How many customers will buy your product?

 

Let's build an operating model.

 

The first step is for Big Dean's to estimate how many total new customers will buy their product each month. For now, we're not factoring in customers that cancel, which would give us our net customer base. We derive cancels (and net customers) by combining total new customers and the customer decay curve. 

 

It's important to separate the assumption for total new customers and the decay curve. The decay curve can vary quite a bit between different products. In other words, two products with the same amount of customer additions can have quite different net customer bases.

 

What to do for pre-launch products?

 

Back to estimating total new customers. Being pre-launch makes this quite challenging for Big Dean’s. Our best bet is to use comparable products and hope it puts us in the right ballpark. 

 

Comparable Products

 

The Big Dean's team finagles their way into talking with other food-oriented newsletters. Some are food critics, others are diet aficionados. The most comparable are a few restaurants that started operating a paid newsletter. It's a tight-knit community of operators, who are more than generous providing guidance and sharing best practices. Beyond being incredibly insightful, these conversations provide a narrow range for new customers. As usual when there’s limited data, it’s probably safe to err towards being more conservative.

 

[Pizzana's Tomato Sauce & Cheese on Dough // Gjelina's Gjam // Big Dean's Newsletter // Cafe Gratitude's Question of the Day... TOTAL new customers during the first year in green chart... squiggly lines for actuals showing that it fluctuates... BD's with a stable curve - "our best guess"]

 

Take Rate on Existing Audience

 

Besides using comparable products, we can also assume a take-rate by pre-existing audiences. "Audience" may refer to traffic to your website, Instagram followers, etc. Take-rate refers to the share of that audience that will buy your product. 

 

Big Dean's assumes that Twitter and Instagram will be the primary marketing channels. These are also solid sources of customers for the other operators mentioned above. They provide ballpark estimates for take-rates, which we apply to our own audience. Hopefully this provides support for the range above, or perhaps even exceeds it. If the estimates are lower, it may signal potential customer acquisition challenges. In this scenario, we may want to revise down estimates.

 

[follower count... avg monthly take rate for twtr & ig... monthly new customers, showing it going down over time - mo 1 vs mo 3 vs. mo 12]

 

[free audience on Substack]

 

What to do after we launch?

 

Though still hard to forecast the future, performance data takes quite a bit of the guesswork out of it. Over time, our month-over-month growth ("MoM") will likely stabilize. Same for year-over-year growth ("YoY"), which can help account for seasonality (i.e. if there's a Q4 holiday bump). As growth becomes increasingly stable, using data to drive assumptions becomes increasingly helpful. On the other side of the same coin, in the early stages of growth, there are a few pitfalls to avoid.

 

Abnormal Events

 

Growth is often gradual with intermittent moments of step-function increases. These growth bumps are usually driven by major product improvements or marketing efforts. Moments of step-function growth are more common early on, before the law of large numbers kicks in. There are a few things to keep in mind during these periods of step-function growth.

 

[image showing bumpy growth w/ callouts for notable mktg events]

 

First, step-function moments are tough to predict and risky to include in forecasts. There will be key product and marketing moments, surrounded by excitement & optimism. And that's important. But compartmentalize the positive outlook and don't let it cloud your forecast.

 

[image showing internal optimistic estimates of impact vs our business projection]

 

After a surge in new customers, we should expect MoM declines in new customers. The key is to stabilize on a higher growth trajectory. For forecasting after these events, bring expectations back towards the previous run rate.

 

[image showing major bump for few months w/ a forecast of big MoM declines, back towards previous growth rate]

 

In a similar light, be cautious in using exceptional events to model future efforts. It's inevitable to attempt to replicate the success behind step-function moments. Unlocking new marketing channels and partnerships, or launching important product features. These are important, but don't expect the same degree of impact as previous efforts. Diminishing returns are more likely than back-to-back grand slams.

 

[image showing don't of using past events to model exceptional events]

 

The above shows more extreme examples. As performance data flows in and we update forecasts, we'll face more subtle flavors of this dynamic. Like other pieces of the puzzle, we want to be accurate on a blended basis, not the daily peaks & valleys. And we want to pleasantly surprise ourselves, not vice versa. Err towards these and your forecasts will be a valuable tool.

 

Absolute vs. Relative Growth Rate

 

Relative growth rates (MoM & YoY) are likely to be higher when the absolute numbers are small, and vice versa. Right after launch, when user volume is low, triple digit MoM growth rates are not uncommon. Rather than use MoM growth, it's probably prudent to use absolute growth (i.e. ~20 customers / month). 

 

[show blue font for drivers - customers over relative growth rate early on]

 

As relative growth becomes more stable, we can start to favor it more in our forecasts. Like other pieces of the model, we can gut-check absolute & relative growth against each other. Perhaps we're holding MoM growth consistent and we see massive customer volume. We can start to peel back off those assumptions.

 

Growth & Gravity

 

As an extension of the above, it's hard to maintain growth rates over a long period of time. With most products or businesses, year-over-year growth tends to fall towards 0%. Like an apple falling from a tree, "gravity" places downward pressure on YoY growth. 

 

[revenue growth for FAANG & M companies over past 10 years?]

 

This is important for longer-term planning. First, it's probably safe to assume YoY declines when looking out over multiple years. Second, from a strategic view, it's important to think about how to combat gravity. This usually returns us to the two main levers of growth:

 

1. Grow the number of customers.

2. Increase the value of those customers (“CLV”.

 

Perhaps we explore launching products to reach new audiences or unlock new revenue streams. Or building partnerships with complementary products. Depending on the relationship, bundling can expand audience or drive up APRU. The key is to explore how to create new layers of growth that combat gravity in a cost-effective way.

 

[5-year plan w/ key strategic levers?]