Showing posts with label bootstrap. Show all posts
Showing posts with label bootstrap. Show all posts

Bootstrapped CPC rule of thumb: ARPU/25

Mike's Notes

Ajabbi is a bootstrapping social enterprise, but the useful measures outlined in this excellent article still apply.

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Last Updated

09/07/2025

Bootstrapped CPC rule of thumb: ARPU/25

By: Jason Cohen
A Smart Bear 01/07/2025

In the first year of business, you have no data for decision-making.

Even after the first hundred customers, half of those were serendipitous one-offs, not representative of repeatable, predictable customer acquisition, and the scale of the data isn’t statistically significant.

One of the fundamental data-driven questions (but you don’t have data) is: What’s the maximum I should bid for CPC (cost-per-click) campaigns like Google AdWords?

The answer for a funded startup is “Bid as much as possible, to get as many customers—and data!—as you can, as quickly as you can, then rapidly iterate from there in the presence of that data.”

That’s a smart use of money: To “pay to find out.” But what about a bootstrapped, profit-driven business? You don’t have that budget, and you’re keen on getting a reasonable return on investment reasonably quickly.

Here’s my way.

(Tune the exact numbers if you disagree with my assumptions!)

LTV = ARPU x 20

ARPU (Average Revenue Per User) is the amount you charge the average customer every month, which is typically a mixture of different quantities of customers at different tiers, special add-ons, etc..

LTV (Life-Time Value) is the total amount of money you expect to collect from a customer over their entire tenure. A simple version [1] is ARPU ✕ [expected months] meaning the average number of months a customer sticks with you.

[1] The correct version also includes multiplying by Gross Profit Margin, i.e. the cost to serve customers, which for SaaS is tech support, server infrastructure, and payment fees. You should include this for a more accurate calculation; small bootstrapped companies often have very high GPMs, so ignoring it for this back-of-the-envelop calculation was simpler.

Some customers cancel in one month, some cancel in a year, some in five years, and some never cancel! So it can be difficult to compute LTV accurately for small companies, and impossible to know for young companies (where five years hasn’t elapsed yet to see how many customer stuck it out that long). These are among the reasons that I dislike the LTV metric, but it’s common to use it in this context.

If you do have data, the simplistic calculation is [expected months] = 1/c where c is your monthly cancellation rate.

But since you don’t, in my experience (and in a non-scientific survey of some of the 100 startups currently officed at the fabulously Capital Factory co-working space in Austin), a good pre-data rule of thumb is 20 months.

If you have an average customer lifetime smaller than 20 months (i.e. cancellation rate higher than 5%/mo), that’s a dangerously high cancellation rate for almost any SaaS business, and you need to focus on addressing the business issues before acquiring more unsatisfied customers. Use surveys and one-on-ones to try to understand whether it’s technical failings, lack of features, missed expectations, bad service, doesn’t hit pain points, or what.

A healthy SaaS company will have a higher number of expected months, but at the start you also will have lots of mis-steps with weird early-adopters and non-ICPs where your product is at its worst—least features, least quality, etc—so it’s good to assume a low LTV instead of inflating it to where it might be in future.

CAC = LTV / 5

CAC (Cost to Acquire a Customer) is your average total cost to get a new customer, which includes direct costs (AdWords spend, affiliate payouts, the fees your affiliate system charges to process them) and indirect costs (consultants and your own time). So to compute CAC, take your total costs to acquire new customers and divide by the number of customers you acquired.

In general of course CAC needs to be less than LTV, otherwise it costs so much to get the customer that you will never make money. A surprising number of startups have CAC > LTV. Many justify this either by not correctly computing CAC (e.g. ignoring indirect costs) or saying they’ll “fix that later” by raising prices or finding other channels of revenue. Others justify by saying they’re doing a “land-grab” for customers, and just having a customer at all has intrinsic value.

Profit-seeking bootstrapped companies cannot afford those delusions. Also you need something far stronger than CAC = LTV, because you need to pay for other business expenses and still produce a profit. So how big can CAC be before it’s “too big?”

Growing, funded SaaS companies who treat CAC with respect often commonly target CAC = LTV / 3.

Back at my second startup IT WatchDogs, my co-founder Gerry Cullen used to say “A third to built it, a third to get rid of it, and a third to keep,” meaning a third of revenue goes to pay for hardware/inventory/shipping costs of the sale, a third goes to what I’m calling “CAC” here, and a third for the overhead costs, development costs, and profit.

That’s a good model, and I think a bootstrapped company can copy it, but I urge profit-seekers to instead adopt an even more strict model of CAC = LTV / 5. The reason is that at the start you should be able to find a few efficient ways of acquiring customers, even if those get saturated over time.

CAC = ARPU x 4

If you combine the previous two results, you see that the cost to acquire a customer should be no more than four months of revenue.

Another good way to think about it is: “The payback-period for my cost to acquire a customer is four months.” Also, ideally you’re getting the first month of revenue back immediately, so it’s really three months of cash-float.

Companies with large budgets to deploy at scale will often be happy with 12 month payback periods; some very high volume businesses like shared hosting will accept 24 or 36 months! But a bootstrapped company’s cash-flow won’t allow it, even if the math would work in the long run.

Conversion Rate = 1%

Conversion Rate is the percentage of visitors to your website who convert to a paying customer.

This is another step which in practice should be completely data-driven, segmented by customer type and marketing channel, segmented by landing page, A/B tested and iterated, blah blah blah. But since you don’t have data, and you don’t have enough visitors to have real ratios, you have to take a swag at this number.

In that same informal survey I ran, and bolstered by other formal surveys, a huge number of bootstrapped SaaS companies report a 1% conversion rate.

Another way of saying the same thing is “You need 100 visitors to make 1 sale.”

And since you need to incur no more than CAC dollars in the making of that sale, you need to incur no more than CAC/100 dollars in the making of each of those visitors.

And if you’re running a CPC campaign, that means you can pay up to CAC/100 dollars per click.

And since CAC is ARPU x 4, we can substitute and get the end result:

CPC = ARPU / 25

So for example if your average customer generates $50/mo, you can spend $2/click.

Indeed, this is a great way to prove one of my main arguments for all bootstrapped companies, which is that you should charge a lot more than you think, in part because it enables you to pay quite a lot per click, which enables a wide number of marketing channels, and out-bidding parsimonious competitors whose paltry LTVs preclude them from competitive marketing spend.

Customized

“But my numbers are different!” Of course, but now you have a formula you can plug them into, to arrive at the answer:

CPC = (ARPU) r/5c

Where:

  • c = monthly cancellation rate
  • r = visitor → purchase conversion rate from the paid marketing source in question

Innovation Accounting in Practice

Mike's Notes

Ajabbi is a bootstrapped not-for-profit startup. Which is a tough way to go. Innovation accounting will help. I had an online meeting earlier this year with Tristan Kromer, and I gained valuable insights from him. Kenny was originally a musician.

The Kromatic resources are testable and strongly maths-based. They also don't make a fetish of Business Canvases like the innovation theatre crowd. The focus is on finding tools that are actually useful in a specific context. If they don't quite work, tweak them so they do, or invent one.

The Monte Carlo simulation is fantastic.

Ajabbi will pay for support from Kromatic once it has the financial resources. 

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Last Updated

27/06/2025

Innovation Accounting in Practice

By: Tristan Kromer & Elijah Eilert
Kromatic: Copied 25/06/2025

It is not enough to say, “We’re early stage and we shouldn’t focus on a business plan or metrics.” It is not enough to say, “We’re focusing on qualitative data.” And it is certainly not enough to say, “We’ll figure out how to monetize later.”

As an early-stage venture, you don’t need a business plan, but you do need a business model from Day Zero.

You don’t need a financial plan projecting cash flows 4 years out, but you do need a financial model on Day Zero.

Pointing to examples like Twitter and Facebook and how they found their financial model much later is not a good excuse. Even social media products have clear metrics that they measure in the early stages. Social media and game companies count on the fact that they are acquiring a user’s attention and data, and those are valuable assets that can be quantified and monetized later. But we can measure the user’s attention and willingness to relinquish data right away.

Social media companies are similar to a mining operation. If you were digging up gold from a mine shaft, no one would complain that you didn’t have a detailed plan and metrics to sell it in the market. We know gold is valuable and we can figure out how to sell it later. The value is clear. We just need to know if there is gold down there and how much.

To say, “we’re an early-stage mining operation and we don’t need to focus on a business plan or metrics,” would be an absurd statement. We can quantify how deep, far, and fast we’re digging. We can quantify the mineral content of the soil and the geology of the area. No one would accept the qualitative data of a dowsing rod to make a serious mining investment.

Startups, more than ever, should start with a hypothesis-driven financial model from Day Zero. That is why we use innovation accounting.

In the last article, we discussed why standard business cases don’t work in an innovation context, and the three principles we need in order to replace the standard business case with something better. In this article, we’ll go through how to actually do it. Using innovation accounting, we can build a financial model that is accurate, true, and testable.

How to Solve the Problem

To implement innovation accounting for an early-stage project we need to:

  • Identify assumptions
  • Construct a visual model
  • Build a hypothesis-driven financial model
  • Integrate uncertainty


Four steps innovation accounting method

1. Identify Assumptions

step one - innovation accounting method

There are a ton of assumptions that we make when starting a new innovation project. Fortunately, there are a number of different templates and frameworks that capture those assumptions, such as the business model canvas and customer personas. But none are as useful to innovation accounting as a Storyboard.

A storyboard, similar to a user-journey map, maps each step of the user journey from start to finish. This includes hearing about the product or service, actually using it, renewing their subscription, inviting friends, or simply finishing their use and throwing it in the trash.

Storyboards can be used to organize our assumptions into a clear series of actions that the user must take in order for us to both provide value and capture the revenue (or impact if you are a non-profit organization.) The advantage of a storyboard is that it represents observable moments that we can measure. Frame to frame, step to step, each moment in the user journey transitions into another — and we can measure that conversion rate from moment to moment.

If the first step of the story is downloading an app, and the second step is signing up for an account, that is a conversion rate we can measure — the % of people who sign up for an account after downloading the app. If the next step is applying a filter to a photo, then we can measure the % of users that apply a filter. From qualitative data about what the customer wants (to take beautiful pictures) we can map out our ideal story to deliver that value on quantitative metrics.

Even from Day Zero with just a nascent idea, we can create the step by step measurable process by which a person becomes a customer. We may not know the actual conversion rates, but we know what we need to estimate and measure. From there, it is tempting to go straight to a spreadsheet, but sometimes a quick detour will help.

2. Visual Business Model


step two - innovation accounting method

Once you have the basic story down, it’s useful to abstract this into a visual financial model. This really is the same thing as a storyboard where the user’s journey from acquisition to purchase is mapped out. However, we will want to simplify some aspects and include retention (if and how customers buy again) and virality (if and how customers refer their friends to become new customers) which are often left out of the storyboard.

A storyboard or user-journey map is often too detailed for what we need in our financial model. We don’t need to know what % of users apply a filter to a photo, we need to know how many users upgrade, stick around after four weeks, or purchase something so we can zoom out to the bigger picture and only use the most critical metrics that signify important progress towards our business model.

Startup Metrics for Pirates is a widely adopted framework with the right level of simplification for the purposes of innovation accounting. The five components of this framework are Acquisition, Activation, Revenue, Retention and Referral (AARRR, hence the pirate name.)

Acquisition (getting a user to your service or product), Activation (getting the user to have a great first experience and recognize the value), and revenue (getting the user to pay something) should already be on your storyboard. It is only a matter of identifying the step in the storyboard that represents the critical points in your business and thus represent the most useful metrics. This simplified, three-step user journey is often represented as a vertical funnel (although representing it horizontally makes no difference).

However, Retention and Referral are usually not included. That’s just because a storyboard or user-journey map are typically linear. Assembled with yellow sticky notes, it’s hard to represent retaining a customer or referring a friend (although we’ve seen some creative uses of blue sticky tape). But with a journey simplified into a shorter conversion funnel, loops can be more easily added to show where a user retains or refers a friend from a later stage (such add Revenue) back to Acquisition.

With an easy-to-understand visual model and these last two Pirate Metrics in place, we’re ready to make the leap to a spreadsheet.

3. Hypothesis-Driven Financial Model


step three - innovation accounting method

A hypothesis-driven financial model sounds complex, but it is not. It can and should be as simple as your visual model. Each step can be converted into a row in a spreadsheet, starting with Acquisition for the top line to represent the consistent number of organic visitors to your website or storefront.

However, unlike a traditional financial model, the number of visitors is not guessed from month to month and hard coded. Instead, a single assumption sets the number for that variable, and a formula varies the value from month to month in the spreadsheet. That way, if the assumption turns out to be wrong, changing a single cell in the spreadsheet will correct it throughout the model.

Each subsequent row applies the same logic as the visual model. The % of visitors that activate in your user journey becomes a variable that is held constant from month to month, changing visitors into users. The next row might convert users into paying customers who have taken a trial of your product and decided to buy based on another variable, the % of customers who purchase after trial.

Referral and Retention loops require a bit more thought as they impact Month 2 based on Month 1, but still only require a couple of additional rows of calculation.

With minimal effort – most teams take 1-2 hours to do this under guidance – a simple spreadsheet is constructed which is driven by a few variables. Those variables can be updated as more information is available.

Of course, this is a wild oversimplification. But with startups, start simple. We can add complexity over time.

With a tech startup, we often don’t even model costs on Day 1 because user growth might be all that matters for a social media app or game. However, costs can be introduced and more complexity added as the company grows.

This basic model allows us to do some basic scenario testing. We can immediately start testing out different acquisition and retention rates to see what the impact on our growth will be. We can even set certain conditions our business must reach in order to meet our growth targets.

With a limited number of variables, we can see that if our actual retention rate falls below 20%, our referral rate must increase accordingly if we are to continue to grow. This sort of scenario analysis is basic, but effective for helping early-stage innovation tests set pivot-or-persevere thresholds for their business and start designing tests to establish the actual numbers.

Although entrepreneurs can dictate the shape of their business model, reality will ultimately dictate what numbers go into the variables.

Here is a financial modeling template for startups if you would like to try it out.

4. Integrate Uncertainty

step four - innovation accounting method

Lastly, we have to integrate uncertainty.

Although the basic hypothesis-driven financial model allows us to play around and try out different scenarios, it doesn’t actually tell us what is going to happen or the likelihood of success. But we can do this if we get a little data and integrate uncertainty.

Statisticians have a few tricks we can adopt here. Hurricane forecasts, baseball games, and even startups can use a technique called the Monte Carlo Method to predict outcomes based on uncertainty.

Instead of entering a single number into each of our variables, we enter two numbers to represent the range and a distribution curve which tells us the likelihood of any individual outcome within that range. This is not easy.

For example, I may not know the outcome of rolling two six-sided dice and adding the numbers, but I know for certain that it is between 2 and 12. It’s most likely that it’s 7, but 50% of the time the number will be between X & Y.

We can make the same estimations with our business model variables. We may not know what price the customer is willing to pay, but we should be able to say that they will pay between $10 and $100. That’s all the information we need to start building a Monte Carlo simulation.

The math behind choosing the right distribution curve is tricky, and the art of choosing the right range requires a bit of training. But both can be accomplished with a little effort. Building the right spreadsheet is even more complicated, but more and more tools are being created that allow teams to run Monte Carlo simulations right in their spreadsheet or in a specialized application.

Here is a Monte Carlo simulation example if you would like to try it out.

In practice, this means that innovation teams and executives can create go / no go criteria for their pivot / persevere decisions. You want your project to be at least 10% likely to reach 1B in revenue? The Monte Carlo simulation can tell you if your project has reached that threshold. If not, you can stop the project with confidence that it would not achieve your goals and move on to test the next idea.

The output of the Monte Carlo simulation is a chart that shows a range of possible outcomes at any given point in the future and allows you to calculate the likelihood of any individual outcome.

Lessons Learned

This process is repeatable and is applicable to all types of business models. It doesn’t matter if it’s B2B, B2C, B2G, a network, a platform, or anything else not yet invented. Building a model from Day One allows innovation projects to make better decisions, make useful predictions, and demonstrate real progress to stakeholders.

Start by:

  • Identifying assumptions
  • Constructing a visual model
  • Building a hypothesis-driven financial model
  • Integrating uncertainty