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The ultimate guide to decision-making: a managerial approach

Updated: Oct 15, 2021

As a marketer, I’ve always been attracted by the psychological aspects that influence people in decision-making.

They help me understand what happens in a prospect’s mind when they are deciding whether to buy a product or service and guide me to make effective strategic decisions.

If you want to learn how to effectively evaluate alternatives, discover what disruptive elements come into play during the decision-making process and the best tools to use, then keep reading my guide!

Table of contents


Decision-making is the process which enables people to make a decision between two or more alternatives. The outcome of each alternative is associated with an expected value and a certain degree of uncertainty.

Due to this uncertainty, the decision-making process requires great attention in evaluating the alternatives.

The triangle below represents the three components of decision-making.

Decision-making framework
Decision-making is formed by three components: problem management, decision analysis and risk management.

The new rational manager: an updated edition for a new world, published in 1997 by Charles H. Kepner and Benjamin B. Tregoe, shows how problem management should be carried out before making a decision. The information gathered in this phase can be used to perform decision analysis.

Instead, evaluating risk is necessary for future objectives or strategies.

For definition, a decision regards an action that still has to happen. That’s why decision-making needs a risk analysis of the alternatives.

Defining decision analysis

Decision analysis is a process to determine the pros and cons of all possible outcomes.

Decision analysis requires a finite number of alternatives. Sometimes, the less information, the better.

As Robert Duncan explains in his work, Characteristics of organizational environments and perceived environment uncertainty published in 1972 by Administrative Science Quarterly, a decision maker seeks information to reduce or eliminate risk (uncertainty).

On the other hand, information overload occurs every time there is a gap between volume of information and assimilation capacity. In 2007, Monash Business Review issued Decision-making: too much info! by Ambalika D. Kutty, Himanshu Kumar Shee and R.D. Pathak who explained how excessive information can negatively affect decision-making.

A simplified step-by-step process to decision analysis should be:

  1. Listing all the possible alternatives;

  2. Setting the parameters to evaluate each alternative;

  3. Reordering or prioritizing the alternatives according to the degree of attractiveness set by the decision maker;

  4. Making a final decision.

The last step may sound obvious, but analysis paralysis is very common in organizations. Over-analyzing or over-thinking a situation paralyze the outcome, since a decision is never made.

7 steps of the decision-making process

According to the two experienced business managers and educators Phil Higson and Anthony Sturgess, who published Uncommon leadership: how to build competitive advantage by thinking differently in 2014, the decision-making process can be divided into 7 steps.

7 steps to effective decision making
7 steps to effective decision making.

A structured decision-making model, based on a rationalistic approach, can be made by 7 steps:

  1. Identify the decision. The first step is realizing what decision you need to make and its nature;

  2. Gather information. Get relevant information and insights about your decision: what’s relevant and what’s not? Who can influence the final event?;

  3. Identify the alternatives. What different courses of action do you have? What different data interpretations may be possible?;

  4. Weight the evidence. Based on the intel you have, list the pros and cons of each alternative and imagine its final outcome. Then, order the alternatives according your specific value system;

  5. Choose among alternatives;

  6. Take action.

  7. Review the decision and its consequences. Has the outcome satisfied the need in step one? You may want to review the previous steps and acquire more info on your options, add additional details and explore other alternatives.

The 7 steps of the decision-making process is an example of rational decision-making model. It brings logic and order to decision-making and consists of a series of steps which start by identifying a problem or opportunity and end with actions taken upon guided decisions.

What are the benefits in using a rational decision-making model?

In 2002, the professor of management Paul C. Nutt at Ohio State University published Why decisions fail: avoiding the blunders and traps that lead to debacles, a study based on over 400 strategic decisions made by top managers of different organizations.

His analysis revealed that 2/3 of all decisions are based on error-prone or controversial tactics. He said that:

[Managers] believe that following recommended decision-making practices would take too much time and demand excessive cash outlays.

He identified three main blunders:

  1. Rush to judgment occurs when managers find an issue and patch it up with the first solution that they come across. They fear to leave concerns unanswered and feel the pressure from colleagues and supervisors;

  2. Misuse of resources happens when decision makers spend money (sometimes millions of dollars) on an evaluation to support a hurried selected decision. They should also invest in other aspects of decision-making, like gathering information about the concern, set expectations and find who may stop action;

  3. Failure-prone tactics occur every time a manager approaches decision-making with a wrong methodology. Nutt highlighted how critical participation is: decisions with a prior participative phase grants over 80% of success. He revealed that only 1/5 of the decisions are founded on participation.

In conclusion:

Following good decision-making practices actually costs very little, especially when you compare it to the costs of dealing with the consequences of a debacle.

Defining problem management

Problem management is a process which takes care of the life-cycle of every problem in an organization.

It includes all the activities required to identify what caused a problem (root cause), how it can be resolved, how to implement the resolution in the organization and how to prevent the same incident over time.

Another scope of problem management is to store issues’ information and workarounds to reduce future number and impact of incidents.

It activates when something negatively affects the organization.

How can you diagnose and fix problems in your business?

You should craft a service blueprint: a detailed process flow chart.

Defining risk management

The International Organization for Standardization (ISO) codified risk management standards in ISO 31000 and gave the following definition:

Risk management represents the coordinated activities to direct and control an organization with regard to risk.

In the same document, risk is defined as:

The effect of uncertainty on objectives.

In other words, risk management is the process to decrease or eliminate risks.

Risk is composed of two elements:

  1. The probability that something goes wrong;

  2. The costs or negative consequences of the worst scenario. In other words, the outcome when something actually goes wrong.

How can you identify and decrease risks in a business?

In brief, the prior step to risk management is risk analysis. It includes all the activities required to identify threats and estimate their likelihood of materializing.

The first move is listing all the possible threats. You can brainstorm them or use two powerful tools: SWOT analysis and Porter’s Five Forces analysis.

Then, you want to estimate the risk of each threat using this formula:

Risk value = probability of event x cost of event

To calculate the probability of a single random event with mutually exclusive outcomes (e.g. in a horse race, a certain horse wins or it doesn’t), you can use this formula:

Probability = event ÷ outcomes

Let’s consider a 6-sided die. If it rolls a 3, it can’t also roll a different number at the same time (event with mutually exclusive outcomes).

Your event is rolling a 3 and the total number of outcomes is 6 (as many as the die’s sides). The probability that the event occurs is: 1 ÷ 6 ≅ 16.7% (see image below).

Likelihood of rolling a 3 on a 6-sided die
How to calculate the probability of rolling a 3 on a 6-sided die.

Let’s now consider a jar with 5 red, 4 green and 11 blue balls. What’s the probability of drawing randomly a red ball from the jar?

You divide the number of events by the number of outcomes: 5 ÷ 20 = 25% (see the image below).

Finding the probability of a single random event
A jar contains 4 green, 5 red and 11 blue marbles. If a marble is drawn from the jar at random, what is the probability that this marble is red?

How can you calculate the probability of multiple events?

You consider each event separately and then multiply their probability together:

Probability event 1 x probability event 2 x … = probability of multiple events

Let’s consider the same jar mentioned above. How can you find the probability of drawing a red ball the first time, a green ball the second time and a blue ball the third time?

Calculate the likelihood of each event separately, but keep in mind that you are extracting a ball every time. It means the total amount of balls decreases of 1 per drawing.

5/20 x 4/19 x 11/18 = 44/1368 = 0.032 = 3.2% (see image below).

Calculating probability for dependent events
A jar contains 4 green, 5 red and 11 blue marbles. If 3 marbles are drawn from the jar at random, what is the probability that the first marble is red, the second is green and the third is blue?

You can replicate this formula for the rolling 6-sided die. In this case, the events are separate and don’t influence each other. The probability to roll the same number for two consecutive times is:

1/6 x 1/6 = 0.0277 ≅ 2.8%

Once you have estimated the risk value, you can proceed by choosing a risk management strategy:

  • You would avoid the risk when there are no relevant advantages for your organization to take it;

  • You would share the risk with partners, team or third party organizations. For instance, when you purchase health insurance, the deductible is the risk value you are willing to take. Another example can be represented by joint-products (where multiple companies unite to produce and market a product/service) or a differentiated investments portfolio;

  • As a last resort, you could accept the risk when the insurance cost is higher than the value of potential losses or when the possible gain is worth the risk;

  • Even if you accept the risk, you should activate preventive strategies to identify possible weak points and take actions to reduce the probability of unfavorable events.


Decisions can be divided into two categories:

  1. They can be programmable when they regard routines or repetitive processes governed by standard procedures. They are characterized by a low degree of risk;

  2. They are not programmable when they are related to unexpected or new situations which require specific tactics. They are characterized by a higher level of risk.

In both cases, you can use a rational approach by evaluating the possible options and analyzing them, in terms of costs and benefits, to maximize earnings and minimize losses. However, emotions will always limit the quality of logic and generate irrational decisions.

Emotion is the most disruptive factor in decision-making. They are inevitable, can mislead and alter perception. They can be:

  • Fear;

  • Contempt;

  • Anger;

  • Delight;

  • Joy;

  • Panic;

  • Anxiety;

  • Frustration;

  • Enthusiasm;

  • Excitement.

Emotions are additional information to consider in decision-making. They guide attention towards certain aspects rather than others and can be used in heuristic decision-making strategies.

Defining heuristic decision-making

Heuristic decision-making focuses on unconscious rules and some variables of the decision rather than others.

It decreases costs and time in decision-making, resulting far quicker than step-by-step processing.

Although heuristics allows to drop evaluative elements, it can mislead and produce inaccurate outcomes.

An example of heuristic decision-making is the gambler’s fallacy, which is also known as the Monte Carlo fallacy or fallacy of the maturity of chances. It represents the wrong belief that if an event occurs more often than in the past, its probability to happen in the future decreases (or vice versa).

As I’ve already explained in risk management, the probability of an event is not related to what happened in the past (statistically independent events).

Another heuristic behavior is the compromise effect. Decision makers would prefer moderate alternatives rather than extreme ones.

In 2012, the journal of the Society for Judgment and Decision Making (SJDM) issued The effect of incomplete information on the compromise effect by Shih-Chieh Chuang, Danny Tengti Kao, Yin-Hui Cheng and Chu-An Chou who explained how, in an incomplete information scenario, the most regular decision results more appealing, because it contains elements of either extreme.

Think of a value ladder used in marketing or a simple offering. Companies should always offer three alternatives. At the movies, you can buy a small, regular or big soda. About 60% of people would pick the middle alternative. The middle alternative represents the core business of such companies.

The theory of prospect

In 1979, Econometrica (journal of the econometric society) published Prospect theory: an analysis of decision under risk by the two Israeli psychologists Daniel Kahneman and Amos Tversky.

As opposed to the rational decision-making model, they offered a more realistic key to reading based on empirical experiments of cognitive psychology. The latter studies the mental processes responsible to acquire, elaborate, memorize and eventually recover information.

The lottery dilemma, case A.

Think of being $300 richer. You must choose between:

  1. A sure earning of $100;

  2. 50% chance of winning $200 and 50% chance of winning nothing.

The lottery dilemma, case B.

Think of being $500 richer. You must choose between:

  1. A sure loss of $100;

  2. 50% chance of losing $0 and 50% chance of losing $200.

What alternatives did you choose?

According to Kahneman and Tversky’s research, 72% of people opt for the sure earning in case A (choice number 1). In case B, only 36% picks the same alternative.

In case A, the majority of people are risk-averse towards a potential profit. In case B, the same people are prone to risk towards a potential loss.

People perceive moderate losses almost double times more valuable than modest profits.

The two dilemmas are presented in different ways, but they share the same choice: $400 for sure or a lottery with equal possibility to win $500 or $300. According to the principle of invariance, we would expect people to choose the same alternative in both dilemmas.

The theory of prospect explains this phenomenon with three psychological aspects that affect decision-making:

  • Framing effect. During a decision-making process, people are influenced by their past and surrounding context. In particular, distinct dilemma formulations affect the perception of the starting point (or status quo) from which people evaluate their options;

  • Loss aversion. Most people are more motivated to avoid a loss than realizing a profit. For example, it’s easier to give up on a discount than accepting a price increment, even if there is no difference between the starting and final price;

  • Isolation effect. People are prone to isolate consecutive probabilities, instead of treating them together (see the next example). Afterwards they choose an alternative with the best utility value. Utility reveals a consumer’s preferences and, for this reason, is a matter of opinion. It’s not an objective variable.

It took me a while to understand the first part of the isolation effect. Let’s figure it out with an example.

Dilemma A.

At the end of a game you can choose:

  1. 20% chance to win $400;

  2. 25% chance to win $300.

Dilemma B.

You are playing a game with two phases. You have a 75% chance you finish the game without winning and a 25% chance to pass the first phase. If you pass to the second phase, you have this choice:

  1. $300 for sure;

  2. 80% chance to win $400.

What alternatives did you choose?

According to Kahneman and Tversky, 65% of the audience chooses the alternative 1 in dilemma A and 78% picks the alternative 1 in dilemma B.

The two dilemmas are formulated differently, but they have the same outcomes.

According to what I’ve already explained in the risk management paragraph, the probability of multiple events is calculated by multiplying distinct events with each other. So, the final probabilities of dilemma B are:

  1. 25% chance to win $300 (since you have to pass the first phase of the game);

  2. 0.25 x 0.80 = 0.20 = 20% chance to win $400 (since you have to pass the first phase and win the 80% chance consecutively).

This is the isolation effect.

In conclusion, the prospect theory explains how people’s risk appetite increases in circumstances of loss and decreases in circumstances of profit. They tend to choose the closest alternative to their starting condition (status quo).

Affect heuristic and the “Asian disease problem”

Daniel Kahneman and Amos Tversky also demonstrated how affect heuristic influences decision-making.

Affect heuristic shortens the decision-making process and lets people attribute a value to alternatives according to their emotional response (or affect in psychological terms).

If you read the word “lung cancer”, your emotional response is characterized by fear, but if you read “mother’s love”, you generate a feeling of affection and comfort.

It means words play a powerful role in the decision-making process.

Let’s use the “Asian disease problem” to better understand this concept.

Problem A.

A new Asian disease invades the United States and the life of 600 people is at stake. You can choose between two action programs:

  1. 200 people are saved;

  2. 1/3 chance of saving everyone, 2/3 chance of saving no one.

Problem B.

A new Asian disease invades the United States and the life of 600 people is at stake. You can choose between two action programs:

  1. 400 people die;

  2. 1/3 chance that no one dies, 2/3 chance that everyone dies.

What alternatives did you choose?

The content of the two problems is the same, the only difference is the language used. 72% of people picked the program 1 in problem A and 78% chose the program 2 in problem B.

When you read problem A, you are exposed to a series of positive elements that encourage you to pick a certain outcome. In problem B, you are exposed to negative elements and your risk appetite increases. That’s why people preferred a probabilistic solution in problem B.

The endowment effect

The loss aversion described in the prospect theory is due to the endowment effect as defined by Daniel Kahneman, Jack L. Knetsch and Richard H. Thaler in Experimental tests of the endowment effect and the coase theorem published by the Journal of Political Economy in 1990.

The endowment effect
The charachter Sméagol (also known as Gollum), from the movie The lord of the rings, perfectly represents the endowment effect.

The endowment effect describes how people attribute a higher value to their belongings rather than the same goods available in the market. In other words, once you acquire an item, it’s difficult to let it go.

This behavioral model is used by companies to set price according to consumer willingness to pay. The maximum willingness to pay for a good is usually lower than the least amount people are willing to accept to give up the same object when they own it.

As a marketer, I’m very curious about the endowment effect in decision-making.

During summer, shores are populated by holiday-makers and… hawkers. They usually sell bracelets and necklaces, they invite you to wear the product and say: “keep it, it’s yours!”. They are exploiting the power of the endowment effect.

Beliefs bias in decision-making

We perceive reality through certain representations generated by a mix of emotions, information and experiences. When these representations are consolidated over time, they are called beliefs.

Our brain tends to confirm our beliefs as much as possible in an attempt to save psychic energy and increase our sense of identity.

Besides, how could it be possible to make faster decisions on a daily basis without beliefs?

The problem is that their effect isn’t always positive and generates bias in decision-making:

  • Illusory truth effect. People believe false information after extended exposure. The illusory truth effect was first discovered in 1977 by a study of Lynn Hasher, David Goldstein and Thomas Toppino called Frequency and the conference of referential validity and published by Journal of Verbal Learning and Verbal Behavior. For the same effect, people perceive information as true when they recall the same information from their past: even if the source wasn’t reliable;

  • Illusion of control. People overestimate their ability to control events (e.g. stock market). If you are interested in this topic, you can read Illusions of control: how we overestimate our personal influence written by Suzanne C. Thompson in 1999 and published by Current Directions in Psychological Science;

  • Excessive optimism. There is the tendency to overestimate the probability of positive outcomes and underestimate negative outcomes. Source: How unrealistic optimism is maintained in the face of reality written by Tali Sharot, Christoph W. Korn, Ray J. Dolan and published by Nature Neuroscience in 2011;

  • Selective perception. We underestimate information which contradicts our prior beliefs. In other words, selective perception encourages people to ignore opposing viewpoints and select only the elements that they want to assimilate. You can learn more about this phenomenon in Perception and misperception of bias in human judgment written by Emily Pronin and published by Trends in Cognitive Sciences in 2007;

  • Attribution bias. People tend to award themselves the successes and the insuccesses to others. Source: Attribution of success and failure revisited, or: the motivational bias is alive and well in attribution theory - Miron Zuckerman, Journal of Personality, 1979;

  • Authority bias. People think they are able to predict the outcome of a probabilistic event just because the source of their information is an individual that they esteem and respect. Reference: Behavioral study of obedience - Stanley Milgram, The Journal of Abnormal and Social Psychology, 1963;

  • Home bias. People prefer choices which are geographically close (national, regional or local) or with which they have an emotional tie. In 2012, The Review of Financial Studies issued No place like home: familiarity in mutual fund manager portfolio choice by Veronika K. Pool, Noah Stoffman and Scott E. Yonker who showed that U.S. managers prefer to invest in a portfolio of stocks from their home states;

  • Status quo bias. People have discomfort to change a present situation, so they prefer outcomes which keep them as close as possible to their status quo;

  • Partial information. There is the tendency to consider information overreaching and all-embracing while they are only partially true;

  • Scope neglect. It is also called scope insensitivity and represents the tendency to ignore the extent of a category. For example, the sum of many little costs is perceived lower than the same final amount presented as a whole. Source: Evaluation by moments, past and future - Daniel Kahneman and Amos Tversky, 2000.

Bounded (or limited) rationality

In 1978, Herbert A. Simon identified an alternative bias for decision-making and explained how people perceive the surrounding context according to the bounded rationality principle.

Decisions are affected by transitory perceptions and emotional experiences associated with the decisional context. In the same situation, different people act differently.

Bounded rationality: the adaptive toolbox, published in 2002 by Gerd Gigerenzer and Reinhard Selten, explains that human rationality is limited. People have to elaborate so much data in a short time that prefer to make the most satisfying decision rather than the optimal one.

This heuristic decision-making strategy, called satisficing, requires a search among different alternatives until a satisfying threshold is met. It’s used when circumstances don’t allow to determine an optimal solution.

Bounded rationality by Herbert Simon
Quote from: Carnegie Mellon University's Simon reflects on how computers will continue to shape the world - Interviewer: Byron Spice; interviewee: Herbert A. Simon; Publisher: Post-Gazette Science, 10/16/2000.


If you have arrived at this point, then you should know what decision-making is, how to do it and what disruptive factors influence it.

It’s now time to learn how to design a decision-making process.

The six thinking hats

I want to introduce you to a very simple tool which can be used offline with your team.

The six thinking hats is a designing system presented in 1985 by Edward de Bono, a physician and psychologist, in Six thinking hats: an essential approach to business management.

It is a tool which supports groups or single individuals in design thinking and thinking processes.

The six thinking hats
The six thinking hats system by Edward de Bono, 1985.

De Bono discovered that human brain could be challenged in six different directions and used hats as a metaphor to describe them:

  1. White hat is data analysis. It represents facts, intelligence gathering, analogies, similar past events and other elements that can be used for a following evaluation;

  2. Red hat represents an immediate expression of feelings, intuitions and suggestions. All the factors connected to emotions and sensitivity;

  3. Black hat is the devil's advocate, a list of reasons why the alternatives can’t work or result in negative outcomes;

  4. Yellow hat is the angel’s advocate, a list of positive aspects, advantages and opportunities;

  5. Green hat represents creativity. This is where new ideas, development propositions and different perspectives are shared;

  6. Blue hat represents the process: identifying priorities, methods, planning, organization, rules and coordination. For instance, the 7 steps of the decision-making process can be a right strategy to organize the work.

Decision tree

In decision analysis, you can use a tree-like model to depict all the alternatives, probability, outcomes, costs and utility.

Decision tree example
Reworked version of Lucidchart's decision tree example.

The image above represents an example of decision tree where a company is deciding on whether or not to test the product before launch. The different branches record the probability of success and estimated payout, so the company can see which option will bring in more revenue.

Decision matrix

A decision matrix is a series of values ordered in rows and columns which allow analysts to study and compare the performance of multiple alternatives.

Decision matrix example
Decision matrix example.

The example above depicts a decision matrix of a company which is finding the best learning management system to help employees develop new skills.

Three reasons why you should use a decision matrix:

  • It decreases time to decision-making;

  • It increases objectivity;

  • It clarifies and prioritizes alternatives.

To master this tool you need alternatives (usually listed in the first row) and comparative variables (usually listed in the first column).

Then, you choose an assessment criterion. In the image above, it is represented by a scale from 1 to 5, where 1 stands for a variable that doesn’t match the expectations and 5 for one that exceeds them.

To build my decision matrices, I use Google Sheets: it’s free, reliable and easily shareable.

How can you craft a decision matrix?

Pre-requirements: you need to have a decision to make and a series of alternatives.

The first step is making your matrix table ready.

This should be easy, right?

You open Google Sheets and here is your table.

The second step is brainstorming the comparative variables. You can ask other stakeholders to join this activity and help you list the criteria you will use to evaluate each option.

In the previous example, these variables are:

  • Quality;

  • Costs;

  • Ease of use;

  • Support.

The third step is determining a ranking method for the variables:

  • I usually give a value from 1 to 5 according to the performance (or expected performance) of each variable;

  • Another method could be choosing an option as a baseline and compare the other options to it. For instance, you can rate a variable against the baseline as worse (-1), same (0) or better (1). This is called Pugh matrix method, from its inventor, Stuart Pugh, who described it in Concept selection: a method that works published in 1981 during the first Proceedings of International Conference on Engineering Design (a series of events hosted by Design Society).

The image below is an example of Pugh matrix where a company is determining the best prototype among three alternative proposals.

Pugh matrix method example
Reworked example of CItoolkit's Pugh matrix method.

The fourth step is weighing the variables. This passage is optional, but it’s useful if you attribute a different value to each criterion.

The image below represents a decision matrix which breaks down the rankings of four restaurants. This time, variables are in the first row and alternatives in the first column.

Decision matrix restaurant example
Decision matrix restaurant example with weighted alternatives.

The fifth step is scoring your options by multiplying a criteria weight by each value.

Decision matrix restaurant example with weighted variables
Decision matrix restaurant example with weighted variables.

As you can see from the example above, the Japanese restaurant is the winner.

Pros and cons list

Decision-making can be carried out with a classic pros and cons list which is technically known as a decisional balance sheet or a decision balance sheet.

Decision-making pros and cons example
Decision-making pros and cons example.

The image above depicts a list of pros and cons on social media advertisement.

With this method, you can point out the options that meet the necessary criteria or that are too risky to be supported.


After discovering that about 67% of your decisions are based on deceptive methods, will you introduce some decision-making best-practices in your company? What tools will you use first?

Comment below. I can’t wait to know your progress in decision-making!

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