Local Service Score Methodology

This page explains how the Local Service Score and supporting data on our directory suburb pages are produced — the sources we use, how the score is composed, how often we update it, and the guardrails we apply before publishing.

1. Our data sources

Our directory pages combine two kinds of data:

Verified customer reviews — submitted by real customers after using a business. Each review includes star ratings for quality, service, value, and overall experience, plus a written explanation of the job.
Customer-reported pricing — optional price information attached to a review (what the job cost, what service was performed). This is customer-entered, not sourced from the business.
Post-service trust signals — structured follow-up responses asking customers whether they would choose the provider again, whether the provider arrived on time, and whether they were professional.
Service awards — our annual Service Award program recognises providers with consistently high ratings over a calendar year.

For local context on each suburb page, we use the ABS 2021 Census at the Suburb and Locality (SAL) level — population, dwelling type, median age, household size, income, rent, and mortgage data. SAL is the finest-grained geography that the Australian Bureau of Statistics publishes, and it maps directly to named suburbs (unlike the broader SA2 areas often used by other directories).

2. How the Local Service Score is calculated

The Local Service Score is a 0–100 composite that summarises how well a suburb is served for a given category. It is computed from the first-party data described above, benchmarked against state and national averages for the same category.

The score combines these components:

Depth of choice — number of active providers servicing the suburb.
Rating quality — the average overall rating across local providers, weighted against the state/national benchmark for the same category.
Review volume and recency — how many reviews are on file and how recent they are.
Cost competitiveness — customer-reported pricing vs. the state average for the same category.
Trust signals — the share of customers who would choose the provider again, reported on time, and described the provider as professional.
Service awards — the concentration of recent Service Award winners in the suburb.

No component is weighted to favour any specific outcome. Paid business subscriptions do not affect any component of the score — ratings and awards are based entirely on customer-submitted data.

3. How often the data updates

The underlying reviews, trust signals, and pricing appear on provider profile pages as soon as they are submitted and moderated. The aggregate Local Service Score and supporting stats on each suburb page are recomputed monthly from the live review data.

ABS Census data is updated when a new Census is released (every five years). The current figures on our pages are from the 2021 Census.

4. Quality guardrails

We apply several safeguards before surfacing data on a directory page:

Minimum thresholds — the Local Service Score only appears when a suburb has enough reviews to form a meaningful sample. Trust-signal percentages are only shown when the underlying sample meets a minimum response count.
Outlier handling — customer-reported prices go through IQR-based outlier removal to exclude obvious data-entry errors. Category-specific caps apply where the typical industry price is well-known (for example, conveyancer fees are capped at $6,000 to exclude entries where the property purchase price was entered by mistake).
Sample-size caveats — pages built on a smaller sample than our typical suburb page display a visible note alerting readers that ratings may shift as more data arrives.
Cost-variance disclosure — for categories where prices vary widely with project scope (renovations, roofing, painting, etc.), we display a disclaimer recommending customers obtain multiple quotes.
Population check — ABS Census figures are only displayed for suburbs with a resident population of at least 200, to avoid misrepresenting very small localities.

5. What we don't do

We don't scrape the web. Reviews are submitted directly to Word of Mouth by the customers who used the business. We do not import or aggregate reviews from other platforms.
We don't publish unverified business-entered data. Pricing, ratings, and trust signals come exclusively from customers, not from the provider.

Questions?

If you have a question about how a specific number on our pages was calculated, or spot something that looks off, get in touch — we respond to methodology questions individually.