Machine Learning is the process of using your existing data to discover the inputs that most significantly affect your KPIs.

As an example, I want to compare a more traditional marketing strategy with the buzz word “Machine Learning” without using any statistics or technical terms. So for this post, let’s consider two approaches to answering the business question: “How do I get more customers?”

First, the more traditional approach is to develop a marketing strategy to appeal to a specific segment. So, by looking at your customer’s buying factors and your relative strength at providing value, you can decide on a segment to target. In this scenario, we tend to ask inductive questions of the data:

Does our segment like Product A or B more?

What is the demand curve?

When do my potential customers buy my product?

Where should I advertise to connect with my target?

There are hundreds of business intelligence tools and services to help your organization answer these sorts of questions (using reporting, dashboards, ad hoc querying).

Alternatively, let’s consider the Machine Learning approach. Here, the process is to simply feed information about your existing customers into a statistical model and allow the computer to profile the characteristics that best identify a “good customer”. Then, armed with most significant associations, your marketing can focus on finding prospects that are similar. So, now the questions we ask become focused on specific inputs:

How do I best advertise to men who like baseball and make between $40–50K a year?

How do I expand operations in Chicago in the summertime?

What are the strongest characteristics for creating a market segmentation?

Simply put, if I were using a computer to decide how to maximize the number prospects to convert given a limited budget, I can either:

Try to describe my target customer to the machine and have it find prospects that fall into that segment OR give the computer a bunch of information about my existing customers, allow the machine to “learn” what characteristics are most important, and then rank prospects based on the results

The pro of Machine Learning is that the speed, repeatability, and cost of the analysis is great. Users can get powerful, timely insights on how to improve their KPIs. However, at the end of the day, it is hard for a machine to consider the greater mission of the company or the context of the data…in other words, a machine can know a lot, but will never understand. As a result, businesses will likely settle on a hybrid of the two approaches.

To conclude, think of your company’s key performance indicators:

Profit

Customer Lifetime Value

Membership Retention

% of Product Defects

Employee Turnover

Etc, etc…

What levers do you pull to improve these metrics? Machine Learning allows a computer to generate data-driven insights into how you might improve these operations with a deductive analysis of your existing information!