Unit economics

Unit Economics: Striking a Balance between Customer Lifetime Value and Acquisition Cost

Is your business profitable? Well, it might be. You’ve invested $100k or your startup acquired a few million in VC, and now you generate ten times more even after expenses. Sounds like success, right? Next step - to grow. Or is it?

The metric of unit economics, though having simple calculations, can dictate the future of your business and help understand its long-run sustainability. It allows you to take a microscopic look at each of your business's transactions beyond revenue and cost. And sometimes - even save you from taking unjustified scaling steps. Here we will tell you how.

What is unit economics?

Unit economics (UE) is a calculation of profit and loss for a particular business model on a per-unit basis. Basically, it tells you how much value each item or unit creates for the business.

Unit is a fundamental, measurable piece creating value for a business that can be measured. An airline might look at seats sold. For a car dealership, the unit may simply be a car. While Uber takes per-hour revenue as a unit of measurement. We'll talk mainly about digital products, and here, a unit is usually a customer or a user. In terms of software products, unit economics is considered as the ratio of Customer Lifetime Value and Customer Acquisition Cost.

Unit economics calculates the value a customer brings to your business during the time they stay loyal, in relation to how much you invest to acquire that customer. The desired ratio will depend on your company and the industry you operate in, so even if the numbers don’t look good, it makes sense to compare them to benchmarks.

While reviewing your unit economics metric quarterly is recommended, for startups, sensitive to the smallest market blips, it makes sense to track changes every month. Consider it one of the vital metrics for adjusting the marketing strategy of your product.

How to calculate unit economics

As we mentioned before, unit economics is based on a ratio of two metrics:
  • Customer Lifetime Value (LTV), which estimates how much a company receives from a customer during the entire engagement time before this customer churns.
  • Customer Acquisition Cost (CAC), which estimates how much it costs to attract a client.

Calculating these indicators is a separate, nuanced task.

How you count LTV depends on your revenue and business models. So, for more accuracy, you should make adjustments to your industry and business strategies. In general terms, there are two main methods:
  • predictive LTV
  • flexible LTV

Predictive LTV: looking into the future

What for: modeling the transactional behaviors of customers to predict their actions.

Customer preferences change over time influencing the way they purchase. If you don’t want to be taken aback by a sudden drop in sales, you have to be prepared. Calculating predictive LTV will let you forecast how your customers are likely to behave in the future. So here’s a formula for measuring predictive LTV:

Predictive LTV = T * AOV * AGM * ALT /number of customers for the period

T - the average number of transactions. For instance, within a chosen period of, say, five months, T equals the number of total transactions divided by this time span. Let’s take 600 transactions within five months - 120 transactions per month on average.

AOV - the average value of an order. AOV is a proportion of total revenue divided by the number of orders. As an example, we divide a five-month revenue ($13,680) into the overall orders quantity (600), and get AOV of $22.80.

AGM - the average gross margin. AGM identifies the actual profit against the total revenue (TR) deducting cost of sales (CS). For starters, we need to determine gross margin for each month, and then find the average one for five months.

gross margin calculation

Supposing, CS stands at $7500, we subtract it from our Total Revenue of $13,680 and get 45 percent of Gross Margin. Adding this number to the other four GMs that don’t fluctuate much, and dividing the sum by five, gives the average gross margin of 45 percent.

ALT - the average lifetime of a customer. ALT equals 1 divided by the churn rate figure.

The churn rate is measured by the number of clients at the beginning of the period (CB) versus the ones left at the end of it (CE):

churn rate calculation

Let’s say you had 500 customers early this month but were left with only 400 when the month was over. It follows, that your churn rate is 20 percent and ALT, consequently, - five months. By the way, did you know that machine learning can now predict churn rates? For details, go to our article on churn rate prediction for subscription businesses.

Summing up the above figures, we can finally calculate the predictive LTV: (120 T × 22.8 AOV × 0.45 AGM × 5 ALT) / 500 users = $12.30 per user.

Flexible LTV: covering possible changes in revenue

What for: taking predictive LTV one step further having retention and discount rates in mind.

Many businesses - startups, in particular - are far from flat revenue, constantly undergoing numerous changes. If that’s your case, counting your LTV, you may want to take into account such indicators as discount rate and retention rate for a more precise calculation.

Here is a formula that unites the mentioned inputs:

Flexible LTV calculation

GML - average gross margin per customer lifespan. GML shows how much profit your business generates from a single customer during an average lifespan.

GML calculation

As we have already calculated GM, we simply enter all the values into the formula: 0.45 GM × ($13680 / 500 users) = $12.30

D - discount rate. Also called required yield or required rate of return, D indicates

the rate of return on investment. Let's assume that the discount rate accounts for 10 percent.

R - retention rate. R can be calculated by the following formula, where Cb and Ce are numbers of customers who made repeat purchases or extended their subscription at the beginning and at the end of the period respectively. As for Cn it’s a number of new customers acquired within this period.

retention rate calculation

We suggest having 500 customers first of the month, resulting in 600 customers by its end, plus 200 new customers during the month. When we plug these figures into the formula above, we’ll get a retention rate of 80 percent.

The ultimate LTV in this case will be: 12.3 GML × (0.8 R / (1 + 0.1 D - 0.8 R)) = $32.80

Based on the calculations above, if our business acquired a new customer for less than $32.80, our unit economics is healthy. Otherwise, we’re losing money.

Complementary metrics: How to get a fuller picture on LTV

Using some indexes along with unit economics will be useful depending on your revenue and business models. So, for more accuracy, you can make adjustments to your industry and business strategies. To get a better picture of your profit and customers, we recommend calculating ARPU and conducting cohort analysis.

Average Revenue Per User: How is your business doing?

What for: project and company benchmarking, revenue forecasting, quick estimation of financial alterations.

Average Revenue Per User is an LTV-related metric that measures the overall health of the business on an ongoing basis. To calculate ARPU, choose a period and take a total revenue earned within this time. Then, divide it by the corresponding number of subscribers.

Average revenue per unit

Interpreting ARPU allows for:

Forecasting revenue. For example, let’s say 30 customers brought the profit of $960 in a two-month period. Then, the ARPU for this timeframe equals $32. Multiplying this result by six, we can calculate how much revenue these customers will generate in a year, which turns out to be $192 per customer.

Choosing the most promising project. Investors use ARPU figures to compare projects and choose the most effective one - the one offering the highest average revenue per user.

Judging the success of any changes made. If ARPU jumps up after implementing a promotional deal, it means that the effort paid off. Counting ARPU can help evaluate any financial alterations - if its value increases, kudos! You did everything right.

Cohort analysis: Understanding customer groups

What for: behavioral patterns, retention rate, valuable customers.

LTV varies greatly between different customers, so it makes sense to segment them into certain groups - cohorts - based on the time of their first purchase.

Cohort analysis is a matrix where, instead of per-user revenue, you keep a record of the monthly/quarterly/annual revenue each customer segment generates since they’ve opted for this company. The following example shows that the customers acquired during the first month initially generated $100 revenue, which lowered to $90 in the second month, ending up with $60 in month five. The same logic applies to the second-month customers - beginning at $100, their contribution went down to $85 over the course of time.

Revenue cohort for a five-month period

Revenue cohort for a five-month period

Cohort analysis can be helpful in terms of understanding customer retention rate, providing an immediate insight. Have a look, the following table shows a number of customers - cohorts - acquired within a certain timeframe (e.g. 2010-01-01 - 2010-12-31). Then, the table tells us the percentage of customers from one cohort who purchased again in a year (12-24, 24-36, etc.), in other words, those who were retained as customers.

Cohort table identifying average retention rate per year

Cohort table identifying average retention rate per year, Source: GormAnalysis

Tracking cohorts over time will eventually let you see the common patterns in their behavior and estimate how new customers should act. Juxtaposing cohort results from different periods, as well as among other cohorts, you can identify most valuable customers, determine retention rate, and find the points where purchasing drops off.

Cost of acquisition (CAC)

Acquiring new customers is one of the most difficult challenges a new business deals with.

High-level analyses while reflecting overall expectations won’t provide the results of the needed accuracy. It takes capturing value at a per-unit level.

It’s a common practice for companies to start with high CAC to build scale. As a business matures, it is usually able to gradually lower acquisition cost while relying more on higher LTV. Take Netflix as an example: With an estimated LTV of 25 months, they don’t mind losing money during the first-month free trial, as it will pay off yielding long-term customer loyalty.

General CAC formula amounts to marketing and sales costs divided by the number of acquired customers. The former constant involves various expenses: ad spend, marketing agency costs, payment processing fee, sales team salaries and commissions, you name it. All of these factors must be taken into account to have a fair CAC result.

CAC calculation

Using the CAC metric, you can assess and compare how different advertising campaigns are performing. If at first sight campaigns produce an equal number of customers, calculating from a per-customer perspective will allow us to notice that each ad earns customers at a different price. As a result, it’s easy to decide upon the most profitable ad campaign to pursue.

Unit economics results: What now?

Comparing LTV and CAC metrics, you can see if your marketing efforts are profitable. Here are three basic scenarios you can face:

CAC < LTV - strong unit economics. The best-case scenario is that LTV is 3x higher than CAC, meaning that for every dollar you spent on acquiring a new customer, you get 3 dollars back over the course of their lifetime. In this case, it’s safe to assume that investing more money in marketing will pay off and growing to other markets won’t lead to a scaling catastrophe.

CAC = LTV - business stagnation. In terms of cash flow, the outcome is negative for you, as it will take customers their whole lifetime to repay you the initial spend. Try avoiding expenses in areas that don’t bring relevant traffic (like blogging and social media ads). Instead, invest resources in increasing customer lifetime value using upsells and cross-sells and providing a personalized experience.

CAC > LTV - poor unit economics. Should acquisition costs take the lead over the LTV, that’s a sign of financial loss coming. Pursuing such a strategy, as odd as it sounds, the more customers you acquire - the more money you will lose. Stop growing. If more investments come your way, don’t grow either, as they will soon drown under the weight of a larger market. Instead, reconsider your business approach before it’s too late.

Why use unit economics?

Usually, the task of tracking any financial metrics in a company belongs to its CFO. But, in the context of software development, unit economics is one of the product management metrics. As a product manager is the one strategizing and making sure the product is following its success route, this person will make decisions on the metrics results. (If you’re interested in more KPIs for product management, check our article offering a comprehensive list.)

So, when should you apply unit economics?

To expose gaps hindering profitability. On estimating how sound a product is, the UE draws attention to problematic issues: overpricing or underestimation, weak advertising, or maybe outdated service.

To identify optimal strategies. UE is a handy tool in decision making. Not sure if there’s a point in scaling the project or changing the pricing plan? Or maybe you intend to roll out a promotion? UE can put you on the right path.

To calculate reasonable spendings. UE helps understand how much can be spent on attracting users. UE metrics demonstrate when product growth isn’t worth the cost and when forcing product discounts and partner incentives makes no financial sense.

To evaluate the potential. Not only does unit economics assess the product's current situation, it can also predict its future development. Looking at the direct revenues and associated costs of a company’s business model, the UE projects how successful a company may or may not be, and whether profit is forthcoming.

To analyze startup performance. Unit economics is particularly applicable for startups to estimate their market sustainability. To prevent a startup from failing requires a good handle on the UE. Benchmarking can be used to foresee conversion rates. Analyzing business performance of others, you can use their data to calculate how much customer acquisition will cost you in the early stages.