Map 5–10 Competitors Fast: Positioning Map Examples With Templates

A positioning map is a two-axis scatterplot that shows how customers perceive brands relative to competitors, helping you spot underserved market space and crowded clusters. It works best when you plot 5 to 10 competitors against a target segment's own ratings, not your own marketing claims. Success looks like a map with four distinct quadrants, not one diagonal smear of dots.

TL;DR:

• Selecting two independent, buyer-relevant axes that are specific and relevant ensures the map effectively reveals white space and clusters without redundancy.
• Using 20 to 30 qualified responses per brand per segment produces stable averages and accurate positioning, while fewer responses increase noise.
• Validating perceived gaps through additional surveys prevents mistaking empty space for genuine market opportunities that buyers will consider.
• Advanced methods like PCA and MDS can better capture complex attribute data when more than two dimensions influence customer perception.
• Avoid projecting claims instead of actual customer perceptions, as it often results in maps that do not accurately reflect the competitive landscape.

Table of Contents

• What a positioning map is and why it matters strategically
• Choosing axes: ready pairs and rules for selection
• How to build a positioning map using direct rating
• Advanced methods: PCA and MDS for multi-attribute data
• Reading the map and turning insights into decisions
• Positioning map examples across categories
• Tools, templates, and workflows for building your map
• Building validated maps faster with a phase 0 planning tool
• What most positioning map projects get wrong
• Try Klaritea as an alternative way to build your map
• Sources
• FAQ

What a positioning map is and why it matters strategically

People often use "perceptual map" and "positioning map" interchangeably, but there's a useful distinction. A perceptual map plots how customers actually see brands based on survey data. A positioning map can plot either that perceptual data or the strategic attributes a company wants to claim. The Georgia Southern proceedings on perceptual mapping describe the tool as a two-dimensional scatter plot that visualizes how target customers perceive a brand relative to competitors on two strategic attributes, typically covering 5 to 10 main players.

The strategic value comes from three things a map makes visible at a glance:

• White space: gaps in the grid where no competitor sits, signaling a possible unclaimed position.
• Clustering: groups of brands crowded together, showing where competition is fiercest and differentiation is weakest.
• Movement over time: repeated mapping shows whether a repositioning effort actually shifted perception.

The limitation is built into the format. Two axes force a simplification of a brand that customers actually judge on ten or more attributes, so a map is a starting hypothesis, not a verdict. It only holds up when the underlying ratings come from a properly screened sample of the target segment, not from internal guesses about how the brand is perceived.

Choosing axes: ready pairs and rules for selection

The single most common mistake is picking two axes that move together. If you plot price against feature depth, most brands will land in a rough diagonal line because premium products tend to pack in more features, and the map wastes two of its four quadrants. The Umbrex guide to competitive positioning maps is direct about this: axes should be independent of each other, relevant to what buyers actually decide on, and specific enough to produce four distinct quadrants rather than a cloud.

Three rules cover most cases. First, test independence before you survey: ask whether a brand could plausibly score high on one axis and low on the other. Second, anchor both axes in something the buyer weighs when choosing, not something only the company cares about. Third, avoid vague labels like "quality" without defining what that means to the segment you're studying.

Here are axis pairs that hold up across common industries, each phrased as a bipolar survey item using a 1 to 7 scale:

How to build a positioning map using direct rating

Direct rating is the method most teams should start with. It's a survey-based approach that works for most cases before you ever need statistical software. The Asgard AI skills reference on perceptual mapping lays out the standard steps: pick axes, have respondents rate brands on 1 to 7 scales, average the scores, and plot the result.

1. Select 5 to 10 competitors that genuinely compete for the same buyer decision, and define the target segment you're surveying (industry, company size, or demographic, depending on your market).
2. Screen respondents for brand familiarity before you let them rate anything. Someone who has never used or evaluated a brand shouldn't score it.
3. Write bipolar 1 to 7 items for each axis using the wording patterns above, keeping the poles concrete rather than abstract.
4. Collect enough responses per brand to average out individual noise. A common practical floor is 20 to 30 qualified respondents per brand per segment, since fewer than that makes the average unstable.
5. Average the scores for each brand on each axis, then plot the pairs as (x, y) coordinates in a spreadsheet scatter chart.
6. Run a gap validation check on any white space you find before treating it as an opportunity.

That last step matters more than most teams give it credit for. A gap on the map is not automatically a market opportunity. Anything below that threshold suggests the space is empty because nobody wants what would go there.

Pro Tip: Run the gap validation survey on a separate sample from the one that generated your map, so you're not just confirming the same group's opinion twice.

Advanced methods: PCA and MDS for multi-attribute data

Direct rating works cleanly with two attributes chosen in advance. Once you have six or more attributes and you're not sure which two matter most, it's worth letting the data choose the axes instead of guessing. That's the case for Principal Component Analysis (PCA) or Multidimensional Scaling (MDS).

The practical steps look like this:

• Build an attribute by brand matrix, where each row is a brand and each column is an average rating on one attribute.
• Run PCA on that matrix to compress the attributes into two components that capture as much of the variation as possible.
• Interpret the components by their loadings, meaning you label an axis based on which original attributes contribute most heavily to it, rather than naming the axis before you see the results. The Asgard reference is explicit that labels should follow the loadings, not precede the analysis.
• Check variance explained before trusting the 2D map. A common rule of thumb is that the first two components together should explain at least 50% of total variance; below that, a two-dimensional plot is hiding real differences and should carry a caveat.

MDS takes a related but distinct route: instead of starting from attribute ratings, it starts from similarity judgments between brand pairs and converts those into distances on a map, which is useful when you suspect customers judge brands on dimensions you haven't thought to ask about. Strategic Management Insight notes that this geometric approach can surface decision dimensions managers didn't anticipate going in.

Reading the map and turning insights into decisions

A finished map is only useful once you know what to do with what you see on it. Three patterns show up repeatedly, and each implies a different move.

• Tight clusters signal a crowded segment where brands compete mainly on execution rather than positioning, meaning a new entrant needs a real differentiator to avoid getting lost in the pack.
• Isolated outliers often mark a brand that has claimed a defensible niche, or one that has drifted so far from buyer expectations that it's simply not being considered.
• Genuine white space is the pattern worth the most scrutiny, since it can mean either an opportunity or a space nobody wants.

Separating those two require a three-part gap test. First, check sample coverage: does the white space sit inside the range of attributes your segment actually cares about, or is it a combination nobody would buy regardless? Second, look for a demand signal outside the map itself, the kind of workaround behavior, community discussion, or search pattern the Asgard methodology points to.

Distance on the map also matters for repositioning decisions. A small shift, moving a brand's perceived position slightly within its existing cluster, is achievable through messaging alone. A medium shift usually needs a visible product or service change to be believable. A large shift, jumping across the map into an entirely different quadrant, rarely succeeds without a genuine product overhaul, since customers don't update their perceptions faster than the brand actually changes. Adding bubble size for market presence and arrows for movement over time, a technique Oscom.ai recommends, turns a single snapshot into a basic forecast of where the competitive field is heading.

Positioning map examples across categories

Seeing how axis choices play out in real categories makes the abstract rules concrete. The Rework resources library walks through several categories where axis choice directly shaped a repositioning decision.

Automotive: price vs. sportiness. Plotting brands on affordability against sporty versus practical positioning tends to reveal a "sporty for the price" gap, a space where no brand offers performance styling at an accessible price point. Rework's writeup cites Mazda's use of exactly this insight to reposition around affordable driving enjoyment rather than competing head-on with luxury performance brands.

B2B SaaS: ease of use vs. feature depth. This pairing consistently shows a sparse upper-right quadrant, meaning genuinely easy and genuinely deep products are rare. Most SaaS tools land as either simple-but-shallow or powerful-but-complex, which is exactly why the empty corner is worth testing rather than assuming it's unfilled for a good reason.

Fast food: price vs. quality perception. Rework's example points to Chipotle exploiting the gap between fast-food pricing and sit-down-restaurant quality perception, a space that looked empty until a brand built a menu and supply chain around filling it.

A simple scoring table makes the pattern easy to read before plotting:

Reading this table before plotting, Brand C stands out as the "quality for the price" position, while Brand D and Brand B sit close together, suggesting real competitive overlap rather than distinct strategies.

• CPG and soft drinks typically map convenience against health positioning, and clusters here often reveal that "healthy and convenient" is underclaimed relative to how many brands crowd the "indulgent and convenient" corner.

Tools, templates, and workflows for building your map

Once you have averaged scores, the plotting itself is simple. The choice of tool depends on what you're doing with the output next.

• Sheets or Excel works best for the scoring and plotting stage itself, since a basic scatter chart with labeled data points handles the math and the visual in one place.
• PowerPoint is the right tool once you need an annotated, stakeholder-ready slide with quadrant labels, callout boxes, and a clean brand color scheme.
• Miro fits the collaborative stage, when a team is debating axis choice or sorting competitors into rough positions before formal survey data comes in.

A complete template has five pieces: a raw scoring sheet, the plotted scatter itself, annotated quadrant labels explaining what each corner means strategically, trajectory arrows showing movement since the last mapping round, and bubble sizing tied to market presence such as review volume or estimated revenue. Oscom.ai's guide recommends triangulating that market-presence data from sources like G2, Capterra, or ad libraries rather than relying on a single source. For SaaS teams specifically, mapping axes like self-serve versus sales-assisted alongside SMB versus enterprise is a common next step once the core map is built, an approach covered in Manaxo's guide to self-service analytics rollouts.

Building validated maps faster with a phase 0 planning tool

Klaritea is built for the planning stage before a founder writes a line of code or commits budget to a repositioning move, and a positioning map is exactly the kind of artifact that stage needs. You type a one-line description of your idea, and Klaritea builds a connected model that includes competitor analysis alongside market sizing and feature scope, viewable through different lenses covering Clarity, Build, and Run & Scale.

• The AI advisory board, three advisors covering marketing, business strategy, and operations, can draft axis wording and cross-check competitor placement against multiple angles rather than one person's assumption.
• Outputs include scoring tables and a printable report, so the map data doesn't stay stuck in one spreadsheet.
• Because the model is connected, a positioning insight updates the competitor analysis and feature scope it touches, instead of living as an isolated slide.

What most positioning map projects get wrong

The most common failure isn't a bad axis choice, it's skipping validation entirely and treating an empty quadrant as a strategy on its own. A close second is plotting what a brand claims about itself instead of what customers actually perceive, which produces a tidy map that has nothing to do with reality.

Run more than one map with different axis pairs before committing to a repositioning bet, and document how each cluster moved over time rather than trusting a single snapshot. A quick, cheap experiment testing purchase intent beats a expensive repositioning campaign built on an untested gap.

— Karl

Try Klaritea as an alternative way to build your map

Building a positioning map by hand means juggling a spreadsheet, a survey tool, and a slide deck across three separate steps, then redoing all three every time a competitor moves. Klaritea keeps competitor lists, axis questions, and the scoring data in one connected model, so a positioning insight flows straight into the feature and market sections it affects instead of sitting in an orphaned slide.

If you're validating a new product idea rather than repositioning an existing one, start with the Free plan to export a sample report and see how the connected model handles competitor placement. Read more about the approach on the why Klaritea page, or move to the Klaritea plan at $19 per month once you need the full workflow.

Sources

• Perceptual mapping and related proceedings (Georgia Southern)
• Competitive positioning map and axis guidance (Umbrex)

FAQ

What is a positioning map?

A positioning map is a two-axis chart that plots brands or products based on how customers perceive them on two strategic attributes, such as price and quality. Companies use it to spot underserved gaps and crowded clusters among 5 to 10 main competitors.

How do I create a positioning map?

Pick two independent, buyer-relevant axes, then survey a screened sample of your target segment using bipolar rating items for each brand. Average the scores per brand and plot the pairs on a scatter chart, following the direct rating method most practitioners start with before moving to statistical methods like PCA.

What are some examples of positioning strategy?

Common examples include Mazda's use of a "sporty for the price" gap in the automotive category and a fast-food brand exploiting the space between quick-service pricing and sit-down quality perception. Both examples came from mapping price against a second strategic attribute and then validating the resulting gap before repositioning.

What are the four types of positioning?

Definitions vary across marketing frameworks, so there's no single universally agreed list. Positioning maps themselves don't rely on a fixed typology, they work with whichever two strategic attributes matter most to the buyer segment being studied, whether that's price, quality, convenience, or feature depth.

How many competitors should I include on a positioning map?

Most practitioner guidance points to plotting 5 to 10 main competitors on a single map. Fewer than that makes clusters hard to spot, and more tends to crowd the chart past the point of readability.

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