Heap alternative (2026): Analytics insight vs Shopify experiments
Heap is a product analytics platform that autocaptures every click and page view, so teams can analyse funnels retroactively without pre-tagging events. Omniconvert Explore is a Shopify-native CRO platform that runs A/B, multivariate, and checkout experiments measured in revenue per visitor. Heap sizes the drop-off; Explore tests the fix. They complement, rarely compete.
- Heap is an autocaptured product analytics platform, with a 4.4 out of 5 G2 rating across 1,098 reviews, now part of the Contentsquare group. [G2, 2026]
- Heap autocaptures every click and page view so teams can answer retroactive funnel and retention questions without pre-tagging events.
- Heap has no A/B testing engine, no variant delivery, no visual editor, and no native Shopify checkout workflow.
- Omniconvert Explore runs Shopify-native A/B, multivariate, and personalization experiments on product, cart, and checkout, measured in revenue per visitor.
- The two rarely compete: eCommerce teams typically run Heap to size the drop-off and Explore to test the fix on the same brand.
Teams comparing Heap vs Omniconvert Explore are usually asking two questions dressed as one. Heap is a product analytics platform that autocaptures every interaction, so any funnel or retention question can be answered retroactively without pre-tagging events. Omniconvert Explore is a Shopify-native CRO platform that runs experiments on product, cart, and checkout, and measures results in revenue per visitor. This page explains where each fits, and why an eCommerce team usually needs both.
What is Heap, and what does it actually do?
Heap is a product analytics platform that autocaptures every click, page view, form submission, and session event without engineers instrumenting them in advance. Teams use it to answer funnel and retention questions retroactively, size drop-offs precisely, and explore behaviour they never thought to tag. It now sits inside the Contentsquare group and rates 4.4 out of 5 on G2 across more than a thousand reviews. [G2, 2026]
Heap is well established in product analytics, with a 4.4 out of 5 rating on G2 across 1,098 reviews. [G2, 2026] The product is popular with product and analytics teams because autocapture removes the usual gap between a business question and an event schema that was never planned for it.
The category Heap sits in is autocaptured product analytics. Every interaction is recorded by default, funnels and retention curves are defined after the fact, and the raw dataset can be sliced without going back to engineering for a new event. That retroactive posture is the point of the product.
The question this page answers is narrower: is autocaptured analytics the same job as running conversion experiments on a Shopify store? And if not, where is the gap?
Autocaptured product analytics means the platform records every user interaction by default and lets teams define funnels, cohorts, and retention curves after the fact, without pre-tagging events. It is powerful for sizing where users drop off and for answering questions the team did not know to instrument. It is a separate concern from running a controlled experiment to prove which change recovers the revenue lost at that drop-off.
Where Heap is genuinely strong
- Autocapture: every click, page, and form event is recorded by default, so analysts can build a funnel for a question they never planned to instrument.
- Retroactive funnel analysis: the strongest single feature, teams can define a funnel today and see months of historical data in it immediately.
- Broad market standing: 4.4 out of 5 across more than a thousand G2 reviews, now part of the Contentsquare group with enterprise reach. [G2, 2026]
- Session and behavioural depth: combined with session replay and heatmaps, it gives product teams a rich picture of what visitors did across the funnel.
Where Heap hits its ceiling for an eCommerce store
- No experimentation engine: Heap does not run A/B tests, multivariate tests, or variant delivery. It quantifies drop-offs but never tests a fix.
- No visual editor for changes: there is nothing to edit on the page, only data to analyse; a separate tool is required to ship a variant.
- Low native Shopify integration: autocapture works, but there is no Shopify-native workflow for product, cart, and checkout testing.
- Quote-based pricing at the enterprise end: custom pricing shaped for larger product orgs, with analyst time needed before the autocaptured volume produces decisions.
- No eCommerce revenue reporting: outcomes are framed as events and funnel steps, not revenue per visitor or order rate at the store level.
None of this makes Heap a weak product. It makes it an analytics tool. The friction shows up specifically when the team using it for eCommerce CRO can size a checkout drop-off precisely and then still needs a separate platform to prove which change recovers the revenue.
What Heap cannot do for an eCommerce store
Heap explains what visitors did across a funnel but offers no route to test a different funnel. Teams using it for eCommerce CRO can size a drop-off precisely and then still need an experimentation platform to prove which change recovers the revenue. That is the gap an eCommerce-first testing platform closes.
Omniconvert Explore is built for the layer Heap leaves open. Heap tells a store exactly where the checkout leaks, on which step, for which segment, at what rate. It does not tell the store which variant of that step recovers the lost revenue, because it does not run experiments.
Most product analytics tools are built around a generic event and a generic funnel. They optimise the fidelity of the observation. They are not built around the surfaces where eCommerce revenue is actually won or lost, or around the metric that matters to a store, revenue per visitor rather than an event count.
eCommerce CRO is the practice of running controlled experiments on the revenue surfaces of an online store, product pages, cart, and checkout, and measuring the result in revenue per visitor and order rate rather than generic conversion rate. Omniconvert Explore is defined as an eCommerce conversion rate optimization platform for product, cart, and checkout experiments, native to Shopify and priced for store traffic.
What Heap cannot tell an eCommerce team
- Which variant recovers the revenue. Heap sizes a drop-off but has no way to run the variant that would recover it or measure the lift in revenue per visitor.
- Which surface to test first for revenue impact. Funnel percentages rank the leak but not the expected revenue payoff of testing one surface over another.
- How a change behaves in Shopify checkout. Heap does not run natively against the Shopify catalog, variants, or checkout flow, so it cannot ship a test there.
- Whether the win holds for high-value customers. The Customer Value Optimization question, whether an experiment holds for repeat, high-value buyers, is not something Heap is designed to answer.
Across the 7,000+ eCommerce websites in Omniconvert's CROBenchmark Report 2026, the stores that turn analytics insight into revenue are the ones where the team can take a funnel drop-off surfaced in Heap and ship a controlled test on that exact surface the same week; the benchmark shows testing cadence, not analytics coverage, is the strongest predictor of eCommerce conversion rate improvement year over year. [CROBenchmark Report 2026, Omniconvert]
Explore runs the experiment on the store's real revenue surfaces and reports the outcome in revenue per visitor. AliveCor used Omniconvert Explore to run a structured A/B testing program and achieved +21% conversion rate, +5% revenue per visitor, and 94% statistical relevance across their experiments. [Omniconvert, AliveCor case study]
Heap vs Explore: the capability comparison
Side by side, Heap and Explore share almost no overlap in daily job. Heap autocaptures every interaction and lets teams build funnels and cohorts retroactively. Explore ships Shopify-native experiments on product, cart, and checkout, adds surveys and overlays, and reports in revenue per visitor. Heap answers what happened; Explore tests what to change, and the fit rarely forces a choice.
| Capability | Heap | Omniconvert Explore |
|---|---|---|
| Primary function | Autocaptured product analytics for retroactive funnel and cohort analysis | eCommerce CRO on product, cart, and checkout pages |
| A/B testing | No analytics only, no variant delivery | Yes visual editor plus code editor |
| Multivariate testing | No | Yes |
| Server-side testing | No | Yes |
| Visual editor | No nothing to edit, data-only tool | Yes no developer required |
| On-site surveys and overlays | No not part of the product | Yes surveys and overlays built in |
| Shopify integration | Low autocapture works, no native Shopify workflow | Yes native |
| eCommerce focus | Medium broad product-analytics fit, not eCommerce-specific | High built for store revenue workflows |
| Pricing model | Quote-based, custom quote, free trial available | Session-based, built for store traffic, free trial |
| Best for | Product teams wanting retroactive analysis without tagging events in advance | Shopify and eCommerce teams optimizing product, cart, and checkout for revenue |
Competitor pricing and plan details reflect publicly listed figures as of 2026 and can change. Explore uses session-based pricing; see the Omniconvert pricing page for current plans.
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Get the CROBenchmark ReportFrequently Asked Questions
Should you choose Explore over Heap?
If your team already runs Heap and needs to test the drop-offs it surfaces, choose Explore: it ships Shopify-native A/B, multivariate, and checkout experiments through a visual editor and reports outcomes in revenue per visitor. Heap remains the right tool for retroactive funnel analysis and event exploration without pre-tagging. The two rarely compete; most eCommerce teams run Heap on analytics and Explore on the storefront experimentation layer.
Heap earns its rating. Autocaptured product analytics with retroactive funnels is a genuinely different job from experimentation, and Heap is one of the most established tools in that category, which is exactly what a product-analytics org wants for behavioural exploration.
The question for a store is narrower: once you have sized the drop-off, do you have a way to ship a controlled variant of that product page or checkout step, natively on Shopify, and measure the lift in revenue per visitor. That is the surface Explore is built for, and it is why the two products so often sit side by side.
Stop guessing.
Start testing what moves revenue.
Explore runs A/B, multivariate, and personalization experiments on your product pages, cart, and checkout, then measures the outcome in revenue per visitor, not just clicks.