Sales metrics: CAC, LTV, churn and what every business owner should track

If you ask most business owners which sales numbers they track, the answer is almost always the same: revenue. How much they sold this month, whether they hit the target, whether they grew compared to last year.

Revenue matters, of course. But it is the final result. There is a whole set of metrics that show how that revenue is being built, whether it is healthy and whether it will repeat. And most of them are ignored.

The problem starts before the metrics

To measure, you need data. And that is where the first problem appears: on average, companies do not have a CRM to manage the sales process. When they do, it is usually poorly structured, with no configuration strategy, no clear stages and no defined communication.

Business owners get stuck in the sales team’s day-to-day operations and cannot see the big picture. Here at Oxigenweb I have seen companies with revenues in the millions where, in practice, that revenue was in the hands of the salespeople. Each salesperson had their own spreadsheet, their own way of tracking customers, their own contacts.

One case really stuck with me. At one of these companies, a salesperson passed away. He was the only one who knew how to sell a specific product. Nobody else had the technical knowledge, and nobody had the contacts of the customers he dealt with. The result was that product sitting in the warehouse, because there was no one who knew who to sell it to or how.

If sales knowledge lives only in people’s heads, it does not belong to the company.

That is why structure comes before metrics. With a well-configured CRM and a clear process, the numbers appear. And with the right numbers, you stop managing in the dark.

The sales metrics every business owner should track

To make it easier, I will use a fictional example throughout: a recurring services company that bills R$120,000 per month with 80 customers, has a 40% margin and invests R$30,000 per month in marketing and sales.

Average ticket

It is how much, on average, each customer pays you.

How to calculate: revenue for the period divided by the number of customers (or sales) in the period. In the example: R$120,000 ÷ 80 = R$1,500 per month.

Why it matters: it shows whether you are selling to the right customer profile and is the basis for calculating almost every other metric. Increasing the average ticket is often cheaper than winning new customers.

CAC (Customer Acquisition Cost)

It is how much you spend, on average, to win each new customer.

How to calculate: add up everything invested in marketing and sales in the period (media, sales team salaries and commissions, tools, agency) and divide by the number of new customers won in the same period. In the example, if the company won 15 customers in the month: R$30,000 ÷ 15 = R$2,000 per customer.

Why it matters: without knowing your CAC, you do not know whether your marketing and sales investment is paying off. Many companies grow revenue while the cost of winning each customer grows even faster.

Churn

It is the rate at which you lose customers in a given period.

How to calculate: number of customers lost in the period divided by the number of customers at the start of the period, times 100. In the example, if the company lost 4 of its 80 customers in the month: 4 ÷ 80 × 100 = 5% per month.

Why it matters: high churn is like filling a leaky bucket: you invest to bring customers in and they leave through the other end. It also tells you how long, on average, a customer stays with you. With a 5% monthly churn, the average lifetime is about 1 ÷ 0.05 = 20 months.

LTV (Lifetime Value)

It is how much a customer generates for your company during the entire time they remain a customer.

How to calculate: average ticket × average lifetime × margin. In the example: R$1,500 × 20 months × 40% = R$12,000. Some companies calculate LTV using revenue, without applying the margin, but using the margin gives a much more realistic view.

Why it matters: LTV shows how much each customer is really worth and, as a result, how much you can invest to win them.

LTV/CAC ratio

It is the metric that combines the previous two and answers the most important question: does each customer generate more than it costs to win them?

How to calculate: LTV ÷ CAC. In the example: R$12,000 ÷ R$2,000 = 6. In other words, each customer generates six times what it cost to acquire.

Why it matters: if this ratio is below 1, you lose money with every new customer. A widely used market benchmark is to aim for at least 3 to 1. When the ratio is very high, it may be a sign that you are underinvesting and missing out on growth.

Payback

It is the time a customer takes to return what you spent to acquire them.

How to calculate: CAC divided by the monthly profit generated per customer (average ticket × margin). In the example: R$2,000 ÷ (R$1,500 × 40%) = R$2,000 ÷ R$600 = about 3.3 months.

Why it matters: payback shows how much cash you need to grow. The longer the payback, the more money is tied up before the customer starts generating profit. This directly affects financial planning and budgeting.

Metrics summary

Metric	How to calculate	Example
Average ticket	Revenue ÷ number of customers	R$120,000 ÷ 80 = R$1,500
CAC	Marketing and sales investment ÷ new customers	R$30,000 ÷ 15 = R$2,000
Churn	Customers lost ÷ customers at start × 100	4 ÷ 80 = 5% per month
LTV	Average ticket × lifetime × margin	R$1,500 × 20 × 40% = R$12,000
LTV/CAC	LTV ÷ CAC	R$12,000 ÷ R$2,000 = 6
Payback	CAC ÷ (average ticket × margin)	R$2,000 ÷ R$600 = 3.3 months

To make these calculations easier, we created the Marketing and Sales Metrics Calculator at Oxigenweb, a free tool where you enter your numbers and calculate indicators like CAC, LTV, churn, LTV/CAC ratio and ROI, among more than 15 calculators. It is a good starting point for anyone who has never tracked these metrics.

Organized data + artificial intelligence

If you struggle to calculate these metrics, here is some good news. If your information is organized in a database, and not scattered across a bunch of loose spreadsheets, artificial intelligence can help a lot.

Today you can use AI to take your sales, customer, cancellation and investment data and clearly arrive at your CAC, churn, LTV or any other indicator you need. And it goes beyond calculation: it helps cross-reference information, find patterns and build strategies from what the numbers show.

For example: which customers have the highest LTV? Do they have something in common? Which channel did they come from? Questions that used to take days of analysis can now be answered in minutes, as long as the data exists and is at least minimally organized.

AI does not create data you do not have. But with organized data, it turns numbers into strategy.

That is why a CRM and a sales structure are so important. They are the foundation for AI to work in your favor. I wrote more about this mindset in the post about AI First companies.

Why the sales team needs to know these numbers

These metrics are not just a finance topic. They need to be part of the company’s strategic, sales and financial planning and its budget. And the sales team needs to know them.

When the team knows the CAC, LTV and churn, it understands which customers are most worthwhile, where it is worth investing time and which opportunities are bad, even if they look good at first glance. And the business owner can clearly see whether investments are paying off and by how much.

The ultimate goal: predictable revenue

When you have structure, data and the right metrics, you can build predictable revenue. You stop depending on the market, referrals or luck to generate revenue. You know how much you need to invest, how many customers you need to win and how much you will bill, and you can plan for it to happen.

And it all starts with knowing who your ideal customer is. If you have not read it yet, I recommend the post Sales start with the ICP, which is the first step on this journey.

In the next posts, I will talk about the day-to-day metrics of the sales team and about marketing metrics and data analysis. Revenue is the scoreboard. Metrics show how the game is being played.