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Answer Any Question With Fact-Based Decision Making
by Moe Kelley

 
   
 
   

Every day, executives make difficult decisions with incomplete information and limited resources under tight deadlines, with millions of dollars at stake, never mind the lives and careers of their employees. Yet too often these decisions are made not by analyzing relevant and reliable data, but through organizational politics, formal authority, or persuasive champions.  The loudest voice or the highest-ranking participant makes a call based on gut feel.  Or the organization slowly grinds to a too-late, too-compromised solution that no one really supports but no one can quite kill, either.

This intuitive, personality-driven approach is the norm in large corporations we’ve worked with throughout Europe, the US and Asia, and it’s probably the norm in parts of your company, too.  Worse than just resulting in poor decisions (too frequently the “wrong” decisions), this approach leads to hard-to-execute decisions because those responsible for execution are all heading in slightly different directions.  In retrospect, it can be difficult to tease apart whether a poor decision or poor execution led to failure, but the two often go together.

But there is another, better way.  We call it fact-based decision-making, but other proponents use terms like “evidence-based management” (mostly in healthcare) or “data-driven decision-making” (mostly in public education, where this is a truly radical idea). Fact-based decision-making is both a methodology for executive decision-making under uncertainty as well as a philosophy of how to tackle business problems. In this article, we’ll describe the four components of fact-based decision-making, and explain why it’s a successful approach.

Moving the emphasis away from personalities and organizational hierarchy, fact-based decision-making focuses on the facts.  The decision to develop and launch a new product, for instance, relies on knowing who will buy the product and how much they will be willing to pay.  The decision to invest in or acquire a technology startup relies on knowing whether or not the technology in question works as advertised and whether it is well positioned against competing solutions.  Pricing decisions require in-depth knowledge of demand elasticity, costs, and customer economics.

But it seems the necessary information is never at your fingertips. Sometimes, it isn’t even clear which facts are important and which aren’t.  There’s a hope that someone, in some department, must have this information.  In situations like this, executives mentally chase each other around the conference room table, unable to reach agreement because they have incompatible but hidden mental models, different incomplete sets of information, and competing theories about what to do. 

By contrast, fact-based decision-making is the scientific method applied to business decisions.  This approach has four key characteristics:

  • The decision is based on facts and analysis -- not intuition and personality.

  • The structure of the decision is well understood, and the decision is carefully framed to reflect the reality of the outside world.

  • Analysis is hypothesis-driven, focused in areas that drive choices between options.

  • Data gathered to support the decision is relevant and reliable (key drivers, apples-to-apples comparisons, data integrity, quantifying qualitative information).

 

 

1.  A fact-based mindset comes first

Behind every tough decision is a set of critical unanswered questions. The first order of business is deciding what those questions are and what facts would provide the needed answers to move forward in a positive, unambiguous manner. The first and hardest step is simply to let go of opinions, assertions, and anecdotes and embrace the facts.  Sometimes the facts are uncomfortable, and it takes a little work to get the facts you need.  But in an environment where “facts are friendly,” people can de-personalize the decision and believe in the process.  To create such an environment:

  • Validate opinions and assertions or turn them into testable hypotheses.
     
  • Check facts. 

  • Make mental models explicit. 

  • Put anecdotal evidence in context. 

  • Encourage questions and disconfirming data from all participants, regardless of rank.

How does this work in practice? A wireless company was looking at partnering with a bank. In the past, a key sticking point had been the percentage revenue share on each transaction that the wireless company received versus what the bank would receive. The client's decision-making process had become political and unproductive, and negotiations with the banking partner had stalled. So we modeled six or seven different possible revenue streams and determined that this per-transaction revenue share was the least important of the possible opportunities. Our analysis cleared the way for all parties to strike a win-win deal.

2. Frame the decision at hand

What is the decision you are trying to make? This may seem like the most obvious thing in the world, but it is surprising how often different people on the same team or in the same organization are actually trying to solve different problems and don’t even realize it.  For a new product, the decision could be “Should we launch this product?” or it could be “What is the best way to launch this product?”  Frequently, the executive team is trying to decide whether or not to fund and launch a product while the product team is trying to determine how to launch it.

So, the second step in a fact-based decision-making process is to lay out a set of options under consideration and the circumstances or set of beliefs that would support each possible course of action.  If you are making a go/no go decision on a new product, then the set of options is fairly straightforward.  What set of facts supports a “go” decision?  What set of facts would lead to killing the project? Notice how these facts relate to one another: Which facts are most important?  Which are dependent on others? Then, choose the favored option and turn it into a testable hypothesis.

3.  Conduct a hypothesis-driven analysis

Turning the favored option into a testable hypothesis enables you to focus the analysis on answering the key questions that will support a decision—using the least amount of time and resources. The more specific the hypothesis, the better.

For instance, a client was trying to decide whether to reduce the price of a struggling product. The team’s hypothesis was this: “Reducing the price of our product by 33% will lead to a 40% increase in customers and better overall economics.” Based on this hypothesis, we were able to zero in on two key questions:

  • Will reducing the price by 33% lead to a 40% increase in sales?

  • Will 40% more sales at the lower price point produce better economics?

Answering the first question would have required expensive market research, while  answering the second question required only simple financial modeling.  A negative answer to either question would invalidate the hypothesis, so we saved both time and money by starting with the financial analysis and performing a sensitivity analysis on the 40% assumption. We discovered that cutting the price by 33% destroyed the economics of the product under a wide range of sales increase scenarios.  Thus, the company killed the new pricing strategy and the team explored other approaches to improve the business.

Another client theorized that one of their minor products reduced customer care calls and that it would be worth investing a great deal more in the product because every new customer in this area saved them money elsewhere. We dug deep into customer records, looking at a random sample of a few thousand customers and demonstrated that the hypothesis was likely true -- and was projected to save over $40 million in customer care costs if extended. This analysis took the discussion from the realm of anecdote and personal theorizing to hard data and real dollars. The client stepped up their support of the product, with additional investment coming from the customer care organization.

4.  Emphasize relevant and reliable data

Assembling the facts is a key step in any fact-based approach.  Facts are all around, however, and it is important to distinguish between facts that matter and facts that don’t.  Having a strong hypothesis as discussed above is key.  Keep in mind these rules of thumb:

  • Data should be relevant:  Does it relate explicitly to the decision at hand and address the hypothesis being tested?  If not, ignore it.

  • Disconfirming data is best and most interesting: Finding data that seems to refute the current hypothesis? Digging in deep to understand disconfirming data will either solidify a hypothesis or send you back to the drawing board.

  • Data should be reliable: Separating anecdotal evidence from hard data is vitally important; often we try to extrapolate up from our own experience or overstate the importance of one person’s opinion. Use random samples to turn large, unwieldy data sets or customer groups into manageable information. Where possible, validate external data from multiple sources.

  • Data should be apples-to-apples:  Be extremely careful comparing benchmark data across companies or industries or time. Whenever possible, frame the data in terms of a common denominator such as “per customer” or “per product sold” relative to the addressable market.
A fact-based decision-making approach is both critical and successful in today’s business environment. It is critical because large organizations often have trouble making decisions and moving forward. A fact-based approach is successful because 1) such an approach makes it easier to make a decision by getting people on the same page, and 2) it improves the odds of making a good decision that will lead to a successful outcome because the decision is based on hard data and an understanding of what will drive value -- not on organizational politics or intuition.

       
   
 
       
   

The Author

Moe Kelly

As partner with Endeavour Partners, Moe Kelley is a recognized thought leader in fact-based decision-making, with 15 years of professional experience in strategy, finance and project management focused on the converging worlds of broadband and mobile. He has led engagements at technology businesses across the US, Europe, and Asia. A hallmark of Moe's consulting work is a hypothesis-driven, fact-based approach to business problem solving.

Contact him at: moe@endeavourpartners.net, (617) 953 7824, or www.endeavourpartners.net.

 
       
   
 
       
   
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Copyright 2009 by Moe Kelley. All rights reserved.

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