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How to Define Business Objectives for a Predictive Analysis Model

A predictive analytics model aims at solving a business problem or accomplishing a desired business outcome. Those business objectives become the model’s goals. Knowing those ensures the business value of the model you build — which is not to be confused with the accuracy of the model.

Hypothetically you can build an accurate model to solve an imaginary business problem — but it’s a whole other task to build a model that contributes to attaining business goals in the real world.

Defining the problem or the business question you want your model to solve is a vital first step in this process. A relevant and realistic definition of the problem will ensure that if you’re successful in your endeavor of building this model and once it is used, it will add value to your business.

In addition to defining the business objectives and the overall vision for your predictive analytics model, you need to define the scope of the overall project. Here are some general questions that must be answered at this stage:

  • What business problems would your stakeholders like to solve? Here are some immediately useful examples:

    • Classify transactions into legitimate versus fraudulent.

    • Identify the customers who are most likely to respond to a marketing campaign.

    • Identify what products to recommend to your customers.

    • Solve operational issues such as the optimal scheduling of employees’ work — days or hours.

    • Cluster the patients according to their different stages of the disease.

    • Identify individualized treatments for patients.

    • Pick the next best-performing stock for today, the quarter, or the year.

  • If you develop your predictive model as a solution, you have another range of questions to address:

    • What would stakeholders do with that solution?

    • How would they use the model?

    • What is the current status with no model in place?

    • How is this business problem handled today?

    • What are the consequences of predicting the wrong solution?

    • What is the cost of a false positive?

    • How will the model be deployed?

    • Who is going to use the model?

    • How will the output of the model be represented?

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