Bernt
aee0f09db8
- Datafabrik: Dockerfile fix, agentorkestrering fungerar - Vision: Identify-modell, FAISS, OCR alla testade - API: Alla 7 integrationstester passerade - Upplösare: Entitetsupplösning verifierad
87 lines
6.1 KiB
HTML
87 lines
6.1 KiB
HTML
<!DOCTYPE html>
|
|
<html lang="en">
|
|
<head>
|
|
<meta charset="UTF-8">
|
|
<meta name="viewport" content="width=device-width, initial-scale=1.0">
|
|
<title>Retail Site Selection Data | Landvex</title>
|
|
<meta name="description" content="Foot traffic and demographics are not enough. The physical condition of a location — accessibility, surroundings, and maintenance — predicts store performance better than census data alone.">
|
|
<link rel="canonical" href="https://landvex.com/insights/retail-site-selection-data/">
|
|
<meta property="og:title" content="Retail Site Selection Data | Landvex">
|
|
<meta property="og:description" content="Foot traffic and demographics are not enough. The physical condition of a location — accessibility, surroundings, and maintenance — predicts store performance better than census data alone.">
|
|
<meta property="og:type" content="article">
|
|
<meta property="og:url" content="https://landvex.com/insights/retail-site-selection-data/">
|
|
<script type="application/ld+json">
|
|
{
|
|
"@context": "https://schema.org",
|
|
"@type": "Article",
|
|
"headline": "Retail Site Selection Data: Why the Street Matters More Than the Spreadsheet",
|
|
"description": "Foot traffic and demographics are not enough. The physical condition of a location — accessibility, surroundings, and maintenance — predicts store performance better than census data alone.",
|
|
"author": {"@type": "Organization", "name": "Landvex"},
|
|
"publisher": {"@type": "Organization", "name": "Landvex", "logo": {"@type": "ImageObject", "url": "https://landvex.com/apple-touch-icon.png"}},
|
|
"datePublished": "2026-08-10",
|
|
"dateModified": "2026-08-10",
|
|
"mainEntityOfPage": {"@type": "WebPage", "@id": "https://landvex.com/insights/retail-site-selection-data/"}
|
|
}
|
|
</script>
|
|
<style>
|
|
body { font-family: -apple-system, BlinkMacSystemFont, 'Inter', sans-serif; line-height: 1.6; max-width: 760px; margin: 0 auto; padding: 40px 20px; color: #1a1a1a; }
|
|
h1 { font-size: 2.5rem; font-weight: 700; margin-bottom: 0.5em; line-height: 1.2; }
|
|
h2 { font-size: 1.5rem; font-weight: 600; margin-top: 2em; margin-bottom: 0.5em; }
|
|
p { margin-bottom: 1.2em; }
|
|
.meta { color: #666; font-size: 0.9rem; margin-bottom: 2em; }
|
|
.cta { background: #f5f5f5; padding: 24px; border-radius: 8px; margin: 2em 0; }
|
|
.cta a { color: #0066cc; text-decoration: none; font-weight: 600; }
|
|
footer { margin-top: 4em; padding-top: 2em; border-top: 1px solid #eee; font-size: 0.85rem; color: #666; }
|
|
</style>
|
|
</head>
|
|
<body>
|
|
<article>
|
|
<h1>Retail Site Selection Data: Why the Street Matters More Than the Spreadsheet</h1>
|
|
<p class="meta">Published August 10, 2026 · Commercial Intelligence</p>
|
|
|
|
<p>Retail site selection has become a data-heavy discipline. Demographics, foot traffic, competitor density, and spending power are modelled with precision. Yet stores still fail in locations that looked perfect on paper. The missing variable is usually the <strong>physical condition of the location itself</strong>.</p>
|
|
|
|
<h2>What the spreadsheet misses</h2>
|
|
<p>Standard site selection models treat the location as a point on a map with associated data. They rarely account for what a customer experiences when approaching the store:</p>
|
|
|
|
<p><strong>Accessibility.</strong> Is the entrance visible from the street? Are there steps, uneven paving, or obstacles that deter entry? Is parking available and clearly marked?</p>
|
|
|
|
<p><strong>Surroundings.</strong> What is the condition of neighbouring properties? A well-maintained street signals safety and quality; a neglected one signals the opposite, regardless of the store's own investment.</p>
|
|
|
|
<p><strong>Maintenance burden.</strong> A location with deteriorating infrastructure — broken lighting, damaged pavements, overgrown vegetation — creates costs and risks that do not appear in the rent calculation.</p>
|
|
|
|
<p><strong>Competitor adjacency, physical.</strong> A competitor across the street may be invisible in census data but highly visible to a pedestrian. Conversely, a complementary business nearby may drive foot traffic the model does not capture.</p>
|
|
|
|
<h2>The physical condition score</h2>
|
|
<p>Landvex's retail site selection layer adds a physical condition dimension to standard location data. For any candidate site, the system scores:</p>
|
|
|
|
<p><strong>Street condition.</strong> Pavement quality, lighting, cleanliness, and signage legibility in the immediate vicinity.</p>
|
|
|
|
<p><strong>Access quality.</strong> Ease of approach by foot, bicycle, car, and public transport, including obstacle detection.</p>
|
|
|
|
<p><strong>Neighbourhood maintenance.</strong> The condition of surrounding properties, public assets, and green space — a proxy for area investment and safety perception.</p>
|
|
|
|
<p><strong>Change trajectory.</strong> Whether the area is improving, stable, or declining, based on repeated observation over time.</p>
|
|
|
|
<h2>From score to decision</h2>
|
|
<p>A location with strong demographics but poor physical condition is a risk — the customer base exists, but the experience of reaching the store may suppress conversion. A location with moderate demographics but improving physical condition may be undervalued by competitors who rely on spreadsheet models alone.</p>
|
|
|
|
<p>The physical condition score does not replace demographic analysis. It complements it, adding a dimension that is observable, comparable, and predictive — but almost never included.</p>
|
|
|
|
<h2>Portfolio-level application</h2>
|
|
<p>For retailers with multiple locations, physical condition data enables portfolio-level analysis. Which stores are in declining areas? Which are in improving areas where additional investment may pay off? Where should the next store go, based on both customer potential and street-level experience?</p>
|
|
|
|
<div class="cta">
|
|
<strong>See how physical condition data improves your site selection.</strong><br>
|
|
<a href="/enterprise/">Request a pilot →</a>
|
|
</div>
|
|
|
|
</article>
|
|
|
|
<footer>
|
|
<p><strong>Related:</strong> <a href="/insights/what-is-a-city-health-index/">What Is a City Health Index?</a> · <a href="/insights/urban-growth-index-nordic/">Urban Growth Index: Nordic Methodology</a> · <a href="/insights/official-data-urban-infrastructure/">Official Data and Urban Infrastructure</a></p>
|
|
</footer>
|
|
|
|
</body>
|
|
</html>
|