Primary operating owner
Network Planning; Real Estate; Expansion
P39 · Representative solution case · Retail / QSR / BFSI
Retail/QSR Site Selection & Store Intelligence
How many proposed sites would change rank if catchment demand, travel time and cannibalisation were measured consistently?
Evidence distinction: this is a representative solution case constructed from the source requirements and Mappls delivery pattern. It is not presented as a named-customer outcome unless a separate verified customer story is explicitly linked.
01 · Operating context
The decision environment and the problem to change
Site decisions depend on fragmented catchment, competition and cannibalisation assumptions, while store networks need location-aware performance diagnostics.
Economic decision
Chief Strategy/Expansion Officer
Technical influence
Analytics; Finance; Operations; GIS
Why action becomes urgent
Expansion plan, network rationalisation, new format, M&A, weak stores, market entry.
02 · Before and after
Change the operating model, not only the interface
Fragmented context and delayed action
Site decisions depend on fragmented catchment, competition and cannibalisation assumptions, while store networks need location-aware performance diagnostics.
- Location, operational and enterprise data remain separated
- Stakeholders make inconsistent decisions from partial context
- Exceptions are found late and evidence is difficult to reconstruct
A governed location-aware decision loop
Catchment and drive-time analysis, POI/demographics, competition, demand scoring, cannibalisation, network gaps and store/ATM/branch dashboards.
- One role-aware workspace in Mappls Location Analytics Studio
- Live context moves directly into the responsible workflow
- Every decision, scenario and outcome can be measured and audited
03 · Stakeholder experience
One shared system, different role moments
Own and govern
Network Planning
Own and govern
Real Estate
Decide and coordinate
Expansion
Decide and coordinate
Chief Strategy/Expansion Officer
Decide and coordinate
Analytics
Execute and verify
Finance
Execute and verify
Operations
Execute and verify
GIS
Inside Mappls Pro, the same user can hold this role here and a different role in another application. Membership, saved runs and administration remain app-specific.
04 · Day-in-the-life journey
How the solution turns information into an operational result
Frame the operating decision
What defines a successful site? Which historical stores and KPIs can train/validate the model?
Connect the location foundation
Bring the required map, address, road, place, imagery, asset, customer and operational data into a governed Analytics workspace.
Act through role workflows
Data workspace, Catchments, Models, Scenarios, Decision board give each stakeholder the information, decision and action appropriate to their role.
Prove the intervention
Score one city and back-test model against existing high/low performing sites before ranking new candidates.
Scale and continuously improve
Operationalise the model through Data/analytics project + platform subscription + per-market updates., adoption governance, service measurement and a managed improvement backlog.
05 · Solution and data architecture
The Mappls capabilities behind the experience
Location foundation
Map, address, place, route, imagery, boundary and context layers
Operational context
Customer, asset, order, sensor, incident, task or transaction records
Application workflow
Data workspace · Catchments · Models · Scenarios · Decision board
Outcome loop
Model separates performance cohorts; shortlist accepted by expansion team; decision time reduced.
Geo-demographics & Custom Data
Population, market, administrative and customer-enriched location layers
Product detailsStandard 2D Map & Road Data
Road network, attributes, administrative boundaries and cartography
Product detailsClarityX Location Analytics
Market, territory, network and spatial decision analytics
Product detailsGeoAI & Spatial Decision Models
ML/AI, risk, demand, accessibility, image and anomaly models
Product details06 · Outcome and evidence
Make the value claim measurable before scaling
Bounded proof
Score one city and back-test model against existing high/low performing sites before ranking new candidates.
- Confirm the baseline and decision scope: What defines a successful site? Which historical stores and KPIs can train/validate the model?
- Use representative and, where approved, customer data in the configured workspace
- Review measurable success with the operating and economic owners: Model separates performance cohorts; shortlist accepted by expansion team; decision time reduced.
- Document data quality, adoption, security, service and scale findings
- Convert evidence into a phased production scope and accountable value plan
07 · Governance and scale
Operate the solution safely and sustainably
Identity and access
Server-enforced identity, least-privilege app membership, role-specific actions and auditable administration.
Data governance
Source lineage, quality thresholds, update cadence, retention, consent and controlled data sharing.
Operating governance
Named process owners, exception SLAs, escalation, change control and adoption measurement.
Value governance
Baseline, intervention and outcome metrics reviewed on an agreed cadence with accountable owners.
Your outcome, powered by Mappls
Put Retail/QSR Site Selection & Store Intelligence into your operating context
Bring the baseline, workflow, users, data and success criteria. Mappls will shape a bounded proof and production path.