Shopify Analytics Adds Scatter Plots and Radar Charts

For most Shopify merchants, the native analytics suite has meant line charts and bar charts. Both are good at tracking a single metric over time, and both fall short the moment the question becomes how two or more metrics relate to each other. Which products sell in volume but earn little margin? Which traffic sources convert rather than simply arrive? Answering those questions has usually meant exporting the data and rebuilding it in a spreadsheet or a separate dashboard. A cleanly built store makes this kind of reporting more reliable from day one, which is something we plan for in every Shopify web design project.

The addition of scatter plots and radar charts narrows that gap inside the reporting tool itself. The change is small in surface area and larger in what it enables. Both chart types are available now in the visualisation picker when you create or edit a custom report with compatible data, so there is no plan upgrade, no app to install, and no configuration step to unlock them.

How the new chart types work

A scatter plot maps two metrics against one another, one on each axis, with every record drawn as a single dot. The pattern the dots form is the point: a tight diagonal line suggests the two metrics rise together, a flat scatter suggests they are unrelated, and the dots sitting well away from the crowd are the outliers worth investigating. The format has been a staple of statistical analysis for well over a century, and its strength is exactly this ability to expose relationships and anomalies that a table of numbers hides. For a fuller definition, the scatter plot entry on Wikipedia is a useful primer.

A radar chart takes a different job. Instead of two metrics, it compares several at once by arranging them as spokes on a shared circular axis, then drawing each item as a shape across those spokes. Where a scatter plot answers "how do these two numbers relate", a radar chart answers "how does this thing compare across many numbers at the same time". The shape that emerges, wide on some axes and pinched on others, gives a quick read of relative strengths and weaknesses without flicking between half a dozen separate bar charts.

Both options sit in the same visualisation picker you already use to choose a line or bar chart, and both require compatible data in the report. In practice that means a report built around a dimension (such as product or channel) carrying more than one numeric measure. If a report does not offer the new chart types, it is usually because the underlying data does not suit them rather than a fault.

Examples UK store owners will recognise

Shopify's own announcement gives two scenarios that map cleanly onto everyday retail decisions. The first is a scatter plot of revenue against units sold per product. Plotted this way, the products that move high volume but contribute little revenue separate visibly from the products that earn well on modest sales. That distinction matters when you are deciding what to feature on the homepage, what to push in paid campaigns, and what is quietly tying up stock budget without paying its way.

The second is a scatter plot of session count against conversion rate per traffic source. A channel can look impressive on traffic alone and still convert poorly, and the dots make that gap obvious at a glance. A source clustered high on sessions but low on conversion is a candidate for a landing-page review or a closer look at audience targeting, not more budget.

Radar charts come into their own for side-by-side comparison. Place your product categories on one radar across average order value, conversion rate, and return rate, and the shape each category draws tells a story: a category strong on order value but heavy on returns looks very different from one with steady conversion and low returns. Reading three metrics on a single shared axis is faster than comparing three separate charts, and it tends to prompt better questions about why the shapes differ.

What this means for UK Shopify merchants

For independent UK retailers and growing brands, the practical win is that more of the analysis can stay inside Shopify. Smaller teams rarely have a dedicated analyst, and the work of relating two metrics or comparing categories has often been deferred precisely because it meant an export and a manual rebuild. Lowering that effort makes the analysis more likely to actually happen, which is usually the real barrier rather than any shortage of data.

For agencies and consultants running Shopify retainers, the new chart types are a quicker route to the kind of diagnostic that informs a strategy conversation. A revenue-versus-units scatter is a fast way to open a merchandising discussion, and a sessions-versus-conversion scatter frames a paid-media review without a slide deck. This is the sort of analysis that feeds directly into ecommerce planning and into paid advertising decisions, where the question is always which products and which channels deserve more attention.

The caveat is the same one that applies to every analytics upgrade: a chart shows what is happening, not what to do about it. The value lands when the visual is paired with clear targets for traffic, conversion rate, and customer acquisition cost. A scatter plot can tell you a channel underperforms on conversion, but the fix still depends on the store experience behind the click, which is where a considered build earns its keep.

Practical considerations before you rely on them

Compatible data is the first constraint. Not every report will offer scatter or radar as an option, because both need the right structure underneath. If the new chart types do not appear, revisit how the report is built rather than assuming the feature is missing.

Sample size is the second. Scatter plots are most readable when there are enough data points to show a genuine pattern and few enough that the dots do not merge into a single mass. Radar charts mislead quickly if you load too many items or too many axes onto one chart, so keep the comparison focused: a handful of categories across three or four metrics reads far better than a dozen of each. Remember too that a visible relationship is not proof of cause. A scatter can show two metrics moving together without one driving the other, so treat it as a prompt to investigate, not a verdict.

Finally, these charts complement rather than replace the rest of the reporting suite. Period-over-period comparison, cohort analysis, and full-funnel attribution still call for dedicated reports and, in many cases, an external analytics platform alongside Shopify. Shopify's own guidance on building and visualising custom reports is documented in the Shopify Help Center. The new chart types also sit alongside two other analytics changes worth knowing about: the dashboard’s ranked daily insights feed, and the Shopify Flow action that queries analytics data with ShopifyQL and hands the result to a workflow.

How to get value from them quickly

Two reports are enough to start. Build a scatter of revenue against units sold per product to surface your volume movers and your margin earners, and build a radar comparing your main product categories across average order value, conversion rate, and return rate. Save both, then fold them into whatever weekly or monthly review routine you already run.

From there, let the questions the charts raise drive the next report rather than building charts for their own sake. A scatter that flags an underperforming channel earns a follow-up on landing pages and targeting. A radar that shows a category with strong order value but heavy returns earns a look at product descriptions, sizing, and expectation setting. Used that way, the new visuals become a triage layer that points you to the next decision, which is the most either chart type is meant to do.

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