different user on Monday morning than on Friday night: more or less motivated, able or not, open to a nudge or wanting to be left alone. A persona freezes them in one demographic. An adaptive product has to read the state they are in.
The persona trap
A profile that never changes.
- A static PDF, frozen in demographics
- “A busy 34-year-old”, and nothing your AI can act on
- Nothing about motivation, stress or readiness
The Audience Factors way
A state your product can read.
- Five stages of change, from not ready to habit
- What to detect, infer and do at each stage
- When to prompt, and when to stay silent
How the map turns into behaviour
Meet Sarah, a user of an AI fitness coaching app, in the Action stage (a worked example from the framework guide). This is the reasoning the map holds.
- DetectWhat you can observe
The product notices
- She skipped a planned session
- Her sessions are getting shorter
- InferWhat it means
It reads the state
- Her motivation is dropping
- After a missed day she is at risk of a guilt spiral, so her ability is low
- AdaptWhat the product does
It responds
- A gentle same-day nudge to come back
- No streak-break or guilt messages after two missed days: it stays silent
The same three answers, for each of the five stages, make up the map.
The map you build
That reasoning, for all five stages: five stages across, eight questions down. Fill it in with your team, one stage at a time.
Product Behaviour goal Date
| PrecontemplationNot thinking about change yet | ContemplationThinking about it, but ambivalent | PreparationDecided to act, making plans | ActionJust started, behavior is fragile | MaintenanceSustaining the behavior over time | |
|---|---|---|---|---|---|
| DetectWhat you can observe | |||||
| Behavioral SignalsObservable actions (or inactions) that tell you someone is at this stage. | |||||
| InferWhat it means | |||||
| MotivationWhat drives or blocks the desire to act | |||||
| High Motivation SignalsWhat does it look like when someone at this stage is motivated? | |||||
| Low Motivation SignalsWhat does it look like when motivation is dropping? | |||||
| AbilityWhat enables or constrains action right now | |||||
| High Ability MomentWhen is a user at this stage most capable of acting? | |||||
| Low Ability MomentWhen is action hardest for someone at this stage? | |||||
| Prompt timingWhen to intervene, and when to stay silent | |||||
| When to PromptWhen should the system reach out proactively? | |||||
| When to Stay SilentWhen would prompting do more harm than good? | |||||
| AdaptWhat the product does | |||||
| System AdaptationHow should the AI respond differently at this stage? | |||||
app.jcerejo.com · The Anticipatory Design Framework
Two proven models, one map
The five stages come from Prochaska’s Stages of Change. The momentary read comes from Fogg’s behaviour model. Together they tell your product where a user is heading, and whether now is the moment to act.
Precontemplation
Unaware or unready: they don't yet see a reason to change.
The product: No pushing and no guilt. Light, educational content only.
Contemplation
Ambivalent and easily overwhelmed: weighing it up, not committed.
The product: Reduce friction and surface the barriers, without shaming.
Preparation
Committed to act, and needing a push over the line.
The product: Offer a micro-commitment: “just 10 minutes today.”
Action
Forming the habit, and at the highest risk of dropping off.
The product: Check in daily, reinforce wins straight away, and plan for relapse.
Maintenance
Routine established: the behaviour has stuck.
The product: Offer advanced challenges and community. Turn off the patronising reminders.
Behaviour = Motivation × Ability × Prompt
A behaviour only happens when motivation, ability and a prompt come together at the same moment. The map asks these questions at every stage, so your team knows when a prompt will land, and when to stay quiet.
Built for teams designing adaptive experiences
Senior product designers
Design stage-specific affordances and progressive disclosure, instead of one static interface for everyone.
You get
A stage-by-stage brief for what your interface shows, and what it holds back.
AI PMs & data scientists
Turn qualitative research into the signals to watch and the rules for when to prompt.
You get
A written list of signals to detect and rules for prompting, ready to turn into requirements.
Behavioural design researchers
Plan behaviour-change products (fitness, fintech, learning, health) on a recognised model.
You get
A map your team can test with users, stage by stage.
Free for everyone
The whole tool is free, AI included. The only limit is one project. Pro lifts it.
Free, for one project
- All five stages, all eight questions
- “Suggest ideas”: AI suggestions on every field
- Download as text, or email it to your team
Before you start
Do I need to throw away our existing user personas?
No. Keep them as shared vocabulary if they help, but treat them as a starting hypothesis, not the model your AI reads. Audience Factors sits underneath: it turns “who someone is” into “what state they're in, right now,” which is the part a static persona can't give an adaptive system.
How is this different from event-tracking or analytics?
Analytics records what happened. Audience Factors is where you decide which behaviours to look for and what they mean: which stage someone is in, how motivated and able they are, and what the product should do about it. It does not collect any data. It is the planning layer you write before, or alongside, your analytics.
How do we use behavioural signals without over-tracking or hurting privacy?
Map from the signals you already legitimately have, and keep the model deliberately coarse: a stage and a readiness read, not a surveillance profile. The goal is to prompt less and better, so “collect everything” works against you. Your map is held in the EU-hosted, GDPR-first workspace.
Can we share the map with our data science team?
You can download the Audience Factor Map and the behavioural signal inventory as text files, or email them to your team. There is no direct Jira, Figma or Notion integration, so paste what you need into the tools you already use.
What is free, and what is Pro?
The tool is free for everyone, including the AI suggestions on each field (“Suggest ideas”), for one project. Pro adds unlimited projects.
How does this prevent annoying or patronising prompts, the “Clippy effect”?
That's the whole reason to map state. Fogg's B=MAP says a prompt only fires behaviour when motivation and ability are already present, so the model's default is often silence. It tells the system when NOT to prompt (precontemplation, low ability, settled maintenance) as clearly as when to, which is exactly what Clippy never learned.
More questions? See the full FAQ →
Ready to map your first stage?
Start with one stage and add the rest as you go. Free for your first project, no card required.
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