Original STRINGERSEO research analysing 880 anonymised retail search queries across eight European markets.
A retail keyword can be highly commercial without being particularly local.
Someone searching for a pair of trainers may be happy to buy online. Someone searching for a sports shop is much more likely to need a physical retailer.
That distinction sounds obvious. What is less obvious is how strongly it appears in actual search results.
I analysed an anonymised set of retail keyword research carried out across eight European market and language combinations during 2026. The usable quantitative dataset contained 880 non-branded retail query records. Within that, 155 queries were tested across three or four different locations, producing 539 location-specific Local Pack checks.
The result was significant enough to change how I would approach keyword research for multi-location retailers.
In the location-specific sample, shop and store-intent searches triggered a Local Pack in 79.7% of tests. Generic product and category searches did so in just 26.5%.
In other words, simply having transactional intent does not mean Google interprets a query as having local intent.
Google says local results are mainly determined by relevance, distance and prominence. Distance in particular depends on where the searcher is, or what Google understands about their location. This research does not attempt to reverse-engineer those ranking factors. Instead, it looks at something that happens one stage earlier: which types of retail queries actually cause Google to surface local results at all.
The headline finding: store intent was much more local than product intent
The cleanest part of the dataset came from two markets where the same unmodified queries were tested from multiple locations.
One market used three test areas and the other used four. That produced 539 individual query-location observations.
| Query type | Queries analysed | Location-specific checks | Local Pack observed | Local Pack rate |
|---|---|---|---|---|
| Generic product/category | 119 | 411 | 109 | 26.5% |
| Shop/store intent | 36 | 128 | 102 | 79.7% |
A shop or store-intent query was therefore about three times as likely to surface a Local Pack as a generic product or category query.
The difference becomes even clearer when looking at consistency.
Of the 119 generic product queries, 69.7% failed to trigger a Local Pack in every location tested.
Not one of the 36 shop/store queries behaved that way. Every shop/store query generated a Local Pack in at least one of its test locations.
That does not mean shop queries always trigger local results. Only 17 of the 36 shop/store terms, 47.2%, produced a Local Pack in every location tested.
For generic products, only 19 of 119 queries, 16.0%, were consistently local across every test location.
That distinction matters for local SEO measurement.
A keyword should not be treated as a local-search keyword merely because somebody could buy that product in a shop.
The pattern held across a wider six-market sample
I then compared the result with six additional European retail datasets where usable market-level Local Pack values were available.
After excluding explicit city/proximity queries, irrelevant navigational searches and records without usable Local Pack evidence, the secondary sample contained 725 non-branded retail query records.
The pattern was even stronger.
94 of 115 shop/store queries triggered a Local Pack, that’s 81.7%.
By comparison, only 79 of 610 generic product/category queries did, that’s 13.0%.
The absolute percentages should not be directly merged with the location-specific experiment because the underlying validation methods were not identical. The useful finding is that both datasets independently pointed in the same direction.
| Market | Generic product/category | Shop/store intent |
|---|---|---|
| Lithuania | 6.1% · 3/49 | No comparable shop sample |
| Portugal | 16.7% · 2/12 | 87.5% · 7/8 |
| Austria | 15.0% · 32/214 | 67.4% · 29/43 |
| Bulgaria | 3.1% · 2/65 | 77.8% · 14/18 |
| Switzerland, German-language research | 21.3% · 30/141 | 90.9% · 20/22 |
| Croatia | 7.8% · 10/129 | 100% · 24/24 |
I would treat those percentages as descriptive results from this dataset, not universal benchmarks for each country.
SERPs change. Location changes. Retail categories change. The methodology also matters.
But it is difficult to ignore how consistently the relationship moves in the same direction.
Transactional and local are not the same thing
This was probably the most useful finding from the project.
SEO keyword research commonly groups phrases into informational, commercial and transactional intent. That classification is useful, but it does not tell you whether Google thinks the transaction should happen online or at a physical location.
The data contained numerous highly commercial product searches that did not produce local results.
The reverse was true when the wording explicitly described a physical retailer.
In Bulgaria, for example, the generic equivalents of “shoes” and “trainers” did not trigger a Local Pack in the reviewed market-level data. The equivalents of “shoe shop” and “clothing shop” did.
Portugal showed a similar distinction. The generic equivalent of “sneakers” did not generate a Local Pack in the research, while “outlet” and store-focused terms did.
The implication is useful beyond retail.
Google does not appear to be asking only whether someone wants to buy something.
For local SEO, the more useful question is: does this query imply that the person wants to find somewhere?
That is a different intent model.
Product modifiers can change whether Google sees the query as local
The multi-location data also showed why building keyword lists by formula can be misleading.
In the Hungarian-language research, the local equivalent of a broad “sports shoes” query produced a Local Pack in all three locations tested.
Adding a male modifier changed that completely: the equivalent of “men’s sports shoes” generated no Local Pack in any of the three test locations.
The equivalent of “running shoes” sat between the two, producing local results in two of the three locations.
Ireland showed the same instability from another angle.
“Sports shop” produced a Local Pack in all four locations tested.
“Sports shoes” appeared in three of four.
But “trainers” produced none in four.
These terms are semantically close enough that a conventional keyword-mapping exercise might put them in the same broad theme. Their SERPs were not behaving in the same way.
That is why a translated keyword list, or even a well-built search-volume list, is not enough for multi-location SEO.
Local intent changes by location
The project methodology deliberately tested the same unmodified keyword from different locations rather than appending town names to it.
The underlying research process required one primary city and at least two secondary cities for shortlisted generic and store-intent queries. The unmodified keyword was searched from each location, with the presence or absence of a Local Pack recorded separately.
That distinction matters.
Testing “running shoes” from Dublin is not the same experiment as searching “running shoes Dublin”.
The second query tells Google that Dublin matters. The first lets us observe whether Google infers local intent from the query and the searcher’s location.
The research framework also deliberately separated genuine shop/store language such as shop, store, outlet or flagship from city names, addresses, “near me”, “nearest” and similar proximity modifiers.
Google’s own explanation helps explain why the same phrase can produce different results in different places: distance is one of the principal inputs into local results.
So asking whether a keyword “triggers a Local Pack” without also asking where it was tested can produce a false sense of certainty.
Search volume was a poor substitute for local intent
Another practical lesson was that the largest keyword was not necessarily the most useful local-search keyword.
Some comparatively broad, high-demand product terms produced no local results, while more specific shop/store terms with much less demand did.
That does not make search volume unimportant.
It means volume answers a different question.
Search volume tells us how much demand appears to exist. Local Pack testing tells us whether Google currently interprets that demand as geographically relevant.
The research methodology therefore treated search volume as supporting evidence rather than the sole selection criterion, with store relevance and category coverage also required.
That is a useful safeguard against a surprisingly common workflow: finding the biggest retail keywords, putting them into a local rank tracker and discovering later that many of them do not consistently produce local results.
One Local Pack flag should not be treated as ground truth
There was another data-quality issue worth publishing because it affected the methodology.
In one additional market that I excluded from the aggregate comparison, a non-branded sporting-goods store query had a third-party Local Pack field marked No, while the project’s manual validation recorded Yes.
Rather than arbitrarily choosing one, I excluded the conflicting record from the comparative dataset.
Third-party SERP-feature databases are useful for research at scale, but they should not be treated as perfect substitutes for location-specific validation.
This was also why incomplete markets were excluded rather than “completed” through inference. Reviewed datasets containing broken, missing or insufficient Local Pack evidence were not used to calculate prevalence.
Missing evidence is not the same as “No”.
And an unvalidated “Yes” should not quietly become a research finding.
Local-language research matters more than literal translation
The project also reinforced why international keyword research should not simply translate a master English list.
The research process required each market to use its own language and observed search behaviour rather than relying on direct translation. Keyword, category, intent, search volume and local-result status were recorded separately.
The SERP data explains why.
Two phrases can have nearly identical dictionary meanings and still produce different search-result layouts.
Likewise, the same broad retail concept can behave differently between countries.
Generic footwear searches showed very low Local Pack incidence in Bulgaria and Lithuania, for example, while the German-language Swiss data showed a noticeably higher rate.
That does not mean users in one market are inherently “more local” than another. There are too many variables for that conclusion.
It does mean intent should be validated in the market rather than imported from another language.
Translate the meaning, then research the behaviour.
What this changes in a retail Local SEO workflow
For a multi-location retailer, I would use Local Pack research as a qualification layer rather than beginning with rankings.
- Build the keyword universe in the actual market language. Research product, category and store-intent wording independently rather than mechanically translating an English master list.
- Separate product intent from physical-retailer intent. “Running shoes” and “running shop” may belong to the same commercial theme but they should not automatically be treated as the same local-search opportunity.
- Test unmodified queries from multiple target locations. Avoid proving local intent by adding the location to the keyword you are trying to validate.
- Use search volume after relevance and local-result behaviour are understood. High demand is useful, but it should not override what the SERP is actually showing.
- Record inconsistent results rather than forcing a Yes/No answer. A term that triggers a Local Pack in two cities and not a third is a more useful finding than converting it into a misleading universal flag.
That approach also fits Google’s broader guidance for Business Profiles: represent the real business accurately and provide relevant, useful information rather than adding wording purely for visibility.
The original research framework followed the same principle. Approved keywords were intended to provide search themes for Business Profile content, not instructions to paste exact-match phrases into descriptions.
What the study does not prove
This is an observational SEO dataset, not a laboratory experiment.
It measures whether a Local Pack or prominent local-results module appeared. It does not measure whether a particular retailer ranked within that pack.
The research also does not establish that adding “shop” or “store” to a page will cause Google to show that business locally.
The individual country samples are different sizes, and several broader project markets were excluded from the quantitative analysis because their Local Pack evidence was incomplete, broken or unsuitable for a prevalence calculation. Nothing was imputed to make the study larger.
Branded and semi-branded searches were also kept outside the main comparison.
That makes this study primarily a comparison of non-branded product intent versus non-branded physical-retailer intent.
The conclusion
For retail Local SEO, commercial intent is not enough.
Across 539 location-specific searches, shop/store queries surfaced a Local Pack 79.7% of the time, compared with 26.5% for generic product queries.
A separate six-market sample produced almost the same strategic conclusion: 81.7% of shop/store queries carried a Local Pack signal compared with 13.0% of generic product/category terms.
The exact percentages will change by market, category, location and date.
The useful finding is the relationship between them.
If the objective is to measure whether physical stores are visible in local search, start with the queries that actually express a need for a physical retailer. Then test the product terms rather than assuming they are local because the product happens to be sold in shops.
Search volume tells you what people search for.
The SERP tells you what Google thinks they are trying to find.
For Local SEO, you need both.
About this research
This analysis was produced by Jonathan Stringer FCIM at STRINGERSEO from anonymised multi-market retail keyword research conducted during 2026.
The underlying retailer has not been named, and brand-specific keywords and raw commercial search-volume data have been withheld.
Percentages were calculated only from records containing usable Local Pack evidence. Missing or conflicting data was not inferred.
The study measures Local Pack presence, not local ranking position. Findings should therefore be interpreted as evidence from this dataset rather than universal benchmarks for individual markets.
