106. Building an Operating Model

We now have a CLV model, an important tool to help grow our business. More important, we're starting to understand how different drivers of growth impact each other. 

The next step is to build an operating plan ("OP"). To do so, we have to make two new key assumptions:

  1. How many customers will buy our product? 

  2. What are our fixed costs? 

 

Similar to the CLV model, data is immensely helpful in guiding our assumptions. With more limited data (i.e. pre-launch), we’ll have to research and cobble together what we can to provide guardrails.

 

We combine our new assumptions with elements of the CLV model to produce the OP. At its most basic level, the OP is a simple profit & loss statement. The OP is hugely helpful in allowing us to estimate annual earnings for our product.

 

[image of operating plan w/ emphasis on annual earnings]

 

The OP also serves as the cornerstone in allowing us to explore different scenarios. There's a few different flavors of scenario planning exercises. Predicting the future with complete accuracy is impossible. It's more a matter of reducing the margin of error. Data can help us reduce that margin of error. When we're pre-launch, we don't have the luxury of data to help guide our assumptions. As a result, we can use scenario planning to sensitize our key assumptions. In this case, we may hold some drivers steady (i.e. price), and apply a general buffer for our assumptions (i.e. decay curve). This creates a "cone of possibilities".

 

[image helping show this... +10% / Base / -10% across key drivers]

 

 

 

 

 

 

 

 

We can also use scenario planning to see how big decisions impact growth & earnings. Compared to above, we take a more concerted approach towards adjusting specific drivers. For example, if we feel that our price is low, we can observe how increasing price impacts customer growth and earnings. Or if we think we can grow faster, we can model out scenarios where we invest more.

 

[same as above but with narrative points vs. the general buffer]

 

Data & the ability to experiment is hugely helpful in this type of scenario planning. We can do this kind of exercise pre-launch or with limited data. For example, it may help to see how different prices impact new customers & growth. But we should be aware that, at the end of the day, these are just numbers in a worksheet. With limited data, our margin of error is high, and we should view any output through that lens.

 

With the preamble out of the way, let’s start to build an operating plan. We'll bring our hypothetical Substack newsletter, Big Dean's, along to continue guiding us. Our first step - estimating how many customers will buy our product.

 

Let’s assume Big Dean’s is about to launch their newsletter and hoping to build an operating plan ("OP"). Being pre-launch, the team is only focused on the first year of operations. The team wants to understand how much they can expect to earn, and how much they will need to invest. 

 

There's also continued uncertainty around big decisions. Like price and whether to offer an annual plan. But also whether (and to what degree) to invest in marketing & hire additional team members. Big Dean's wants to adjust these drivers and explore potential pathways forward.

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