People are accustomed to thinking of accounting as dry and boring, a necessary evil used primarily to prepare financial reports and survive audits, but that is because accounting is something that has become taken for granted.
Historically, under the leadership of people such as Alfred Sloan at General Motors, accounting became an essential part of the method of exerting centralized control over divisions.
Accounting allowed GM to set clear milestones for each of its divisions and then hold each manager accountable for his or her division’s success in reaching those goals. All modern corporations use some variation of that approach. Accounting is the key to their success.
Unfortunately, standard accounting is not helpful in evaluating entrepreneurs. Startups are too unpredictable for forecasts and milestones to be accurate.
I recently met with a phenomenal startup team. They are well financed, have significant customer traction, and are growing rapidly. Their product is a leader in an emerging category of enterprisesoftware that uses consumer marketing techniques to sell to large companies.
For example, they rely on employee-to employee viral adoption rather than a traditional sales process, which might target the chief information oficer or the head of information technology (IT).
As a result, they have the opportunity to use cutting-edge experimental techniques as they constantly revisetheir product. During the meeting, I asked the team a simple question that I make a habit of asking startups whenever we meet: are you making your product better? They always say yes.
Then I ask: how do you know? I invariably get this answer: well, we are in engineering and we made a number of changes last month, and our customers seem to like them, and our overall numbers are higher this month. We must be on the right track.
This is the kind of storytelling that takes place at most startup board meetings. Most milestones are built the same way: hit a certain product milestone, maybe talk to a few customers, and see if the numbers go up.
Unfortunately, this is not a good indicator of whether a startup is making progress. How do we know that the changes we’ve made are related to the results we’re seeing?
More changes we’ve made are related to the results we’re seeing? More important, how do we know that we are drawing the right lessons from those changes?
To answer these kinds of questions, startups have a strong need for a new kind of accounting geared specifically to disruptive innovation. That’s what innovation accounting is.
An Accountability method which works for startups
I call it startup accounting which enables startups to prove objectively that they are learning how to grow a sustainable business.
Every business plan has some kind of model associated with it, even if it’s written on the back of a napkin. That model provides assumptions about what the business will look like at a successful point in the future.
For example, the business plan for an established manufacturing company would show it growing in proportion to its sales volume.
As the profits from the sales of goods are reinvested in marketing and promotions, the company gains new customers. The rate of growth depends primarily on three things: the profitability of each customer, the cost of acquiring new customers, and the repeat purchase rate of existing customers.
The higher these values are, the faster the company will grow and the more profitable it will be.
These are the drivers of the company’s growth model.
By contrast, a marketplace company that matches buyers and sellers such as eBay will have a different growth model. Its success depends primarily on the network effects that make it the premier
destination for both buyers and sellers to transact business.
Sellers want the marketplace with the highest number of potential customers. Buyers want the marketplace with the most competition among sellers, which leads to the greatest availability of products and the lowest prices.
For this kind of startup, the important thing to measure is that the network effects are working, as evidenced by the high retention rate network of new buyers and sellers. If people stick with the product with very little attrition, the marketplace will grow no matter how the company acquires new customers.
The growth curve will look like a compounding interest table, with the rate of growth depending on the “interest rate” of new customers coming to the product.
Though these two businesses have very dierent drivers of growth, we can still use a common framework to hold their leaders accountable. This framework supports accountability even when the
model changes.
How does it work? Three steps
Startup accounting works in three steps: first, use a minimum viable product to establish real data on where the company is right now. Without a clear-eyed picture of your current status—no matter how far from the goal you may be—you cannot begin to track your progress.
Second, startups must attempt to tune the engine from the baseline toward the ideal. This may take many attempts. After the startup has made all the micro changes and product optimizations it can to move its baseline toward the ideal, the company reaches a decision point.
That is the third step: pivot or persevere. If the company is making good progress toward the ideal, that means it’s learning appropriately and using that learning effectively, in which case it makes sense to continue.
If not, the management team eventually must conclude that its current product strategy is flawed and needs a serious change. When a company pivots, it starts the process all over again, reestablishing a new baseline and then tuning the engine from there.
The sign of a successful pivot is that these engine-tuning activities are more productive after the pivotthan before.
Establish the Basecamp
For example, a startup might create a complete prototype of its product and oer to sell it to real customers through its main marketing channel. This single MVP would test most of the startup’s
assumptions and establish baseline metrics for each assumption simultaneously.
Alternatively, a startup might prefer to build separate MVPs that are aimed at getting feedback on one assumption at a time. Before building the prototype, the company might perform a smoke test with its marketing materials.
This is an old direct marketing technique in which customers are given the opportunity to preorder a product that has not yet been built. A smoke test measures only one thing: whether customers are interested in trying a product.
By itself, this is insufficient to validate an entire growth model. Nonetheless, it can be very useful to get feedback on this assumption before committing more money and other resources to the product.
These MVPs provide the first example of a learning milestone. An MVP allows a startup to ll in real baseline data in its growth model—conversion rates, sign-up and trial rates, customer lifetime
value, and so on—and this is valuable as the foundation for learning about customers and their reactions to a product even if that foundation begins with extremely bad news.
When one is choosing among the many assumptions in a business plan, it makes sense to test the riskiest assumptions first. If you can’t find a way to mitigate these risks toward the ideal that is required for a sustainable business, there is no point in testing the others.
For example, a media business that is selling advertising has two basic assumptions that take the form of questions: Can it capture the attention of a defined customer segment on an ongoing basis? and can it sell that attention to advertisers?
In a business in which the advertising rates for a particular customer segment are well known, the far riskier assumption is the ability to capture attention.
Therefore, the first experiments should involve content production rather than advertising sales. Perhaps the company will produce a pilot episode or issue to see how customers engage.
Tuning the Engine
Once the baseline has been established, the startup can work toward the second learning milestone: tuning the engine. Every product development, marketing, or other initiative that a startup undertakes should be targeted at improving one of the drivers of its growth model.
For example, a company might spend time improving the design of its product to make it easier for new customers to use. This presupposes that the activation rate of new customers is a driver of growth and that its baseline is lower than the company would like.
To demonstrate validated learning, the design changes must improve the activation rate of new customers. If they do not, the new design should be judged a failure. This is an important rule: a good design is one that changes customer behavior for the better.
Compare two startups. The first company sets out with a clear baseline metric, a hypothesis about what will improve that metric, and a set of experiments designed to test that hypothesis.
The second team sits around debating what would improve the product, implements several of those changes at once, and celebrates if there is any positive increase in any of the numbers. Which startup is more likely to be doing effective work and achieving lasting results?
Pivot or Persevere
Over time, a team that is learning its way toward a sustainable business will see the numbers in its model rise from the horrible baseline established by the MVP and converge to something like the ideal one established in the business plan.
A startup that fails to do so will see that ideal recede ever farther into the distance. When this is done right, even the most powerful reality distortion field won’t be able to cover up this simple fact: if we’re not moving the drivers of our business model, we’re not making progress.
That becomes a sure sign that it’s time to pivot.
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