Chrome extension user growth: how to calculate your growth rate from weekly users

Chrome extension user growth is the number every extension developer checks and few measure the same way twice. One week you compare the dashboard with last Monday, the next week with the start of the month, and after a launch spike you are no longer sure whether the extension is growing, flat or slowly shrinking. A growth rate fixes that, as long as you calculate it from the right number and compound it instead of averaging it.

This guide explains which user number to use for each store, the three formulas worth knowing (week-over-week, compound weekly or monthly growth, and net new users), a script that computes them from a dashboard export, the counting effects that distort them, and how to watch growth for your own extension and your competitors without checking the dashboard every morning. Every store behavior cited below was checked against the stores' documentation on October 13, 2026.

Key takeaways

• Calculate growth from weekly users in the Chrome Web Store dashboard, not from installs and not from the rounded number on the public listing.
• Week-over-week growth is this week / last week - 1. It is noisy, so also track a compound rate over four weeks or more.
• Never average weekly percentages. Going from 1,000 to 1,500 and back to 1,000 "averages" +8.3% a week while the real growth is zero.
• Small rates compound into big differences: 1% a week is about 1.7x a year, 5% a week about 12.6x.
• Compare like with like across stores: Chrome reports weekly users, Firefox reports average daily users.

Which number measures Chrome extension user growth#

The Chrome Web Store gives you three numbers that sound alike and behave differently:

Google's store listing metrics documentation is clear on one limitation: "The Users stats only captures installations; it doesn't monitor whether users are active or not." Weekly users is therefore an installed footprint, not engagement. It is still the best growth base the store offers, because it falls when people uninstall or stop using Chrome, while installs only ever go up. Our explainer on why Chrome extension weekly users drop covers how the number is counted in detail.

Paul Graham's Startup = Growth essay makes the same choice for startups that do not charge yet: "The best thing to measure the growth rate of is revenue." The next best is "active users." If your extension is paid, track paying users with the same formulas below. If it is free, weekly users is your number.

Other stores count differently#

• Firefox Add-ons reports average daily users. The public add-on API returns it as average_daily_users for any listing, so you can record it yourself every day. Our guide to Firefox add-on stats shows where it comes from.
• Microsoft Edge Add-ons reports installs and related metrics in Partner Center, explained in our guide to Edge add-on analytics.

Daily users and weekly users are different measures of the same population, so compare growth rates across stores, never the raw counts.

The formulas behind Chrome extension user growth#

Three calculations cover almost every question you will ask. Here they are on nine weeks of example weekly users for a mid-sized extension:

Week-over-week growth#

WoW growth = users this week / users last week - 1

From 5,560 to 5,810 is 5810 / 5560 - 1 = 4.5%. It is the most responsive measure and the noisiest: in the table it swings between 1.2% and 6.1% with no change in the product.

Compound weekly and monthly growth#

To see the trend through the noise, compound the rate over a longer window:

Compound weekly growth  = (users at end / users at start) ^ (1 / weeks)  - 1
Compound monthly growth = (users at end / users at start) ^ (1 / months) - 1

Over the eight weeks in the table: (5810 / 4200) ^ (1/8) - 1 = 4.1% a week. That single figure says more than eight separate percentages, and it stays stable when one week is unusually good or bad. The monthly version, often called CMGR (compound monthly growth rate), works the same way: going from 12,000 to 14,900 users over three months is (14900 / 12000) ^ (1/3) - 1 = 7.5% a month.

Do not average growth rates#

Averaging weekly percentages looks reasonable and gives wrong answers. Take an extension that goes from 1,000 to 1,500 users (+50%) and back to 1,000 (-33.3%). The average of the two rates is +8.3% a week. The real growth is zero. Compounding from the first and last values avoids that, which is why the compound formula uses only the endpoints.

Net new users#

Net new users = users this week - users last week

Percentages shrink as you grow: 250 net new users is 6% at 4,000 users and 0.5% at 50,000. Track the absolute number next to the rate, so a falling percentage on a growing base does not look like a crisis.

Calculate it from a Chrome Web Store dashboard export#

Google's metrics page says "You can also export all the reports described below as CSV files." Export the weekly users report for the longest range you have, keep one row per week (the same weekday each time), and rename the columns to date,users. Column names in store exports change over time, so check the header before you run anything on it.

In a spreadsheet, with weekly users in column B:

• Week-over-week growth in C3: =B3/B2-1
• 4-week compound growth in D6: =(B6/B2)^(1/4)-1
• 8-week compound growth anywhere: =(B10/B2)^(1/8)-1

Or in Node.js, which is handy if you want to run it on a schedule or feed it into alerts:

// growth.mjs: week-over-week growth and a 4-week compound rate from weekly users.
// Usage: node growth.mjs weekly-users.csv   (columns: date,users; one row per week)
import { readFile } from 'node:fs/promises';

const rows = (await readFile(process.argv[2], 'utf8'))
  .trim()
  .split('\n')
  .slice(1) // skip the header
  .map((line) => {
    const [date, users] = line.split(',');
    return { date, users: Number(users) };
  })
  .filter((row) => Number.isFinite(row.users) && row.users > 0);

const pct = (x) => `${(x * 100).toFixed(1)}%`;

for (let i = 1; i < rows.length; i++) {
  const { date, users } = rows[i];
  const prev = rows[i - 1].users;
  let line = `${date}  ${users}  net ${users - prev}  WoW ${pct(users / prev - 1)}`;
  if (i >= 4) {
    // Compound weekly rate over the last 4 weeks smooths one-off spikes.
    line += `  4-week compound ${pct((users / rows[i - 4].users) ** (1 / 4) - 1)}`;
  }
  console.log(line);
}

Run on the table above, the last line reads 2026-10-05 5810 net 250 WoW 4.5% 4-week compound 4.2%. The 4-week compound rate stays between 3.8% and 4.2% for the whole period while the weekly figure jumps around, which is exactly what you want from a trend line.

If you record users yourself every day, store the snapshots in a database built for it. Our guide to MongoDB time series collections for extension stats shows a schema for daily users, ratings and versions.

What drives the number: installs in, users out#

Weekly users is a stock, and two flows move it: people arriving and people leaving. A simple way to think about any week:

users this week = users last week + new users who stayed - users who left or went quiet

That gives you two levers and two diagnoses:

• Growth from acquisition. Installs come from store search, your website, reviews and word of mouth. If installs per week are flat, growth will flatten as your base grows. Our guide to the Chrome Web Store conversion rate shows how to see which source is working.
• Growth lost to churn. If installs are rising and weekly users are not, people are leaving faster than they arrive. Uninstalls are one signal; browsers that simply stop reporting are another. Our guide to measuring extension retention and uninstall rate shows how to measure the second one without collecting personal data.

Weekly users is never exactly installs minus uninstalls, because returning users, synced browsers and idle machines all move it. Use the identity as a way to ask the right question, not as an equation that must balance.

Five things that distort a growth rate#

1. Launch spikes. A Product Hunt day or a newsletter mention can add more users in a week than the previous month. The weeks after look like a collapse when they are just a return to normal. Start the compound window after the spike, or report it separately.
2. Seasonality. Holidays, school breaks and summer reduce the number of browsers online. Compare a December week with the same week last year, not with November.
3. A small base. Going from 20 to 40 users is 100% growth and tells you very little. Below a few hundred weekly users, watch net new users and reviews instead of percentages.
4. Updates that disable the extension. An update that adds a permission warning disables the extension until each user accepts it, and the growth rate turns negative overnight. Break weekly users down by item version after every release.
5. The rounded listing number. The public listing shows weekly users in brackets for larger extensions, so it can stay flat for weeks and then jump. Never calculate your own growth from it.

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

There is no published benchmark for extensions, and growth depends heavily on age and category. What you can do is understand what a rate means over a year:

For context, Paul Graham wrote in 2012 that "A good growth rate during YC is 5-7% a week", and that "a company that grows at 1% a week will grow 1.7x a year, whereas a company that grows at 5% a week will grow 12.6x." That benchmark is for young startups in a three-month program. A mature extension with hundreds of thousands of users that grows 1% a week is doing well, and a new one that grows 1% a week has a problem with acquisition.

A more useful comparison is your own category. If similar extensions are growing while yours is flat, the cause is probably your listing or product. If everyone in the category is flat, it is the market or the season.

Track competitors' growth the same way#

You cannot see a competitor's dashboard, but you can record their public listing every day: user count, rating, rating count and version. Two caveats apply. The listing user count is rounded, so measure competitors' growth over months rather than weeks, and treat a jump between brackets as one step, not as a sudden surge. And compare rates, not raw counts, since a competitor ten times your size grows by more users even at a lower rate. Our guide to the Chrome Web Store stats API explains how to read those public numbers programmatically.

When you record those snapshots daily, the interesting part is the change between them. Our guide to JSON diff in Node.js shows how to compare two snapshots and turn the differences into readable alerts.

Watch your growth rate with Extenify#

Calculating a growth rate once is easy. Noticing the week it changes is the hard part, and that is what Extenify is for. It is a chrome extension stats tracker and browser extension analytics dashboard that records users, ratings, reviews and versions for your extensions on the Chrome Web Store, Edge Add-ons and Firefox Add-ons, and for your apps on Google Play and the App Store, every day, next to your competitors. It computes a 7-day install growth rate for each listing and flags extension user growth changes in your daily digest: growth that turns negative or positive, accelerates or slows, alongside rating drops, new reviews and new versions. Alerts go to email, Slack, Discord, Telegram or Microsoft Teams. If new reviews are what you want to catch first, our guide to Chrome extension review notifications covers each store's built-in options. Every account starts with a 7-day free trial, and the pricing page lists the plans.

Frequently asked questions#

How do I see how many users my Chrome extension has?#

Open the Chrome Web Store Developer Dashboard, select the item and go to the users report. It shows weekly users, with breakdowns by country, language, operating system and item version, and you can export it as CSV. The number on your public listing is the same measure, rounded for larger extensions.

How do I calculate the weekly growth rate of my extension?#

Divide this week's weekly users by last week's and subtract 1. For example, 5,810 after 5,560 is a 4.5% weekly growth rate. For a steadier figure, compound over four or eight weeks: divide the last value by the first, raise it to the power of 1 divided by the number of weeks, and subtract 1.

What is CMGR?#

CMGR is the compound monthly growth rate: (users at end / users at start) ^ (1 / months) - 1. It gives the constant monthly rate that would take you from the starting number to the ending one, which is more honest than averaging month-by-month percentages.

Why do my installs not match my weekly users?#

Installs count install events, including returning users, while weekly users counts browsers with the extension installed over a 7-day window. Users who uninstall, stop using Chrome or keep a computer switched off all reduce weekly users without changing installs, so the two numbers drift apart over time.

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

No store publishes a benchmark. As a reference, 1% a week compounds to about 1.7x a year and 5% a week to about 12.6x. Young products often grow faster, mature extensions slower. Compare your rate with similar extensions in your category over the same months.

Summary#

Measure Chrome extension user growth from weekly users in the developer dashboard, not from installs or the rounded listing number. Use week-over-week growth for the latest signal, a compound weekly or monthly rate for the trend, and net new users to keep the percentages in perspective. Never average growth rates. Break the number into acquisition and churn when it moves, remember that launches, holidays and permission warnings distort it, and compare rates rather than raw counts when you look at other stores or competitors.

Image credits#

• Cover photo: Young green seedlings by Diganta Sarkar on the WordPress Photo Directory, CC0.