What Customer Retention Is, How the Rate Is Calculated, and Why It Rises on Its Own
Your rate can climb while nothing improves, and the people most likely to leave are often the worst ones to chase. Here is what to measure.
Customer retention is the work of keeping the customers you already have, and the rate that measures it is the number a marketing, lifecycle or product owner gets asked to report on.
That number can climb while nothing about the business improves. And the customers a churn model flags as most likely to leave are often the worst ones to spend a retention budget on.
Both of those come out of published research, and neither of them changes what to do first.
Retention, Loyalty and Satisfaction Are Three Different Measurements
Customer retention is keeping the customers you already have buying, measured as the share of a starting group still active at the end of a period, with anyone acquired inside that period excluded from the count.
That last clause is doing the work. Without it you are measuring growth, because new signups would paper over everyone who left.
Loyalty and satisfaction are different objects. Satisfaction is what a customer says when you ask, loyalty is what they prefer, and retention is what they did.
Only the third is already in your records. That matters when someone asks you to prove a program worked, because a survey score moves for reasons a purchase record does not.
Fader and Hardie, writing in the Journal of Interactive Marketing in 2007, give the tightest definition of the rate itself: the proportion of customers active at the end of the previous period who are still active at the end of this one.
The Three Rs, and Where They Came From
The three Rs are retention, related sales and referrals.
They name the three ways a kept customer pays you. They keep buying, they buy adjacent things over time, and they bring other people in.
The phrase belongs to the loyalty literature and not to any standards body, so treat it as a memory aid and not a model.
The third R is the one most reporting never counts. A customer who brought two others is worth more than the rate shows, and nothing in the formula below will tell you that happened.
The Arithmetic of a Customer Who Stays
A kept customer is worth more than a new one because the money to find them has already been spent. That is the whole of the arithmetic, and everything else is a multiplier on it.
The multipliers get quoted often and they do not agree with each other. Zendesk's guide credits Harvard Business Review with the finding that acquiring a customer can cost between five and 25 times more than keeping one.
The Postal Service inspector general, auditing customer retention at that agency in 2014, cited research putting it at six to seven times, and put the odds of selling to an existing customer at four times those of selling to a new prospect.
Bain's figure sits in the same pile. Zendesk reports it as a 5% improvement in retention that can improve revenue by 25% to 95%.
Use numbers like these to justify the direction of the spend, never to forecast a result.
A claim that has traveled through three retellings has usually lost the conditions it was true under.
Open the Footnote Before You Quote the Number
A multiplier like that is worth checking at its own footnote before it goes into a deck.
Zendesk's page carries no year for either of the multipliers it repeats. The audit does carry one, and it is the more useful case, because its footnote credits the six-to-seven-times figure to a marketing blog post from June 2011 rather than to a study.
IBM's guide states that increasing customer retention rate can increase profits by over 90%. The footnote under that sentence points at a 2020 Forrester report titled "Customer Recommendations Have Only A Small Business Impact For Big Brands".
The other footnote on that page checks out. It goes to a McKinsey piece from August 2023 for the line that a company needs three new customers to make up the business value of losing one existing customer.
Before the Formula Works, You Have to Settle Three Things
The formula is one division, and it gives a different answer depending on three decisions you make before you run it.
Settle them first, and write them down where whoever reads the number can see them.
- The window. A month, a quarter and a year on the same business give three different rates, and the shorter the window the higher the number looks.
- Customers or revenue. Counting logos treats your largest account and your smallest one the same. Counting revenue tells you whether the money stayed.
- A cohort or the whole base. A cohort is one group that arrived together and is followed forward. The whole base mixes people who joined last week with people who joined years ago, and that mix moves every month.
Then the arithmetic. Count everyone you have at the end of the period, subtract the ones you acquired during it, and divide by the number you started with.
Salesforce's guide works it on a quarter. A business starts with 1,000 customers, gains 200 and ends with 1,150, so subtracting the 200 new arrivals and dividing by 1,000 gives a retention rate of 95%.
The Six Metrics That Sit Beside the Rate
The rate on its own answers one question. These six sit beside it, and each answers something the rate cannot.
| Metric | The formula | The question it answers |
|---|---|---|
| Churn rate | Customers lost in the period, divided by the customers you started with | How fast the base is draining |
| Repeat purchase rate | Customers who bought more than once, divided by all customers | Whether a first purchase leads to a second |
| Purchase frequency | Total purchases, divided by the number of unique customers | How often the people who stayed come back |
| Customer lifetime value | Average purchase value, times purchase frequency, times how long a customer stays | What one relationship is worth before you decide what to spend on it |
| Average order value | Total revenue, divided by the number of orders | Whether the people who stayed are spending more |
| Retention cost | What you spent on keeping customers, divided by the number you kept | Whether the program is cheaper than acquisition |
Two more are worth naming if you sell a subscription. Net revenue retention is the same measurement run on revenue, so an account that stayed and upgraded can carry one that stayed and downgraded.
Involuntary churn is the other. Some of what looks like a decision is an expired card, and no amount of relationship work fixes a failed payment.
Why a Cohort's Retention Rate Rises Even When Nothing Improves
A cohort's retention rate goes up as the cohort ages, and it does so without a single customer becoming more loyal.
Fader and Hardie showed why. The customers with the highest chance of leaving go first, so the group left behind is made of the people who were always going to stay.
Their words for it: the rise is "a sorting effect in a heterogeneous population", where "the high-churn customers drop out early in the observation period, with the remaining customers having lower churn probabilities".
Their published survival data shows the size of it. Two segments of one subscription business, followed from the year they joined:
| Years since joining | Regular segment still active | High End segment still active |
|---|---|---|
| 0 | 100.0% | 100.0% |
| 1 | 63.1% | 86.9% |
| 2 | 46.8% | 74.3% |
| 3 | 38.2% | 65.3% |
| 4 | 32.6% | 59.3% |
| 5 | 28.9% | 55.1% |
| 6 | 26.2% | 51.7% |
| 7 | 24.1% | 49.1% |
| 8 | 22.3% | 46.8% |
| 9 | 20.7% | 44.5% |
| 10 | 19.4% | 42.7% |
| 11 | 18.3% | 40.9% |
| 12 | 17.3% | 39.4% |
Read down either column and the yearly loss shrinks, steeply at first and then barely at all. The Regular segment falls from 100% to 63.1% in the first year, and from 18.3% to 17.3% between the eleventh and the twelfth.
The falling column is the share of the original group. The rate that rises is a different division, each year's survivors over the year before, and it climbs as those losses shrink.
So a rising rate is not evidence that anything you did worked. It is the expected shape of any cohort left alone.
That is also the honest answer to what a good retention rate is. It depends on the window and on how old the cohort is, and a published industry average with no edition on it cannot tell you.
One cohort, period by period
Put in how many of one group that arrived together were still active at the end of each period. It does the division the section above leaves to you: the survivors this period over the survivors last period, and the same group against the size it started at.
| years since joining | Still active | Retention rate for the year | Share of the original group |
|---|---|---|---|
| 1 | 63.1% | 63.1% | |
| 2 | 74.2% | 46.8% | |
| 3 | 81.6% | 38.2% | |
| 4 | 85.3% | 32.6% | |
| 5 | 88.7% | 28.9% | |
| 6 | 90.7% | 26.2% |
The rate per year went from 63.1% to 90.7%, while the group fell to 26.2% of what it was. Two lines moving in opposite directions is the sorting effect, not a cohort becoming more loyal. The customers with the highest chance of leaving went first.
A rising rate is not evidence that anything you did worked. The question worth asking is whether this cohort is above the one before it at the same age.
Seeded with the Regular segment of Fader and Hardie, Journal of Interactive Marketing, 2007, as printed above. Every figure here is one division on the numbers in the boxes, and nothing is projected forward.
What Happens If You Project the Curve Forward
The same paper shows what happens if you project that curve forward with a regression, which is what a data-mining textbook of the period did.
Fitted on the first seven years of the High End segment, a linear model underestimates year twelve survival by 81%. A quadratic one overestimates it by 92%.
Use this chart — embed code and citation
<a href="https://neerajjivnani.com/blog/customer-retention/"><img src="https://neerajjivnani.com/infographics/customer-retention/sorting-effect.png" alt="Two columns of horizontal bars reproducing every year of Fader and Hardie's 2007 survival table, the Regular segment falling from 100.0% to 63.1% after one year and on to 17.3% by year twelve, beside the High End segment falling from 100.0% to 86.9% and on to 39.4%, with no year skipped so the shrinking yearly loss is visible down the column, and a quoted panel naming the cause as a sorting effect rather than growing loyalty." width="1200"></a>
<p>Chart: <a href="https://neerajjivnani.com/blog/customer-retention/">Neeraj Jivnani</a></p>Neeraj Jivnani, "What Customer Retention Is, How the Rate Is Calculated, and Why It Rises on Its Own", neerajjivnani.com, https://neerajjivnani.com/blog/customer-retention/Free to republish with a link back to this page.
What 513 Customers Told a Government Auditor About Leaving
When a United States government auditor asked 513 current and former customers why they had left, the top two answers were both inside the company's control.
The audit was the Postal Service inspector general's, published in September 2014. It sent 6,632 surveys to the commercial customers whose revenue was falling fastest, received 390 back, and added 123 telephone interviews.
Customers could name more than one reason, so the percentages below are shares of the answers given rather than of the people asked.
These are the counts as the audit published them.
| Reason given for leaving | Respondents | Share of answers |
|---|---|---|
| Customer service or service quality | 114 | 23.2% |
| Lack of resolution to issues | 93 | 18.9% |
| A competitor offered better value | 66 | 13.4% |
| Operational issues | 62 | 12.6% |
| Different product or service offerings | 25 | 5.1% |
| Stopped mailing altogether | 14 | 2.9% |
| Other reasons given | 117 | 23.8% |
The last row of that table is not a seventh reason. The audit lists what went into its residual bucket, and the examples it gives are poor customer service, unreliable delivery, inconsistent mail pickup and rigid pricing.
The Half You Can Do Something About
Two things in that table are worth sitting with. The first is that service quality and unresolved issues together account for more of the leaving than competitor value and product offerings put together.
The second is the stopped-mailing row. Only 2.9% of the answers named stopping mailing altogether, which is the one reason on the list a mail service could do nothing about.
The audit's own reading was the same. It noted that customer service and service quality are more easily controlled by the Postal Service, while stopping mailing altogether is not.
That is the useful way to read a churn list. Sort the reasons into the ones you can act on and the ones you cannot, because in this count the actionable half was the bigger one.
The Employees Answered a Different Way
The same audit put the question to the people who deal with those customers, and got a different order back.
It surveyed 206 bulk mail entry unit clerks and 1,851 postmasters and Consumer and Industry Contact managers on why customers had left in the past two years.
Of the answers the clerks ranked first or second, 35% were decreased demand for mailing and shipping services and 25% were better service quality from a competitor. For the postmasters the order flipped, with competitor service quality at 27% and decreased demand at 21%.
That gap is worth knowing before you build anything on either list. When management objected that stopping mailing and decreased demand were being treated as one thing, the auditor agreed in writing that they are two separate issues.
So the customer survey settles what departing customers say, and it does not settle how much of the leaving is demand rather than service. Two surveys in one document, and they do not agree.
Use this chart — embed code and citation
<a href="https://neerajjivnani.com/blog/customer-retention/"><img src="https://neerajjivnani.com/infographics/customer-retention/why-they-left.png" alt="Horizontal bars of the six reasons 513 current and former Postal Service customers gave in the 2014 inspector general audit, each percentage a share of the answers given, with customer service at 23.2% and lack of resolution at 18.9% picked out in orange as inside the company's control, competitor value at 13.4%, operational issues at 12.6% and product offerings at 5.1% below them, stopping mailing altogether at 2.9%, and the audit's 23.8% residual bucket set apart underneath as not a reason of its own." width="1200"></a>
<p>Chart: <a href="https://neerajjivnani.com/blog/customer-retention/">Neeraj Jivnani</a></p>Neeraj Jivnani, "What Customer Retention Is, How the Rate Is Calculated, and Why It Rises on Its Own", neerajjivnani.com, https://neerajjivnani.com/blog/customer-retention/Free to republish with a link back to this page.
The Retention Campaign That Talks People Into Leaving
A retention campaign can produce churn it would not otherwise have had, and the people it is usually pointed at are the ones least able to respond to it.
This comes from a 2017 review in Customer Needs and Solutions, written by twelve researchers including Eva Ascarza, Peter Fader and Bruce Hardie.
On targeting, its wording is direct: "At first blush, it seems we should target customers who are at the highest risk of not being retained."
It continues: "However, this may not be the best approach. The highest-risk customers may not be receptive to retention efforts."
Someone who has already decided to go is not persuaded by a discount, and the budget lands where it cannot move anything.
The review's own words for that customer are "so turned off by the company that nothing can retain them".
The second half is the part that costs money. The review reports that some customers who would not have churned can be provoked into it by the retention effort itself.
Its explanation is that a renewal running on habit gets interrupted. Retention offers may "disrupt renewal habits, make people realize they are not happy with the status quo, and paradoxically cause churn".
The same paragraph says the effect can run the other way, and that a delighted non-churner may end up more loyal than before. The review adds that the field has not yet unpacked which way it goes.
The provocation is the half that was measured. It comes from a 2016 field experiment in the Journal of Marketing Research that tested proactive churn prevention by recommending customers a different plan.
Who Is Worth the Offer, and Who Is Better Left Alone
The rule the review proposes is to target on effect, not on risk level.
Its phrasing is that the best targets are "customers who are at the risk of leaving and are likely to change their minds and stay if targeted".
That is a different question from the one a churn model answers. Predictive scoring ranks people by how likely they are to leave, and says nothing about whether your offer would change their mind.
The review names the technique for the second question. Uplift models "attempt to model directly the incremental impact of a campaign on individual customers", which is what you need before you spend.
Run a campaign on the risk score alone and the budget goes to people who were leaving anyway and people who were staying anyway.
The review is blunt about the size of that second group: "some customers (in fact, often most) targeted in a proactive campaign will be those whom the company would have retained anyway".
The cheap version of this needs no model at all. Hold back a random slice of the people you were about to contact, and compare what happens to them with what happens to the ones you contacted.
Without that holdout you cannot tell a campaign that saved accounts from a campaign that talked to accounts nobody was going to lose.
Use this chart — embed code and citation
<a href="https://neerajjivnani.com/blog/customer-retention/"><img src="https://neerajjivnani.com/infographics/customer-retention/target-on-effect.png" alt="A two-by-two grid of churn risk against whether the customer takes the retention offer, with the high-risk taker marked as the only square worth spending on, the low-risk taker marked as a customer the company would have retained anyway, the high-risk refuser marked as someone no offer can move, and the low-risk refuser marked as a habitual renewer the offer can provoke into churning." width="1200"></a>
<p>Chart: <a href="https://neerajjivnani.com/blog/customer-retention/">Neeraj Jivnani</a></p>Neeraj Jivnani, "What Customer Retention Is, How the Rate Is Calculated, and Why It Rises on Its Own", neerajjivnani.com, https://neerajjivnani.com/blog/customer-retention/Free to republish with a link back to this page.
The Tactic List, and Which Parts Hold Up
The tactics themselves are not in dispute. What decides whether the money works is which one you start with.
Only the first here rests on a named sample. The rest are convention, ordered by how much of the leaving they act on:
- Fix service and resolution first. The two reasons the audited group cited most often were service quality and unresolved issues, and both sit inside the company's control.
- Close the loop on feedback so the customer sees it. Collecting a score changes nothing on its own. What the audit measured was resolution, and an unresolved issue is what people said they left over.
- Onboarding and customer education. Someone who never reached the point where the product becomes useful has nothing to be retained by. This acts on the first period, which is where the survival table above shows the biggest loss.
- Dedicated account management, for the accounts that carry it. A single point of contact removes the reason to go looking elsewhere when something breaks. It is expensive per account, so it belongs to the accounts whose value pays for it.
- Self-service, live support and flexible billing. These are the three places a small friction turns into a cancellation, and the failed payment behind involuntary churn is the version nobody counts.
- Loyalty and reward schemes. Widely used and the hardest of these to evaluate, because the people who join one tend to be the people who were already staying.
- Community and personalization. Both work on the reasons people stay rather than the reasons they leave, which makes them slow to show up in a number.
- The tooling underneath. A customer relationship management system or an analytics platform holds the record that makes any of this measurable. It decides nothing about where the money goes, which is the decision every item above turns on.
The Five Factors Question
There is no canonical set of five factors. The lists that circulate are each a different company's five, and none of them cites a standard.
If you want the five, they are service quality and how fast an issue gets resolved, onboarding, feedback the customer can see a result from, loyalty schemes and personalization. Service leads because it is the one departing customers named for themselves.
Getting a Former Customer Back Is a Different Job
Getting a former customer back is a different program from keeping one, with its own list, its own offer and its own rate.
The Postal Service audit is unusually direct about this. It found that the retention strategy "does not include a process to contact former customers", and that 94% of the customers surveyed said nobody contacted them when they reduced or stopped their business.
The economics are better than they look. That same audit cites research finding companies twice as likely to win a former customer back as to win a new one.
Offboarding belongs here too. What happens in the cancellation flow decides whether a customer leaves as someone who might return or as someone who will not.
There is a reporting consequence as well. A returning customer is not in the cohort you were tracking, so a win-back program can work without moving the rate you report.
The list to build is short. Everyone who left in the last twelve months, why they left where you know it, and what has changed since.
The audit recommended reaching out to former customers, and cited experts who treat them as leads that deserve attention.
Retention Programs That Left a Public Record
The retention programs worth studying are the ones an outsider wrote down, and the fullest public record here belongs to the United States Postal Service.
Most of what gets published is a company's own account of its own success, which tells you what it wants to be known for and not whether the program worked.
In fiscal 2012 the Postal Service began laying the foundation for a retention program on the standard pattern. The Sales group reorganized around retention, and a Business Customer Intelligence team built a model to predict which customers it risked losing.
In fiscal 2013 a pilot fed that model's output to seven of the 67 district offices and two specialized call centers. Those made proactive contact with a prioritized list of at-risk customers each month.
The plan was to take it to the remaining 60 districts in fiscal 2014.
That is a risk-targeted campaign built exactly as the field recommends, and it is the shape the 2017 review questions.
The audit did not find that the model was wrong. It found that departments were not sharing what they knew about customers at risk, and that nobody was contacting the ones who had already gone.
Chewy's Refund and Starbucks Rewards
The other kind of record is a news report, and two examples have one behind them.
Zoom's guide relays a 2022 Today story about Chewy. A customer asked to return dog food after her dog died, and the company told her to keep it and donate it to a shelter, refunded her in full and sent flowers.
Starbucks Rewards is the other, and the company itself attributes as much as 40% of total sales to it, on 2019 figures relayed by Zoom's guide.
The part worth copying there is not the discount. The scheme runs through an app that also takes payment and lets people order ahead, so the reward and the convenience arrive together and cannot be separated in that number.
Measure It Honestly, Then Spend Where It Moves Something
Retention is measured before it can be managed, and the measurement has to be honest about what it cannot tell you.
Fix the window, the unit and the cohort, and publish all three next to the number. A rate with no window on it is not something anyone can act on.
Expect the rate to rise as a cohort ages, and do not book that as a win. The question worth asking is whether this cohort is above the one before it at the same age.
Spend the budget where it changes an outcome rather than where the risk score is highest. The cheapest way to find out which is which is a holdout.
And start from the reasons people give. In the audit of 513 current and former customers, service quality and unresolved issues were the two largest answers, and both were things the company could have changed.
Retention work goes wrong in two places, and neither of them is the tactic list. The number gets read without its window, and the money gets aimed at the risk score instead of at the people it could move.