The right visualization can turn numbers into decisions. In Shopify Editions, Spring 2026, Shopify is adding four new visualization types—scatter, bubble, radar, and sunburst—plus multi-metric support on existing line and bar-and-line charts. Together, they help you spot patterns faster and move with confidence. Here's what's new, when to use each one, and how to put them to work.
Table of contents
Scatter plot: See relationships between two metrics
A scatter plot maps every item (product, order, customer, campaign) as a single dot across two metrics with one on each axis. The pattern shows whether two things move together, move apart, or don't relate.
When to use it:
- You suspect a relationship between two metrics and want to confirm it
- You want to spot outliers, like certain products that don't behave like the rest
- You're testing whether a lever (discount depth, ad spend, price) is driving the outcome you think it does
In practice: A homeware store plots its orders on a scatter chart with the time of day on the x-axis and order value on the y-axis—one dot per order. Mornings are sparse and the few orders that come in are small. The evenings tell a different story: between 6 p.m. and 9 p.m., the orders cluster is bigger and the biggest order values of the day land here, including a $310 order. The store owner shifts their ad spend toward evening hours and schedules new product drops for 6 p.m.
Bubble chart: Add a third metric
A bubble chart is a scatter plot with one more variable encoded as the size of each dot. You're reading three metrics at once: x-axis, y-axis, and bubble size.
When to use it:
- You want to layer in scale (inventory, customer count, revenue) on top of a two-metric comparison
- You're prioritizing: which items are big and moving in the right direction?
- You want to spot small bubbles outperforming big ones, indicating early signals of future winners
In practice: An apparel store owner plots their products on a bubble chart: gross sales on the x-axis, net sales on the y-axis, and bubble size showing how much was given away in discounts. Most products land on the diagonal: as gross sales rise, net sales rise with them, and the bubbles stay small because discounts are light.
Two bubbles stand out. One product on the bottom-left has tiny sales but is the biggest bubble on the chart. It's being discounted heavily and still barely moving. Another sells $12,000 gross but only $6,000 net, with a large bubble. Half of its revenue is going to promotions. The store owner pulls both from the next sale and revisits their pricing.
Radar: Compare multiple metrics across one dimension
A radar chart plots multiple metrics across the values of a single dimension. Each value of the dimension—a country, a product line, a customer segment—becomes an axis radiating from the center, and each metric becomes a shape drawn across those axes. Strong scores push the shape outward; weak ones pull it in.
When to use it:
- You're comparing how a set of metrics plays out across the same collections, products, segments, regions, or stores
- You want to overlay two to four metrics on one chart and see where they agree and where they diverge
- You want to spot the entity that's strong on most metrics but weak on one
In practice: A global retailer reviews five markets—the US, UK, Germany, Canada, and Australia—on a radar chart. It includes four metrics overlaid as shapes: gross sales, orders, average order value, and gross margin. Each country is an axis. The gross sales and orders shapes both stretch hardest toward the US—the high-volume market. The average order value shape pulls toward the UK—fewer transactions but bigger baskets. Gross margin runs wide across most of the chart, with the US, UK, Canada, and Australia all strong, but pinches in at Germany, the only market where margin is noticeably weak alongside volume. The team digs into pricing, product mix, and acquisition costs there before pouring more spend into the market.
Sunburst: Drill through a hierarchy without leaving the chart
A sunburst shows how a total breaks down, layer by layer. The center is the whole picture; each ring outward is one level deeper. You read it from the inside out.
When to use it:
- Your data is naturally hierarchical (product category → product type → product variant)
- You want composition and depth in one visual—totals, sub-totals, and individual items across nested rings
- You're answering questions like "which sub-segment of which segment is driving this number?"
In practice: An apparel brand breaks down gross sales by category and product on a sunburst. The inner ring shows the categories: Apparel is the biggest slice, Outerwear is second, Footwear and Bags sit in the middle, and Accessories is the smallest. The outer ring shows the products inside each category.
In Apparel, one product carries most of the slice—the classic tee. In Outerwear, the winter coat does the same. Accessories looks different: many small, similar-sized slivers, with no standout product. The team restocks the classic tee and winter coat ahead of demand, and rethinks the Accessories lineup—fewer products, with a closer eye on what's actually working.
Multi-metric support: Read more than one metric on the same chart
Line charts and bar and line charts now show more than one metric at once. Line charts hold up to four metrics; bar and line pairs a bar metric with a line metric over time. When the metrics use different units (currency, percentage, count), Shopify automatically splits them across two y-axes, so a percentage and a dollar figure can sit on the same chart.
When to use it:
- You're investigating a correlation: "did conversion rate dip when sessions spiked?"
- You're stitching a story together on one timeline: traffic, conversion, and revenue
- You're telling two sides of the same story on one chart — how many orders (bars) and how much each was worth (line)
Line chart
In practice: A store owner plots gross sales, orders, sessions, and returns together on a single line chart for the last month. Gross sales and returns share the left y-axis (monetary value); orders and sessions share the right y-axis (count). Sessions surge to about 325 around October 1, well above the steady 250–300 baseline. Three days later, gross sales follow with a spike to nearly $7,000. Orders tick up modestly, and returns hold flat throughout. The story all four metrics tell together: the traffic event drove genuine, sticky revenue and no return wave followed. The team builds that three-day lag between traffic and sales into the timing of their next campaign.
Bar and line chart
In practice: A store owner pairs gross sales (bars) and sessions (line) on a single bar-and-line chart for the last month. Each metric gets its own y-axis with currency on the left and session count on the right. On October 1, sessions spike to roughly 350, well above the typical 200–250 baseline. But gross sales that day stayed flat around $5,000. Three days later, gross sales jump to about $6,500. The signal: the traffic surge didn't convert immediately—it built consideration that turned into purchases a few days later. The team plans to time follow-up emails three to five days after the next traffic event.
