You feel like something is off with the product page. Bounce rate is high, conversion is flat, and you have three tools open: heatmaps, session recordings, and an A/B testing platform. Which one do you start with?
The heatmaps vs session recordings debate misses the point — they're not competitors. Together with A/B tests, they form a three-step diagnostic loop, and using them in the wrong order is how teams burn weeks debugging the wrong thing. Here's the framework.
Each tool answers a different question
The shortest version: heatmaps answer where. Session recordings answer why. A/B tests answer which. Use them in that order and you save yourself a lot of pain.
- Heatmaps — aggregate clicks, scrolls, and attention into a single image of behavior. Best for answering "is anyone seeing the section we built?" or "what are people clicking that isn't a link?"
- Session recordings — let you watch real shoppers move through real sessions. Best for "what's actually going wrong on this page?" or "why are mobile users dropping at checkout?"
- A/B tests — measure whether a specific change improves a specific metric. Best for "is variant B actually better than A, or did I just hope it was?"
Mixing these up wastes time. Trying to figure out why from a heatmap leads to overinterpretation. Trying to prove a change works from a recording leads to false confidence.
Start with heatmaps when you don't know where the problem is
Heatmaps are the wide-angle lens. Open one on your top three pages — usually homepage, top category, top product — and look at three things: scroll depth, click distribution, and "rage clicks" if your tool tracks them.
You're looking for surprise. The hero banner everyone scrolls past in 3 seconds. The section below the fold no mobile user reaches. The non-clickable image people keep tapping because it looks like a link. The size guide that gets more clicks than the buy button.
Heatmaps are great at telling you where shoppers spend attention. They're terrible at telling you why. Treat them as a map, not an explanation.
One real example from a Salla store: the heatmap showed 40% of mobile users tapping the product image expecting it to zoom. It didn't. The fix took an hour. The shoppers who didn't bother going back to the description converted 8% better after.
Use session recordings when you've spotted something weird
Once a heatmap surfaces a strange pattern, you need to see real sessions to understand it. Recordings show the full sequence — where the shopper landed, what they hovered, what they clicked, where they hesitated, and where they left.
Don't watch random sessions. That's a waste of an afternoon. Filter for the ones that matter:
- Sessions that reached checkout but didn't complete
- Sessions on mobile that lasted more than two minutes (signals confusion or comparison)
- Sessions that triggered a rage click or dead click
- Sessions from a specific traffic source — paid ads especially, since the cost-per-failure is highest
Watch ten of each. You'll start seeing patterns by the fifth. Maybe shoppers can't find the size selector. Maybe the coupon field traps them. Maybe the address form rejects valid Saudi phone numbers because it expects a leading zero. None of these would show up in a heatmap, and none would be findable through an A/B test alone.
Run an A/B test only after you have a hypothesis
The recordings tell you what's broken. The A/B test proves the fix works. If you skip the first two steps and go straight to testing, you're testing whatever you happened to think of, which is usually not the actual problem.
The pattern that works: heatmap reveals shoppers ignore the size guide → recordings show three out of ten shoppers clicking around looking for sizing info before leaving → hypothesis is that exposing the size chart inline (not behind a tab) will lift add-to-cart → A/B test confirms a 7% lift. That's a defensible result you can replicate.
The pattern that fails: "I think the button should be green" → A/B test → no significant result after a month → frustrated team. The change wasn't grounded in evidence, and even if it had won, you wouldn't know why.
For the actual mechanics of getting a test running on a Salla store, see how to run an A/B test on a Salla store. And if you've run a test that won't conclude, the underlying problem is almost always sample size — covered in why most A/B tests don't show a winner.
When to use which: a quick reference
If you have one of these symptoms, here's where to start:
- "Bounce rate is high but I don't know why." Heatmap first. Look at scroll depth and what they click before leaving.
- "People add to cart but don't complete checkout." Session recordings, filtered to checkout abandons. Watch ten and you'll spot the issue.
- "I want to redesign the homepage." Heatmap first to understand current behavior, then A/B test the redesign against the current page. Don't ship the redesign blind.
- "My agency wants to change the CTA copy." A/B test it. Don't assume the agency is right.
- "Mobile traffic converts way worse than desktop." Recordings on mobile-only sessions. Then A/B test the fixes you find.
Next steps
This week, pick one page that worries you and run all three steps in order. Start with a heatmap and look for one surprise. Watch ten recordings to understand it. Form one hypothesis. Then — and only then — set up an A/B test. The whole loop takes a few days and replaces months of guessing.