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Crafting Data-Driven Strategies: The Power of Understanding Machine Learning

In order to take advantage of the insights and capabilities that AI enables, organizations need to be prepared on several fronts:

  1. The organization must have a solid understanding of the problem they want to solve.
  2. They must have access to the data that provides insights they are looking to exploit.
  3. They must hire individuals with technical capabilities (both data scientists and programmers) to access the data and implement the machine learning process.

It may seem counter-intuitive, but the step that most companies get wrong is the first. Managers try to apply machine learning to problems that cannot be answered by the data available or they misunderstand the capability of the algorithms and what questions they can answer. In order to increase managers’ ability to ask the correct questions and improve the effectiveness of AI decision-making, we need to craft a better understanding of the difference between supervised machine learning and unsupervised machine learning.

In a previous blog, I outlined the capabilities of supervised machine learning and provided some examples of business problems that can be solved with these algorithms. This method is only part of the story. Unsupervised machine learning can solve a whole different suite of business problems and obtain insights that are otherwise invisible to human analysis.

Unsupervised machine learning is best for instances when the data set is so large that humans cannot classify or define relationships between variables. The process allows decision makers to rely on an algorithm to review the data and come up with insights that are missed by human review.

Some business cases for unsupervised machine learning include:

  • Recommend what product a customer may like based on the preferences of customers with similar attributes (Netflix uses this to suggest content you may like)
  • Create segments of employees based on their likelihood of leaving the company
  • Create a micro-segmented groups of users of a credit card to determine better rewards programs to offer.

To provide further understanding, let's look at an example:

Imagine you are a manager at a large grocery store chain and are looking to solve the problem of decreasing sales. Because you understand the capabilities of unsupervised machine learning, you suggest that the company use unsupervised algorithms to identify products often purchased together and provide personalized offers based on the results.

You collect the receipts for all purchases being made in your store and apply the algorithm to this dataset. The algorithm identifies several products that customers often buy together.

By understanding the applications of different machine learning algorithms the manager was able to:

  1. Identify a business problem and understand how machine learning can solve it.
  2. Understand the dataset needed and how the algorithm would be applied to this dataset.
  3. Receive specific insights that allow the manager to craft a data-driven strategy.

When managers have an understanding of machine learning they can create a data-driven strategy, make better decisions, and drive positive change.