Categories
The Categories group contains six dashboards that analyze product category performance from multiple angles: overall contribution, supplier and brand share, cart affinity, brand loyalty and switching, customer migration between periods, and the taxonomy hierarchy itself.
Navigate to Retail Analytics → Categories and select the relevant tab.
Category's Contribution
Path: Retail Analytics → Categories → Category's Contribution tab
The primary category analysis dashboard. Enables category managers to understand which product categories are performing better or worse across all key commercial metrics: sales, customers, growth, penetration, and private label share.
Summary KPIs (top of dashboard)
| Metric | Description |
|---|---|
| Total Sales | Sales for selected categories in the base period, with % variation vs. compared period |
| Total Purchases | Ticket count, with % variation |
| Average Ticket | Sales / Purchases, with % variation |
| Total Customers | Unique customers who purchased from selected categories |
| Frequency | Average purchases per customer |
| Penetration | % of all active customers who bought from the selected categories |
| % Sales Weight | Selected category sales / total company sales |
| % Private Label | Proportion of private label sales vs. supplier brands within selected categories |
| Top 15 Products | Products with highest sales within selected categories |
Detail Table
Per-category breakdown with 24 metrics including: Sales, % Var Sales, Identified Sales, % Sales Weight, Units, Unitary Price, Purchases, Average Ticket, Customers, Margin, Average Expenditure, Frequency, and Penetration — each with period-over-period variation.
Evolution Chart
Metric evolution over time for each selected category (one line per category). Selectable metrics: Sales, Carts, Customers, Average Purchase, Average Expenditure, Private Label, Units, Products per Purchase, Penetration, % Sales Weight. Configurable chart granularity.
Filters
Base date period, Compared date period, Taxonomy, Product Categories, Supplier, Brand, Store categories, Stores, Carts (all/identified/unidentified), Chart Metric, Chart granularity, Segment, Homogeneous Stores.
Supplier/Brands Performance
Path: Retail Analytics → Categories → Supplier/Brands tab
Analyzes the weight of sales by supplier or brand within a category. Helps understand which suppliers drive the most sales, which ones are growing, and which are declining — enabling better assortment management and supplier negotiation.
Summary Block
| Metric | Description |
|---|---|
| Top Sales | Supplier/Brand with the highest sales |
| Top Customers | Supplier/Brand with the most unique customers |
| Top Penetration | Supplier/Brand with the highest penetration |
| % Private Label | Share of private label vs. supplier sales |
| Top 3 Sales Increase | Fastest-growing suppliers/brands vs. previous period |
| Top 3 Sales Decrease | Fastest-declining suppliers/brands vs. previous period |
Detail Table
Per-supplier or per-brand breakdown with 16 metrics: Sales, % Var Sales, % Sales Weight, Tickets, Average Products per Ticket, Unitary Price, Average Ticket, Customers, Frequency, Average Expenditure, Penetration, and Loyalty (% of buyers who exclusively bought this supplier/brand in the period).
Evolution Chart
Up to Top 10 suppliers/brands visualized side by side, with selectable metric (Sales, Units, Carts, Customers, Average Purchase, Average Expenditure, Products per Purchase) and configurable granularity.
Filters
Base date period, Compared date period, Audience, Taxonomy (mandatory), Product Categories (mandatory), Supplier, Brand type, Store categories, Stores, Carts, Supplier/Brand toggle, Chart Metric, Chart granularity, Comparable stores only.
Affinity
Path: Retail Analytics → Categories → Affinity tab
Analyzes what customers buy together in the same cart for a selected target category. Identifies cross-category affinity patterns to improve store layout, cross-selling strategies, and campaign targeting.
Use cases:
- Improve shelf layout to place high-affinity categories nearby (e.g. beer and diapers)
- Build cross-selling campaigns for customers likely to redeem both categories
- Evaluate category potential based on cart co-occurrence
Key Metrics
Top charts (first section): Top 10 categories by Affinity Index, by Ticket Affinity Weight, and by Customer Affinity Weight for the selected target category.
Affinity detail table — Top 30 categories with highest affinity to the selected target, sorted descending by Affinity Index:
| Metric | Description |
|---|---|
| Common Carts | Tickets where both the target category and the comparison category appear |
| Unique Customers | Customers who bought both categories in the same ticket |
| Target Group Sales | Sales of the target category in common tickets |
| Comparison Group Sales | Sales of the comparison category in common tickets |
| % Target Sales Weight in Common Cart | Target category sales in common carts / total sales of target category |
| % Comparison Sales Weight | Comparison category sales in common carts / total sales of comparison category |
| % Tickets Affinity Strength | Common tickets / total tickets of target category |
| % Customers Affinity Strength | Common customers / total customers of target category |
| Affinity Index | Tickets Affinity Strength / % of all tickets containing comparison category. Removes effect of high-penetration categories that appear together simply because they are bought everywhere |
:::note Minimum threshold Only categories where common carts ≥ 5% of target category tickets are shown, to avoid noise from very low co-occurrence. :::
Filters
Date period, Audience, Taxonomy, Product Categories (target — mandatory), Store categories, Stores, Carts (all/identified/unidentified).
Transferability
Path: Retail Analytics → Categories → Transferability tab
Measures brand and supplier loyalty and switching behavior within a category. Identifies which brands customers are exclusive to, which ones benefit from being the "refuge" when customers can't find their preferred brand, and which have a high risk of being substituted.
Use cases:
- Manage assortment: high-loyalty brands are essential; low-loyalty brands could be candidates for removal
- Improve supplier relationships and contracts based on actual loyalty data
Key Metrics
Simplified Vision table — for each brand or supplier:
| Metric | Description |
|---|---|
| Identified Sales | Identified sales for the brand/supplier in the period |
| Sales Fidelity | % of category sales captured from the brand's own buyers (how much of what brand X buyers spend on the category goes to brand X) |
| Sales Transferability | 100% − Sales Fidelity. % of brand X buyers' category spend going to other brands |
| Sales Transferability Reception | % of category sales from customers who buy brand X AND other brands. High value = brand is a refuge for non-exclusive buyers |
| Customers | Unique customers who bought the brand/supplier |
| Customers Loyalty | % of brand X buyers who exclusively buy brand X |
| Customers Transferability | % of brand X buyers who also buy other brands |
Extended Vision — matrices:
- Sales Transferability matrix: For brand X (row), shows % of X-buyers' category sales spent on brand Y (column)
- Customers Transferability matrix: For brand X (row), shows % of X-buyers who also bought brand Y (column)
Only identified sales (loyalty members) are used in this dashboard. Brands and suppliers must be defined as taxonomies associated to products.
Filters
Date period, Audience, Taxonomy, Product Categories (mandatory — single select), Store categories, Stores, View by Brand/Supplier, Top Brands/Supplier (Top 10 or All).
Won, Lost and Maintained
Path: Retail Analytics → Categories → Won, Lost and Mantained tab
Tracks customer migration between periods at category, product, and supplier level. Classifies customers as Won (new to the category this period), Lost (bought in previous period but not this one), or Maintained (bought in both).
Use cases:
- Understand if a category is acquiring new customers or losing its base
- Identify which categories have the highest customer churn
- Quantify the revenue impact of won, lost, and maintained customer cohorts
Key Metrics
Five analysis tiles:
1. Customers by Product Category
| Status | Description |
|---|---|
| Base Customers | Customers who bought the category in the compared period |
| Lost Customers | Bought in compared period, did NOT buy in base period |
| Maintained Customers | Bought in both periods |
| Won Customers | Did NOT buy in compared period, DID buy in base period (from other categories — not new company customers) |
| Current Customers | Customers who bought the category in the base period |
2. Margin by Product Category — same cohort breakdown for profit margin (Sales − tax − cost) per category.
3. Customer Expenditure by Product Category — sales amounts for each cohort per category.
4. Customers by Product — same won/lost/maintained classification at individual SKU level.
5. Customers by Supplier (only shown when a supplier is selected in filters) — won/lost/maintained at supplier level.
6. Customer Expenditure by Supplier — sales amounts for each cohort per supplier (when supplier is selected).
Filters
Base date period, Compared date period, Audience, Taxonomy, Product Categories (mandatory), Supplier, Brand type, Store categories, Stores, Comparable stores only.
Category Hierarchy
Path: Retail Analytics → Categories → Category Hierarchy tab
Displays the hierarchical structure of product features as an interactive collapsible tree. Allows users to explore how the product taxonomy is organized — from top-level sectors down to individual category levels.
Key Feature
An interactive Collapsible Tree visualization. Click any node (sector, section, category) to expand or collapse sub-levels. Useful for understanding the taxonomy structure that drives all other Retail Analytics dashboards.
Filters
Taxonomy (mandatory) — all displayed data restructures based on the selected taxonomy type.