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Geomarketing

How many visits is too many? Saturation analysis for a touring business

An Excel model with an adjustable radius that counts earlier nearby events and the rest time between them, to show where a market is saturated.

How many visits is too many? Saturation analysis for a touring business
Client
Touring events company (demo with simulated data)
Location
United States (simulated)
Year
2026
Industry
Retail & Sales
PythonExcelSpatial analysisHaversine distance

The challenge

A business that visits the same areas again and again needs to know when it is overusing a market. Two questions matter: how many times have we already been near this place, and how long has it been since the last time?

The solution

For every event I calculate, as of its date, the number of earlier events within a chosen radius and the date of the most recent one. Both fields live in an Excel workbook where the radius is a single input cell: change 25 miles to 10 or 50 and every column, and the summary, recalculates without rebuilding anything.

  1. Distance: great-circle (haversine) distance between every pair of events.
  2. Saturation fields: prior visit count, most recent prior visit, visit number and days since last visit.
  3. Analyse: average revenue by visit number and by rest time, plus a map of where events pile up.

Results

  • A 360-event simulated history, analysed at any radius with one cell
  • In this demo, revenue per event drops from about $43,000 on a first visit to about $20,000 from the 11th visit on
  • Rest time matters too: events less than two weeks after a nearby one average about $22,000, against about $38,000 after four months or more

About the data. This case is a demonstration built with simulated events and revenue. The patterns above come from the simulation and are not real results. The method is the same one I apply to real client data.

Deliverables

Download the simulated Excel model · Saturation map · Summary charts

Map of simulated events colored by prior visits

Average revenue by visit number

Average revenue by rest time

Next step

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