Competitor mapping per location, fed straight into your content
Competitor density, drive-time overlap, and market-saturation scoring per location — feeding your content systems so each location page leads with the right differentiation for its actual market.
The problem
Your location pages should call out the right differentiation per store. But you do not know your competitive landscape per location. The Denver store has 4 competitors within a 2-mile drive time. The Austin store has 12. Denver should lean into "the only place in town that does X." Austin needs a totally different angle. Today every location page ships the same generic differentiation.
You have a Buxton subscription for site selection, but it lives on the franchise developer's laptop. Marketing has no per-location competitor awareness. New service launches get sequenced without per-market competitive intensity. Google Business Profile categories are not tuned to under-served versus saturated markets.
Generic competitive intelligence tools (Crayon, Kompyte, Klue) cost $9,000 to $100,000+ a year and track competitor digital activity — strong for marketing teams chasing CI, weak on physical location density. Site-selection enterprise platforms (Buxton, Tango/eSite Analytics, SiteZeus, ESRI Business Analyst) cost $20,000 to $100,000+ a year and ship the density math in a seat-based interface for franchise-development teams. Neither one reaches your marketing production.
What success looks like
Every location has a current inventory of competitors within a configurable drive-time radius — 3 to 15 miles for service brands, 1 to 3 miles for retail. Density math computes competitor count, competitors per square mile, drive-time overlap with each one, and a market-saturation score.
Industry-specific classification distinguishes direct competitors (same vertical, same price tier) from adjacent (same vertical, different tier) from tangential (related but different vertical). Foot-traffic data (SafeGraph, Foursquare Places, Placer.ai where licensed) adds market-share estimation.
When a new competitor opens within a trade area, your content systems react automatically — local content generates "how we differ from the new competitor" angles, location-page differentiation language refreshes. When a competitor closes, opportunity-mode messaging kicks in. Your franchise development team consumes the same density math for next-unit market scoring.
Marketing operations stops shipping generic differentiation across markets with wildly different competitive landscapes.
How most operators solve this today
Generic CI, site-selection enterprise platforms, and price-competitive tools all exist. None of them build per-location density data and feed it into marketing production:
Generic competitive intelligence (Crayon, Kompyte, Klue, Similarweb, Visualping)
$13/month to $100,000+/year
Built for marketing and product teams tracking digital competitor activity. Strong on web, social, and ad tracking. Weak on physical-location density. No automatic feed into per-location content.
Site-selection enterprise platforms (Buxton, Tango/eSite Analytics, SiteZeus, Pitney Bowes)
$20,000 to $100,000+/year
Built for site-selection consultants and franchise development teams. Strong physical-location mapping. Lives in a seat-based UI. Does not reach marketing operations.
Price competitive intelligence (Prisync, Wiser, Competera)
$99 to $10,000/month
Focused on retail pricing. Different problem from physical-location density mapping. Works side by side when both are needed.
In-house (Google Maps + competitor website scraping)
Internal time
Falls apart past 30 locations. No density math, no foot-traffic data, no feed into your content systems.
Build it in-house
Senior engineer ($130-220k) + ongoing maintenance
You can pull from Google Maps Places. The drive-time density math, the competitor classification, and the foot-traffic integration are multi-quarter projects.
What changes when this is an agent skill
Pulls competitor locations from Google Maps Places (baseline), Yelp and Google Business Profile (service brands), and foot-traffic sources (SafeGraph, Foursquare Places, Placer.ai where licensed) into one competitor inventory per location.
Density math computes competitor count per location, competitors per square mile, drive-time overlap with each one, market share estimation (where foot-traffic data is licensed), and a trade-area saturation score. Industry-specific classification distinguishes direct competitors from adjacent from tangential.
When a new competitor opens within a trade area, your content systems react automatically — local content gets "how we differ from the new competitor" angles, location-page differentiation language refreshes. When a competitor closes, opportunity messaging kicks in. When a competitor adds a service that overlaps yours, your content adjusts. Competitor pricing shifts (where price competitive intelligence is licensed) trigger pricing-aware messaging.
Your existing subscriptions get wrapped in rather than replaced — Buxton, Tango, or SiteZeus density data where licensed; Crayon or Kompyte digital CI where licensed; Google Maps Places as baseline. The same density data feeds your franchise development team for next-unit scoring.
The total cost replaces a Buxton seat sitting on the franchise developer's laptop while delivering the data to marketing operations at the same time.
Agents that include this skill
Skills live inside agent rentals. To get this skill in production, hire any of the agents below — context-tuning at onboarding is included in the first month.
Local Context Ingestion Agent
Ingests per-location external signal — events, news, demographics, weather, competitive density — and emits the canonical local-context feed.
FAQ
- What does the competitor mapping actually do?
- Builds a current inventory of competitors within each of your trade areas, computes density math (count, drive-time overlap, market saturation), and feeds your content systems automatically when the competitive landscape changes.
- How is this different from Crayon, Kompyte, or Klue?
- Those tools track competitor digital activity (web, social, ads) for marketing and product teams. This maps physical-location competitor density per trade area and feeds your content systems.
- How is this different from Buxton, Tango, or SiteZeus?
- Those are enterprise site-selection platforms used by franchise-development and corporate-real-estate teams. Heavy implementation, $20,000 to $100,000 per year per seat. This brings the same data into your marketing stack with automatic content reactions, and your existing site-selection subscriptions wrap in rather than getting replaced.
- What geographic resolutions are supported?
- Drive-time radius (typically 3 to 15 miles for service brands, 1 to 3 miles for retail), ZIP cluster, or trade-area polygon. Configured per location.
- How are competitors identified?
- Google Maps Places as baseline. Yelp and Google Business Profile for service brands. Foot-traffic sources (SafeGraph, Foursquare Places) for high-fidelity identification where licensed. Custom competitor lists can be added.
- What does the density math output?
- Competitor count per location, competitors per square mile, drive-time overlap with each one, market share estimation (where foot-traffic data is licensed), and a trade-area saturation score.
- What changes trigger content reactions?
- A new competitor opening within a trade area (most important), an existing competitor closing (opportunity), a competitor adding a service that overlaps yours, or a competitor pricing shift (where price competitive intelligence is licensed).
- Can this feed our franchise development team too?
- Yes. The same density data feeds new-unit market scoring for franchise development. Marketing operations consumption is the day-to-day use case here.