What Is a Decision Tree Analysis? Definition, Steps & Examples
Decision trees can help business owners make complex decisions about anything from finances to staffing.
Have you ever heard of the decision tree method? Aside from being an amazing idea for a childrenās book, a decision tree is one of the most common methods used by business owners to make a decision.
A decision tree diagram can be used for questions like: āshould I upgrade our office chairs?ā. Also, āshould I hire another salesperson?ā, and āshould I add gummy bears to the snack bar?ā (do you really need a decision tree for that last one?). Decision trees can either be drafted out with a pen or created with a decision tree software program or decision tree maker for that extra bit of accuracy.
Hereās What Weāll Cover:
The 4 Elements of a Decision Tree Analysis
The 4 Elements of a Decision Tree Analysis
A simple decision tree consists of four parts: Decisions, Alternatives, Uncertainties and Values/Payoffs.
Letās say you are trying to decide if you should put on sunscreen today. The decision would be: āShould I wear sunscreen todayā. The alternatives would be: āyes or noā, the uncertainty would be: āthe weatherā and the payoff would be: āhealth and happinessā.
1. The Decision
Every good decision tree analysis starts by writing out the initial decision, which in most software programs is represented by a square. If we use the sunscreen decision as an example, it might look something like this:
(the words: āShould I wear sunscreen today?ā surrounded by a square)
2. The Alternatives
So now that youāve written out and boxed your decision, itās time to add your āalternativesā. Alternatives are the many directions the decision could go in or “future choices”. In this case, there are only two potential outcomes to the question āshould I wear sunscreen todayā and thatās āyesā and ānoā. So now, from your decision box, you would draw two branches, one labelled yes and the other no:
(Add two branches, one labeled yes, one labelled no)
Let’s say your decision is: āwhat type of business should I openā. This may warrant more than 2 alternatives and in this case, you would have many branches stemming from the decision box.
3. The Uncertainty
Now for the āuncertaintyā. In most decision tree analysis software programs, the uncertainty is represented by a circle, but again, any shape will do. In this case, the uncertainty is āthe weatherā and more specifically āwill there be sun?ā.
At the end of each branch, write your uncertainty surrounded by a circle (or other shape)
(Add a circle at the end of each branch with the words: will there be sun?)
Now add two more branches stemming out from each circle labeled āyesā and ānoā.
(Add two branches to each circle, one labeled āyesā and one labeled ānoā)
4. The Value/Payoff
So, what is the payoff for wearing sunscreen? Could it be…happiness and health? So if you decide to āyesā wear sunscreen and āyesā the sun is out, your level of health and happiness will be high. You can assign this level a number that will be useful in analyzing the decision tree once youāre done. So letās make your happiness/health level a 9, pretty good.
If you decided to wear sunscreen and itās a cloudy day, your level might be at a 6. Your skin is obviously going to be protected, but youāre kind of annoyed you wasted your $30 sunscreen. Onto the ānoā branch.
Now letās say you didnāt decide to wear sunscreen and the sun is out that day, a risky choice. Your level will be at an all-time low, maybe a 3 and the sunburn doesnāt help. The final alternative is you not wearing sunscreen and no sun in the sky. That might warrant an 8, as you didnāt waste any sunscreen and life stayed pretty much the same.
You can use this decision tree model for any business decision. If, for example, you are making a money-related decision: āShould I hire a new salesperson?ā. The decision would be āshould I hire a new salespersonā, the uncertainty would be āmoneyā and the payoff would be āmore revenueā. Any decision with uncertain outcomes could benefit from the decision tree model.
Key Takeaways
A decision tree analysis is made up of 4 key parts:
- The decision
- The alternatives
- The uncertainty
- The value/payoff
The decision tree analysis technique is a great tool to use for any uncertain events you might find yourself coming up against, whether that be something as menial like: āwhat kind of cake should I buy for the office partyā or something life-changing like: āshould I open a storefront?ā.
Find more information about learning techniques on our resource hub.
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