This analysis evaluates user behavior in an e-commerce product using the HEART framework over a 28-day period.
Are users meaningfully engaging with product content, and how successfully does the engagement translate into purchases and retention?
Dataset is a public, obfuscated GA4 sample provided by Google in BigQuery.
For Happiness I have used the behavioral proxy due to absence of data on human feedback and reviews.
- Total purchasers: 1363
- Repeat purchasers: 354
- Repeat purchase rate: ~25.97%
Roughly 1 in 4 purchasers return to buy again which is a positive signal. While the absolute number of purchasers is low, those who convert show meaningful signs of satisfaction and trust.
- Average events per user: 18.43
Users who are active generate a relatively high number of events. Meaning that once users engage with the product they tend to explore multiple pages or features rather than bouncing immediately.
High engagement alone doesn't guarantee value delivery, so it must be looked at alongside conversion and retention metrics.
- Viewing item adoption rate: 26.98%
Only about 1 in 4 users viewed at least one product during the analysis window. This suggests that a majority of users never reach meaningful product exploration.
Possible explanations:
- users may drop off before discovering products
- landing pages or navigation may not clearly drive users towards exploring
- problematic onboarding that makes users leave early
Adoption, not engagement, appears to be the first bottleneck.
- Cohort size: 73792
- 7-day retention rate: 10%
Short-term retention is relatively low, with nearly 9 out of 10 users not returning within a week of their first interaction.
This suggests:
- initial visits do not create habit formation
- value may be perceived as transactional
- non-purchasing users don't have reasons to return
I define task successfully completed, if users go through funnel of viewing items to buying items.
- Users who viewed items: 19908
- Users who purchased: 1363
- Conversion rate ~6.8%
Among users who explore products only a small fraction complete a purchase. This points to a friction between discovering a product and buying it.
Possible explanations:
- insufficient product information or trust signals
- incomplete data (missing add to card feature)
- checkout complexity
- lack of urgency
- price sensitivity or unexpected costs
- Adoption is the primary bottleneck
- Engagement is strong once users activate
- Conversion from interest to purchase is weak
- Retention mirrors conversion
- The product delivers value to a small segment
- Improve early activation
- drive users more aggressively towards item views
- simplify navigation from landing pages
- Reduce friction between view and purchase
- improve product details clarity
- surface pricing, delivery earlier
- simplify checkout steps
- Re-engage high-intent non-purchasers
- target users who viewed items but did not purchase
- use reminders, incentives, or social proof
- Leverage satisfied users
- encourage repeat purchasers through loyalty or referral mechanisms
- user their behavior as a benchmark for ideal user journeys
The product demonstrates strong value for a small subset of users, but struggles to guide majority towards meaningful engagement. Improving early adoption and reducing friction would likely have the largest impact on overall performance across all metrics.