AI Product Analytics
Heap
SF 8.6Autocapture analytics for digital product teams
Free · PaidPro from $22/moFree plan available; paid plans scale by sessions.
AI Experience Analytics
FullStory
SF 8.4Session replay with digital friction insights
Free · PaidPro from $22/moFree plan available; paid plans scale by sessions.
Quick decision guide
Choose based on your workflow
Heap may fit better if...
- Autocapture Tracking
- Retroactive Events
- Funnel Reports
FullStory may fit better if...
- Autocapture Events
- Friction Signals
- AI Summaries
Overview
How each tool is described
Heap
Heap is a product analytics platform for product teams needing autocaptured user behavior.
It helps teams turn analytics work into clearer decisions while keeping the output easier for non-technical users to understand. The strongest value appears when the team has reliable data, clear ownership, and repeatable questions that need faster answers. Before choosing it, test one real workflow, one messy data source, and one stakeholder review. That shows whether the platform reduces confusion or simply adds another place to manage analytics work. This matters more than a long feature list.
- Best fit: Product teams needing autocaptured user behavior.
- Check first: data readiness, integrations, pricing, governance, and daily adoption.
Bottom line: Heap is most useful when its strengths match the analytics work your team repeats often.
FullStory
FullStory is an experience analytics platform for digital teams investigating user experience issues.
It helps teams turn analytics work into clearer decisions while keeping the output easier for non-technical users to understand. The strongest value appears when the team has reliable data, clear ownership, and repeatable questions that need faster answers. Before choosing it, test one real workflow, one messy data source, and one stakeholder review. That shows whether the platform reduces confusion or simply adds another place to manage analytics work. This matters more than a long feature list.
- Best fit: Digital teams investigating user experience issues.
- Check first: data readiness, integrations, pricing, governance, and daily adoption.
Bottom line: FullStory is most useful when its strengths match the analytics work your team repeats often.
Side-by-side
Key differences
Criteria
AI Product AnalyticsHeap
AI Experience AnalyticsFullStory
Best for
AI Product Analytics
AI Experience Analytics
Score
8.6/10
8.4/10
Pricing
Free · PaidPro from $22/mo
Free · PaidPro from $22/mo
Category / audience
AI Analytics Software › AI Product Analytics
- session replay
- funnels
- autocapture
AI Analytics Software › AI Experience Analytics
- session replay
- UX analytics
- experience analytics
OVERLAP
Where Heap and FullStory are similar
Both tools cover similar catalog signals. The deciding factor is usually workflow fit, implementation needs, and ecosystem fit.
1 capabilities0 workflows
Shared capabilities
Capability overlap
Feature check
Side-by-side feature check
Feature
Heap
FullStory
Autocapture TrackingRecords clicks and views without manual tagging
-
Retroactive EventsDefine key actions after data collection
-
Funnel ReportsTrack conversion steps across user journeys
-
Retention AnalysisCompare cohorts by product behavior over time
-
AI AssistantAsk guided questions about captured behavior
-
Autocapture EventsCapture behavior without upfront tracking plans
-
11 capabilities compared.10 differentiating rows are shown first.
Use cases
Who they're built for
Heap
- Find checkout funnel drop offIdentify where users abandon key purchase steps online
- Review confusing product user journeysWatch sessions tied to unclear navigation patterns quickly
- Measure feature adoption after launchTrack users engaging with newly released features weekly
FullStory
- Investigate checkout and form frictionWatch user sessions around abandonment and errors closely
- Debug issues with replay evidenceShare exact user journeys with engineering teams quickly
- Analyze rage clicks and dead clicksPrioritize interface problems causing repeated user frustration clearly
The trade-offs
Pros & cons of each tool
Trade-offs
Heap
Pros
- Autocapture reduces engineering work for analytics questions
- Retroactive definitions help teams answer new questions
- Replay context connects numbers with real sessions
Cons
- Autocapture still needs governance to stay trustworthy
- Pricing can rise quickly with session volume
- Strict schema-first teams may prefer other tools
Trade-offs
FullStory
Pros
- Replay quality helps teams understand real behavior
- Friction signals speed up issue discovery quickly
- AI summaries reduce manual session review time
Cons
- Session volume can affect paid plan costs
- Privacy configuration requires careful upfront planning steps
- Not a replacement for full business intelligence
Final verdict
Best fit depends on your workflow
Current catalog data shows meaningful overlap between Heap and FullStory. Use the signals below to decide based on workflow, ecosystem, pricing, and implementation fit.
Shared catalog overlap
Heap and FullStory share 1 catalog signal, so the decision should focus on fit rather than broad capability alone.
Differentiators available
Heap has 4 visible decision signals and FullStory has 4.
Score signal
Heap has the higher SoftFinders Score in the current catalog data.
Choose Heap if…
- Find checkout funnel drop off
- Review confusing product user journeys
- Measure feature adoption after launch
- Autocapture Tracking
Choose FullStory if…
- Investigate checkout and form friction
- Debug issues with replay evidence
- Analyze rage clicks and dead clicks
- Autocapture Events
TRY THEM YOURSELF
See which one fits your workflow
Both tools have their strengths, the best way to decide is to spend a few minutes inside each.