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Executive Summary

FavTrip is an independent convenience store and fuel retailer in the Kansas City metropolitan area. The company grew by buying existing stores and converting them to its own brand, then built its first store from the ground up. Growing past acquisitions meant FavTrip had to judge a site before committing to it, without the real estate department a national chain would use. Using Maptitude, the company profiled the demographics and traffic around the stores it already operated, then screened the wider metro for areas that matched that profile. Competitor locations and traffic volumes were layered on to narrow the results further. The analysis produced a shortlist of look-alike sites and the location that became FavTrip Grandview, along with a screening method the company can run again as it grows.

Business Challenge: Judging a Site Without a Real Estate Department

Babir Sultan did not come from the convenience store industry. He worked in computer networking at JFK Airport in New York before visiting relatives in Kansas City, where family already in the business encouraged him to look at it. He started by leasing a single location and later bought it. He had first worked a convenience store register at a gas station in Grandview, Missouri.

Growth by acquisition hid the hardest question. When a company buys a store that is already trading, the trade area arrives with the building, and the performance record is there to inspect. Building or opening somewhere new reverses that. The site has to be judged before the money is spent.

For an independent operator the question is also narrower than it sounds. FavTrip did not need a model of the entire United States. It needed to know which parts of greater Kansas City resembled the store it already ran well, and which candidate sites were too close to a competitor or to another FavTrip to be worth pursuing. Answering that meant combining population and income data, traffic volumes, competitor locations, and category buying patterns in one place. FavTrip had tried other software before finding a tool that could do it.

The Solution

Profiling the Stores That Already Worked: FavTrip began with the stores it operated rather than with the map. Using the Maptitude buffer tools, the company drew rings around each existing site and calculated the demographics falling inside each one, exporting the results to a single radius report covering every FavTrip location. Naming each ring after the address of the store at its center kept the report aligned with the sites it described. That produced a benchmark: a measurable profile of the population, income, and traffic around the store the company most wanted to repeat, its Independence, Missouri location.

Screening the Metro for Look-Alike Sites: With a benchmark in hand, the problem became a search. FavTrip built a grid of evenly spaced points covering the greater Kansas City area, then tagged every point in that grid with the demographic data included in Maptitude, annual average daily traffic counts from the traffic count data layer, and geodemographic segmentation showing the consumer profile of each area. Selection conditions then did the filtering. Points that failed to match the benchmark profile dropped out, and the survivors formed a shortlist of look-alike sites. FavTrip tightened the screen over time, limiting results to Missouri and to locations carrying at least 10,000 vehicles per day.

Weighing Competition and Category Demand: A demographic match is not enough on its own. FavTrip imported the locations of competing national convenience and discount chains, then used buffers to count how many competitors sat within a mile of each candidate. Sites already crowded with stations were removed, as were sites close enough to an existing FavTrip to take business from it rather than add to it. Because tobacco makes up a large share of sales at some of its stores, the company also compared consumer expenditure on specific categories between a candidate area and a store already trading, which gives a way to reason about likely demand rather than population alone.

Results & Benefits

A Location Found: The clearest result is a site. FavTrip reports that the screening work produced the location it went on to pursue, FavTrip Grandview. For an operator of this size, one good site is not a modest outcome. It is a capital decision that would otherwise have rested on instinct and local familiarity, made instead against population, income, traffic, and competitor data for the whole metro.

A Screen the Company Can Rerun: FavTrip asked to be shown the method rather than handed the answer, and the analysis was documented so the team could repeat it. That has mattered more than any single map. The same combination of traffic counts, demographics, and competitor locations has since been turned around to look outward, including assessing what a newly built competing station nearby would mean for an existing FavTrip store. The screen is now part of how the company thinks about its market, not a one-off report.

Analysis Without an Analyst: FavTrip employs no GIS staff. Sultan learned the software himself, working through the trial with support and training sessions, and was running conditional selections against his own data within weeks. Sultan singles out the support team in his review, and for a company without in-house GIS that responsiveness is the difference between software that gets used and software that sits unopened.

(FavTrip shows what location analysis looks like at the scale most businesses actually operate at. The same tools that national chains apply across thousands of stores are equally workable for an operator weighing its next site, which is the point.)

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