Every customer who uninstalls your technology leaves a pattern behind. LeadsQuantum reads two of them from outside your product: an unusual vertical, and a stack your tool does not fit. Work the riskiest accounts first.
Usage dashboards tell you when engagement drops, often too late. Fit signals tell you which customers were always at higher risk, so you can act early.
Combine fit signals with your own usage data. A low-fit customer whose usage also drops is the first call of the week.
Every one of 5 million domains carries an IAB category. For any technology we know which categories use it far above or far below their share of all sites.
Customers in high-ratio categories are using your type of tool where it is normal. Customers in low-ratio categories are the exceptions, and exceptions churn more easily.
The recommender learned from more than 100 million technology usage data points which tools tend to appear together and which rarely do.
When your technology is an unusual companion for the rest of a customer's stack, the setup is less stable. Those customers are more likely to switch.
Put every customer into one box. Each box gets a different treatment from customer success, so effort goes where it changes outcomes.
| Vertical fit | Stack fit | Risk group | Treatment |
|---|---|---|---|
| Low | Low | Highest risk | Personal check-in this month, tailored onboarding review |
| Low | High | Vertical risk | Show vertical-specific use cases and examples |
| High | Low | Stack risk | Help with integrations and setup alongside their tools |
| High | High | Core customers | Light touch, expansion offers, ask for referrals |
Customers rarely say "we did not fit". They say something more specific. Many of those reasons trace back to vertical or stack fit.
| What the customer says | Often really means | Fit signal that warned you |
|---|---|---|
| "It does not do what we need." | Built for a different kind of business | Vertical fit |
| "It does not work with our other tools." | Integration gap with their stack | Stack fit |
| "Too expensive for what we use." | Only a small part of the product is relevant | Vertical fit |
| "We found something simpler." | A tool that fits their stack better | Stack fit |
| "Our agency recommended another tool." | Their stack follows the agency's standard | Stack fit |
| "We changed platforms." | Migration to a stack your tool does not suit | Stack fit |
| "Nobody on the team uses it." | The category is not normal in their vertical | Vertical fit |
| "We are closing or pausing." | Business reasons outside your control | Neither, but rare in high-fit groups |
Run it quarterly for your whole base, monthly for your largest accounts. The first run takes an afternoon; later runs take an hour.
Export the websites of your current customers from your CRM or billing system.
Match each domain's category against your technology's ratio chart, Tier 2 where possible.
Use the churn ranking from the recommender for your technology to score each customer.
Sort customers into the four risk groups described above.
Every highest-risk account gets a named person and a date for first contact.
Next quarter, compare churn by group and adjust the treatments that did not work.
The goal is not a discount. It is making your product fit better in their world, so staying becomes the obvious choice.
Send examples from customers in their exact category, so the product feels made for them.
Connect your tool to the stack they actually run, not the one you assumed.
A short call to check configuration and find two or three quick wins.
Show what your tool did for them last quarter in numbers they care about.
A cheaper plan beats a cancellation, and keeps the door open to upgrade later.
Point out features that matter in their vertical and that they have not tried.
For large accounts, a personal note from leadership shows they matter.
Ask what would make you indispensable, log it, and tell them when you ship it.
Risk scores only help if they change what people do on Monday morning. This simple weekly plan makes sure they do.
| Day | Focus | Who |
|---|---|---|
| Monday | Review highest-risk accounts with any drop in usage | Team lead |
| Tuesday | Personal check-ins with three to five high-risk accounts | Account owners |
| Wednesday | Send vertical playbooks to the vertical-risk group | CS marketing |
| Thursday | Integration help sessions for the stack-risk group | Solutions or support |
| Friday | Log saves, losses and reasons; update the product feedback list | Everyone |
Churn is everyone's problem. Fit signals give every team the same picture to work from.
Decide which accounts get attention first, every single week.
See which verticals and stacks you serve badly, and fix the biggest gap.
Stop signing customers who will not stay past the first renewal.
Focus spend on verticals where customers stick and refer others.
Forecast revenue with risk built in rather than added at the end.
See whether growth is coming from customers who fit or from ones who will leave.
Customer success leaders have seen many health scores. These are the questions they ask before trusting another one.
Keep it. Fit signals add a view from outside your product that usage-based scores cannot see.
Price complaints often come from customers who use only a small part of the product. Vertical fit often explains why.
You do not need to. The highest-risk group is usually small enough to cover personally.
Compare churn by group next quarter. If the high-risk group churns more, the signal is earning its place.
Fit signals need a website per customer. Most businesses that buy software tools have one.
That is where fit signals shine. They work from day one, before any usage data exists.
Patterns in who churns tell you what to build next, and which customers your product is really for.
If low-ratio verticals churn most, either build features for them or stop selling to them. Both are better than the status quo.
If misfit stacks share a tool you do not integrate with, that integration is a priority for the next quarter.
Feed the same signals into sales, so new customers start with a good fit and stay longer.
The cheapest churn to prevent is the customer you never signed. Use the same fit signals when you build prospect lists.
Before a renewal call or a quarterly review, open the customer's record and run through these six checks.
Where does their Tier 2 category sit on your technology's ratio chart, above or below 1.0?
How high do they appear in the churn list for your technology this quarter?
Is a competing tool running next to yours? That often means a trial in progress.
Has their platform or a core tool changed since they signed with you?
Is their store growing or stalling? Stalling stores cut software costs first.
Combine all of the above with logins and feature use from your own product for the full picture.
Compare churn across risk groups before and after you start the save plays. Four numbers are enough to judge the program after one or two quarters.
Shared definitions keep customer success, sales and product teams reading risk lists the same way.
| Term | Meaning |
|---|---|
| Churn | A customer removing or cancelling your technology. |
| Vertical fit | How common your type of technology is in the customer's IAB category, compared with all sites. |
| Stack fit | How naturally your technology sits next to the other tools on the customer's site. |
| Category ratio | Usage share in a category divided by that category's share of all domains. |
| Churn ranking | Your technology's users ordered by likelihood of churn, based on stack fit. |
| Risk group | One of four boxes formed by combining vertical fit and stack fit. |
| Save play | A planned action to keep an at-risk customer. |
With two outside-in signals: how typical a customer's vertical is for your type of technology, and how well your technology fits the rest of the customer's stack. Both come from data on millions of websites.
No. Fit signals need only the customer's website. You can combine them with your own usage data internally, without sharing that data with us.
Both plans, Advanced and Enterprise. It sits alongside the AI recommender and technology analytics, which it draws on.
For a given technology, a list of websites ordered by how likely they are to churn from it, plus category ratio charts and tables you can export to Excel.
No. It means the customer is more likely to leave than others. Use it to decide where to spend customer success time first, then let conversations confirm or clear the risk.
Tier 1 IAB categories for a broad view and all 440 Tier 2 categories for precision. Tier 2 usually separates risk far better.
If your technology is tracked, yes. If not, the category ratios of the leading product in your category are a good proxy to start with.
Yes. Match your customer domains against the churn ranking and category ratios. For bulk help, send up to 500 domains to [email protected].
Quarterly for the full base, monthly for your largest accounts, and immediately after any big change in a customer's stack.
Either invest in features and content for them, or focus new sales elsewhere. Both are valid; drifting in between is costly.
Yes. They are available from the first day, because they depend on the customer's vertical and stack, not on how long they have used your product.
Re-score them. A stack change, such as a new platform, can move a customer from low risk to high risk overnight, so watch for it.
Not necessarily. Some low-fit verticals are untapped markets. Decide deliberately, and invest in onboarding if you keep selling there.
Look up a former customer's current stack. The tool that replaced yours tells you who you are losing to.
No. Exit surveys explain individual decisions. Fit signals show patterns early enough to prevent them. Use both.
It depends on how well your sales targeting matched your product so far. In most bases it is a minority of accounts, small enough for personal attention.
Yes. Agencies that resell or implement software use the same signals to protect the accounts they manage and to advise vendors.
Assign a named owner and a date to every highest-risk account. Lists without owners do not prevent churn.
Churn signals, the AI recommender and technology analytics, together on Advanced from $999 per year.