Creating an Innovation Accounting Framework

How It Works

Generally when an entrepreneur writes a business plan, it includes two fundamental assumptions:

VALUE HYPOTHESIS

How your target customer will get value out of your offering
That is, that your target customer will actually find the offering valuable. This is your so-called Value Hypothesis.

GROWTH HYPOTHESIS

How your target customers will find your offering and in large enough numbers to sustain your business
In other words, your growth strategy. This is your plan for how you’ll acquire customers. This plan for growth is the so-called Growth Hypothesis.

To be clear, every business plan includes a lot of assumptions, many of which are based on well-established givens drawn from industry experience. Unlike those kinds of assumptions, the Value Hypothesis and Growth Hypothesis are far riskier and substantially more pivotal to the fate of your startup. Generally speaking, the success of the business will usually lie squarely on those two hypotheses, hence the reason Reis aptly deemed them your “leap of faith” assumptions. But never fear, there is a workable solution.

A hypothesis in the traditional scientific use of the term is a “supposition made on the basis of limited evidence as a starting point for further investigation”—meaning hypotheses are a mere starting point for testing and validation. Reis’s Innovation Accounting framework was designed expressly for the purpose of validating, in quantifiable ways, these educated guesses.

So how do you go about doing that? Here’s my step-by-step process:

1. Establish a Standard Measure of Success

First you want to clearly correlate your “Leap of Faith” assumptions with those quantifiable elements in your business plan. Maybe your business today is nothing more than an idea in your head but you’ve written a business plan that projects that in five years it will produce a million dollars in revenue and deliver 20% net profit margins. That projection is your “growth strategy” and it defines an approach for driving growth—your so-called “growth engine”.

To further illustrate, let’s say you have an idea to start an online community that makes money by charging a monthly membership fee. The business model is to give the community something of high value that makes them want to come there over and over—maybe that’s exclusive content—and to access it you’ll charge members a fee. Your Value Hypothesis is that users will find the content valuable, in fact so valuable they will be willing to pay to access it. Your Growth Hypothesis is that those users will find the price-value relationship so compelling that they will be willing to tell others about it (i.e. referrals). The success of the whole business is contingent on those two hypotheses being true. The factors that correlate to those things being true are the drivers of your “growth engine” and can be measured. Things like (taken from Dave McClure’s Pirate Metrics):

Acquisition: Whether visitors will be interested enough to sign up for a trial
Activation: Whether trial members will use it and ultimately convert to paying members
Retention: Whether members will keeping paying month-over-month
Referral: Whether members will tell other people about the site

These business metrics are fundamental to making the growth engine work and can be measured. The sooner you validate them, the sooner you will know whether or not you should stay the course with your current business model or pivot.

2. Run Structured Tests

So now that you have clarified the core assumptions in your engine of growth, you need to design tests to validate them. That is, prove them either true or false.

Here are a few tips:

Focus on one metric at a time: Focus each experiment around a single metric at a time, starting with the early parts of the lifecycle.
Test value assumptions before growth assumptions: You can’t answer the question of whether your growth strategy will work if you’re building something no one wants, right? In other words, seek to answer your Value Assumptions before your Growth Assumptions. For instance, first focus on testing your Acquisition hypothesis then your Activation hypothesis then your Retention hypothesis and so on and so forth.
To draw early learnings you don’t need huge numbers: In the early stages, you don’t need lots of users to test your value metrics because learning tends to be more qualitative.
To validate later phases employ cohort metrics: After you’ve demonstrated your basic value metrics, your focus should shift to the growth phase of the customer lifecycle. In the growth phase the learning will invariably become more quantitative and drawing insights more challenging, which is why at this point you’ll need to employ a cohort metrics approach.

3. Establish Clear Communication

Establishing a regular reporting structure in the form of daily, weekly and monthly team meetings generates invaluable feedback loops that ensure that your startup can pivot quickly and effectively, and your team knows what’s happening on the ground and what next steps are at all times. In these meetings you’ll want to cover:

What’s happening
Where you’re headed
What next steps are
How you’ll accomplish your stated goals

The recommended cadence looks something like this:

Daily “Standup” Meetings: In this meeting, you and your team will go over micro-level progress—individual tasks linked to experiments and blocking issues.
Weekly Meetings: In weekly meetings, you’ll want to communicate progress on current experiments and define new experiments. Additionally, you should use these meetings to discuss customer issues or feature requests, and if action is needed to resolve an issue. Start the meeting by first going over the experiments you’ve completed, break them out into the successful and unsuccessful ones and define clear actions. By analyzing your past successful experiments, you will help determine if the underlying risks those experiments set out to mitigate were in fact eliminated. If not, you’ll want to define a follow up experiment. For failed experiments, determine the “why” behind each, then update your strategy to define a new follow up experiment.
Monthly Meetings: In monthly meetings, you’ll communicate progress at the macro level—the overall business, financial and innovation accounting metrics—for the previous month. Eric Reis deemed this meeting the “pivot or persevere meeting” as it should inform your ongoing analysis of whether or not you are on a sustainable track or might consider a pivot.