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Dealing with Assumptions

Dealing with Assumptions is the time consuming aspect of Reverse Financials, covered in my previous post. Managing assumptions is really about dealing with uncertainty and risk. Let's start by making a distinction between uncertainty and risk.

Risk is uncertainty with a downside.

Uncertainty is your friend. It gives you the opportunity for an upside and a way to beat your competitors. Risk is your enemy. It creates the possibility of loosing accumulated capital. The goal is to turn risk into uncertainty.

Uncertainty Management

Before we dig into the Reverse Financials, let's take a look at uncertainty management in general. The framework I use comes from Hugh Courtney's book 20/20 Foresight. You can read the book if you want a deep dive.

Uncertainty can be categorized into 4 levels.

Level 1 is complete certainty. This does not mean you know everything, but it means everything can be known with enough certainty that you can make decisions without considering uncertainty. In this world, to the extent it still exists, you can use discounted cash flow, Porters, SWAT, and all the traditional tools to make decisions. Life is like a chess game. Whoever is best at reading the board and making strategy wins.

Level 2 consists of a set of mutually exclusive collectively exhaustive (MECE) outcomes. This represents standards wars, regulatory changes, strategy moves in some more stable industries, etc.

Level 3 is bounded outcomes, a range of outcomes. Market share falls into this category.

Level 4 amounts to unbounded outcomes.

Assumptions in Reverse Financials

I want to draw attention to a couple things. First, most of the uncertainty we will deal with in Reverse Financials will be Level 2 and Level 3. Second, product strategy will effect uncertainty. For example, in a disruptive innovation, you probably have more time than in a sustaining innovation. Most companies are scared of disruptive innovations and will watch from the sidelines and will be a fast follower, or a me too. With a sustaining innovation, preemption is far safer, so delaying can have drastic consequences.

The first step in dealing with assumptions is to categorize each assumption by the level of uncertainty.


Let's go through each item in the Uncertainty column from bottom to top.

The first item is General Overhead with Level 1 uncertainty. Management has determined the overhead % and is not going to change. Therefore, this is an assumption that can be ignored.

The next item is a burden rate, probably covers manufacturing. The spreadsheet shows a range of 20% to 30%, but it is categorized Level 1. The implication might be the number can be determined and the range removed, or there is simply a mistake in the analysis so far. It is best to do the research and find out which is the case.

The next item is raw materials we a Level 2. Engineering has proposed several product architectures. One of them includes creating a new platform, several of them propose using an existing platform along with several choices for reusing components from previous product designs. These choices affect cost, development time, and performance. If you are a product manager, this should keep you up at night. The Level 2 uncertainty from engineering affects product value and go to market strategy, implying that the uncertainty of revenue depends on choices made by engineering. (Everyone knows this intuitively, so there should be no surprise here.)

Moving along we get to sales support and cost of warrantee, etc. These items are just not very predictable and nobody has any way to make the uncertainty Level 2, so they are level 3. They are bounded by experience.

We now get to cost of sales and marketing. This is Level 2 because there are some basic choices around using internal sales, reps, and other channels of distribution. However, each channel is reasonably well understood, so this is not Level 3.

Finally, we get to revenue, which is Level 3 uncertainty, but it has some Level 2 characteristics due to Level 2 uncertainty within engineering. As is usually the case, this is the hardest uncertainty to deal with, as information is so much harder to get compared to internal affairs.

Dealing With the Uncertainties

Courtney categorizes strategies into three questions:

  • Shape or Adapt
  • Now or Later
  • Focus or Diversify

I will ignore the third question as my concerns here are more tactical and "focus or diversify" applies more to overall strategy and portfolio management. However, one could make a strong argument against this claim, so feel free to do so :-)

Let's consider the overall question whether to shape or adapt with respect to uncertainty. Because we are developing a product, we must consider uncertainties that are external to the organization differently than those internal. Internal uncertainties are easier to shape than external ones, but not always. There are no rules that say you have to answer the same for both internal and external uncertainties.

Now or later does not have so much independence. If your strategy for dealing with external uncertainty is "now", it is pretty hard to apply a "later" strategy with internal uncertainties because they are bounded by the external timing. There is a similar boundary in a "later" external strategy. Waiting carries the risk of preemption. A competitor can always be first, and there is no way to undo your delay.

Because of the asymmetries, let's start with external uncertainty. We have a Level 3 uncertainty, which means we have a range of possible outcomes. The overall worst case scenario in the spreadsheet says we can have a return on sales of - 5%. The first thing we might do is a sensitivity analysis to get some feel for where it hurts most.

  ROS Profit  
Revenue   12.50% Level 3
Raw Materials 2% 8% Level 2
Tech sales Support 1% 4% Level 3
Install, Warr, Training 1% 2% Level 3
Sales and Marketing 2% 6% Level 2

These values show the resulting change from a 10% change in each parameter. Because revenue and raw material costs are interrelated as discussed above, we can address those first. Let's start with revenue.

The initial post said this was a disruptive innovation. The first question is: can we convert this into a Level 2 risk and use a shaping strategy? Possibilities might include patents, industry standards, tying up a critical resource, or a network externality. If this is possible, it is probably better to shape than adapt. A scenario analysis would prepare for each Level 2 outcome and uncover ways to shape the outcome.

If there is no way to convert to Level 2, it still might be best to shape, but it also might be better to delay decisions using real options techniques. A real option creates a option to execute in the future when there is better data. For example, a critical technology might be developed, but the decision to develop a product might be delayed until the environment is ready. A product launch might be delayed to time the market. Multiple options might be created so that with more data, one option may be chosen and the other discarded.

Part of the analysis is deciding on whether you are making a big bet, or managing downside. Also, you must know if your organization is capable of the strategy. Can your organization support a big bet? Do you have to make a big bet because you are a startup and don't have the cash required to finance multiple options? Are your managers flexible enough to adapt to an unfamiliar strategy?

Let's look at the raw materials uncertainty. It was specified as Level 2. In this case the designers have a finite number of technology and architecture choices and they affect the cost structure. The same issues apply. A scenario analysis can uncover assumptions and issues with each choice. It might be possible to create options by developing prototypes of several architectures in parallel (set based design), then evaluate them, considering how it relates to the revenue risk management strategy.

We must also consider how the design strategy relates to the market strategy. If the market strategy is to shape through an industry standard, the design team has to architect around standard. The design team may want to prototype a couple of options based on guesses as to the final form of the spec. Then wait and see how it plays out. Once the standard is near acceptance, execute on the prototype closest to the standard.

The main point is that all assumptions are uncertainties. Uncertainties should be analyzed to uncover their characteristic, and strategies should be formed to manage it. You have to decide whether to shape or adapt, and whether move forward now or later. You must apply the proper tools for the uncertainty level. And you must align the strategies where they interact.

Other Ideas on Assumption Management

My main point of reference is Discover Driven Growth. McGrath proposes managing assumptions using an options approach. Information is produced by learning, which lowers downside. Checkpoints define strategic places to stop and evaluate, which is a decision point where you stop, pivot, or purchase a new option.

The fundamental concern I have with a process tuned to real options is that it inhibits big bets, and tends to emphasize adaption over shaping. While this might work in many situations, any process that is focused on one approach for managing uncertainty has the downside of misapplication. Like all tools, context matters. So my take away is simply this, use reverse financials and assumptions, but manage assumptions with a rich risk management toolkit and don't commit your process to any one tool or strategy.

However, if you must have simplicity, an options approach is probably best, because most market risk is Level 3. Another assumption to manage ;-)

Reverse Financials

Causal financial models lend themselves situations where inputs to the model are well known. Sustaining innovation falls into this category. But what kind of models should one use for disruptive innovations? This post will demonstrate risk based models intended for disruptive innovations where model inputs are not well defined.

Project Categorization

Jose Briones uses the following Project Categorization in his Beyond Stage-Gate presentation:


Jose then categorizes the financial analysis into three levels going from the top right corner to the bottom left:

  • Level 1 - Reverse Income Statement/Real Options
  • Level 2 - Probabilistic Decision Analysis
  • Level 3 - NPV/DCF

The message is clear, the more certain your innovation, the more you can use traditional tools. Larry McKeough addresses Level 3 in his Rocky Mountain Product Camp 2010 Presentation. However, Larry's DCF spreadsheet tool considers assumptions, so even his tool recognizes the presence of risk in low uncertainty innovations.

Risk Caused By Disruptive Innovation

Christensen uses the following model of disruptive innovations as shown in my Rocky Mountain Product Camp 2010 Presentation:

Disruption 1.png

There are two risky places to innovate. The first is the low-end or new-market disruption. Both of these have considerable market and technical uncertainty, as shown by the circles below. Each of these strategies involve new value networks, new customers, and new definitions of value.

Disruption 3.png

The second place is when sustaining innovation pushes performance beyond user needs and the supply chain begins to reconfigure itself to supply flexibility and speed to the market. When platforms disaggregate and margins shift between players, look out!

Disruption 2.png

DCF is not well suited for these situations, especially the low-end and new-market disruptions. A better tool is reverse income statement and assumption management. The remainder of this post will walk through a reverse financials statement. A follow on post will address assumption and risk management.

Reverse Financials

Let's proceed by building out a spreadsheet step by step. I will roughly follow McGrath's example from his book Discovery Driven Growth. Assume the product is a $100K machine used in manufacturing lines. First, we will frame and scope, then work on deliverables, and finish with the reverse financials.

Framing and Scoping


Management has stipulated $1M in operating profits with a 17% return on sales (ROS) and a 20% return on assets (ROA). This results in a $5M allowance for assets, and requires sales to be approximately $6M. Given a $100K selling price, this implies selling 5 systems a month. Two questions follow:

  • Can manufacturing build 5 systems a month?
  • Can ROS/ROA be maintained?

The benchmark range of return on sales is 25% to 3%. The data came from the public financials of companies in the same product category. This should immediately send shivers down your spine! You should start asking yourself questions like: how do we insure our product lands in the good side of the range?

Notice what has happened so far. Instead of creating a bottom up plan that results in a ROA/ROS figure, we start with a required ROA/ROS and ask, what does this imply? It implies a sales level of sales of 5 systems per month. It implies assumptions about ROA/ROS that may not be real. Not only does the benchmark data have a wide range, but if this is a disruptive innovation, benchmarks may not even apply.

Now have to take the next step, which is the deliverables specification. We have to start breaking down the overall assumptions into smaller assumptions we can manage.



Starting with assumption F10/A3, the sales team has predicted the cost of sales and marketing is 15%, with a range of 13% to 17%. The left side (F10) says that to meet the original ROS/ROA, we require 15%. The right side (A3) says that the possible range is 13% to 17%. Therefore, we have to manage the assumptions on the right side to either meet the requirements on the left, kill the project, or improve some other assumption to compensate.

Reverse Financials


Using the data from the deliverables, a reverse income statement and reverse balance sheet are created. The left side shows a return on sales (F40) of 25% and a return on assets (F47) of 44%. This is better than the original 17% and 20% requirement. Therefore, if the left side of the spreadsheet holds, we have a project. But as my father used to say, IF is the biggest word in the dictionary.

Look at the worst case on the right side. return on sales is -5% and return on assets is -6%. Assumptions matter!

If this were a sustaining innovation, the assumptions would be reasonably accurate with small ranges, and DCF would be a great tool. Diffusion curves could be used to estimate revenues. An IRR could be generated and compared to a weighted cost of capital. Small uncertainties could be accounted for by running a Monte Carlo analysis on the DCF.


We basically framed/scoped management requirements for ROS and ROA. We then worked backwards and created financial deliverables that meet those numbers, and we treated the deliverable components as assumptions with ranges of values. Finally, we created reverse financials that show the best and worst case ROS/ROA.

Take Aways

  • Don't use the wrong tools. Your classic MBA tools don't always work.
  • Work backwards from goal to assumptions.
  • Manage assumptions.

Where do we go from here?

The next post will discuss basic risk management concepts and address how to deal with the assumptions of reverse financial statements.


Introduction to Product Management

Here is a great introduction to the life of a Product Manager from Stanford University

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