Methodology

How we score, in plain language

Every brand runs through exactly the same process.

01

The score

For each brand we average the ratings of the public reviews we hold from each source, normalised to a 5-point scale. The headline score is the equal-weight average of those per-source averages, so every source counts the same. Review counts are the public reviews we hold and link to; totals reported by the platforms themselves are shown separately for reference. If we don't yet hold rated reviews for a brand, we show the platform-reported score and label it as such.

score = (score₁ + score₂ + … + scoreₙ) ÷ n

If we haven't collected any scores for a brand yet, we show “—” and a “Gathering” label rather than a zero. A missing score is not a bad score.

02

Sources

We track public reviews from these platforms and always link back to the original page:

CapterraG2GoogleYelpFacebookInstagramGoogle PlayApp StoreRedditWeb

Individual reviews keep their original rating, date and a link to the source.

Facebook public page comments that evaluate the software are a tracked source when harvested. Facebook has no star rating, so a review carries a rating of 1 (negative) or 5 (positive), and Facebook is included in each brand's equal-weight average like any other source. We never use private groups.

Duplicated reviews are shown once. Reddit posts that only mention a name in passing are filtered out.

03

Update cadence

An automated harvester collects new public reviews and platform scores on a regular cycle (typically weekly). Each per-source score shows when it was last harvested. New reviews are added; existing ones are never rewritten to change their meaning.

04

Neutrality

  • No brand pays to appear, rank higher or remove reviews.
  • We don't write reviews and we never invent them. Empty states stay empty until real reviews arrive.
  • Curators may hide content only for spam, personal data or abuse, never because it is negative.

05

Corrections

Spotted something wrong or outdated? Check the linked original source first. Corrections are assessed against the published review or platform score; a negative rating alone is never a reason to remove a review.

Frequently asked questions

How is the score calculated?

For each source we average the ratings of the public reviews we hold, normalised to a 5-point scale. The headline score is the equal-weight average of those per-source averages.

Why does every source get equal weight?

So one large platform cannot dominate a brand's score. Each rated source contributes the same share, regardless of how many reviews it has; review counts are shown alongside every score so sample size is visible.

Why isn't Software Advice counted?

Software Advice republishes Capterra reviews. When a brand has Capterra reviews, Software Advice is excluded from the score and totals so the same reviews are not counted twice.

How are Facebook and Instagram comments rated?

These platforms have no star ratings. Public comments that evaluate the software are mapped to 1★ (negative) or 5★ (positive) and count like any other rated source. Private groups and accounts are never used.

Do unrated sources count?

Sources without star ratings (for example Reddit posts) count toward a brand's source count and review total, but not toward its score.

What are platform-reported totals?

Some platforms publish their own overall score and review count. We show these for reference only; the headline score uses the public reviews we hold.

What do the red flags mean?

"Possible insider review" means the reviewer appears connected to the company. "Possible fake review" means the review is part of a cluster of similar 5★ reviews posted in a short window. Flags are not findings of wrongdoing, and flagged reviews stay visible and still count in every score and total.

Are reviewer names shown in the data feeds?

No. The machine-readable feeds never include reviewer names or handles; authors appear as placeholders such as "Capterra user".

How often is the data updated?

Public reviews and platform scores are collected on a regular cycle (typically weekly). The most recent data in the directory is from Oct 8, 2026.

Is there an API or AI-readable feed?

Yes, read-only and free: https://kennel-software-reviews.lovable.app/llms.txt, an MCP server at https://kennel-software-reviews.lovable.app/mcp, and JSON at https://kennel-software-reviews.lovable.app/api/brands.json and https://kennel-software-reviews.lovable.app/api/brands/{slug}.json. Requests are rate limited.

Does any brand pay to rank higher?

No. Every brand goes through the same process and appears with the same treatment.