We Analyzed 100 Victoria Contractors: What Actually Predicts Google Maps Rankings in 2026?
We pulled live Google Maps SERP data for five contractor verticals in Victoria, British Columbia, and analyzed 100 ranked business listings against 20 profile variables. The results contradict most of what contractors are told about local SEO. Star rating — the metric business owners obsess over — showed no statistically significant relationship with ranking position. Proximity to the searcher, by contrast, was the single dominant predictor, explaining more variance than review count, photo count, business hours, and category selection combined.
But the more useful finding is subtler: distance conceals the effect of everything else. Once we statistically controlled for proximity, review volume and photo count nearly doubled in predictive strength. The engagement signals matter — they were just being masked by geography in the raw data.
Methodology
Data source
Google Maps SERP data was collected via the DataForSEO Live Advanced endpoint on 5 August 2026, at search depth 20, desktop device, `en` language, from a fixed Victoria centroid of 48.4284° N, -123.3656° W (near Hillside and Blanshard).
Sample
Five service keywords: `plumber`, `electrician`, `HVAC contractor`, `roofing contractor`, and `general contractor`. Twenty organic Maps listings per keyword produced n = 100 listings representing 99 unique businesses (one business ranked for two keywords). Two paid Maps placements returned in the plumber SERP were excluded so the analysis reflects organic ranking only.
Variables extracted
Rating value, review count, full 1–5 star rating distribution, total photos, primary and additional categories, claimed status, website presence, booking and contact URLs, review justification snippets, structured business hours, and geographic coordinates. Straight-line distance from each business to the search centroid was calculated using the haversine formula.
Statistics
Because ranking position is ordinal and most profile variables are heavily right-skewed (review counts ranged from 1 to 2,163), we used Spearman rank correlation as the primary test, Mann-Whitney U for group comparisons, and rank-residual partial correlations to isolate the effect of individual variables. A standardized OLS model was fitted for relative effect sizes. Confidence intervals were bootstrapped over 5,000 resamples. Significance threshold: p < 0.05.
The dataset in aggregate covered 16,425 reviews and 3,166 profile photos.
Finding 1: Proximity is the dominant ranking factor — and it isn't close
Distance from the search centroid correlated with ranking position at ρ = +0.433 (95% CI: 0.262 to 0.580), p < 0.0001 — positive, meaning greater distance predicted worse rank. No other variable came within twice that effect size.
The distance decay is steep and immediate:
| Distance from centroid | Listings | Mean rank | % in top 10 |
|---|---|---|---|
| 0–1 km | 26 | 7.15 | 80.8% |
| 1–2 km | 33 | 9.76 | 48.5% |
| 2–3 km | 22 | 12.77 | 40.9% |
| 3–5 km | 11 | 14.18 | 18.2% |
| 5 km+ | 8 | 13.12 | 25.0% |
A contractor located within one kilometre of the searcher was 4.4 times more likely to appear in the top ten than one located three to five kilometres away. Thirteen of the fifteen top-three positions across all five keywords went to businesses within 2 km.
Critically, this held within verticals, not just across the pooled sample. Distance was independently significant for general contractors (ρ = +0.508), plumbers (ρ = +0.511), HVAC contractors (ρ = +0.465), and roofers (ρ = +0.458). Only electricians broke the pattern (ρ = +0.114), driven by a single service-area business ranking second from 27 km out. Excluding all seven service-area businesses without street addresses, the correlation was unchanged (ρ = +0.437).
The practical illustration is stark. Rather Be Plumbing holds 1,209 reviews at a 4.8 rating — one of the strongest review profiles in the entire dataset — and ranks 13th for "plumber" from 5.5 km out. Meanwhile MacMinn Contracting ranks first for "general contractor" with eleven reviews, sitting essentially on the centroid.
This is why a single rank-tracking screenshot is nearly meaningless for local businesses, and why proper [local SEO for Victoria and Vancouver Island businesses] has to be measured on a geographic grid rather than a single point.
Finding 2: Distance was hiding the effect of everything else
This is the finding most local SEO studies miss. In the raw data, review count looked like a weak predictor (ρ = -0.235). But review counts in our sample were negatively distributed against proximity — the businesses furthest from the centroid tended to be larger, more established firms with bigger review profiles. That correlation suppressed the apparent effect.
When we controlled for distance using rank-residual partial correlation, the signals sharpened dramatically:
| Variable | Raw ρ vs rank | Partial ρ (distance controlled) |
|---|---|---|
| Review count (log) | −0.235 (p = 0.021) | −0.406 (p < 0.0001) |
| Total photos (log) | −0.250 (p = 0.014) | −0.382 (p = 0.0001) |
| Weekly open hours | −0.266 (p = 0.008) | −0.261 (p = 0.010) |
| Days open per week | −0.255 (p = 0.012) | −0.244 (p = 0.016) |
| Star rating | −0.134 (p = 0.190) | −0.216 (p = 0.034) |
| Review justifications | −0.141 (p = 0.169) | −0.209 (p = 0.040) |
The interpretation is straightforward: among businesses competing at comparable distance, profile strength decides the outcome. A contractor cannot change their address, but within their realistic radius, review volume and profile completeness are the levers that move rank.
Finding 3: Star rating, on its own, predicts nothing
Star rating showed no significant raw correlation with ranking position (ρ = -0.129, p = 0.205).
The reason becomes obvious when the sample is bucketed:
| Rating | Listings | Mean rank | Median reviews |
|---|---|---|---|
| Below 4.5 | 16 | 12.50 | 18.5 |
| 4.5 – 4.7 | 26 | 10.23 | 53.5 |
| 4.8 – 4.9 | 27 | 9.48 | 74.0 |
| Perfect 5.0 | 28 | 10.00 | 16.0 |
Twenty-eight businesses held a flawless 5.0 rating and ranked worse on average than the 4.8–4.9 group. Their median review count was sixteen. A perfect rating in this dataset is overwhelmingly a signal of low review volume, not of quality — it is what a profile looks like before it has been tested at scale.
Rating only became statistically significant in the multivariate model (β = -1.32, p = 0.015) — that is, rating predicts rank only once review volume and distance are accounted for. The composite of rating × log review volume was among the strongest raw predictors in the entire study (ρ = -0.268, p = 0.008).
The takeaway for contractors: chasing a 5.0 by soliciting only from guaranteed-happy customers is actively counterproductive. Volume with a 4.8 beats scarcity with a 5.0. This is the core argument for building a systematic review generation process rather than an ad-hoc one.
Finding 4: Photos separate the top ten from the bottom ten
Total photo count was the strongest raw correlate of ranking position in the study (ρ = -0.270, p = 0.007).
| Rank band | Median photos | % with fewer than 10 photos |
|---|---|---|
| 1–3 | 32 | 6.7% |
| 4–10 | 20 | 22.9% |
| 11–20 | 14 | 38.0% |
Photo count and review count were themselves correlated (ρ = 0.503, p < 0.001), and neither survived controlling for the other — statistically, they appear to measure a shared underlying construct we'd describe as profile maturity: how actively the business and its customers maintain the listing. Neither is independently causal on this evidence. But the practical implication is unchanged, because both are within the operator's control and both are cheap. Photo uploads in particular are the lowest-effort item on any Google Business Profile optimization checklist and 38% of bottom-half listings had fewer than ten.
Finding 5: Hours and claimed status carry real weight
Business hours. Total weekly open hours correlated with rank at ρ = -0.251 (p = 0.012), and days open per week at ρ = -0.231 (p = 0.021). Listing Saturday hours was associated with a mean rank of 8.93 versus 11.78 for weekday-only businesses (p = 0.015). Sunday hours trended the same direction (9.06 vs 11.24) without reaching significance (p = 0.075).
Whether extended hours cause better rankings or simply proxy for larger operations, the asymmetry is worth noting: 55% of the sample listed no Saturday hours at all, and the top-ten cohort listed a median of 60 weekly hours against 45 for the bottom ten.
Claimed status. Twelve of 100 listings were unclaimed. They averaged rank 13.67 versus 10.07 for claimed listings (p = 0.044), with a median of one photo versus twenty-one. Only a third reached the top ten. Unclaimed listings are still ranking in Victoria — which means competitors' unclaimed profiles are a real opportunity, and your own is a real liability.
What did not predict rankings
Null results are the most commercially useful part of this study, because each one represents effort contractors are currently wasting:
Number of categories (ρ = +0.032, p = 0.751). Listings averaged 2.58 categories, ranging from one to ten. Category stuffing showed literally zero relationship with rank.
Keyword in business name (84% of sample, p = 0.349). Despite being the classic local SEO exploit, having "Plumbing" or "Electric" in the business name did not predict position in this dataset.
City name in business name (17% of sample, p = 0.326). No effect.
Website presence (95% of sample, p = 0.457). Not predictive — but only because it's now table stakes. There is no variance left to measure.
Online booking link (12% of sample, p = 0.836) and contact URL (19%, p = 0.083). Neither reached significance, though contact URLs trended positive.
Model performance and limitations
A standardized OLS model using distance, review count, photo count, weekly hours, and rating explained 33.5% of variance in ranking position (adjusted R² = 0.298, n = 97). Distance was by far the strongest term (β = +2.62, t = 5.08, p < 0.0001), followed by rating (p = 0.015) and review count (p = 0.050).
Two-thirds of ranking variance therefore remains unexplained — as expected, since Google's local algorithm draws on prominence signals invisible in SERP data: backlinks, citation consistency, on-site relevance, behavioural engagement, and review recency.
Limitations to state plainly. This is a single-point-in-time snapshot from one centroid in one city, n = 100. Correlation is not causation, and several variables here plausibly proxy for business size rather than acting as ranking factors. Distance was measured to a fixed centroid rather than across a grid. Review recency and velocity were unavailable in the dataset and are likely material. Results should be treated as directional evidence for the Victoria contractor market, not as universal ranking factors — which is exactly why a market-specific SEO audit outperforms generic best-practice checklists.
What contractors should actually do with this
1. Stop treating rank as a single number. Your position swings by ten places across a 5 km radius. Measure on a grid.
2. Prioritize review volume over review perfection. The 4.8-with-74-reviews profile outranked the 5.0-with-16-reviews profile. Ask everyone.
3. Upload photos relentlessly. It's the cheapest lever with the strongest raw correlation, and 38% of bottom-half listings are neglecting it.
4. List complete hours, including Saturday if you genuinely operate then.
5. Claim the profile. Twelve percent of ranking Victoria contractors still haven't.
6. Stop stuffing categories and renaming your business. Zero measured effect.
7. If you're outside the core, compensate with prominence. Distance is fixed; profile strength is not — and the partial correlations show it's precisely the businesses fighting a distance handicap for whom review and photo signals matter most.
Van Isle SEO is a Canadian SEO consultancy specializing in local search, GEO/AEO, and technical SEO for service businesses.
Data collected 5 August 2026 via DataForSEO. Dataset and analysis scripts available on request.
Explore the Data Yourself
Use the interactive Google Maps Rank Explorer to compare ranking position against reviews, distance, photos, ratings, and other profile variables across all 100 contractor listings.
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