Heap helped me catch an onboarding problem that was blocking about 15% of trial signups. I thought the drop-off was at the payment step, but the data showed people were leaving much earlier in the flow. So fixing that became the first priority and it directly lifted conversions. The data showed people getting stuck on a form field that looked optional on mobile. They closed out thinking they were done. So after a small update, trial completions went up and paid conversions rose by around 10% over the next two months. What stood out was seeing this without needing new tracking or waiting on engineers. I could pull the numbers, confirm the issue, and move faster on the fix. That saved time and made sure energy went into work that brought a real revenue lift. For me, Heap's value was showing blind spots I wouldn't have noticed. And those small insights shaped product work in ways that made a bigger impact than broad roadmap changes.
Top-line adoption of the updated onboarding flow on one of our core modules appeared steady during early review this year. However, using retroactive analytics from Heap, we identified a hitherto ignored friction point: users who skipped what seemed to be a trivial configuration step were 40% less likely to activate within their first week. Because Heap recorded this event data automatically without requiring prior manual tagging, we could dive into that user behavior almost immediately instead of wasting several more weeks waiting for new tracking to be put in place. This insight provided impetus to a guided prompt and micro-interaction for that step. Within two development cycles of the intervention, first-week activation soared by 23%, while retention figures showed a sustained positive trend. Before Heap, such immediate insights and the philosopher's retrospective perspective on workload needed in prioritization were missing. With the Heap analytics engine, instead of applying minor changes based on guesswork or lagging data, we can see high-impact opportunities as they unfold and align product strategy closer to user behavior, thus speeding the road to tangible outcomes.
Heap has really changed things for me because it removes the did we track that? question. With retroactive analytics, I can go back and answer questions I didn't even know I had when we started a feature. For example, we launched a new checkout process, and conversion looked okay at first. Later, I used Heap to find out that many mobile users were leaving at the promo code step not because of price, but because the field was hidden on smaller screens. Without retroactive tracking, we would have missed that, and we probably would've spent time testing the wrong things. Instead, we quickly fixed the UI and saw conversions increase right away. Heap doesn't just give data; it gives clarity that shapes what we do with our product and saves money.
Marketing coordinator at My Accurate Home and Commercial Services
Answered 6 months ago
In what we do, Heap retroactive analytics played an important role in a recent redesign of our websites where we made changes to the way service request forms were presented. When we first launched the new layout we thought that conversions were going up since submissions were no longer dropping, but Heap showed us that people were leaving the form in the middle at a significantly increased rate. The availability of historical event data without having to manually tag it enabled us to compare behaviors in the two versions and determine the point of friction in short order. That realization changed our priorities and we instead simplified the form fields rather than driving more traffic by means of ads. Not only has the correction improved lost leads but also customer satisfaction has improved as it is now easier to fulfill requests. Heap transformed what would have been a very expensive blind spot into an actionable pivot.
In the past, retroactive analytics by Heap has helped to identify a significant gap that could not have been realized using traditional tracking. We were on the verge of adding our mobile ordering option and we thought that the drop off point would be at the checkout. Following Heap reports, we noted that a significant percentage of users did not get to the checkout since they would abandon the cart at the menu customization point. That observation altered our road map We did not spend resources on integrating payment providers, but instead focused on making the customization process straightforward and trying more understandable defaults. The completion rates posted an increment of 18 percent within two months after the update was released. The capability to study past behavior without being guided by pre-selected tags provided us with the insight to adjust course in a timely manner and it did further emphasize the importance of basing strategic priorities on data instead of assumptions.
Hi, happy to share some G2 review data insights about Heap. Over 88% of G2 reviewers highlight Heap's funnel analysis, and 89% point to user-level analytics as essential. I've seen the same in my own work—when a funnel underperformed, Heap's retroactive analytics allowed us to go back and uncover an adoption blocker we hadn't originally set out to track. Segmentation and multi-product analysis are also key, with 87% and 86% of reviewers emphasizing their role in shaping strategy. These features have helped my team identify which cohorts and product lines create the most value, directly informing prioritization. Taken together, Heap delivers on what G2 users consistently describe: a product analytics tool that surfaces hidden insights and gives teams the clarity to make confident, strategic decisions. Please reach out to me on skoshy@g2.com if you'd like G2 data for any other software products or categories as well. Best, Seba