Illustrative: turning a consumer app into a next-best-action engine
A hypothetical case showing how behavioural signals, experimentation and lifecycle marketing could increase adoption of adjacent product use cases.
Strategy exercise: designing a next-best-action lifecycle system for a consumer app with low multi-use adoption.
The next-best-use-case problem sits at the intersection of customer insight, product data, marketing and experimentation.
This is an illustrative strategy exercise. The numbers are scenario inputs used to make the diagnosis and measurement model concrete.
What was actually stuck?
Assume a consumer app has 4MM monthly active users, but 72% use only one core feature. The company keeps launching cross-sell campaigns with low incremental adoption.
What did the evidence suggest?
The campaign system is product-centric. It starts with the feature the company wants to sell rather than the customer signal that indicates readiness.
From diagnosis to intervention.
Map feature sequences and identify behavioural paths associated with second-use adoption.
Create propensity cohorts with product and data science partners.
Build next-best-action rules with suppression and frequency logic.
Test recommendations against holdout groups.
Optimise for incremental second-use adoption and downstream retention, not CTR.
What should move if the strategy is working?
What changed?
Illustrative target: move the customer base from single-use behaviour toward multi-use behaviour without increasing message volume indiscriminately.
Principles, not playbooks.
Relevance beats volume.
Incrementality matters more than engagement theatre.