Use Machine-Assisted Predictive Analytics to Capture Your Customer’s Heart, Mind, and Pocketbook!


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As users/customers engage with a company (their products, services, surveys), they generate a lot of data about their behaviors and interactions with the brand. Predictive analytics and machine learning capabilities provide a way to extract insights from that data to help you improve the customer experience and optimize customer loyalty.

Machine Learning and Predictive Analytics

Today, businesses can collect hundreds of variables about their customers. Real-time delivery of insights necessitates the quick processing of these data. Toward that end, companies are employing the power of predictive analytics and machine learning (ML) to extract insights from data.

There are generally three types of analytics: descriptive, predictive and prescriptive. Descriptive tells you what happened. Predictive tells you what will happen. Finally, prescriptive analytics tells you what decisions/actions you need to make/take to maximize opportunities/mitigate risk.

Figure 1. Using Descriptive, Predictive and Prescriptive Analytics to Make Decisions / Take Actions
Figure 1. Using Descriptive, Predictive and Prescriptive Analytics to Make Decisions / Take Actions

While companies employ all three types of analytics, machine-assisted predictive analytics is where the most value lies. Simply stated, machine learning is a way to learn from historical data through statistical analysis. When we pair predictive analytics with computational power, we can surface insights quickly and reliably. That is the essence of machine learning. The insights from predictive analytics and machine learning will impact how you market, sell and provide support to your users.

But where do we begin your analysis? How do we get from data to insight?

Adopting a Data Science Mindset

Data science is a way to approach problems. In data science, you can employ a 5-step process designed to deliver insights that are reliable, valid and useful.

  1. Formulate question or problem statement
  2. Generate hypotheses
  3. Gather data
  4. Analyze data
  5. Take action

Figure 2 applies this 5-step process to the problem of managing customer retention. By using this process, you will be able to identify and leverage the insights that help you deliver the right experience to the right customer at the right time.

Figure 2. From Data to Insights to Action
Figure 2. From Data to Insights to Action

Answering Questions about User/Customer Loyalty

The goal of any business is to increase user/customer behaviors that are beneficial to the business. These types of behaviors, referred to as customer loyalty behaviors, fall into three broad categories: 1) retention (will customers stay?), 2) advocacy (will customers recommend?) and 3) purchasing (will customers buy more?). Figure 3 illustrates the types of questions you can answer about your customers’ loyalty behaviors and how you can benefit from the insights.

Figure 3. Use Data and Analytics to Answer Important Questions about Customer Loyalty
Figure 3. Use Data and Analytics to Answer Important Questions about Customer Loyalty

It’s clear that the goal of your predictive analytics needs to focus on identifying ways to optimize these different types of loyalty behaviors. Because each type of loyalty is impacted by different types of factors, you will need to build different algorithms for each type of customer loyalty.

Improving the Customer Experience

Predictive analytics and machine learning capabilities can be used to increase customer loyalty by improving the customer experience for different touch points. Generally speaking, these analytics approaches are used to understand the three broad phases of the user/customer lifecycle:

  1. Marketing – deliver the right message to the right user at the right time to optimize different types of customer behavior (e.g., offer for upgrade)
  2. Sales – target high-value customers by identifying customer segments based on behavioral analytics (what customers do).
  3. Service – deliver real-time service for premier customers.

With the plethora of customer data that are available to companies, it’s important to leverage predictive analytics and machine learning to understand your customers. By capturing and analyzing customer data, companies are able to interact with their customers in relevant, personalized ways to capture their hearts, minds and, ultimately, their pocketbooks.


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