Glossary term

Decision Modeling

Decision-making is a critical part of every business operation, but making the right decision, especially when a vast amount of data is involved, isn’t always easy.  

What’s the best way to go about this? Decision modeling. Decision modeling helps business leaders understand and organize data so that important conclusions can be made quickly, easily, and accurately.  

In this guide, we’ll look at decision modeling in detail including what it does, why it’s important, and the bottom-line benefits it offers organizations. 

What Is Decision Modeling, and Why Is It Important? 

Decision modeling is a structured process that predicts the outcome of certain scenarios, offering valuable insights to business users. Decision models are a forecasting tool that provide an overview of all the potential possibilities of specific actions. 

Every day, executives make dozens of critical decisions. Should we enter a certain market? How should we design our new product? Which partners and distribution channels should we use? These decisions need to happen quickly, and they often determine the business’s profitability and overall success. 

Decision modeling helps teams streamline their decision-making processes so they can prioritize their top business objectives. Even if they don’t have a ton of information at their fingertips, managers can still use decision models to lay the groundwork for their decision networks and alter them accordingly. 

For instance, when businesses plan to develop a new product, they don’t have to wait for it to take shape completely. Instead, the sales team can design several strategies depending on what they know and develop them further when the product is completed. 

Decision Modeling Basics 

Developing a decision model involves four basic stages—let’s explore them. 

1. Formulation 

Before developing a model, you need to know what problem you’re trying to solve. From there, a V1 of the model is developed, typically in a decision tree or similar format. 

The formulation phase can be conceptual, or it might feature all the decision logic needed to define the decision-making. For example, what are the KPIs (risk, churn, NPS) surrounding certain decisions? 

2. Description 

The description stage details the decisions and shows how improving them will affect business metrics and objectives. 

Key information about every decision is captured, including the business context, questions and answers, application context, and organizational context. For instance, what would be the impact of using a prioritization algorithm which includes a Net Promoter Score (NPS) for service-related communications? 

3. Specific Decision Requirements 

A decision model describes the decision requirements in terms of input data, knowledge about the decision, and related decisions. These elements can be collectively joined in a Decision Requirement Diagram

For example, what knowledge is needed to define suitability rules for a cross-sell discount proposition? A marketing leader should provide the inputs required to determine eligibility requirements for the discount. 

4. Refinement 

The final stage is to iteratively refine the model using a graphical notation of Decision Requirement Diagrams. The refinement processes include opportunities to test possible changes in the decision management model to identify their implications and suggest ways to modify the model. 

For instance, eligibility decisions could be refined by suitability and applicability. The process repeats until the decisions are specified completely and every member has a clear picture of how the decisions will be made. 

How Can Businesses Use Decision Modeling to Their Advantage? 

Here are some of the major advantages of using decision models in your business. 

Make Decisions About New Products and Campaigns 

Marketers are tasked with building brands, creating demand, promoting sales, and helping companies increase customer loyalty. To achieve this, they use decision models to gather real-time information about consumer behavior, including their preferences and spending patterns. 

This model helps them address questions such as: 

Determine Crop Yields 

Some companies in the US are using decision models to gather years of data about rainfall and temperature. They then run weather simulations and help farmers decide what to plant in certain seasons and when to plant to get the best crop yields. 

Decide Who’s Best for the Job

If you want to assign specific tasks with tight deadlines to an employee, you can use a decision model to identify which employee performed best on similar tasks in the past, indicating who would be the best fit. 

Common Techniques of Decision Modeling 

Different decision models act as reliable tools that facilitate the decision-making process. The most common decision models include: 

Rational Decision Model 

This model requires you to follow a series of steps to find the best solution. It involves analyzing multiple solutions at the same time to come up with one that offers the best possible outcome. The steps are: 

  1. Begin by defining the problem 
  1. Identify the method you will use to evaluate possible solutions identified 
  1. Determine the importance of each method 
  1. Compile a list of all possible alternatives 
  1. Select the best option or solution 

Bounded Rationality Model 

Bounded reality decision modeling is focused on decisions that are “good enough” rather than perfect. That means that the selected decision is enough to address the current situation, but doesn’t maximize the potential value in the situation – a process known as satisficing.  

This model is used in incidents where quick decisions are required, as it takes less time and provides satisfying results. It’s an ideal model when you’re limited on time and/or context. 

Recognition-Primed Model 

This model uses prior experience and quick thinking to make decisions, often in a fast-paced decision-making environment. It involves the following: 

Recognition-primed modeling works best when you apply knowledge from experience in a similar area. It can come in handy when you have strict time limits and need to make a decision quickly. 

Do More With Machine Learning 

Decision modeling is the initial step in streamlining your organization’s core processes. But, it’s just scraping the surface of what machine learning can do for your business.  

Want more insight into how AI and ML can help you increase profitability and reduce risks? Check out 50 Ways to Impact Your Business with AI for real-world examples of how data science can transform your org. 

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