Excel Tutorials · Lesson 91
A plain list of numbers can't show you how two things relate to each other. A scatterplot can, and adding two reference lines turns it into a quadrant chart that instantly separates what's working from what isn't, like ad spend against the revenue it actually generates.
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A scatterplot plots two numbers per item as a single point: one number on the X axis, one on the Y axis. Instead of reading a table row by row, you see at a glance how the two variables relate, whether high values on one axis tend to come with high or low values on the other.
A quadrant chart takes that a step further by adding two reference lines, usually set at each axis's average, splitting the plot into four regions. An item that's high on both axes lands in one corner; an item that's low on both lands in the opposite corner. The two mixed cases, high on one and low on the other, land in the remaining two corners, and one of those corners is typically the one worth acting on.
The classic use case is exactly what this lesson builds: plotting spend against revenue for a set of ad channels, vendors, or campaigns. Anything sitting in the high-spend, low-return quadrant is visually obvious the instant the chart renders, no scanning a table of numbers required.
The honest framing: building this by hand means setting up the chart, calculating the averages, adding two line series, and labeling four quadrants, roughly ten minutes of setup the first time. Updoot's Competitor Analyzer tool builds this exact spend-versus-return quadrant chart automatically, plotting your data and flagging the high-spend, low-return quadrant for you, with no chart-building involved.
Step-by-step, matching the video above.
List each item with two numeric columns next to it, such as Spend and Revenue, one row per item you want to compare.
Select both numeric columns and go to Insert > Charts > Scatter. Each item becomes one point plotted by its two values.
In two helper cells, use =AVERAGE() on each column. These become the coordinates for the two quadrant lines.
Add a vertical line series at the X average and a horizontal line series at the Y average, both plotted on the same chart, splitting it into four quadrants.
Add data labels to the point series, then set Value From Cells to a name column so each point reads clearly instead of showing raw coordinates.
Add four text boxes or shaded background regions labeling what each quadrant means, such as "High Spend, Low Return: Cut These" in the top-left.
Since the average lines are formula-driven, adding or updating a row shifts the quadrant lines automatically, keeping the read current.
| Channel | Spend | Revenue |
|---|---|---|
| Paid Search | 4,200 | 18,600 |
| Display Ads | 5,100 | 3,900 |
| Referral | 800 | 7,200 |
| Social | 3,600 | 4,100 |
Average spend across these four is 3,425; average revenue is 8,450. Plotted with quadrant lines at those averages, Display Ads lands alone in the high-spend, low-return quadrant, the one worth cutting, while Paid Search sits clearly in the high-return corner worth protecting.
Four building blocks stacked on top of a plain scatter chart.
Two numeric columns, plotted directly. No sorting, grouping or categories needed, just an X value and a Y value per item.
Two extra series: one plotted as a vertical line at the X average across the full Y range, one as a horizontal line at the Y average across the full X range.
Without names on the points, a scatterplot is just dots. Value From Cells pulls the item's name into the label instead of showing raw X and Y numbers.
Four text boxes, one per corner, naming what that quadrant means for your specific data. Light background shading per quadrant makes the read even faster.
What each corner typically means when the axes are spend and return.
Top-right. Whatever is driving results here is working; the risk is cutting budget from something that's actually earning it back.
Top-left. Efficient performers that may have room to absorb more budget before returns start to diminish.
Bottom-right. The quadrant this whole chart exists to surface: spend that isn't converting into results, and the first place to look when trimming a budget.
Bottom-left. Not actively hurting anything, but not contributing much either; usually the last thing worth spending time optimizing.
| Step | Manual in Excel | Updoot Competitor Analyzer |
|---|---|---|
| Chart setup | Insert, format, add series | Already built |
| Average/quadrant lines | Formula plus two series | Calculated automatically |
| Flagging what to cut | Visual read, by eye | Flagged directly |
The manual version and the Competitor Analyzer produce the same read, spend against return, quadrant by quadrant. The difference is the ten minutes of chart-building the tool skips, and that it flags the cut-worthy quadrant outright instead of leaving that judgment to you.
Four things that go wrong on the first attempt.
Excel defaulted to the wrong chart type. Choose Scatter specifically from Insert > Charts, not Line or Column, or the X axis won't plot as continuous values.
A line series needs two points spanning the full axis range, not just a single point at the average. Set its start and end to the axis minimum and maximum.
The default data label shows X and Y values. Switch it to Value From Cells and select the name column instead.
Past 20 to 30 points, labels start stacking on top of each other. Filter to the items that matter most, or turn labels off and rely on hover tooltips instead.
Where a quadrant chart earns its place over a table of numbers.
The exact build in this lesson: which channels are earning their budget, and which are quietly draining it without showing up in a normal report.
Plotting hours invested against results delivered surfaces quick wins (low effort, high impact) the same way it surfaces wasted spend.
Sheets supports scatter charts and additional line series identically, so the same average-line technique carries over with no changes.
The Competitor Analyzer tool inside Updoot plots this exact spend-versus-return quadrant automatically and flags the high-spend, low-return quadrant for you, skipping the chart-building entirely.
A plain scatter without reference lines still shows the relationship between two variables, but forces a visual guess instead of a clear four-way split.
A Line chart treats the X axis as categories, not continuous values, which distorts the spacing between points and defeats the purpose of the chart.
Without names on the points, a quadrant chart shows structure but not which item is which, forcing the viewer to cross-reference back to the table.
Typing the average in as a static number instead of an AVERAGE formula means the quadrant lines stay frozen even as the underlying data changes.
Beyond roughly 20 to 30 labeled items, the chart turns into visual noise. Filter to the items that matter most before building it.
Without labeled axes, a viewer seeing the chart for the first time has no way to know what the X and Y positions actually represent.
Spotting what to cut shouldn't require building a chart first.
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A scatter (XY) chart with two extra reference lines, usually at the average of each axis, splitting the plot into four quadrants so every point's position tells you something at a glance.
A scatterplot is the plain XY chart. A quadrant chart is the same chart with average or target lines added across both axes, turning it into four labeled regions instead of one open plot.
Any two continuous numbers you want to compare per item, such as ad spend versus revenue generated, or hours invested versus results produced.
Calculate the AVERAGE of each axis's values, then add two new data series, one a vertical line at the X average and one a horizontal line at the Y average, plotted on the same chart.
Yes. Replace the AVERAGE-based line with a fixed number, like a spend cap or a revenue goal, if a specific target makes more sense than the dataset's own average.
Add data labels to the series, then use "Value From Cells" (Excel 2013+) to pull in a name column instead of the default X or Y number.
Not directly. A third variable can be represented by point size using a bubble chart instead of a plain scatter, but a fourth variable has no natural place on a 2D chart.
Past roughly 20 to 30 labeled points, the chart gets cluttered and labels start overlapping. Beyond that, filter to the top items or drop labels in favor of a hover tooltip.
It's the same underlying idea, four quadrants from two axes, but a BCG matrix specifically plots market growth against market share. This technique applies the same structure to any two variables.
Yes. Sheets supports scatter charts and additional line series the same way, so the quadrant-line technique carries over with no changes.
A bar chart shows one variable per item well, but it can't show the relationship between two variables at once the way a scatterplot's X-Y position does.
Yes. It plots spend against return for you and flags the low-return, high-spend quadrant automatically, the same read this manual chart is built to produce.
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