Score every candidate against your network strategy.

The problem

Retail expansion is a layered decision. Manual scouting can't keep up with any of the layers.

Datenquellen

Die Quellen hinter jedem Score.

Retail scoring leans heavily on demographic and competitive intelligence layers, complemented by parcel-level imagery and visual evidence for the candidates that surface to the top.

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• Kadaster

  Parcel ownership, lease length signals, transaction history.

• BAG

  Buildings, addresses, frontage, surrounding land use.

• CBS demographics

  Population density, age, income bands, household composition.

• NDW traffic intensity

  Roadside visibility and footfall proxy from traffic flow.

• Competitor location data

  Public chain footprints, density, recent expansions.

• Street View

  Frontage quality, signage feasibility, parking visibility.

• Aerial / parcel imagery

  Roof surface, parking layout, parcel envelope.

So starten wir

How we start.

Most retail engagements open with one of two pilots: a market-entry scouting run on a defined region, or a network-optimization run across your existing footprint. Either is two to three weeks, fixed scope, fixed price.

1. 01Week 1: network strategy + scoring prompt definition
2. 02Week 2: scoring run across the candidate set
3. 03Week 3: pipeline-ready dossier + integration handoff

FAQ

Retail & restaurants: häufige Fragen.

• What is Locata for retail and restaurants?

  Locata is an AI site selection platform for retail chains and quick-service restaurants. It scores every candidate against your network strategy — market entry, network optimization, real-estate pipeline triage, custom criteria per format — and integrates with the demographic and competitive intelligence you already have.

• Why pick Locata over Placer.ai or in-house GIS scripts?

  Off-the-shelf foot-traffic tools like Placer.ai are excellent for US retail but optimised for US data. Internal GIS scripts don't scale to real expansion programs. Locata is European-data-native (CBS, Kadaster, BAG, competitor footprints), runs multi-model AI scoring instead of a single confidence number, and delivers per-candidate reasoning the real-estate committee can defend.

• Can Locata handle drive-through QSR versus urban formats?

  Yes — same platform, different scoring prompts. Drive-through QSR weights traffic flow, manoeuvring space, and signage visibility; urban grab-and-go weights walking traffic, frontage quality, and dwell-time demographics. The scoring backbone is identical; the prompt and constraint set differ per format.

• What data does Locata use for retail site selection?

  Kadaster (parcel ownership and transaction history), BAG (buildings, frontage, surrounding land use), CBS demographics (density, age, income bands, household composition), NDW traffic intensity, public competitor footprints, Google Street View, and PDOK aerial imagery for roof and parking layout.

• What does a Locata retail engagement cost?

  Network-scale retail work is scoped per chain and per expansion program — typically enterprise engagements rather than a fixed rate card. Talk to us about scope, integration with your real-estate pipeline, and refresh cadence.

• How long until we get a pipeline-ready output?

  Two to three weeks fixed-scope from kickoff. Week one is network strategy plus scoring prompt definition with your team, week two is the scoring run across the candidate set, week three is the pipeline-ready dossier plus integration handoff into your real-estate workflow.