To enable experienced investors and aspiring business enthusiasts alike to evaluate the results and financial future of a company, there is unit economics (UE). This is a tool that was invented by venture capitalists for evaluating potentially promising projects. Before investing in a business, it was necessary to make sure that the project would be financially successful. Unlike the classical calculation of the return on investment ratio, unit economics helps to calculate the profitability of a product or service unit.
Unit economics allows you to decompose a business into its components (units) and answer questions such as:
- How much do we spend per client and how much do we get from him?
- is there a financial sense in the enterprise and its scaling, and where the company has a breakeven point,
- where is the business growth point,
- how the product can be optimized.
The calculation of the economy for a specific business unit fits very well for those industries that have an insignificant share of variable costs. These include cloud solutions, mobile applications, online software stores, subscription services, and others.
How to calculate unit economics
In order to calculate the prospects and payback through units, you need to know the indicators of income and expenses. In income, we take into account the revenue received on average from one customer. In costs, we focus on variable costs that are needed to fulfill the customer’s order (fixed costs are not considered).
We also need to understand our unit of business. A business unit is something that brings money to a company, something without which the company loses its meaning of existence. For example, for a marketplace, the main unit of business is the deal, as it gets paid for the deal that it provided. For an online store, it is a client who exchanges money for a product. And for media, for example, this is the user and the average number of views of ads per user.
Unit economics contains many different metrics. We will consider only the most necessary ones:
- Average Revenue Per User (ARPU) – average income from one attracted user,
- Cost Per Acquisition / Customer Acquisition Cost (CPA / CAC) – the cost of attracting a customer.
If CAC> ARPU, then you are likely to lose money, and if ARPU> CAC, then you earn. It is conventionally believed that in a successful unit economy, the average income should be three times more than the cost of attracting.
For example, let’s calculate the CAC:
CАC = Acq Costs / UA
To calculate the cost of customer acquisition, you need to know the value of two other metrics:
- Acq Cost – user acquisition costs,
- User / Lead Acquisition (UA) – the flow of users or potential customers (number of users).
Now we calculate ARPU by the formula:
ARPU = ARPPU * C1
To calculate ARPU, we calculate the following metrics:
- Average Revenue Per Paying User (ARPPU) – income per paying user,
- C1 – first purchase conversion.
How to calculate C1? To do this, you need to know:
- Buyers – the number of paying users,
- User / Lead Acquisition (UA).
Calculation formula: C1 = Buyers / UA
How to calculate ARPPU? Necessary indicators:
- AvPrice – average customer bill,
- Cost Of Goods Sold (COGS) – the cost of goods sold.
Calculation formula: ARPPU = AvPrice – COGS
These are the basic metrics that every business needs to track.
It is important to remember that it is worth calculating the UE for each advertising channel separately.
Data-driven decision making
Using unit economics, we can find points of business growth – which means making decisions about expanding or mastering new marketing channels based on the company’s key indicators. This is data-driven decision-making.
Data-driven decisions also include insights into your sales funnel. Its user passes from the moment when he saw your ad to the moment he paid the order, becoming your client. You can analyze the funnel using your own analytics systems, paid solutions, or free ones, such as Google Analytics.
If you have an online store, you should look at metrics such as
- Bounce rate,
- average page depth per session (Pages / Sessions),
- average session duration.
The perfect picture is when the bounce rate is low, the average pageview depth is more than two, and the session is long enough to get acquainted with the content and take the targeted action.
End-to-end analytics
In order to draw the right conclusions, you must have complete data about everything that happens to the business. Usually, to get a complete picture of business end-to-end analytics is introduced.
End-to-end analytics allows you to track all important business metrics almost automatically in real time. This is a whole system or even a methodology that allows you to analyze the customer journey at all stages of the sales funnel, as well as assess its value to the company. End-to-end analytics show how a customer gets closer with a brand across all company-controlled points.
It is implemented in order to understand what is happening with the business and track such moments as:
- Evaluation of marketing effectiveness of and the client as a whole for the company,
- Customer touch funnel demonstration,
- Omnichannel and multichannel to calculate efficiency for different types of business and transactions stretched over time,
- Evaluation of the advertising sources and channels’ effectiveness.
The implementation of end-to-end analytics is a large project, often individually designed for special client requests, taking into account those indicators that are important for a business to track on a single dashboard.
Any project starts with collecting data from business departments. Each department tells its wishes, what metrics it wants to track. After that, a project is prepared, which includes the data transfer architecture and metrics collected from different departments. The structure tells us where the data come from and where certain data will be transferred, where it is stored, and where it will be rendered.
The simple structure looks like this:
In more advanced analytics structures, all data is stored in the storage – this is a single place where all data is aggregated, after which it can be displayed for visualization. In the most complex structures, there are still ETL solutions that bring data to a single format, and only then visualize it.
The structure of end-to-end analytics is selected for each business individually because it depends on its type. For example, for an online store, there may be two completely different structures, and it may depend on whether it has an offline retail store. If so, it means that there is a ROPO effect (Research online purchase offline) – it is important for a business to understand what proportion of offline purchases is generated by an Internet resource. Without end-to-end analytics, it is impossible to predict this share and evaluate ad campaigns.
So, unit economics, combined with end-to-end business analytics, can provide owners with a summary of the company’s current health or startup success. Moreover, this tool helps to identify potential points of growth and customer acquisition based on the received data.
