Innovation Accounting sounds like one of those semi-humorous oxymorons like “accurate rumors,” “the rules of anarchy,” or “airline schedules.”
Innovation sounds exciting! It’s about ideas and novelty. Things we’ve never seen before. It’s ambiguous and full of uncertainty. It’s mysterious and seems utterly unquantifiable.
Accounting is the thing you dread doing at tax time. It’s all about numbers, it involves spreadsheets, and (unless you’re a geek like me) it threatens to bore you into submission.
Marrying the words together forms an inscrutable and ambiguous concept that is all the rage in innovation departments, but few are entirely sure what it is or how to do it.
But here’s the promise: Innovation accounting allows us to quantify the progress of a risky project in an uncertain domain. It will also allow us to determine if a project is worth investing resources in.
So why should businesses use innovation accounting? Because without a way to predict future impacts from innovation projects, investing in new product development (NPD) is a waste of time.
In other words, dull or not, without innovation accounting, innovation is just a series of lucky guesses at best and a waste of valuable resources at worst.
The History of Innovation Accounting
How did we get into a situation where everyone is using a term that few understand?
Eric Ries brought the term into our collective vocabulary in the Lean Startup book, defining the term as “…a way of evaluating progress when all the metrics typically used in an established company (revenue, customers, ROI, market share) are effectively zero.”
Essentially, Eric was proposing a way to measure the success or failure of innovation projects early. Ideally, before lagging financial metrics such as revenue or ROI are available. This is the holy grail of innovation: being able to tell if a project will succeed in the first months or weeks.
But the methodology of how to actually do that was largely unexplored and unexplained. Even after Eric introduced an actual approach in his follow up book, The Startup Way, the actual approach lacked rigor. The basic recommendation was to list the project “Leap of Faith” assumptions and then check if the Minimum Viable Product confirmed or disproved each.
Truth Is Binary, Knowledge Is Not
This approach is certainly better than guessing. But didn’t provide much in terms of saying what level of evidence is needed to validate an assumption. It also didn’t acknowledge that our knowledge is never ever binary. There are degrees of confidence.
When we have an assumption, “Marvel’s next movie will be awesome,” we have a certain level of confidence in that assertion. If we have some useful data such as “Robert Downey Jr. will be reprising his role as Iron Man,” that might increase our confidence. We might get some additional data that points in the other direction such as, “The main villain will be Howard the Duck.”
In reality, there is truth and there is fiction. But we don’t have direct access to the truth. We must gather data and make judgments based on imperfect data that is never as simple as “yes” or “no.”
Vague Metrics for Everything
Compounding this lack of detail is the lack of general consensus among VPs of innovation. Innovation executives insisted that innovation accounting must not only tell us whether an individual project is succeeding, but also tell us if the innovation department is doing a good job. Because if the innovation department can’t point to a number and show that it’s going in the right direction, there won’t be an innovation department for long.
Innovation departments need to be able to not only measure the potential ROI of a specific project, but also the potential of the company to innovate at all. And since early indicators of project success are unclear, undefined, and undiscovered, innovation departments went a different route.
Instead of seeking early indicators of success for a project, they sought early indicators of success in the team itself.
Could the skill of the team be a leading indicator? Can we measure a “culture of innovation?” Can we measure our innovation ecosystem?
We can also look at innovation pipeline metrics. How many ideas are there? How many projects discover a true market need? Which projects are able to create a product that fulfills that need? Even if project progress is measured by subjective guesses, it’s at least an attempt at measurement, and it’s better than nothing.
These attempts to find valid metrics are reasonable next steps to take, especially when innovation departments are under pressure to show how their activities result in bottom line results. Thus, the term “innovation accounting” has become a catch-all for measuring innovation in a project, in a portfolio, and in an organization.
Innovation accounting and its methods are just ambiguous enough so everyone can have their own definition. No wonder such little progress has been made to truly measure innovation.
Decisions, Not Numbers
Measuring innovation should not be something we just do to fill a PowerPoint slide. Although innovation departments have to show their value, the true measure of a metric is what actions we take. For example, when we say, “We need innovation accounting,” we are really demanding an answer to the question, “Which projects are going to succeed?”
We are asking that question because we need to make a decision. Should we continue this project or not? Is it worth investing in? Or is this other project a better bet?
The Reality of Limitations
All companies have limited resources. Regardless of their size, they always have at least twice the number of innovation projects as people to execute on them. They need to understand which projects have the best chance of success so they can prioritize.
Similarly, innovation departments want to measure the effectiveness of their workshops, coaching, and skill training, because someone in the company wants to make another decision: “Should the VP of Innovation keep their job?”
Even individual team owners focus on innovation accounting because they are trying to make a decision: “Should I assign this project to these intrapreneurs or to someone else? Do they have the skills to succeed?”
Essentially, we want a business case to make each decision on how to allocate resources.
Cash Is King
Unsurprisingly, all these decisions involve money. (Remember, people * time = money.)
They are about investing in:
- An individual innovation project.
- An innovation strategy across a portfolio of projects.
- Innovation capabilities.
The first is about investing in a project in order to receive an ROI. It is about allocating resources to a specific team with a specific idea. It is about revenue, profit, and/or impact.
The second is about systematically investing in a strategic thesis. It is not about investing in an individual project, but in a portfolio of projects which the company can choose to scale or not. It is about generating options. Specifically, innovation options.
The third is about investing in the capabilities to run more projects more quickly and for less money. Therefore it is about cost.
These innovation investment decisions rely on having sufficient information at each level of the organization that I mentioned in a previous post.
Levels of Innovation Accounting
Lining up these levels with the decisions we need to make, we get some specific questions we need to answer:
- Investing in an innovation project
- Investing in an innovation strategy
- Portfolio – Should we continue to invest in projects in these areas? Are we sufficiently diversified?
- Investing in innovation capabilities
Centering innovation accounting around decisions that need to be made finally allows us to get to a pragmatic definition of the term.
What Is Innovation Accounting?
Innovation accounting is a method of quantifying the uncertain impact of investments in individual projects, strategies, and capabilities.
Traditional financial accounting is about the past. Innovation accounting is about the future. While traditional financial accounting looks backward and tabulates the impact of decisions that have already been made, innovation accounting looks forward and predicts the value of things that have not yet happened and may never happen.
Innovation accounting helps us make decisions in the present about whether certain courses of action will offer value in the future. So what does it actually look like?
What Qualifies as Real Innovation Accounting?
It Must be Quantitative
I love qualitative data. I would rather subjectively judge the softness of a beach and the warmth of the sun than know precisely how many grains of sand there are or exactly what percentage of my body I forgot to put sunscreen on.
But there’s a limit to the usefulness of qualitative data. If innovation accounting doesn’t get us to quantitative data (a.k.a numbers), then it’s not doing its job.
Qualitative data is great for making quick broad decisions. If we’re deciding between investing in blockchain vs. Consumer Packaged Goods (CPG), we don’t need an extensive quantitative analysis. Do we have the skills necessary to succeed in blockchain? If not, but we have the skills for CPG, go for CPG instead.
But if we’re deciding between two different projects within the same domain solving similar customer needs, we need a more precise approach. If two people with two different qualitative assessments disagree, we must have quantitative measures to resolve the conflict.
Innovation accounting must provide an objective set of facts. In traditional financial accounting, if we count 17 one-dollar bills, that is $17. No one can disagree and claim that their qualitative assessment is that the value is $18.
If innovation accounting leaves us with only qualitative subjective assessments, it has failed its promise.
It Must Enable Decision-Making
If innovation accounting just generates dashboards of numbers that don’t help us make decisions, then it is ultimately just theater. Numbers go up and numbers go down, dancing all over a PowerPoint presentation. But without purpose, all metrics are vanity metrics.
We have real decisions to make, and an innovation accounting system should help us make those decisions the same way a real accounting system does.
Innovation funnel metrics may tell us we have 100 projects at the concept stage, 50 in the market research stage, and 20 in the prototype stage, but that doesn’t tell us if our strategy is working.
Are those 20 projects in the prototype stage aligned with our strategy of investing in disruptive innovation? Are they even really innovation projects? Or do we just have 20 projects proposing changes to our company slogan?
We need to make hard pivot or persevere decisions on projects, and a true innovation accounting system should make those decisions obvious and undeniable.
A Complete System Must Consider Projects, Strategy, and Capabilities
Making great decisions on individual projects doesn’t create a cohesive strategy. One project on blockchain, another on CBD scented candles, and another on freezerless ice cream may sound great in principle. But pursuing them all is a bad strategy.
There is no company that is going to be great at producing both ice cream and blockchain at the same time.
Even if we know that our strategy is sound and our portfolio will corner the scented candle market, that’s useless if our company is packed with blockchain experts with hyperosmia (sensitivity to strong smells).
A strategy is useless if we don’t have the capabilities to successfully execute it.
Being able to value an individual innovation project is a starting point. But a complete system of innovation accounting must encompass projects, strategy, and capabilities to truly give us a view of the future.
How Does Innovation Accounting Work?
As mentioned above, there are different ways of approaching innovation accounting depending on whether you are talking about the individual, a team, a project, a portfolio, or an organization.
But the most complex and critical part is at the project level. To actually do innovation accounting for a project, we must:
- Create a hypothesis to identify cause and effect
We first observe the customer and their existing behavior in order to understand their needs and goals. Then we create a hypothesis of how our new product or service will change their behavior in terms of cause and effect.
- Identify observable metrics
We then need to take that hypothesis of behavior and quantify each step of the process by identifying the right way to measure the cause and effect.
- Create a mathematical model
Next we must create a mathematical model. Generally this is done in a spreadsheet, but other tools have been created recently.
- Estimate variables using ranges
Next, we have to estimate the actual numbers that go into that mathematical model. We might think that we’ll have 10,000 customers, 10,000,000, or just 10. At first, we just guess. Then we measure real user behavior whenever possible.
This creates a possible range of outcomes where the width of the range (10 to 10,000,000) represents the level of uncertainty around our guess or the measurement.
- Choose the distributions
Next we have to choose how we think reality matches up with our estimated range. Is it more likely that the true result is in the middle of our range? Or closer to the low end? This is called a distribution in statistics, and involves a bit of mathematical know-how.
- Run the simulations
Finally, we can create a prediction by simulating our mathematical model thousands of times using random numbers. This is just an estimation based on a lot of assumptions, but it’s better than a pure guess. That term for this is a Monte Carlo simulation.
- Collect data and iterate
Lastly, we must actually measure the real world and compare it to the prediction from our simulation. If reality is within our estimated range, then we can keep going.
If it’s way off, then we may need to alter our model. As with anything, iterating based on real data ensures continuous improvement.
What Skills Are Required for Innovation Accounting?
In order to actually accomplish the process above, innovation teams need a few specific skills:
- User Research – To observe, understand, and create a hypothesis of user behavior.
- Logic – To create a model, we need to clearly think through cause and effect in a clean, easy to describe way.
- Spreadsheet skills – To actually implement the logic, it’s usually best to use a spreadsheet. (Although new tools are becoming available.)
- Estimation – Yes, this is actually a skill we can get better at. The ability to estimate will improve our ability to predict user behavior in real, quantitative terms.
- Statistics – To choose the right distributions and create the Monte Carlo simulation, we need a little, but not a lot, of statistics know-how. Most of the technical details can be implemented in a template or tool, but the mindset is critical.
- Instrumentation & Experimentation – To actually measure reality and see if we are correct, we need to know how to collect the data with tools like Google Analytics, but also how to design experiments.
Remember: Innovation accounting is a method for quantifying innovation investment decisions. That includes decisions about individual projects, strategy, and capabilities. It’s a way to find leading indicators for success, not only for a product, but for a company.
The benefits of innovation accounting include:
- Greater accuracy in financial projections for a project
- Accurately predicting the future health of the overall organization
- Representing innovation as a quantity on the balance sheet for public companies
- Showing the ROI of the capability-building efforts by the innovation department
We’re still far from a system of innovation accounting that will complement the Generally Acceptable Accounting Practices (GAAP), but we know what such a system will look like:
- Innovation accounting must be quantitative….
- …must help us make decisions…
- …and should include metrics for projects, strategy, and capabilities.
- Kromatic Experiment Calculator
- Get clear results from quantitative experiments with this step-by-step process.
- Basic Financial Modeling Template (Excel)
- By creating a practical financial model with a small number of key hypotheses, our template represents your business model as inputs and outputs so you can predict if your business will be sustainable.
- Practical Statistics – Rules of Thumb
- Apply these practical rules of thumb when running experiments to avoid common biases in the data you use to make business decisions.
Become a faster, more confident decision-maker
Learn how to make better pivot or persevere decisions on your real project by building a hypothesis-driven financial model. Innovation Accounting is a six-week training program taught by real entrepreneurs with real experience and you’ll get results you can use right away. Our next course kicks off March 13, 2024 and $250 discount is valid through March 6th.
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