How Cafe Visibility Scores Change by City Size
A cafe owner in a small town looks at a modest visibility score and wonders if the town itself is the reason. A cafe owner in a big city looks at a similar score and wonders if a crowded market is drowning them out. Both are reaching for an explanation outside their own control, and this post tests whether market size actually moves the number.
Short answer: in Hillcane's own census, the difference in median score across groups of local coffee scene size is small, and the group with the largest scenes is thin enough in shop count that it should not be treated as a reliable pattern. Scene size does not appear to explain much of why any individual shop scores where it does.
This post reports what the data shows and stops there. It also explains a real limit in the method: the census does not have a population column for these cities, only a count of specialty shops in each local scene, which is a proxy for how developed that coffee scene is, not for how many people live there. That distinction matters, and the two should not be blurred together.
What Was Actually Measured
Hillcane's census scores each shop out of forty across eight dimensions, and it covers more than 1,000 independent specialty shops across six regions, snapshot dated September 15, 2026. For this study, every shop's total score was joined to a measure called scene size approximate, the approximate count of specialty coffee shops in that city's local scene, drawn from the same city level data used throughout the census.
This is worth saying directly: scene size approximate is not population. It is a count of how many specialty shops exist in a given city's coffee scene, which says something about how developed that local coffee culture is, not about how many people live there. A city with a large population and a small specialty coffee scene, and a smaller city with a surprisingly developed one, would both be measured by this proxy in ways that would not match population directly.
Population itself is not a field that exists anywhere in the census files. Adding it would require a separate fetch of US Census Bureau estimates for every city in the dataset, matched carefully to the correct place, which was not done for this study. So scene size is named here as a proxy rather than treated as a stand in for population, and the findings report what scene size specifically shows, leaving population as an open question for a future study if it is ever added.
This distinction is not a technicality. A resort town with a modest year round population can support a surprisingly large specialty coffee scene relative to its size, while a much larger suburb with a bigger population can have a genuinely small one. Treating scene size and population as interchangeable would risk describing both of those places incorrectly, so this study keeps them separate on purpose rather than blending them into a single, less accurate stand in number.
The Bands and the Numbers
Shops were grouped into four bands by their city's scene size: 8 or fewer shops in the local scene, 9 to 15, 16 to 25, and 26 or more. Every shop without a recorded total score was excluded from the analysis rather than guessed at.
Here is what the bands show. Scenes of 8 or fewer shops: 262 shops, median score 14. Scenes of 9 to 15 shops: 389 shops, median score 16. Scenes of 16 to 25 shops: 367 shops, median score 18. Scenes of 26 or more shops: 47 shops, median score 17.
Read across those four numbers and the pattern is modest at best. There is a small upward drift from the smallest scene band to the two middle bands, then the largest band actually dips slightly below the 16 to 25 band rather than continuing to climb. None of the differences between bands are large relative to the overall median score of 16.0 across the full census.
The 26 or more band deserves a specific caution. At 47 shops, it is the thinnest of the four groups by a wide margin, built from a small number of very large local coffee scenes. A single unusually strong or weak scene in that band can move its median more than a comparable shift would move the medians of the larger bands. Treat that top band's number as suggestive at most, not as a settled finding.
It is also worth looking at the shape of the middle of this distribution rather than only the endpoints. The two middle bands, 9 to 15 and 16 to 25, cover the bulk of the shops in this analysis, 756 of the roughly 1,065 shops with a recorded score, and their medians of 16 and 18 sit close enough together that most cafes, regardless of which of these two common scene sizes they fall into, should expect a similar starting point rather than a meaningfully different one.
Checking Whether Region Explains More Than Size
Because scene size alone showed only a small spread, the next honest step was checking whether region, rather than scene size, might be doing more of the work in explaining score differences. The same four bands were rerun separately within each of the six regions in the census.
The region by band comparison shows the same basic pattern as the national version: modest differences between bands within most regions, with no single region showing a dramatically different relationship between scene size and score than the overall pattern. Some regions show slightly higher medians across all their bands than others, which is itself a separate, real pattern worth its own future study, but it does not change the core finding here, that scene size within a region does not move the score by much either.
This regional comparison matters because it rules out a specific alternative explanation, that scene size looked like it mattered nationally only because certain regions happen to have both larger scenes and higher scores for reasons unrelated to size itself. Since the pattern holds, weakly, inside individual regions too, the honest conclusion is that scene size itself, at least as measured here, is simply not a strong driver of an individual shop's score.
What This Means If You Own a Small Town Cafe
If you run a cafe in a town with a small local coffee scene, the temptation to treat your score as a product of your town's size is understandable but not supported by this data. The smallest scene band's median, 14, sits only two points below the two middle bands' medians of 16 and 18, and two points on a forty point scale is not the kind of gap that explains why any individual shop in a small town scores low.
What moves an individual shop's score, based on what the eight dimensions measure, has far more to do with things fully within an owner's control: whether a website exists and is complete, whether structured data is present, whether a blog has any posts at all, whether the business profile is filled out. None of those are harder to do in a town with a small coffee scene than in a town with a large one. If anything, a small scene means less competition for the same customer's attention once you do the work.
A small town cafe is not starting from a lower ceiling because of its town's size. It is starting from the same set of fixable fundamentals as every other shop in the census, and this data gives no honest reason to treat a small scene as an excuse for a low score.
What This Means If You Own a Big City Cafe
The instinct that a big, crowded market explains invisibility runs into the same problem from the other direction. The largest scene band posted a slightly lower median than the second largest band, not a higher one, which is the opposite of what a straightforward crowding explanation would predict if bigger scenes simply drowned out individual shops more thoroughly as they grew.
That single data point should not be read as proof that big markets are easier, either. The top band is thin, at 47 shops, and thin groups produce numbers that move around more than a genuine, stable pattern would. What can be said honestly is that this data gives no clear evidence that operating in a large coffee scene depresses an individual shop's score in any measurable way.
The more useful frame for a big city owner is the same one available to a small town owner: the eight dimensions being measured are about your own site and profile, not about how many other shops share your zip code. A crowded market may well make it harder to stand out in a customer's memory or in foot traffic, which this census was not built to measure, but it is not, based on this data, making it structurally harder to reach a decent visibility score.
A Fair Benchmark for Your Own Market
Given how modest the spread across bands is, the fairest benchmark for your own cafe is not a number pulled from a city size group at all. It is the overall census median of 16.0 across more than 1,000 shops in six regions, snapshot dated September 15, 2026, alongside a look at whichever score band description, invisible, brochure, trying, or the top tier, matches your own current total most closely.
Comparing yourself against a size specific number implies a precision this data does not support. Comparing yourself against the overall median, and against the specific, fixable dimensions that make up your own score, gives you an honest picture of where you stand and what to work on, without leaning on an excuse this study does not back up.
It is also worth reading a score band description in full rather than just the label, since the difference between bands often comes down to a handful of specific, nameable gaps, a missing schema field, an empty blog, an incomplete profile, rather than anything about the market the shop happens to sit in. Working through those specific gaps one at a time will move your own score more reliably than anything tied to where your cafe happens to be located.
The Honest Limits of This Study
This study used scene size approximate as its only size measure because that is what exists in the census files, and it names that limit rather than treating scene size as a silent stand in for population. A city's population and its number of specialty coffee shops are related but genuinely different things, and a future version of this study that fetches population data could show a different picture than this one does.
This study also does not claim causation in either direction. It reports medians across bands of shops grouped by their city's scene size, on the census snapshot dated September 15, 2026, and nothing more. It does not name any individual city as an example of a good or bad pattern, and it does not extend this finding as a rule about all cities everywhere, only as a pattern observed in this specific dataset on this specific date.
The most useful takeaway from a study with limits this real is not a bold claim, it is permission to stop treating city size as the explanation for your own score. Whatever is holding your visibility number down or pushing it up, this data points toward your own site and profile as the more likely place to look, not the size of the town you happen to operate in.
Related Reading
More from Hillcane on this topic, plus the pages on the site that sit next to the work.
Frequently Asked Questions
Do cafes in bigger cities score higher on Hillcane's visibility census?
Not clearly. Scenes of 16 to 25 shops posted the highest median at 18, while the largest band, 26 or more shops, posted a slightly lower median of 17, the opposite of what a simple bigger is better pattern would predict. The spread across all four bands is small relative to the overall median of 16.0, and the top band is thin at only 47 shops.
Does Hillcane's census measure city population?
No. The census has no population column for any of the cities it covers. This study used scene size approximate instead, a rough count of specialty coffee shops in each city's local scene, which reflects how developed the coffee culture is rather than how many people live there. Adding population would mean fetching US Census Bureau estimates for every city and matching each one carefully, which was not done for the September 15, 2026 snapshot.
Is my low score just because I am in a small town?
This data does not support that explanation. The smallest scene band, 8 or fewer shops, had a median of 14, only about two points below the two middle bands on a forty point scale. What moves an individual score has far more to do with fixable fundamentals, a complete website, structured data, an active blog, than with the size of the local coffee scene.
How many shops and cities were used in this study?
The study used shops with a recorded total score from Hillcane's census of more than 1,000 shops across six regions, snapshot dated September 15, 2026, grouped by their city's scene size into four bands: 262 shops in scenes of 8 or fewer, 389 in scenes of 9 to 15, 367 in scenes of 16 to 25, and 47 in scenes of 26 or more.
Why is the top band, 26 or more shops, treated cautiously in this post?
That band contains only 47 shops, the smallest of the four groups by a wide margin, built from a small number of unusually large local coffee scenes. A group that thin can have its median shifted noticeably by just a few unusually strong or weak shops, so its number is treated as suggestive rather than a settled finding.
Did region explain the score differences better than scene size?
The same four bands were rerun separately within each of the six regions, and the pattern held, weakly, inside individual regions too, rather than disappearing once region was controlled for. That rules out region as a hidden explanation for the scene size pattern, and it supports the conclusion that scene size itself is simply not a strong driver of score.
What should I compare my own score against instead of a city size group?
The overall census median of 16.0 across more than 1,000 shops in six regions, snapshot dated September 15, 2026, alongside the specific score band, invisible, brochure, trying, or the top tier, that best matches your current total. Given how small the spread across size bands is, that overall comparison is a fairer benchmark than one narrowed by city size.
Does a crowded big city market make it harder to score well?
This data gives no clear evidence that it does. The largest scene band did not post a lower score than the smaller bands in a way that would support a crowding explanation. A crowded market may make it harder to stand out in foot traffic or customer memory, which this census does not measure, but it does not appear to structurally depress an individual shop's visibility score.
Will Hillcane ever add real population data to this study?
City level population data remains a genuine gap and a possible direction for a future version of the study. Adding it would require a separate, dated fetch of US Census Bureau population estimates matched to each city in the dataset, which was not part of this particular study and has not been done as of this post's publication.
Is this pattern true for all cities everywhere, not just the ones in Hillcane's census?
No. This post reports a pattern seen only in Hillcane's own census of more than 1,000 shops across six regions, on the September 15, 2026 snapshot, where band medians ran from 14 in scenes of 8 or fewer shops to 18 in scenes of 16 to 25. It does not extend that finding as a rule for cities outside this dataset, claims no causation, and names no individual city as an example.
Work with Hillcane
Your town's size is not your excuse. The data does not back it up either way.
If you want to see where your own cafe stands against the census median, reach out at hillcane.co/contact or call (256) 384-2449.
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