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4 Ways Predictive Analytics Make Customer Service More Efficient

Read Time: 3 minutes

Have you ever gotten caught up on Facebook clicking one after another on the videos and articles it throws up on your page? Or possibly on Amazon where you end up buying something you didn’t even know you needed 10 minutes ago? Many online sites are using predictive analysis to target and retain customers by creating personalized content for them.

With the rise of Big Data, along with Machine Learning, Predictive Analysis is becoming the status quo. This technology applies to every industry where returning customers are a vital part of their strategy. Companies are now consolidating their customer data with what they say on social media and other public platforms. This information helps them build tools that can offer exceptional customer satisfaction by reaching out to them on different channels.

Predictive Customer Analytics is transforming customer service to be proactive and personalized. Here are four of the ways that Predictive Analytics is making customer service more efficient:

1. Reading the Customers Mind and Keeping Them Loyal

Companies have long known that acquiring a new customer is difficult and retaining a customer is even more important. Every interaction with a customer, whether it is in person, on a call or through a product purchase, is a chance to know the customer better. Feeding in data from social media or their online reviews provides additional insights.  All this can be used by the customer support desk to remove the mundane questions and transform the interaction to a more personalized call. No more exercise in futility, where it can take up to 15 minutes to connect to someone who we hope will help us but instead asks us questions we suspect are already in their systems.

Customer support has always been a decisive factor for a customer switching loyalties. Predictive analysis now uses a neural network model that uses multiple variables that will calculate customer satisfaction and hence customer retention.

2. Predicting Churn Customers and Retaining Them

Churn Modeling is a popular application model used in Predictive Customer Analytics to identify customers who are at a risk of leaving. It is, in fact, commonly used by cell phone companies to identify possible risk customers and to find ways to retain them. FedEx is another company that uses the churn model on an regular basis. Their Predictive Analytics tools have a high accuracy percentage when it comes to identifying such churn customers.

This is why customer segmentation becomes important. It helps group customers with similar profiles to create better packages and programs that can be used for old and new customers alike.

3. Segmenting Customers to Build Predictive Patterns

For any business, manually segmenting customers is a tedious process that does not lend itself to bulk data. Machine learning does this much more effectively, creating segments on diverse characteristics based on their probability to react to certain conditions. It has been able to answer many critical business insights to make marketing strategies a much more efficient engine. Some of these key areas are

  • How responsive each customer segment will be to different kinds of stimulus
  • What key criteria led customers in a segment to make a purchase decision
  • Which instances led to a positive conversion
  • How much to promote to each segment to maximize efficiency

4. Personalizing Content By Anticipating Needs

Creating customer stickiness is what every company hopes to do. If only we could know the important events in a customer’s life cycle and then have the ability to reach out with suitable products or services that can increase business revenue. Easy to say but difficult to do? The often repeated Target example highlights the importance of predictive analysis while showing the possible downside too. The Target model identified buying patterns of its women customers to identify which products could trigger a further sale. This led them to promote baby products for a teen who had not yet let her parents in on the news of her pregnancy.

Companies are using the Decision Tree Model to decide what could galvanize the purchasing instincts in each segment and, even further, for each individual. In the pyramid, the company’s products form the base of the pyramid with the customer being the single entity on top. Predictive analysis simulates the customer’s journey to a particular product. This helps marketing strategies to reach out to customers on the best channels.

Predictive Customer Analytics helps companies target and retain customers by cultivating a proactive and personalized customer service experience. By predicting customers’ needs and patterns, you can determine those most at risk for leaving and find out how to improve the value you offer them.

Reach out to our team today!


Venkatesh Kalluru

Venkatesh “VK” Kalluru is a technology and business expert with executive and hands-on experience in automating multi-million dollar enterprises and a strong record of success in creating robust information technology architectures and infrastructures. VK brings proven ability in using IT to solve business issues to the Sertics team. Sertics is SevenTablets' sister company, specializing in predictive analytics solutions.

VK studied computer science at Jawaharlal Nehru Technological University in Hyderabad, India and earned a Master’s Degree in computer science at George Mason University.


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