Algoshop AI Sales Chatbot gives shoppers a way to ask for help, but not everyone explains what stopped them. When visitors leave a product page without adding an item to cart, the merchant still needs to distinguish unclear information, an awkward control and an unavailable product. Behavior analytics helps investigate that silent part of the shopping journey.
Bigdelta combines Shopify analytics, heatmaps, session replays, and funnels to investigate that silent friction. Its listing also describes customer profiles and purchase history. For a product-page investigation, the practical sequence is: locate a pattern with a heatmap, examine individual paths with replay, and use a funnel to understand its scale.
This guide uses sizing help as a practical question to investigate, not a claimed customer case. It explains which Bigdelta views to use, how they complement each other, and what to check before changing the page. There are no invented session results or conversion improvements.
Heatmaps, session replay and funnels show different evidence
Choose a view based on the question, rather than opening every dashboard.
Swipe horizontally to read the full table.
| Evidence | Question it helps answer | Limitation |
|---|---|---|
| Click or tap distribution, when available | Where do recorded interactions concentrate? | Concentration does not establish whether the action succeeded |
| Scroll reach, when available | How many recorded visits reach a page region? | Reaching a region does not prove reading or attention |
| Session replay | What happened before and after an interaction? | A few selected visits may not represent the population |
| Event funnel | Where does progression decrease between defined stages? | A missing event can be tracking failure rather than customer exit |
The Bigdelta Shopify listing describes website analytics, session replays, heatmaps, funnels and customer profiles. Its funnel description covers product viewing, cart addition and checkout progression. It does not specify every heatmap type or counting rule. Where a particular view is unavailable, use another supported signal rather than label an unobserved action as measured.
A hot area near a size selector might mean successful size selection, repeated failed taps or multiple changes while comparing variants. The heatmap locates a pattern; replay and reproduction help distinguish those explanations.
Use Bigdelta to connect the three kinds of evidence
Bigdelta's official listing brings analytics, heatmaps, replays and funnels into one product. In this task, analytics defines the relevant population, the map locates a pattern, replay checks its sequence and the funnel records progression. That is a concrete use case for the product without attributing every merchant decision to an automated feature.
Before using recordings, establish the configured masking, exclusions, access and retention settings with the vendor. Avoid recording sensitive input merely to debug a size selector. The exact privacy controls and specialized heatmap filters need confirmation for your setup; broad category labels in a listing do not define them.
The official session-replay page explains an important distinction: replay reconstructs a visit from page structure and interaction changes rather than recording a conventional video. It describes filtering by page, event or error, an event timeline, and skipping inactivity. That makes a specific question—what happened around the size selector—more manageable than watching unrelated visits from beginning to end.
The same page describes input masking and exclusions. Review those settings against the pages you intend to analyze, and confirm how the Shopify installation is configured before collecting sensitive journeys. A general website installation guide is not evidence that every setting or setup step is identical in the Shopify app.
For questions specifically about behavior around a chat widget, the separate chat-widget analytics guide develops exposure, opening and conversation denominators. This article's task remains the whole product-page selection path.
Bring the shopping context back into the investigation
A heatmap can show a busy size selector, but the commercial question is whether that pattern deserves attention. Bigdelta's Shopify listing also describes visit and source analytics, converting pages, and customer profiles with purchase history. Those capabilities broaden the investigation beyond a picture of taps. Use whichever contextual views are available in your setup to decide which page and journey to examine next.
For example, a product page that receives substantial traffic but contributes little cart progression warrants a different review from a rarely visited information page. Traffic sources can also change the question: visitors arriving from a specific product campaign may expect the exact color or offer shown in that campaign, while someone browsing a collection may still be comparing alternatives. Check the landing page and product availability before deciding the interface is the problem.
Customer purchase history adds another useful perspective where it can be associated with the relevant customer. A returning buyer may know the sizing already; a first-time visitor may need the guide. This does not mean every anonymous session can be identified or that purchase history proves intent. It means the merchant should avoid assuming that all visitors need the same explanation.
A focused review, not a dashboard tour
Choose the commercially important journey
Use the supported analytics to identify a page worth reviewing, then define the shopper task: selecting a size, finding delivery information or choosing an available variant.
Locate the point of uncertainty
Use the heatmap to find an interaction pattern and replay to examine the sequence. Compare successful and incomplete journeys rather than watching only abandoned visits.
Check how widely it occurs
Use the supported funnel to put the observed issue in context. A repeated obstacle on an important path deserves priority over one unusual recording.
Make a page change that answers the problem
Move inaccessible sizing help, repair a control or clarify unavailable stock only when the investigation supports that action. Revisit the same journey after the change.
The product's appeal here is the combination: analytics helps choose the question, visual evidence makes the journey understandable, and funnel progression helps prioritize the work. A merchant still needs to reproduce the issue and choose the remedy. That gives the investigation a practical finish—a better shopping path—rather than simply another screenshot to discuss.
Choose one page and segment
For a shirt store, difficulty finding sizing help is a useful question to investigate when actual support messages or observed behavior point to it. Keep missing stock and a broken control as competing explanations; do not decide that sizing is the problem before looking at the evidence.
Replace “Why is conversion low?” with “Can mobile visitors to the regular-shirt template find sizing information and add an available variant to the cart?” That identifies a page, device and task.
Use a stable page version and a defined period. Record campaign changes, stock problems and theme releases during that period. A sale attracting new shoppers can change behavior even if the layout remains identical. Out-of-stock sizes can explain exits after size selection without any interface defect.
The investigation brief is deliberately small: mobile visitors, the regular-shirt template, seven complete days and one layout version. The task is to find size information, choose an available size and add it to the cart. If relevant visitors find the guide easily but leave because L is unavailable, the evidence redirects the work toward inventory.
This brief prevents cherry-picking any dramatic session into a story about the guide. If the leading problem is inventory, moving a link will not solve it.
Read a mobile product-page heatmap
First inspect the page structure: gallery, price, size selector, cart button, description and size-guide entry. Use the recorded version where possible. If the page changed, a current screenshot can place old interactions over a different layout and produce a misleading interpretation.
Bigdelta's heatmap page describes click, scroll, attention and move maps, alongside device and traffic-source segmentation. Each adds a different perspective: click maps show interaction concentration; scroll maps show page reach; attention maps add time spent in an area; move maps use desktop cursor movement as a proxy. None reads a shopper's thoughts.
For sizing help, compare the guide's placement with the selector and purchase control. If the guide is far below the initial screen, examine reach before calling its low interaction count a lack of interest. If a control attracts repeated clicks, inspect whether it responds. The official page describes dead-click and rage-click signals, which can help prioritize that inspection but are not a diagnosis by themselves.
Look for three separate patterns. A concentration around a working selector can be normal engagement. Low reach at the guide may indicate poor placement or simply no need to scroll. Repeated taps on a label that looks like a link may indicate a false affordance or broken control. Each produces a different next check.
Heatmaps highlight the parts of a page where shoppers interact most.
Inspect device and layout differences when your configured tool supports them. A desktop guide may sit beside the selector while the mobile layout places it much lower. An aggregate map can obscure that distinction. Keep page version, population and collection coverage attached to the map you discuss.
Use session replay to check the explanation
Choose sessions relevant to the original brief, including successful and incomplete tasks. If you watch only exits, every observed path ends badly by definition. A fixed review sample should also include shoppers who find the guide and complete selection.
For each replay, record the sequence without inventing emotion. “Selected M, scrolled down, returned to the selector and exited” is an observation. “Became frustrated with sizing” is a hypothesis unless other evidence supports it.
Swipe horizontally to read the full table.
| Possible observation in an actual replay | Question to investigate | Practical follow-up |
|---|---|---|
| Repeated interaction with a guide label without visible expansion | Is it a link, static text or a failing control? | Reproduce the same control and layout |
| A shopper reaches sizing help and returns to selection | Is the guide understandable and relevant to that product? | Review the displayed measurements and units |
| Selection is followed by an unavailable state | Was the desired variant in stock at that time? | Compare the observation with available stock records |
These are review questions, not invented recording summaries. Fill the investigation record with what your real sessions show. Separate the visible sequence from the explanation: a replay can reveal that an interaction went nowhere without proving why the visitor left.
Session replays show the steps shoppers take before and after a click.
Use the replay sequence to inspect a specific task. Avoid copying customer identifiers into the investigation record; anonymous reference labels are enough for most layout questions.
Build a funnel with consistent event definitions
The Shopify Listing explicitly describes progression through product viewing, cart addition and checkout. Start there rather than adding a size-selection stage that has not been instrumented. A missing custom event is not evidence that no one selected a size.
Bigdelta's funnel page describes combining pageviews and events, arranging the steps, and opening underlying replays from a drop-off. For a merchant, the useful connection is between scale and detail: the funnel identifies the stage worth investigating, while replay helps inspect the actual experience around that stage. Confirm the available events and counting rules in your Shopify installation.
Before comparing reports, check the date range, audience and whether the report counts sessions, people or events. Keep the same definitions across stages. A product-view-to-cart transition cannot be interpreted consistently if one side counts unique visits and the other counts repeated button clicks.
Use a test shopping journey to check that the intended events appear in the report. Then ask what the loss between stages could mean: incomplete browsing, an unavailable variant, unexpected shipping information or a control problem. The funnel helps prioritize those questions; it does not decide which explanation is correct.
Apply one fix and record the result
Choose a fix only after reproducing the actual obstacle. If sizing help is hard to reach, a clearly labeled guide near the selector may address access. If the control fails, repair its behavior. If stock is the problem, moving the guide is not the remedy. These are possible actions, not findings from a Bigdelta test.
Inspect the revised page at the same phone viewport: entry visible, chart readable, closing behavior works and the cart button remains usable. The size-guide tutorial explains the measurement content to show once customers reach it.
Record the release date and compare the same journey afterward using real observations. Check stock, campaign mix, tracking and page changes before attributing a difference to the fix. If the report is inconclusive, say so. A useful investigation does not need to end with a positive percentage.
Use a randomized comparison when practical for a causal estimate. A sequential review is still useful for diagnosing a reproducible issue, but report it as a before-and-after observation with the relevant limitations. Do not project a small change into guaranteed revenue.
Keep a reusable investigation record
Keep a concise record of the question, page version, observed sequence, reproduced issue, change, and before/after counts. Attach an owner and the evidence that would change the decision.
Keep the handoff concrete: the affected URL and device, the observed interaction, a link to permitted evidence, the action taken and the date to review it. That lets the person changing the page understand the problem without inheriting an unsupported story about shopper motivation.
Start with one page and one task using Bigdelta Heatmaps & Replays. The useful outcome is an obstacle you can reproduce, a fix you can inspect and a result you can interpret with consistent definitions.
