Predicting Business Outcomes Using Payoff Tables & Decision Trees

Instructor: Scott Tuning

Scott has been a faculty member in higher education for over 10 years. He holds an MBA in Management, an MA in counseling, and an M.Div. in Academic Biblical Studies.

The quality of strategic decisions is heavily dependent on data provided by statistical analysis. Payoff table and decision trees are tools that can turn raw data into actionable information.

Knowing is Better Than Hoping

Business decisions are, by nature, often tough calls to make. They can involve decisions about someone's employment, the organization's financial future, or its strategic direction. The good news, however, is that these important decisions don't have to be guesses or full of conjecture.

Instead, business statistics and other forms of analysis can provide decision-makers with valuable tools to guide them to the right course of action. Two of these important tools are the payoff table and the decision tree. In fact, an accurate decision tree or payoff table is often the difference between simply hoping an alternative will work and knowing that it'll work.

Payoff Tables and Decision Trees

While both tools provide some of the same information, they have a few notable differences. A payoff table is a tool that provides information about the probability of various outcomes--usually related to potential profit or loss. A decision tree also provides some of the same type of information, but it's more informative in terms of the consequences of actions or decisions. Let's illustrate each of these with an example.

Applying a Payoff Table and Decision Tree

Let's imagine that you're the CFO of a medical group that employs several different types of specialty physicians. Coincidental events beyond your control have resulted in the nearly simultaneous departure of all but one of the eight orthopedic surgeons. The CEO has asked you to research and recommend a course of action moving forward. You'll be considering several options including:

  • Replacing all departing surgeons with full-time employees (the status quo or default action)
  • Using contracted, temporary physicians (known as locums)
  • Discontinuing the service line altogether

Obviously each of these options is a legitimate possibility, but are they equally as impactful? Let's use a payoff table to find out. As we build our table, it's important to remember that we're accounting for all outcomes or influences--including those outside your span of control. In this scenario, you'll use the reference data provided below, but you'll want to keep in mind that the projections made by a payoff table or decision tree are only as the data upon which they are based. In our payoff table, the term alternative is used to describe one of the possible courses of action.

As we analyze, it's important to remember that you're not making a decision in isolation. Since the situation will influence how patients or customers behave, we'll not be looking only at the three alternatives, but also at the impact if we lose, gain, or retain our customers during the process.

Example Payoff Table

Here's our payoff table:

Alternative Worst Case Likely Case Best Case
Fill open positions with permanent, full-time physicians ($233,449) $11,334 $43,019
Use temporary or locum physicians ($1,223,330) ($445,332) ($19,231)
Eliminate the service line altogether ($2,112,465) N/A N/A

Example Decision Tree

Using the same data we used to create the payoff table, we can also construct a simple decision tree. It might look something like this:

A typical decision tree
An Example Decision Tree

Analyzing the Results

In this example, we're exploring a few alternatives for a healthcare service line. In this payoff table (which can also be called a payoff matrix) we're analyzing three alternatives under three conditions. In this case, our biggest concern is the relationship between patients (customers) and the costs associated with retaining them. Our first alternative is what we could call the status quo. In other words, what will the results be if we keep doing things exactly as we have been.

But what if our customers leave because their physician has gone somewhere else? To account for that possibility, we're analyzing the 'worst-case' scenario in which we stay the course, but lose customers anyway. Conversely, maybe our changes are positive and attract more customers than we expected. We also account for the 'best-case' scenario. We then apply that to all three alternatives and prepare to make a decision.

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