HOW INNOVATION ACCOUNTING WORKS—THREE LEARNING MILESTONES

 

Innovation 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 pivot than before.

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Establish the Baseline

 

For example, a startup might create a complete prototype of its product and offer 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 fill 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.