This post is part of the Growthzilla Book series, which is an online draft of the print edition that will be available in 2018.
When modeling a business, it’s best to begin with its core: how it generates revenue. By starting at the most fundamental level, you can progressively drill deeper into the variables that affect revenue to understand what factors are ultimately responsible for the success of your company. Furthermore, you can map key stages of the customer lifecycle to the factors that drive revenue. For example, one common realization that business leaders make when they really evaluate their revenue model is that increasing how often existing customers make purchases can be just as effective as garnering new customers. You could also just focus on modeling pure customer growth, which we will cover in the next section, but starting with how your business makes money will help you to tie everything together. In this section, we’ll start with the simplest revenue model and explore how it can be applied to different types of businesses.
Simplest Revenue Model
How much money a company makes quite simply depends on how much stuff it sells and how much revenue it makes per unit. This is the most basic revenue equation known to mankind, and even though it’s trivial, it’s the perfect jumping off point for us to delve deeper into more sophisticated models. We can start by breaking down what factors drive the number of transactions (or sales). The more active customers, the higher the number of transactions. Moreover, if each customer makes more purchases, that will also increase the total number of sales. Therefore, the number of transactions that a company gets is a product of the number of customers that it has and how many purchases each of those customers makes.
The amazing thing is that even by digging one level deeper, we can start to see insights! Since the number of transactions is a function of the number of customers and their average purchase volume, we can ask ourselves how can we get more customers or how can we increase how many purchases each customer makes. We can start to form hypotheses around increasing each of those factors independently. For example, we can assume that we can get more customers by increasing marketing spend or by introducing a feature where customers can invite their friends for cash credits.
The underlying factors that drive overall revenue vary with different business types, which we will explore next, but I will also note that there is no right way to break out the revenue model for any business. The examples below are simply meant to give you a starting point, and you’ll need to contextualize them to your business. Another important note is that some of the models below do not take time into account, but you can simply add a temporal component by applying the equation to any period of time be it a month, quarter, or year.
E-Commerce Revenue Model
In order to model an e-commerce business or any retail business you should explore what drives the volume of transactions as well as average revenue per transaction. There are many ways to think about this, but one common perspective is to consider the transaction or sales volume to be a function of the total number of products that are being sold in the online store as well as how likely they are to be sold. The probability with which a sale is likely to be made could also be influenced by how many products the average customer views as well as how likely they are to purchase each product. You could dive even deeper and assume that the number of product views is a function of how many visitors you get and how many products each one views, on average.
Let’s not forget that we could also increase revenue by increasing how much an average customer spends. What drives the revenue per transaction? That question might be a little more tenuous. It could be the nature of the goods you’re selling (budget vs. luxury), how easy it is to find products, the checkout conversion rate, or the amount of cross-selling that you do on each product page. In reality, probably all of these things have an effect on the average revenue per transaction, and the best way to figure out on what to focus is to go back to your trusty customer journey map and identify how your customers perceived each of those experiences.
SaaS Revenue Model
The following model has a time component since software as a service businesses tend to worry about revenue on a monthly basis given the typical billing cycle for this class of products. At the simplest level, monthly revenue for SaaS companies is a product of the total monthly subscribers and the average monthly subscription cost. One could break down the number of monthly subscribers as being the product of the total number of customers that are paying for subscriptions and the average number of accounts or seats that each buys. Delving a little deeper into the model, one could assume that the number of customers is a function of marketing effectiveness or the size of your sales team. Furthermore, the number of subscriptions that each customer buys might be greatly affected by how easy it is to invite colleagues.
What is a bit unique in the SaaS model is that it’s often more difficult to affect the monthly subscription cost if the business is selling just one product, in which case the pricing is often set by market conditions. In other cases, when the business sells multiple products or tiered plans for access to advanced features, it is possible to affect the monthly subscription cost. Moreover, the cost of enterprise subscriptions is often tied to the size of the client company, so larger companies often pay higher subscription fees for bigger teams. For SaaS companies that have tiered pricing, as most do, it’s wise to create a model for each tier when performing more rigorous analysis or creating predictive models.
Marketplace Revenue Model
Marketplaces such as eBay or Uber bring buyers and sellers together and make money by charging a transaction fee. The total revenue that the company makes from the marketplace is a product of the number of items available for sale from sellers, the probability of a sale, and the average transaction fee. Each of these factors can be further broken down into underlying factors that drive them. For example, we can assume that the number of items for sale in the marketplace is driven by the number of sellers on the platform as well as how much each seller posts, on average.
We can also think of the probability that a sale will be made as a function of how many times buyers view the item as well as the conversion rate for purchases (e.g. how many purchases are made for every thousand views of products). The number of views of product also depends on how many buyers are on the marketplace and how engaging the buying experience is. For example, if the product images are really unattractive, buyers probably won’t spend a ton of time browsing products.
Finally, the average transaction fee can also be affected by a number of factors. The two most obvious ones are the fee that the company is charging as well as the average sale price since many marketplaces charge a percentage commission. The average sale price could also be determined by the type or quality of goods that are being listed on the marketplace. It’s also worth noting that, in some cases, the marketplace charges flat fees. For example, many job sites charge a flat fee for every job posting that the employer publishes.
Community Revenue Model
There are a large number of online and mobile products that enable people to connect and share content with each other. The class of products include social networks and user-generated content sites such as Facebook, Twitter, Instagram and Quora. One thing they have in common is that they make money by displaying pay-per-click advertisements on their pages and in their apps. The revenue that these kinds of companies make is a product of the total number of clicks on ads and the average cost per click (or revenue per click). This model, with slight variation, can also apply to any business driven by cost-per-click advertising.
The easier of the two variables to optimize is usually the number of clicks on ads, which we can think of as a function of the average ads per page, the number of times that users view those pages (page views), and the rate at which those users click on the ads. Diving a level deeper, we might assume that the number of ads on the site or in the app is determined primarily by the number of advertisers and the average number of ads that those advertisers buy. The number of pages that are viewed by users is probably a function of the number of users and how engaging the content on those pages is.
The average cost per click is a little more difficult to model, but it is usually driven by two key inputs: the number of advertisers bidding on ad placements and the categories that are being advertised. For example, costs of ads for lawyers are usually a lot more than for common commodities such as grape jelly. Another thing to note is that the cost per click of ads almost certainly impacts how many of those ads or impressions advertisers buy.
Key Takeaways for the Sample Revenue Models
The main point to understand is that the above models are just simple examples, and although we hope they are very relevant for your business, you should use them as starting points to develop your own model. As you are creating a revenue model for your business, you should lean heavily on the customer journey map that you developed since it will provide you with great insights about what variables are important for growth and how they relate to each other. Finally, it’s important to remember that other growth models such as those for viral growth usually plug into the left hand side of the above models and drive the number of customers that a business has. We’ll cover customer growth models and other useful models in the next section.
Be sure to check back tomorrow to learn about different kinds of growth models that you might use. New sections of Growthzilla are published every weekday.