How often is the data updated?

The whole directory is refreshed on a monthly cycle, and each listing shows a last verified date so you can see exactly when it was last checked rather than assuming it's current.

Can a business pay to improve its score?

No. Scores come only from the rubric applied to public review and listing data. Paid placement, where it exists, is always labelled and never changes a business's score.

Why does sentiment matter more than the star rating?

Star ratings can look identical for two very different businesses. Reading what recent reviews actually describe, praise and recurring complaints alike, is the only way to spot patterns a plain average hides, which is why sentiment carries the heaviest weight.

What does a low-confidence label mean?

It means the business has too few recent reviews to support a reliable score. We label these openly rather than present a thin data set with the same confidence as a well-reviewed one.

How we score cleaning services in Petaling Jaya

PJ Cleaning Service Guide currently scores 231 cleaning service businesses across Petaling Jaya. Every score is generated from a fixed rubric applied to public data, mainly Google reviews and business listing details. This page explains what goes into that number, why it's weighted the way it is, and where it falls short so you can read the rankings with the right amount of trust.

The five signals, heaviest first

Each business gets a composite score from 0 to 100, built from five measured signals. Every business is scored against the same weights, so a listing at the top earned it across the board, not just on one strong point.

Sentiment: 28%

This is the biggest single factor, and deliberately so. A star rating alone can hide a lot. Two cleaning companies can both sit at 4.5 stars, but one has a scattering of unrelated gripes while the other has a pattern of the same complaint showing up review after review, missed rooms, cancelled bookings, staff turnover, damaged items. You only catch that pattern by reading what recent customers actually wrote, not just averaging their star clicks. We synthesise the themes running through recent reviews, the praise and the complaints alike, into this part of the score. We don't republish review text wholesale; we summarise the pattern and link back to Google so you can read the originals yourself.

Rating: 26%

The Google aggregate star rating still matters a lot, it's the simplest signal of how satisfied past customers were overall, and it carries the second-heaviest weight for that reason.

Volume: 20%

A 5.0 rating from four reviews and a 4.7 from four hundred are not the same claim of reliability. We log-scale review volume so that a handful of reviews doesn't get treated as equal proof to hundreds, while still giving smaller but well-reviewed businesses fair credit.

Recency: 12%

A cleaning business that was great in 2019 may have changed hands, staff, or standards since. We weight how recently people have actually left reviews, so the score leans toward what customers are experiencing now rather than years ago.

Completeness: 14%

Whether a listing has a working phone number, website, posted hours, and a full address feeds into the score too. It's a practical signal: a business that keeps its basic contact details current is easier and safer to actually book, and it suggests the listing itself is being maintained.

Where the score has limits

Businesses with only a few recent reviews don't have enough signal behind them for a confident score, and we label these as low-confidence rather than let a thin data set masquerade as a solid one. We're also working from what's public and structured: we don't call businesses, mystery-shop them, or independently verify service quality first-hand. The score reflects a synthesis of public review data and listing details at the time it was refreshed, nothing more, and nothing less.

Scores are earned, not sold

Scores come from the rubric above and the underlying data only. They are never edited by hand to move a business up or down. Where paid placement exists anywhere on this site, it is labelled clearly as such and it never changes the underlying score. If any list on this site reflects editor-reviewed picks or ordering rather than the pure rubric, that is disclosed directly on that page. Our best residential cleaning list and other roundups follow this same rule.

Who's behind this

PJ Cleaning Service Guide is published by Meridian Guides, which has run independent city directories for everyday services across Malaysia since 2025. Every Meridian Guides site uses a published scoring method built on public customer reviews, rankings aren't for sale, sponsored placements are always labelled, and no payment changes a score. Listings across the network are re-checked on a monthly cycle. You can read more at meridianguides.my or reach the publisher directly at hello@meridianguides.my.

Editorial oversight for this directory sits with Janice, Editor, who maintains the rubric and the listings. Data across PJ Cleaning Service Guide is refreshed monthly, and each listing carries a "last verified" stamp so you can see when it was last checked rather than taking freshness on faith. You can browse the full directory from the home page.

FAQ

How often is the data updated?: The whole directory is refreshed on a monthly cycle, and each listing shows a last verified date so you can see exactly when it was last checked rather than assuming it's current.
Can a business pay to improve its score?: No. Scores come only from the rubric applied to public review and listing data. Paid placement, where it exists, is always labelled and never changes a business's score.
Why does sentiment matter more than the star rating?: Star ratings can look identical for two very different businesses. Reading what recent reviews actually describe, praise and recurring complaints alike, is the only way to spot patterns a plain average hides, which is why sentiment carries the heaviest weight.
What does a low-confidence label mean?: It means the business has too few recent reviews to support a reliable score. We label these openly rather than present a thin data set with the same confidence as a well-reviewed one.