Chrome extension metrics: the 10 numbers to track every week and where each one comes from

Chrome extension metrics are easy to collect and surprisingly hard to read. The Chrome Web Store dashboard gives you installs, uninstalls, impressions, page views, weekly users and ratings, each with its own chart and CSV export. What it does not give you is an answer to the question you actually have on Monday morning: is the extension healthier than it was last week, and if not, which part broke?

This guide turns those charts into a short weekly scorecard. It lists the ten Chrome extension metrics worth tracking, the formula behind each one, where the number lives in Chrome, Edge and Firefox, and a small script that flags the ones that moved. The store behavior described here was checked against Google's, Microsoft's and Mozilla's documentation on October 16, 2026.

Key takeaways

• Track ten numbers in five groups: discovery, audience, churn, quality and releases. Anything more becomes a dashboard nobody reads.
• Weekly users is an installed footprint, not activity. Google says the Users stats "only captures installations; it doesn't monitor whether users are active or not."
• Use ratios for diagnosis and counts for context: listing conversion, growth rate and uninstall rate tell you what changed, the raw counts tell you whether it matters.
• Compare each week with your own recent weeks, not with someone else's benchmark. There is no published "normal" uninstall rate or conversion rate for extensions.
• Store dashboards cannot see competitors or usage inside the extension. Add public listing data for the first and a minimal ping for the second.

Why most Chrome extension metrics dashboards mislead#

Three kinds of numbers sit side by side in the Developer Dashboard, and mixing them up causes most bad conclusions.

• Events happen on a day: installs, uninstalls, page views. They add up over a week.
• Stocks describe a state: weekly users, the current rating, the share of users on the latest version. They do not add up; you compare them across weeks.
• Ratios combine the two: conversion rate, growth rate, uninstall rate. These are the numbers that tell you something changed, because they hold steady when traffic simply goes up or down.

The classic mistake is to treat weekly users as last week's users plus installs minus uninstalls. It never reconciles, for reasons covered in our guide to measuring extension retention and uninstall rate: synced browsers, dormant laptops and profile deletions all move the stock without a matching event. Keep events, stocks and ratios in separate columns and the scorecard below stays honest.

The second mistake is reading the public listing as if it were your dashboard. The user count on the store page is rounded as an extension grows and can lag, which is fine for watching a competitor and wrong for measuring yourself. Stack Overflow has carried the question "Weekly Users vs Installations" for over a decade, and the answer has not changed: they measure different things.

The 10 Chrome extension metrics worth a weekly look#

Discovery: impressions, page views, installs and conversion#

Google's store listing metrics documentation defines impressions as the number of users "that discover your extension while searching or browsing the Chrome Web Store," and adds that an impression occurs "when your extension is featured in any collection or direct visits to your store item." Page views count visits to the listing itself and, according to the 2022 analytics revamp announcement, break down by utm_source, utm_medium and utm_campaign when your links carry UTM parameters.

Installs are daily events that "include new and returning users." Divide installs by page views for the week and you have the listing conversion rate, the single best measure of whether your name, screenshots, description and rating do their job. Our guide to the Chrome Web Store conversion rate splits that further into a listing visit rate and an overall store conversion, with a worked example.

Audience: weekly users and growth rate#

Weekly users is the base for growth. Calculate week-over-week growth as this week / last week - 1, and once you have a month of data, a compound rate over four weeks, because single weeks are noisy. Never average weekly percentages. The full set of formulas, with an example that shows why averaging lies, is in our post on Chrome extension user growth.

Churn: uninstall rate#

Uninstalls per install is easy to compute and swings with every marketing push. Uninstalls divided by the average weekly users for the same period measures the share of your base you lose, which is the number that compounds and the one to put on the scorecard. If it jumps, open the Weekly Users page as well: Google's revamp post lists an enabled versus disabled breakdown there, and an update that adds a permission warning leaves users disabled rather than uninstalled. Our guide to a sudden weekly users drop walks through that diagnosis.

Quality: rating and new reviews#

The average rating moves slowly, so it is a poor alarm. In August 2026 Google announced that the Chrome Web Store rating will focus on more recent reviews, as explained in our summary of the Chrome Web Store changes in 2026, which makes it faster than it used to be but still a lagging signal. The leading signal is the mix of new reviews: count the reviews posted this week and the share that are one or two stars. Three one-star reviews in a week that usually brings one is worth reading today, whatever the average says. For reading the pattern behind the stars, see reading review and rating trends.

Releases: latest-version share#

The Weekly Users page breaks users down by item version. After a release, the share on the newest version should climb within days as Chrome updates extensions in the background. If it stalls, users are stuck on an older build, often because the new version is disabled behind a permission prompt. Our Chrome extension version history guide shows how to track this alongside the versions themselves.

Where each metric lives in Chrome, Edge and Firefox#

If you publish on more than one store, the same scorecard works, but the inputs differ.

Sources: Microsoft's Edge extension analytics documentation and Mozilla's guide to monitoring extension usage statistics. Because Chrome counts weekly users and Firefox counts average daily users, compare growth rates across stores, never raw counts. The store-by-store detail is in our guides to Edge add-on analytics and Firefox add-on stats.

Build a weekly Chrome extension metrics scorecard#

Every chart in the Chrome Web Store dashboard exports to CSV. Column names in those exports are not documented and can change, so do not parse them blindly: once a week, copy the weekly totals into a small file you control, one row per week, and let a script do the arithmetic.

week,impressions,pageViews,installs,uninstalls,weeklyUsers,newReviews,lowStarReviews,latestVersionUsers
2026-09-14,18200,2410,612,288,40100,9,1,39100
2026-09-21,17900,2380,598,301,40350,7,1,39500
2026-09-28,18650,2520,640,295,40800,11,2,40100
2026-10-05,18400,2490,629,310,41050,8,1,40400
2026-10-12,18100,2470,455,540,40650,14,6,21300

The numbers above are illustrative. The script below computes the ratios and flags any metric that moved more than a set amount against the median of the previous four weeks:

// scorecard.mjs - run with: node scorecard.mjs weeks.csv
import { readFileSync } from 'node:fs';

const [header, ...lines] = readFileSync(process.argv[2], 'utf8').trim().split('\n');
const cols = header.split(',');
const weeks = lines.map((line) => {
  const values = line.split(',');
  return Object.fromEntries(cols.map((c, i) => [c, i === 0 ? values[i] : Number(values[i])]));
});

const ratio = (a, b) => (b > 0 ? a / b : null);
const rows = weeks.map((w, i) => {
  const prev = weeks[i - 1];
  return {
    week: w.week,
    conversion: ratio(w.installs, w.pageViews),
    growth: prev ? ratio(w.weeklyUsers, prev.weeklyUsers) - 1 : null,
    uninstallRate: prev ? ratio(w.uninstalls, (w.weeklyUsers + prev.weeklyUsers) / 2) : null,
    lowStarShare: w.newReviews >= 5 ? ratio(w.lowStarReviews, w.newReviews) : null,
    latestVersionShare: ratio(w.latestVersionUsers, w.weeklyUsers),
  };
});

// Allowed relative change against the 4-week median before a metric is flagged.
const LIMITS = { conversion: 0.2, uninstallRate: 0.3, lowStarShare: 0.5, latestVersionShare: 0.25 };
const median = (xs) => {
  const s = xs.filter((x) => x !== null).sort((a, b) => a - b);
  return s.length ? s[Math.floor(s.length / 2)] : null;
};

const latest = rows.at(-1);
const history = rows.slice(-5, -1);
console.log(`Scorecard for week of ${latest.week}`);
for (const [metric, limit] of Object.entries(LIMITS)) {
  const base = median(history.map((r) => r[metric]));
  const now = latest[metric];
  if (base === null || now === null || base === 0) continue;
  const change = now / base - 1;
  const flag = Math.abs(change) > limit ? 'CHECK' : 'ok';
  console.log(`${metric.padEnd(20)} ${(now * 100).toFixed(2)}%  vs ${(base * 100).toFixed(2)}%  ${flag}`);
}
console.log(`growth               ${(latest.growth * 100).toFixed(2)}% week over week`);

On the sample data it flags conversion (installs fell while page views held), uninstall rate, low-star share and latest-version share in the same week. That combination, with weekly users dipping, is the signature of a release that added a permission warning or broke something. One look at the scorecard sends you to the right place instead of five charts.

Two design choices matter more than the code. The low-star share is skipped below five new reviews, because one bad review in a quiet week is not a trend. And every flag compares the extension with its own recent history, since a fixed target borrowed from another extension says nothing about yours.

When a change deserves attention#

A scorecard that flags everything gets ignored, the same failure we describe in store change alerts your team will actually read. These rules keep the noise down:

1. Act the same day when the latest-version share stalls after a release, the low-star share jumps on five or more reviews, or the listing disappears from the store.
2. Look this week when conversion or uninstall rate moves beyond its limit for two weeks in a row.
3. Note and move on when impressions or installs move alone. Seasonality, a holiday or a Chrome release can explain a single week.

Put your own release dates and Chrome stable release dates in the same sheet. Most sudden changes line up with one or the other, which our post on what to watch after an extension release turns into a day-by-day checklist.

Metrics the store cannot give you#

The store sees installs, not use. Three questions need data from inside the extension:

• Active usage. How many people opened the popup or ran the main feature this week. A single event per active day is enough; our comparison of Chrome extension analytics tools covers GA4, PostHog and lighter options in Manifest V3.
• Cohort retention. What share of people who installed in August still use the extension in October. The minimal ping in the retention guide answers it without a user ID.
• Uninstall reasons. runtime.setUninstallURL opens a page when someone removes the extension, the only place you can ask why.

Collect the least you need and declare it. The store's Limited Use policy and Firefox's consent rules apply to analytics data as much as to anything else.

Add competitor context#

Your metrics only become meaningful next to the market. If your growth slows while the two closest competitors keep adding users, the cause is probably your listing or product; if everyone slows, it is the season or the store. Public listing data gives you users, rating, rating count, version and last update for any extension. You can collect it yourself, as described in our guide to the Chrome Web Store stats API, or let a tool keep the history.

Extenify is a Chrome extension analytics dashboard built on that public store listing data. It records users, ratings, reviews, versions and category rank daily for your extensions and competitors across the Chrome Web Store, Edge Add-ons, Firefox Add-ons, Google Play and the App Store, and sends the changes that matter in one daily digest. If your team lives in Teams, our guide to the Microsoft Teams webhook in Node.js shows how to post your own scorecard flags there, and if you ship an Android app too, the Google Play Developer Reporting API adds crash and ANR rates to the same routine. Plans start with a 7-day free trial; see Extenify pricing.

A 15-minute weekly review#

1. Export the week from Installs and Uninstalls, Impressions and Weekly Users, and copy the totals into your weekly file.
2. Run the scorecard and read the flags first, the counts second.
3. For any flag, break the metric down by version, country and operating system in the dashboard.
4. Read every one and two star review from the week.
5. Check the two or three competitors you care about for users, rating and version changes.
6. Write one sentence on what changed and why. Next week's review starts by checking whether you were right.

Frequently asked questions#

What metrics does the Chrome Web Store provide?#

The Developer Dashboard reports daily installs and uninstalls, impressions, listing page views with a UTM breakdown, weekly users by country, language, operating system and item version, and ratings. Each chart exports to CSV, and you can opt in to Google Analytics 4 for aggregated store-item data.

Does weekly users mean active users?#

No. Google's documentation says the Users stats "only captures installations; it doesn't monitor whether users are active or not." Weekly users counts browsers with the extension installed over a 7-day window. To measure activity, send your own minimal event from the extension.

What is a good uninstall rate for a Chrome extension?#

There is no published benchmark, and developer anecdotes vary too widely to be useful. Calculate uninstalls per average weekly users, keep the definition fixed, and compare each month with your own previous months. A sustained rise matters more than the absolute level.

How do I track Chrome extension installs by source?#

Add UTM parameters to every link that points to your listing. The Impressions page then breaks page views down by utm_source, utm_medium and utm_campaign, and installs divided by page views per source gives you a conversion rate for each channel. The conversion rate guide shows the setup.

How often should I check Chrome extension metrics?#

Weekly for the scorecard, daily only for the signals that need same-day action: a new low-star review burst, a release that is not reaching users, or a takedown. Daily ratios on a small extension are mostly noise.

Can I see metrics for a competitor's Chrome extension?#

Only what the public listing shows: the rounded user count, rating, rating count, version and last update date. Record them daily and the changes become a usable signal, which is what a competitor extension monitoring tool automates.

Summary#

Ten numbers cover what matters about a Chrome extension: impressions, page views, installs, listing conversion, weekly users, growth rate, uninstall rate, rating with recent reviews, low-star share and latest-version share. Keep events, stocks and ratios apart, compare each week with your own recent history, and let a small script flag what moved. Add usage data from inside the extension for the questions the store cannot answer, and competitor data for the context it does not show.

Image credits#

• Cover photo: Interior of a vintage red racing car by Hardeep Asrani on the WordPress Photo Directory, CC0.