White Paper: 7‑Brand Geographic Gap Analysis

Data, methodology, qualification criteria, and deliverables for territory delineation and expansion opportunity identification using SpatialXL and Geoscope

Executive summary

This white paper sets out the proposed approach to a multi-brand geographic gap analysis across Gauteng, Northwest, and Limpopo. The analysis applies standardized store and competitor classifications, layered GIS outputs (reader files), and drive-time-based coverage modelling to identify whitespace opportunities by brand. Outputs include (1) a PowerPoint report per brand and (2) Geoscope reader files containing the agreed layers, competitor views, delivery overlays, and identified gaps to support internal decision-making.

Background and objectives

The objective of the project is to assess market coverage and identify expansion gaps for a portfolio of seven brands using a consistent spatial framework. The initial geography in scope comprises Gauteng, Northwest, and Limpopo, with provincial boundaries retained for reporting and internal planning purposes. The business development footprint will align to a two-manager geographic split, which must be confirmed and mapped to enable clear ownership of identified opportunities.

In-scope brands: The analysis covers seven brands. Brand inclusions/exclusions to confirm include Brand 1 (currently not in scope) and Brand 2 (status to be confirmed).

Approach overview

Standardise store categories and subcategories by brand, including iconography and labelling rules to improve map readability.
Develop competitor layers by brand (direct, positive marker, and regional competitors) using a consistent two-level categorisation.
Define current territories using an urban–rural typology and model coverage using drive time rather than distance.
Apply qualification filters (population, demographics, income/NLI, and density) to limit gaps to commercially viable demand areas.
Overlay relevant shopping centre categories by brand to translate demand gaps into actionable site targets.
Package outputs into presentation-ready insights and layered reader files to support internal review and decision-making.

Data layers and cartographic standards

To ensure consistency across brands and outputs, the following map standards will be applied:

Store classifications: Define main categories and subcategories per brand; each subcategory is represented by an icon.
Labelling rules: Set a zoom threshold at which labels turn on; labels display store name and subcategory, while the main category is conveyed via the icon.
Store scene tips: Configure store pop-ups to include franchisee name and average monthly turnover (calculated as a 24‑month rolling average from the underlying spreadsheet).

Competitor framework

Competitor mapping is structured per brand to reflect different competitive dynamics. All competitor points are categorized to two levels below the top level to enable filtering and more nuanced interpretation.

Direct competitors: Primary like-for-like competitors for the brand.
Positive marker competitors: Co-location/side-by-side brands that indicate complementary demand.
Regional competitors: Location-specific competitor sets (e.g., province- or market-specific brands).

Example layer structure (illustrative): A brand layer (e.g., “Brand 3 Stores”) split into operational subcategories (e.g., delivery vs take-out), plus a competitor group split into direct, positive marker, and regional competitor sublayers by province/area.

Delivery coverage layers

Geoscope Reader files will include legacy third-party delivery coverage areas (for example, historical Uber Eats and Mr D coverage), where available.
In-house delivery heatmaps will be included as map snapshots at appropriate zoom levels, aligned to an eight-minute drive-time scale where applicable.

Territory definition and coverage modelling

Territories will be defined using an urban–rural typology, supported by population density and drive-time accessibility. Coverage and gaps will be assessed using drive time (not distance), with the applicable drive-time thresholds confirmed per brand and/or decision area.

Rural Town (typically a couple or one main road with a strip)
Small Town (single CBD and industrial area)
City Suburban (Low Density) (e.g., Midrand-type context)
City Suburban (High Density) (e.g., Tembisa-type context)
City CBD (e.g., Johannesburg CBD-type context)

Qualification and filter criteria

Gap identification will be constrained using a set of filters to ensure opportunities are evaluated in viable demand areas. The intent is to align each brand’s target customer profile to measurable demographic and socioeconomic indicators.

Minimum night-time population: Threshold to be confirmed and tested for usefulness.
Age profile: Use agreed age bands (as per reference provided) to ensure alignment with each brand’s focus.
Neighbourhood Lifestyle Index (Data available from Geo Terra Image) (income proxy): Translate each brand’s primary and secondary income focus into NLI bands; use the dominant NLI band as a qualification criterion, with secondary bands discussed qualitatively in reporting.
Night-time population density: Minimum threshold of 1,000 people per km² and above (as stated in notes).

Gap identification and site targeting

Following coverage modelling and the application of qualification filters, the resulting gaps represent candidate demand areas for expansion. To translate these demand gaps into actionable site targets, the analysis will overlay shopping centers and filter them to those center categories that are viable for each brand.

Shopping center targeting (example): For Brand 3, target center types include strip/medium malls, mapped to center classifications such as Convenience, Neighborhood, and Community (classification definitions to be confirmed).

Include store turnover metrics in area/gap analysis outputs.
Include delivery heatmaps as background context in presentation slides, using an 8‑minute drive-time scale where applicable.

Reporting outputs

The PowerPoint report and supporting exhibits will summarize the analysis per brand and per identified gap/territory using the following standard breakdowns:

Area land use set: Proportional aggregation of Residential, Commercial, Industrial and Other land uses for each gap or territory.
Demographic profiles: Age breakdowns and race breakdowns.
Socioeconomic profile: Neighbourhood Lifestyle Index (Data available from Geo Terra ImageGTI) breakdowns, with primary/secondary focus highlighted per brand.

Deliverables

PowerPoint report: A presentation summarising finding by brand, including mapped gaps, target areas, and supporting metrics.
Reader files: Layered files containing stores, competitor layers, delivery overlays, shopping centre layers, territory typologies, and identified gaps.
Pre-analysis approval note: A short document, per brand, summarising agreed inputs (for example, competitor sets, Neighbourhood Lifestyle Index (Data available from HOME - GTI) focus, and drive-time thresholds) for approval prior to running the full analysis.

Assumptions, dependencies, and open items

Confirm the geographic split across the two business development managers and capture it on a map.
Confirm the final list of the seven in-scope brands (including the status of Brand 2) and any exclusions (for example, Brand 1).
Obtain shopping centre classification definitions (for example, translations for “Community” and “Neighbourhood” centres) for brand targeting.
Confirm drive-time thresholds, by brand and/or decision area, used to compute coverage and gaps.
Confirm the minimum night-time population threshold and how it will be applied.
Provide brand-specific primary and secondary Neighbourhood Lifestyle Index (Data available from Geo Terra Image) focus bands (including numeric thresholds where applicable) for qualification and reporting.