Retail Site Selection: Lassen Sie Daten sprechen, nicht die Intuition
KI-Analyse Fußgängerfrequenz + Wettbewerber + Demografie + ROI für jede Adresse weltweit — in 60 Sekunden, ab 99€.
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Last updated · Reviewed by Nicholas Todeschini
Retail site selection is the process of identifying the optimal physical address for a new retail store, restaurant, bar, gym, salon, boutique or pharmacy. It combines foot traffic analysis, competitor mapping, demographic profiling, rent benchmarking and accessibility scoring to forecast a site's revenue potential before you sign a 5–10 year lease.
WhereToOpen.ai is AI-powered retail site selection built for SMBs: same rigor as a site-selection consultant, delivered in 60 seconds for $99 per report — instead of 4–8 weeks and $5,000–$20,000+.
Coverage
Built for every retail format
Each business type weights signals differently. Our AI tunes the model per format so you get the right answer for your category.
Restaurants & bars
Foot traffic, competitor menu/price overlap, evening vs lunch demand split.
Coffee shops
Morning commuter flow, residential density, work-from-home patterns.
Gyms & fitness
Residential proximity, parking availability, evening accessibility.
Salons & beauty
Female demographic concentration, repeat-visit drive-time tolerance.
Boutiques & retail
Pedestrian traffic, tourist flow, complementary co-tenants.
Pharmacies & services
Aging-population density, competing pharmacy walking distance.
Methodology
7-step retail site selection process
Whether you DIY, use software or hire a consultant, a credible retail site selection follows these seven steps.
1
Define the customer
Who are they, where do they live, what's their spending power, when do they buy?
2
Set the trade area
Walking radius for coffee, 10-min drive for gym, 5-min walk for boutique. Each format has its own catchment.
3
Quantify foot traffic
Daily, weekly and seasonal pedestrian and vehicle flows around the candidate address.
4
Map the competition
Direct and indirect competitors with ratings, price tier, opening hours and review sentiment.
5
Profile demographics
Age, income, household composition and lifestyle within the trade area.
6
Score rent vs revenue
Project monthly revenue and confirm rent stays under 10–15% of revenue (format-dependent).
7
Stress-test the decision
What if foot traffic drops 20%? What if a competitor opens? What's your break-even month?
Vertical playbooks
Retail playbooks by vertical
Each retail format weights site-selection signals differently. Here's the short playbook for the four most common formats we see operators open.
Food & beverage
Lead signals: morning vs evening foot traffic split, competitor menu/price overlap, sentiment around 'wait time' and 'noise level'. Watch out for: rent-to-revenue creeping above 12%, saturated direct competition within 200m.
Fashion & boutique
Lead signals: pedestrian density on Saturday afternoons, presence of complementary co-tenants (cafés, bookstores), tourist share. Watch out for: low repeat-visit pattern, distance from main shopping artery.
Grocery & convenience
Lead signals: residential density within 800m, household income, parking availability, vehicle traffic on the adjacent road. Watch out for: existing supermarket chain within 1km drawing weekly trips.
Services (gyms, salons, clinics)
Lead signals: drive-time catchment population, age and income match, competitor count within the catchment, evening accessibility. Watch out for: low evening foot traffic, lack of parking for service formats.
KPIs
The 5 KPIs that decide a retail site
Skip glamour metrics. These five are the ones that actually predict whether the lease pays off.
| KPI | Healthy range | Why it matters |
|---|---|---|
| Rent-to-revenue ratio | F&B 8–12% · Retail 6–10% | Above 15% is unsustainable in most formats. |
| Direct competitor count (500m) | 2–5 | Zero = no validated demand. 6+ = saturated. |
| Average competitor rating | 3.8–4.4 | Low = market gap. High = entrenched incumbents. |
| Demographic match score | 70+ | Age × income × density alignment with target. |
| Foot traffic at peak daypart | Format-dependent | Compare to format benchmark, not absolute. |
Pitfalls
5 common retail site-selection mistakes
Most failed openings repeat the same handful of mistakes. Spotting them in the report is the fastest ROI you'll get from the platform.
Falling in love with a unit
Anchoring on a specific space and rationalizing weak signals (low traffic, high rent) instead of comparing 3+ candidates objectively.
Ignoring rent-to-revenue
Signing a 'beautiful' lease at 18% rent ratio and hoping volume will catch up. It rarely does in year one.
Misreading competition
Treating zero competitors as 'opportunity' when it usually means there's no validated demand for the format on that street.
Skipping seasonality
Choosing a tourist-heavy address without modeling the off-season cash-flow gap. Many F&B closures happen in Feb/Mar.
Skipping the second look
Signing without re-running the analysis after the landlord's counter-offer. Final rent often pushes a sustainable site into red.
Stop guessing where to open your next store
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Frequently asked questions
Was ist Retail Site Selection?
Retail Site Selection ist der Prozess, die optimale physische Adresse für einen neuen Standort — Laden, Restaurant, Fitnessstudio, Apotheke — auszuwählen. Er kombiniert Fußgängerfrequenz, Demografie, Wettbewerber, Mieten und lokale Regulierung zu einer quantitativen Go/No-Go-Entscheidung.
Wie funktioniert KI-basierte Retail Site Selection?
KI-Plattformen für Retail Site Selection integrieren POIs, demografische Daten, Mobilitätssignale und Mieten und wenden branchenspezifische Modelle an, um einen Score zu erzeugen. WhereToOpen.ai liefert das in 60 Sekunden für jede Adresse weltweit.
Welche Faktoren zählen am meisten?
Die sieben prognosestärksten Faktoren: Fußgängerfrequenz nach Tageszeit, Wettbewerberdichte und -qualität, demografisches Matching, Miete-zu-Umsatz-Verhältnis (<15 % nachhaltig), Erreichbarkeit, Sichtbarkeit und Saisonalität des Formats.
Was kostet eine Retail-Site-Selection-Analyse?
Klassische Berater verlangen 5–25 k€ pro Adresse mit Wochen Wartezeit. Enterprise-Plattformen (Placer.ai, ESRI) starten bei 5.000$+ pro Jahr mit Vertrag. WhereToOpen.ai ist für KMU: 99€ pro Report, ohne Vertrag.
Funktioniert das auch für F&B?
Ja — Gastronomie ist einer der reifsten Anwendungsfälle. Das Modell muss F&B-Spezifika berücksichtigen: Frequenz pro Daypart, Sentiment zum Wettbewerber-Menü, Dichte der Delivery-Aggregatoren und Miete-zu-Sitzplatz-Stresstest.
Kann ich Retail Site Selection vor Vertragsunterzeichnung machen?
Genau dann sollte man es machen. Ein 99€-Report vor der Unterschrift ist eine Versicherungsprämie von 0,02 % gegen eine 500-k€-Verpflichtung. Viele Betreiber nutzen den Report auch, um Miete oder Anreize mit dem Vermieter zu verhandeln.