Upload your survey, map your attribute ratings and an overall satisfaction score, and get the classic importance-performance quadrant map, every attribute ranked by its gap, and a prioritized action list. Free.
Free analyses run on up to 10,000 rows. Larger files are randomly sampled to that size — sign up to analyze your full dataset.
Mapping importance against performance...
Sent to — importance-performance quadrant map, gap ranking, stated vs derived importance comparison, boundary sensitivity, prioritized action list, R code, and AI insights.
Analyze another filePerformance is each attribute's mean rating. Importance is measured two ways. STATED importance is the mean of the importance rating respondents gave that attribute. DERIVED importance is the Pearson correlation between the attribute's rating and the overall satisfaction score, reported with the evidence behind it; a joint standardized regression is also fitted to disclose how much of that association is shared with the other attributes rather than unique. Attributes are placed on a four-quadrant map split at the grand mean of each axis, and ranked by a priority score — the attribute's importance minus its performance, each standardized across the attribute set, so the ranking works whether importance is a rating or a correlation. When both axes are on the same rating scale, the raw importance-minus-performance gap is reported in scale points as well. The whole quadrant assignment is then recomputed under the alternative boundary convention (the scale midpoint, or a fixed correlation cut for derived importance) and every attribute that moves is named.
Use it when you have survey ratings across several attributes and need to decide what to improve first — the classic customer, employee, product, or service prioritization question.
Not for ranking drivers of an outcome without the performance dimension (use the key drivers tool), not for two measurements of the same quantity (use the method agreement tool), and not for deciding whether an attribute improved over time, which needs repeated measurement rather than one cross-section.
Built for: CX and market research analysts, product managers, service and operations leads deciding where to invest
Typical data source: A survey export with one row per respondent and one column per attribute rated, usually with an overall satisfaction question alongside
One row per respondent, one column per attribute rated, plus an overall score. Importance columns are optional — supply them and the analysis compares stated against derived importance:
Minimum 20 rows · Best with 100-10,000 responses across 4-12 attributes
Standard-library analysis: what matters most that we do worst? Classic Importance-Performance Analysis on survey data. Map your attribute satisfaction columns and either an importance rating per attribute or an overall satisfaction score, and get the four-quadrant map (Concentrate Here, Keep Up The Good Work, Low Priority, Possible Overkill), every attribute ranked by the size of its importance-performance gap, and a prioritized action list. When both stated importance and an overall score are available, both are computed and the disagreement between them is reported rather than one being picked silently — and because the quadrant boundary is a methodological choice, the analysis states the values it used and names every attribute that would move under the other convention.
Every attribute placed by importance against performance, with the quadrant dividers drawn — the whole prioritization argument in one picture.
Attributes ranked by how far short of their importance their performance falls, so the top of the list is where the unmet need is largest.
The numbers behind the map: importance, performance, gap, and quadrant for every attribute.
What respondents said mattered against what actually tracks their overall score — and every attribute where the two give different instructions.
Whether the priority list is a real finding or an artefact of where the cross-hair was drawn.
The map as a work order: what to do about each quadrant, and what to check before spending against it.
Plain-English interpretation — what the numbers mean, what's significant, and what to do next.
What should we fix first?
Map your attribute satisfaction columns and either an importance rating per attribute or an overall satisfaction score. You get the four-quadrant map, every attribute ranked by the size of its gap, and a per-quadrant action list — so the argument about priorities becomes a picture.
See our FAQ for details on pricing, data privacy, and how the analysis works. Every report includes a Methodology section showing the statistical test, assumptions checked, and diagnostics run.
Run any analysis on your own data — validated R analyses, interactive reports, AI insights, and PDF export.
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