TL;DR
Problem:
41% of new users dropped off before experiencing product value. New users were overwhelmed by admin-heavy onboarding and a blank family tree canvas, preventing activation which is the strongest predictor of free trial conversion.
Solution:
Designed TreeAssist, an AI-assisted onboarding experience that replaced form-heavy setup with in-product relative suggestions and progressive guidance, helping users build their tree faster and learn by doing.
Impact:
Stakeholders:
New Users, Head of Tree Product, Head of Product Experience
Timescale:
2 Months end-to-end , 2-week experiment cycles
Business problem:
Improve early user activation to increase free trial starts and downstream subscription conversion, unlocking growth from new genealogy users.
User Problem:
41% of new users dropped before experiencing product value. New users felt overwhelmed by admin-heavy onboarding and a blank tree canvas, leaving them unsure how to begin building their family tree.
Hypothesis:
If we replace form-heavy onboarding with AI-assisted relative suggestions and guided tree building, users will reach value faster, increasing activation, tree depth, and free trial conversion.
Experiment 1
—> Reveal immediate value
Intent: Reinforce product value immediately after signup.
Change: Surfaced a user’s likely birth record using signup data, showing that Findmypast could instantly uncover real family history. This experiment could only apply to users with British or Irish ancestry.
Result: 60% task acceptance rate, validating that early value moments increase engagement.
Experiment 2
—> Guide the next action
Intent: Reduce post-onboarding confusion and help users continue building their tree.
Change: Introduced suggested mothers and spouses directly on the tree canvas, giving users an obvious next step without requiring manual record searches.
Result: +32% hint engagement post experience, increasing tree expansion behaviour.
Experiment 3
—> Interactive Onboarding
Intent: Help users reach the core product faster.
Change: Reduced onboarding from 8 forms to 3, then shifted users into AI-assisted tree building with suggested relatives.
Result: –13% reduction in onboarding drop-off and higher activation.
Introduce a personalised task list within the tree canvas to guide new users through key actions during their free trial, helping them discover more product value and increasing the likelihood of subscription.
Prioritised activation over data completeness
Reducing onboarding steps meant collecting less upfront profile information. This improved early engagement and tree creation but resulted in lower short-term profile richness.
Experience limited by record availability
TreeAssist suggestions relied on historical record matches, which meant the experience worked best for users with British ancestry, where record coverage was strongest.
Advanced users needed faster access to complex tools
Simplifying onboarding improved outcomes for new users but created friction for experienced genealogists who preferred quicker access to advanced search and editing features.
Helping users experience value early is often more impactful than collecting perfect data upfront.
Designing for momentum can unlock both engagement and monetisation.
Managing latency in activation moments - We descoped this in interest of release speed
TreeAssist suggestions required anywhere between 2–30 seconds to generate, creating a potential drop-off point. I proposed progressive, personalised content during the wait state to maintain engagement, but the team prioritised delivering the core activation improvements within the release timeline.
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Tips to grow your family tree:
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Ask your family members to help with any information they might know.
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Newspaper headlines on your birthday
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