The (Ir)relevance of Unit Economics

The (Ir)relevance of Unit Economics

How’s your new business coming along? Good? Why do you say that?

Probably because as rational human beings, we intuitively use ratios to measure things. Businesses tend to operate the same way.

You look at your numbers and see that your company is adding 2x new clients every month. Would you think your company is doing well? Your first thought would probably be: yes. But the truth is that while it might look good at first glance, you won’t have the right answer until you look deeper. Your unit economics matter, and if they are adverse, it might just kill your company.

At this stage, what’s more important than the total revenue is your unit economics, which is essentially the revenue and costs associated with each unit you sell.

When you are selling one more thing of what you make – are you making money on it?

In this post, I will be discussing the importance of unit economics, especially for seed-funded companies, both B2B and B2C.

Case Study of a B2C Mobile Gaming Company

Let’s pick up the case study of a B2C mobile gaming company I worked with. A company in that industry will calculate its revenue in the following manner:

It will have X number of Daily Average Users (DAUs), out of which only a few will pay. The company now needs to look at the total money collected over this period.

In this situation, let’s say the company has a total of 1,000 users, and collected $100. So the unit revenue (average revenue per daily average user — ARPDAU) is 10 cents per user per day.

The company, as a service provider, now computes its costs, which are generally divided into two parts: fixed and variable. Variable costs are typically: server cost (partially fixed but some variable), bandwidth, and advertising costs to acquire users.

Assume the advertising cost is $2 per thousand impressions (CPM). On an average, how many impressions does the company pay for before it gets a customer? Let’s assume it to be 1,000 impressions.

Thus, its cost per install (CPI, i.e. the cost of customer acquisition) is $2.

To be profitable to the company, an average user has to last at least 20 days. (20 days * 10 cents / day = $2 revenue) If an average user goes away in LESS than 20 days, the company will never make money.

What happens in B2B?

The situation is completely different in the case of seed-funded B2B companies, because their fundamentals are different. How?

  1. To get any accurate statistical average of a data set, the data set needs to be big (“n” has to be large), and at the seed stage, that is near impossible for a B2B company. At the seed stage, a seed-funded B2B company that has good “traction” will have 4-5 customers, even if that!
  2. A B2B company also has much longer sales cycles – i.e. prospects today become clients next year, which means the costs of acquisition are higher and add up for a longer period of time, for each client. This makes averaging costs over a data set really difficult.

So for B2B entrepreneurs, any unit economics figures derived would have serious validation issues.

Moments like these have made me realize that a lot of startup advice dispensed about is implicitly aimed for consumer startups. While hundreds of people are talking about unit economics for startups, nowhere did I find a distinction between B2B and B2C companies. This is perhaps explained by the sheer volume of consumer startups – AngelList actually shows over four startups tagged “consumer” for every one startup tagged “B2B” or “enterprise.”

However, where unit economics is concerned, I’d like to dispense this myth for B2B startups. Don’t waste precious resources getting those figures right. At this stage, even your investor is more concerned whether you’ve built something that people will pay for. Unit economics gain importance after a Series A round.

If you’d like me to deep-dive into your numbers, I’d be happy to provide a free consultation. Please reach out to me via email at or even an inMail works.

Source for cover image here. I have talked more about the “magic number” that startups need to get right in an earlier article here.

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