Product Investigation · Fictional AI Subscription Product
ChatAI Pro Capability Discovery
A product analytics investigation into feature discovery, value realization, and subscription conversion in AI products.
What if users do not upgrade because they never discover the feature that would make the product valuable to them?
Fictional product: ChatAI Pro. Synthetic data only. This portfolio artifact demonstrates product analytics reasoning, not actual OpenAI, ChatGPT, or real AI product data.
Opportunity
Help users discover high-value AI workflows earlier
Method
Instrument → Analyze → Prototype → Experiment
Outcome
Product recommendations and experiment roadmap
How to read this investigation
This case study shows how I translated a product hypothesis into event taxonomy, funnel analysis, prototype concepts, and experiment recommendations. Each stage connects the product decision, the artifact created, and the PM skill demonstrated.
Decision
Frame the value-discovery problem as a measurable product bet.
Artifact
Event taxonomy, prototype screens, funnel readout, and roadmap.
Skill
Product strategy, analytics design, growth thinking, and AI product sense.
01 Opportunity
The value-discovery gap
As AI products add more capabilities, feature breadth can become invisible to users. A user may sign up, use basic chat, miss advanced capabilities, question the product's value, and fail to upgrade.
The core thesis: AI subscription products may lose users not because the product lacks value, but because users evaluate the subscription before discovering enough high-value use cases.
Decision / Artifact / Skill
Decision
Treat discovery as an activation and conversion problem, not just an onboarding surface.
Artifact
A product investigation focused on discovery cards, premium previews, and re-engagement moments.
Skill
Frames a fuzzy AI product challenge as a testable customer and business hypothesis.
02 Hypothesis
Capability discovery should change activation, conversion, and retention
Users who discover and engage with multiple AI capabilities are more likely to convert, remain active, and retain their subscriptions.
Discovery cards
Surface high-value workflows earlier.
Contextual recommendations
Match capability suggestions to user intent.
Premium previews
Show paid value before the upgrade decision.
Save for later
Capture intent without interrupting the current task.
Proposed solution: Capability Discovery Engine.
03 Measurement Design
Instrument the moments where value becomes visible
Event Taxonomy
account_created
first_chat / chat_started
discovery_card_viewed
discovery_card_clicked
premium_preview_viewed
image_generated
file_uploaded
deep_research_attempted
plus_upgrade
subscription_cancelled
Activation
Discovery card clicked + preview viewed
Conversion
plus_upgrade rate
Engagement
Distinct capabilities used per user
Retention
Return usage after signup / upgrade
Guardrail
Cancellation + prompt dismissal
04 Prototype
Prototype screens for earlier value realization
The prototype explores how ChatAI Pro could introduce high-value workflows at the right moment, preview premium value, and re-engage users before perceived value drops.
Prototype artifact: Figma screens showing discovery cards, premium preview, save-for-later, and Day-3 re-engagement.
Explore the live prototype for screen-level interaction, or open the original slide-style analytics case study for the synthetic Amplitude findings and roadmap.
New Chat Discovery Cards
Activation: surface high-value workflows early.
Deep Research Premium Preview
Monetization: preview premium value before upgrade.
Save for Later
Intent capture: reduce pressure without losing interest.
Day-3 Re-Engagement
Retention: re-engage users before value perception drops.
05 Analysis
Synthetic funnel signals point to discovery as the largest visible bottleneck
158
Synthetic users in latest funnel cohort
72
Users clicked a discovery card
45.6%
Discovery card click rate
61
Reached premium preview and completed Plus upgrade
Key Synthetic Findings
54.4% of users did not click a discovery card, making discovery engagement the largest visible bottleneck.
84.7% of card clickers reached premium preview, suggesting preview access may be a meaningful activation signal.
Once users reached the premium preview, conversion remained very strong in the simulated data. This should be treated as a validation signal, not a proven result.
Churn signals pointed more toward "not enough value" than price, suggesting onboarding and discovery may be better first levers than discounting.
Capability Engagement
Deep Research had the highest feature engagement signal and should anchor early value realization experiments.
Caveat: All data in this project is synthetic and intended to demonstrate product analytics reasoning, not actual user behavior from any real AI product. This is directional synthetic analysis, not causal proof.
06 Product Recommendations
Prioritize discovery before discounting
P0
Surface discovery in the first session
Place contextual discovery cards above the fold on the new chat screen and trigger within the first two minutes.
P0
Anchor premium value around Deep Research
Use the strongest engagement signal as the default premium preview and onboarding example.
P1
Create a Day-3 value intervention
Target active users who have not clicked a discovery card or attempted a high-value capability.
P1
Add save-for-later to reduce pressure
Let users save a suggestion without interrupting their current task and use it as a re-engagement signal.
P2
Personalize discovery cards
Use query type, device, acquisition source, and prior behavior to recommend the most relevant capability.
07 Experiment Roadmap
Validate discovery quality, not just clicks
Card placement
Above-fold vs inline vs modal
Metric: card click rate
Duration: 14 days
Deep Research intro
Guided first-session intro vs standard onboarding
Metric: Day-7 return + upgrade
Duration: 21 days
Day-3 re-engagement
Triggered value email or in-app prompt vs no intervention
Metric: cancellation rate
Duration: 30 days
Premium preview format
Inline preview vs modal preview
Metric: upgrade rate
Duration: 14 days
Personalized cards
Personalized vs generic card content
Metric: CTR + capability use
Duration: 21 days
Rollout Decision Rule
Ship broadly only if treatment improves upgrade conversion and Day-7 retention without increasing cancellation, prompt dismissal, or user complaints. If conversion rises but guardrails worsen, iterate on frequency caps and personalization before rollout.
08 Reflection
Portfolio takeaway
This case study demonstrates product strategy, analytics design, growth thinking, experimentation, and AI product sense.
The strongest product opportunity is not merely adding more features. It is helping users discover the right capability at the right moment, then measuring whether that discovery changes activation, conversion, and retention behavior.
Kelly Connelly · PM portfolio artifact · Fictional product, synthetic data