Data Input
Columns represent one dimension (brands, segments, markets). Rows represent another (attributes, drivers, barriers). Cells must be numeric values.
Drop .csv or .xlsx here, or click to browse
Up to 10 columns and 30 rows
Imagine Brand A scores 80 on every attribute while Brand B scores 40. In raw data, Brand A dominates the map simply because its numbers are bigger — not because it has a distinctive profile. The map shows size differences instead of shape differences. That is the big brand effect.
Standardisation removes this distortion so the map reveals what actually differentiates each column and row from the others — which is typically what you want.
- None (standard CA)
- Uses your data as-is. Best when all columns already have similar totals and no single column dominates. If your map looks like one cluster with an outlier far away, try switching to an equalised mode.
- Equalised (recommended)
- Removes the average level of each row and each column, keeping only the pattern of deviations. Think of it as asking: "Ignoring that Brand A scores higher overall, where does it score relatively higher or lower than its own average?" This is the safest general-purpose correction.
- Fully normalised
- Goes further — each row is rescaled so all rows have the same spread. Use this when some rows (attributes, drivers) have a much wider range of scores than others, causing them to dominate the map. This is the strongest correction; it forces every row to contribute equally.
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Drag any label to reposition. Adjust axes, fonts, and markers above.
When to Use This & How to Read the Map
A correspondence map works whenever you have a grid of numbers where rows and columns represent two different things you want to compare. It compresses all the relationships in that grid into a single visual — showing you which rows are associated with which columns, which ones are similar to each other, and where the real differentiation lies. Any cross-tabulation with numeric cells is a candidate.
Use Cases
Brands vs Attributes
The classic use. Rows are perceptual attributes (trustworthy, innovative, affordable). Columns are brands. The map shows which brands own which attributes in consumers' minds — and where white space exists for positioning.
Segments vs Motivations
Rows are purchase drivers or needs (low fees, mobile app, status). Columns are customer segments. The map reveals which motivations pull which segments — essential for targeting and messaging strategy.
Markets vs Features
Rows are product features or service capabilities. Columns are geographic markets or channels. Reveals which features matter most in each market — useful for localisation and go-to-market planning.
Any Grid You Have
Departments vs competencies, media channels vs audience profiles, product categories vs occasions, touchpoints vs satisfaction drivers. If the data is a table with numeric cells, it works here.
Reading the Map
Closeness = Association
When a column point (circle) sits near a row point (diamond), those two are more strongly associated than points further apart. A brand near "innovative" is perceived as more innovative than a brand on the opposite side of the map. Look for clusters — they tell you which columns share a similar profile across rows.
The Centre vs the Edges
Points near the origin (where the axes cross) are average — they don't stand out on any row or column. Points far from the centre have distinctive, differentiated profiles. If your brand sits dead-centre, it lacks a clear identity in the data. Edge positions are where strong associations live.
Opposite Sides = Contrast
Points on opposite sides of the origin along the same axis represent opposing profiles. If "premium" is top-right and "affordable" is bottom-left, that axis captures the price-perception trade-off. These contrasts reveal the fundamental tensions in your data.
Inertia = Explanatory Power
Each dimension (axis) explains a percentage of total variance, shown in the axis labels. Higher is better — Dimension 1 typically captures the dominant pattern, Dimension 2 the next strongest. If both axes together explain 70%+ of the variance, the map is a reliable summary. Below 50%, interpret with caution.
Pro Tips
Pro Tip
Naming the Axes
Let the attributes name the axis, not the other way around. Look at which attributes anchor each end of the axis, then identify the common theme across them — that's the axis label. Resist picking a single attribute as the name; the axis is the latent construct that the cluster of attributes points to.
Example: "innovative" and "contemporary" can look interchangeable. If the attributes loading on that end are about new products, tech, and industry firsts — it's innovative. If they're about following current trends and staying relevant — it's contemporary. Same-looking axis, different story. The attributes decide.
Pro Tip
When Entity and Attribute Sit Close
Visual closeness between a brand and an attribute is a good first read, but it measures position, not direction. For a rigorous read, draw a line from the origin through the attribute, extend it across the map, then drop a perpendicular from the brand onto that line. Where it lands — and which side of the origin — tells you the true association.
Why bother: A brand can sit visually near an attribute yet project weakly along it (off-axis). A brand on the opposite side of the origin isn't just weakly associated — it's negatively associated. Closeness can't tell you this; projection can. Use closeness for quick reads; switch to projection when defending a finding.