Using a dataset of attribute ratings for brands in [Category] (file: [DatasetName]; n=[SampleSize]; period=[Timeframe]), produce a perceptual positioning map. Standardise attributes, run principal component analysis to derive two dominant dimensions, label axes interpretively (e.g., “Value Density”, “Experiential Richness”), and plot [Company] vs [CompetitorList]. Quantify each brand’s coordinate with component scores and explain variance captured. Provide segment overlays for [Segment] if available.
Output format:
Data summary (rows, attributes, missingness handling).
Axis naming logic (2–3 sentences).
Markdown table: Brand | PC1 | PC2 | %Variance_Explained | Top_Loadings.
6 interpretation bullets (clusters, whitespace, cannibalisation risk).
Action plan: Repositioning levers (feature, price, message) with expected impact on PC1/PC2.
Governance: Approval owners [Role1], [Role2] and decision gates.