How to read Pitch Deck. Metrics & Unit Economics

In addition to the desire to make money, investors and startups have something else in common. It is the fear of slides with numbers. But is it really that complicated?

The topic of metrics excites, and for a good reason because if you miscalculate it, you'll doom a startup. In previous materials about pitch decks, we already mentioned them along with unit economics. The time has come to fulfill the promise and devote a separate piece to this subject. The article will help novice investors understand unit economics and metrics, which they will surely encounter in pitch decks. Understanding the subject will be handy, so you don't get into a deal with questionable math. We'll start with some basic information to refresh your memory. Next, we'll look at what we think are the primary metrics found in most startups. Finally, we'll play situational challenges to help you determine the key metrics for a particular startup on your own.

When writing this article, we consulted with venture partner Pawel Schapiro and investment analyst Daryna Koval from TA Ventures, for which we are very grateful.

Unit Economics in a nutshell

Unit economics is the calculation of costs, income, and profit per product unit. It allows you to look into the future and understand how much money the startup can make, how long it will take, and the risks of incurring losses. For example, if a startup sells software, a license can be considered a product unit. If the startup develops hardware, the product unit could be a device. Knowing the mathematics of one product unit, we can make calculations for the whole startup. Thanks to the figures obtained, we can see – or not – the economic feasibility of the company.

There are two cases where it is acceptable for a startup not to include unit economics in its pitch deck. The first case is a startup at the pre-seed stage. As a rule, there are no sales, and the focus shifts to hypotheses, analytics, and the team. The second case is an R&D-heavy company, where a lot of money goes into creating the technology or product itself. In such a situation, profitability from clients is not the key objective. Investors can nurture such startups with a future exit in mind.

For example, Consumer Edge, a famous data provider from the United States, recently bought our portfolio company Qentnis. The startup is also a financial data provider, but smaller, focusing on the European market. From the beginning, we thought about M&A as a way to go, so unit economics was not the main reason for the investment. As a result, everything went according to plan. Don't rush to throw pitch decks in the trash if there are few metrics or no unit economics. First, pay attention to these two possible situations.

The ocean of metrics

Investing in a startup involves a lot of different indicators that are easy to confuse. But does an investor need to know them all? The short answer is no. Based on our practice, we divide all metrics into primary, key, and product. In turn, primary metrics are divided into revenue, growth, and customer base.

We call the primary metrics that tell core info and can be found in most startups. By memorizing them, you will know most of the metrics needed to decide about investing. With key metrics, it's even easier. These are one or two from the primary pool considered critical for a particular startup. Think of them as the locomotive that drags everything else along. Forgive my love of metaphors.

A logical question arises – how do we choose the key indicators? To do this, we start from the strategy and business model of the startup. Each company selects its own methods for working with clients and making money. Depending on those methods, the key metrics are determined. For example, if a startup sells its product by subscription, its key metric will be MRR, monthly recurring revenue. If the startup is a marketplace, we look at GMV, gross merchandise value. A company that sells a physical product should focus on margin optimization. LTV\CAC is equally important for almost all startups. Last example: a startup with a freemium sales model. In this case, the key indicator for the investor will be the number of paid subscriptions.

Key metrics do not contradict primary. Returning to the analogy, imagine a train without a locomotive, where cars are the primary indicators. We must choose the key ones among them, which will turn into a locomotive.

Beginning investors are likely to face difficulties in understanding business models. It is unlikely to learn them by heart. Many startups work with proven strategies. Along with this, companies often offer new ways to monetize. The investor gets used to it, as trite as it sounds, by practice and experience. After a few hundred pitch decks, you will begin grasping monetization's essence and thinking logically. In other words, you will learn to understand where the startup is getting or wants to get money from, drawing the appropriate conclusions.

To summarize. You, as an investor, open a pitch deck, look at the business model, determine the primary metrics and highlight the key ones. This is what a startup's unit economics breakdown looks like. However, there are typically cases when it is impossible to understand pitch decks. 

The most common mistakes are too many metrics and missing key ones. A startup may try to impress an investor and calculate everything. This is a bad signal for us because founders don't understand what should be counted and what shouldn't. Unit economics is not a competition in arithmetic. Investors only need a few indicators that will answer most questions. Overloaded tables with formulas only tire and distract from the main thing. A startup should calculate its metrics not for the sake of calculation, but for further work.

Primary metrics

Let's start with the income. First, investors are interested in how much money a startup can make. This includes:

• Revenue. The figure is simple and straightforward. For example, fund N prefers those startups that can show $60 million in revenue over three years. 

• Revenue growth. Realistic revenue dynamics over a certain period. We emphasize the word realistic because a startup needs to assess its capabilities reasonably before specifying a particular figure here. A good indicator for a startup at an early stage would be 3x a year. For example, in the first year, the startup expects revenue of $10 million. In the second year – $30 million. In the third year – $90 million. This gives an idea of the scaling prospects. In addition, the metric allows the startup to find its investor. Some funds expect quick results from the team, while others are ready to invest for the long term.

• MRR and ARR (monthly & annual recurring revenue). These metrics are needed for startups that make money through subscriptions and refer to the cumulative income from them over a month or a year. If the startup sells subscriptions weekly, it could be WRR – weekly recurring revenue, for example.

• GMV, aka gross merchandise value. Used only in e-commerce and marketplaces. It means the total value of goods sold over a certain period. For example, a startup says that its GMV for the quarter was $25 million. This figure shows players' activity in the marketplace and directly affects profit. By observing the dynamics of GMV, we can predict the marketplace's success without waiting for revenue & profit reports.

The second most important block of indicators concerns growth and its efficiency. In other words, how effectively the startup spends and earns.

• LTV, aka lifetime value. It means the amount of money a startup receives from one client for a whole time while working with him. The higher the figure, the better. A business always prefers to have a smaller pool of loyal customers who constantly buy something rather than vice versa. Regular payments help to get a steady income and minimize the cost of attracting new customers. Any economics textbook will tell you that it costs less to retain a customer than to find a new one. A good LTV is 3x or more of CAC.

• CAC, aka customer acquisition cost. Means how much money you need to spend to get one client. The lower the figure, the better. To put it simply, it is the cost of marketing. CAC is closely related to LTV and is often seen as one. We will see a high LTV and a low CAC in good unit economics. For B2C, a good CAC is considered 12 months or less. For B2B, up to 6 months. That is, how long it will take to get back the money spent on marketing.

• Gross Margin. Margin means how much revenue we get from one product unit. In other words, it is the difference between the cost of production and the price to the end consumer. The higher the margin, the better. The indicator is vital for companies that sell physical or other products where it is difficult to receive regular payments (e.g., subscriptions). In such a case, the startup's strategy is to maximize revenue per transaction.

• AOV, aka average order value. This is the average receipt. The indicator is used in retail and e-commerce. The higher the average check figure – the better. The startup is interested in selling the client as many goods and services as possible in one visit. A low AOV may indicate problems with the assortment, personalization of search results, and pricing policy.

• Burn rate. Shows how much money the startup has until the next round is raised. It is measured in dollars, euros, or other currency. For example, you have invested $1000. The startup spends $100 a month. So its burn rate = $100. The founders are supposed to achieve moderate spending and have time to fulfill the tasks set out in the strategy before the investments are exhausted. A high burn rate can indicate concerns in unit economics, overstated salaries, poor logistics, and production optimization. A high burn rate gives a startup few chances to make a mistake, offers investors to go for broke, and makes searching for funding in subsequent rounds more difficult. Who, after all, wants to give money to spenders? In general, the lower the burn rate, the better.

The last block of primary metrics concerns the customer base. 

• Paid users. A key indicator for subscription-based and freemium business models. Startups in this cohort often offer a conditionally free product, lure customers, and then try to monetize them.

• Retention. The number of loyal customers who continue to use the product over a certain period. Measured as a percentage. The higher the rate, the better. Retention is related to LTV because the longer the customer uses the product, the more opportunities for additional monetization.

Product metrics

This includes non-financial metrics such as MAU, DAU, WAU, NPS, sign-ups, app installs, beta users, uninstall rate, pre-booked revenue, engagement, consumption, and many others.

Such metrics are used to determine product-market fit, that is, whether the product has found its customer. As a rule, they show activity but do not answer the question of where the money is. Startups like to abuse them by substituting primary metrics. Pay attention to this and always watch context.

The simplest example is the DAU, daily active users. Is a million active users a month a good or bad thing? If they don't pay anything, that's bad. But that does not mean that the startup will not be able to monetize its audience in the future by getting a lot of paid users. Knowing these figures will give you a complete picture of the startup's state of affairs.

Where's the money, Lebowski?

To better understand unit economics and metrics, we suggest looking at examples of startup business models. Such case studies will help you get your wits and determine where the money comes from. By understanding its source, we know what metrics to look for and what to ask about.

Task #1. Startup TopBuild is looking for investment for a marketplace that sells building materials. Of the features, the founder cites personal consultants and in-house educational content. The target audience is professional builders and everyday people who want to do minor repairs but don't know how. What metrics might be considered key? Foremost, ask yourself how the marketplace makes money. It does this through commissions during transactions between buyer and seller. Since that's the case, we're interested in metrics that tell us about sellers and buyers. 

Pay attention to GMV, AOV, and the number of sellers with buyers over time. GMV will show the volume of transactions and the size of the marketplace. If the marketplace is skinny, is it worth the investor's time? AOV, in turn, will give an idea of the amounts spent by participants. A low average check will not allow the marketplace to grow and, consequently, your investment. The dynamics of buyers and sellers joining the marketplace will answer the demand for such a product. 

If that does not seem sufficient, ask the marketplace founder to discuss retention and gross margin. An excellent gross margin for marketplaces is considered 60-70%. As for retention, we want marketplace participants to return to it repeatedly. Otherwise, it will quickly fall into disrepair.

Don't you forget something? Personal consultants and in-house-produced educational content can financially burden a startup. It's a good idea to ask if the founder has considered the costs, how critical they are for attracting participants (customer development), and whether this will affect the high burn rate. Maybe all this is unnecessary, and we are wasting our money?

Task #2. The startup ShinyYou sent a pitch deck about a mobile dating app. The app is not simple but innovative, like everything in the startup world. The program uses unique algorithms that allow it to find couples three times faster than the competition (whatever that means). What metrics would be key in this case?

If the distribution model is freemium, we are interested in the number of paid users and MRR. That is how many people pay and how much. The startup can go a different way and allow to use the application only by paying a monthly subscription. Then we would be interested to see the number of these subscriptions. As you may see, nothing complicated.

Task #3. Startup SharK develops B2B software for optimizing maritime logistics operations. The business model involves selling a license and two levels of optional subscriptions: standard and premium. What to look at?

First, gross margin and MRR/ARR. There are hardly plenty of companies in the world that do water logistics operations. It's a tough niche with mediocre scaling. However, such companies are willing to pay a round sum. So we need to cling to every deal and sell licenses for as high a price as possible.

Considering the limited pool of possible customers, subscriptions will become a serious source of income for the startup. That is why we need to know the number of subscriptions and how much they bring in. MRR/ARR metrics are responsible for that. 

The following important metric is LTV/CAC. LTV will show the financial capacity of the subscriptions to cover the potential lack of new contracts. In turn, CAC is significant because finding and attracting a customer is expected to be costly.

Finally, ask about the retention rate. In this situation, we are unlikely to have customers that can be neglected. The loss of even one of them will be pricey.

Task #4. Startup Sootora makes natural wood furniture for offices. The pitch deck highlights features such as handmade, a wide range of designs, and the ability to design the space individually. As with the previous startup, the key metrics will be gross margin and LTV/CAC.

I don't think I need to explain why making furniture is complicated and time-consuming. In fact, any hardware startup is taking a big risk, unlike its software brethren. Margin comes first, as it will include materials, production optimization, logistics, and possibly assembly. How well the startup manages this stage directly determines the future revenue. 

LTV/CAC will show how effectively the startup can attract clients and what will happen when there is wooden furniture in all the offices.

Task #5. Startup WeirDY is asking for funds for an ArtTech project with an innovative monetization model of artworks by turning them into NFTs and then selling them in a marketplace for crypto. Oh, that web3. We can think about the key metrics together and write our guesses in the comments.

During our conversations with Pawel, we asked if there's a catalog where the business models are written and the key metrics opposite them. This would be a significant relief for venture partners, but unfortunately, nothing like that has been invented yet. In the meantime, pitch decks never cease to amaze, in both good and bad ways. 

Discovering a new and working business model equals finding a treasure trove. So it's not surprising that startups compete with each other in experiments, constantly looking for them. And since that's the case, new situations with equally new metrics await investors. The only thing that remains constant is the money. Always follow in their footsteps.