The Science of Effective Presentations: Using Monroe’s Motivated Sequence to Communicate Analytical Findings

For operations research and management science (OR/MS) professionals, the ability to effectively communicate complex analytical work is not just a skill but a necessity. Although most presentations of analytical work at OR/MS conferences are for technical audiences and focus on the details and process of building mathematical models and analyzing data, the real impact of analytical work is observed through implementation. Therefore, it is crucial for OR/MS professionals to receive training in managerial presentations – especially in delivering concise and compelling demonstrations of credible results to guide nontechnical managers, executives and other decision-makers [1].

Many researchers, educators and industry professionals have developed valuable resources for communicating quantitative analyses to audiences with nontechnical backgrounds, some of which focus on presenting traditional OR/MS methods such as empirical or analytical models. Quantitative modeling techniques have advanced significantly with big data and cheaper and faster computers, and artificial intelligence (AI) has gained tremendous popularity in the 21st century. The potential of AI in the future is immense, promising exciting developments and innovations in the field of OR/MS. It may seem that presenting findings from AI applications would be similar to presenting a quantitative analysis; however, the differences between these methods are significant and can substantially impact the focus of a presentation.

The differences and relationships between more traditional quantitative analyses (e.g., statistical analysis and optimization) and AI have been discussed for decades. In his seminal paper, Leo Breiman defined statistical analysis and machine learning – one of the tools AI depends upon to automate decisions – as methods that analyze data to predict or inform but differ on interpretability, validation, data types and dimensionality. In statistics, easily interpretable data models are built to predict and inform how a response variable relates to predictor variables. When presenting the results of a statistical analysis, it is recommended to use charts that can easily explain the relationships and tables to indicate the significance of predictors. Conversely, AI automates complex algorithms to predict or inform using large data sets, performing calculations and models at a speed and scale that is impossible for humans. The result is usually a black box model that may be more difficult to explain than a linear regression model.

Presenting the results of any quantitative analysis to nontechnical decision-makers involves both educational and persuasive aspects. We use Alan Monroe’s well-known five-step persuasive speech framework, Monroe’s Motivated Sequence, to describe how quantitative analyses or AI-based work can be effectively communicated. We base our suggestions on both communication and OR/MS literature.

Step 1: Get Attention

The communication literature offers multiple examples of attracting your audience’s attention – from telling a story, using shocking statistics, or presenting powerful quotes and humorous anecdotes to drawing hypothetical scenarios or creating a series of vignettes [2]. Science reveals that listening to narratives engages multiple brain areas beyond language processing, including those related to emotions and movement, and a listener’s brain waves can synchronize with the storyteller’s, particularly when comprehension is high [3]. Reliable information alone may not be sufficient; an emotionally engaging story adds authenticity and importance. Stories are also critical in virtual presentations, awakening and humanizing the remote audience [4]. Most OR/MS students or professionals primarily focus on quantitative subjects during their training, often neglecting formal education in verbal presentations. Although certain individuals possess a natural aptitude for storytelling, others may benefit from additional guidance in effectively crafting narratives that captivate an audience’s attention.

Although anecdotes and stories are often used interchangeably, they possess distinct characteristics. Anecdotes typically exhibit brevity and revolve around biographical experiences, whereas stories may or may not be based on reality [5]. Cohen and Rubin (2020) suggest a five-step method for structuring engaging stories: (1) create intrigue or describe a problem, (2) expand on the intrigue created in step one, (3) explain the solution to the problem or intrigue, (4) describe the outcome and (5) end with a clincher or a memorable closing line. In his introduction to the topic, Thomas Saaty – a pioneer of the analytic hierarchy process – uses the story of his son’s high school selection. The story starts with a problem: Saaty’s son refuses to change high schools. He further elaborates on why his son is reluctant to switch schools and explains the solution to the problem. By using the Analytic Hierarchy Process (AHP), a method for making subjective group decisions, the Saaty family can collectively evaluate and resolve the school change dilemma. The story ends with a positive outcome: Saaty’s son changed schools and became a successful engineer, showing the effectiveness of AHP.

Step 2: Establish the Need

In Step 2 of Monroe’s Motivated Sequence, the goal is to convince the audience there is a problem and that the problem is essential and relevant. In managerial presentations, the initial problem is usually presented by the client and explained more as a list of symptoms. For example, in most inventory-related projects, the client describes a low service level, overstocking and spoilage as problems, and a detailed root cause analysis may reveal a lack of data or demand forecasting as the most important root causes. The analyst team may offer two solutions: collecting more accurate data and forecasting demand. In this step, the focus should be on broadly overviewing the possible causes of the symptoms and isolating the most critical root cause to tackle. The importance of focusing on the root cause needs to be clearly communicated to the decision-makers. Some options include using statistics, showing how the problem affects the audience, and expressing the costs of inaction in monetary terms or through other key performance indicators.

Step 3: Satisfy the Need

Once the root cause and its importance are communicated, the next step is to present the solution. In Monroe’s traditional framework, the solution is introduced, supported with facts and defended. However, there are some key differences when presenting statistical analyses or AI applications. Statistical analysis uses carefully selected small to medium-sized data and usually requires a human to handle data and interpret results. The interpretation is straightforward, and the model performance is measured through imperfect but simple indicators. On the other hand, AI uses algorithms trained on large data sets to find relationships between a vast array of predictors and a response. Because much of the model is not transparent in AI, it is vital to describe the data source; pros and cons of the specific algorithms used; and the accuracy, sensitivity, and specificity of the model and to address possible biases and ethical considerations.

Table 1 displays our changes (four subsections) to Step 3 of Monroe’s Motivated Sequence and highlights the differences between traditional OR/MS and AI-related presentations.

Table 1. Comparison of presenting a statistical analysis vs. artificial intelligence.

Step 4: Visualize the Future

Implementing AI projects may be time-consuming and costly; confusion matrices can help quantify the cost of false positives and false negatives, demonstrating the return on investment. In some industries, the cost of a mistake is only measured in monetary value; in other areas, such as healthcare, a mistake may lead to incorrect medical treatment, patient suffering and even death. Consequently, the positives and negatives of the system must be taken into consideration.

To help the audience visualize the potential impacts, consider one of three methods: positive method, negative method or contrast method. The positive method focuses on what the future will look like if the solution is implemented. Conversely, the negative method focuses on what may happen if the solution is not implemented. The contrast method explores both – what will happen if the solution is ignored versus what will happen if it is adopted. Regardless of the approach, the key is to make the vision for the future realistic and credible [6].

Step 5: Introduce a Specific Action

In the final step of Monroe’s Motivated Sequence, guide the audience toward a specific, clear action that is both necessary and urgent. For OR/MS professionals, this step involves recommending a course of action based on the analysis, emphasizing its benefits to the audience.

For example, in an AI-driven presentation, propose investing in a particular AI solution to optimize a business process. Connect this recommendation to broader organizational goals, such as enhancing competitive advantage, and address any potential concerns to reinforce the call to action.

Conclude the presentation with a memorable statement, whether a powerful statistic, quote or challenge. This approach will reinforce the positive outcomes resulting from the recommended action so that the audience is informed and motivated to act.

References

1. Grossman, T.A., Norback, J.S., Hardin, J.R. and Forehand, G.A., 2008, “Managerial communication of analytical work,” INFORMS Transactions on Education, Vol. 8, pp. 125-138.
2. Ballaro, B., 2003, "Six ways to grab your audience right from the start," Harvard Management Communication Letter, Vol. 6, No. 6.
3. Renken, E., 2020, "How stories connect and persuade us: Unleashing the brain power of narrative," NPR.
4. Cohen, S. D. and Rubin, D., 2020, "Supercharging your storytelling and sales," Toastmaster, Vol. 86, No. 10, pp. 28-29.
5. Cohen, S. D., 2011, "The art of public narrative: Teaching students how to construct memorable anecdotes," Communication Teacher, Vol. 25, pp. 197-204.
6. Cohen, S. D., 2020, "Public speaking: The path to success," 2nd ed., San Diego, CA: Cognella Academic Publishing.