Wise Insights

Beyond Demographics: Slicing and Dicing Customer Data the Right Way

Beyond Demographics: Slicing and Dicing Customer Data the Right WayIn our modern, information-driven environment, you are practically assured of having too much raw data to handle when it comes to understanding your customers – existing clients and prospects included.

You know their names, you know their numbers, you have their emails and social media posts, but do you know how to address their needs? The abundance of rich demographic details in customer data can seem overwhelming, but they are extremely valuable assets if you know how to use them properly.

Move Customers Out Of Buckets

What sort of signals do you currently use to sort out and assess the satisfaction level of your customers? Like many organizations, you may be tempted to make relatively broad “buckets” to capture customers by region, product purchased or even the volume of their calls to your support team.

Those categories are all a great start, but by adapting a machine learning algorithm to your voluminous CRM data, you’ll discover an underground river of much deeper behavioral information, which can be used to automatically and instinctively address specific client needs.

Machine learning can help uncover more intelligent customer segmentation and efficiently break down data points to identify patterns, isolate the most valuable elements and very accurately help predict future activity.

Use All Signals and Data Points

The secret is machine learning’s ability to take in a wide pool of data from your customers and sort that into extremely usable information. More than just the standard demographic, geographic or product information collected through your CRM, machine learning takes a deep dive into finer customer details that you may have, such as:

  • Psychographics– Information on your customer’s interests, attitudes and opinions, drawn from survey data or their own social media engagement, plus key data on their company revenue, employee numbers, industry and their own types of customers
  • Product and financial data – Useful numbers on customer revenue, purchase frequency or even the number of cancellations they’ve made in the past; plus clickstream data and full purchase history, unique to their business
  • Summary of engagement – Interactions made with clients in the past, such as sales calls, support resolutions and touchpoints made by marketing, including email performance and campaign engagement


The way machine learning leverages this vast amount of data is key. As complex data streams in, machine learning automatically recognizes behavioral patterns from the inputs. This information gleaned from past behaviors can then be applied to decision-making models that predict future outcomes – freeing your marketing, sales and customer services teams from the time consuming – and nearly impossible - practice of wading through unlimited data points to uncover useful information they can act on.

Plus, as data changes over time, machine learning continually adapts so businesses are not limited to making decisions from a static set of data points. Instead, they are given useful information to determine how a particular customer at that particular moment will act or should be treated to achieve a desired outcome.

Predict Customer Needs

Machine learning is not only useful when assisting those who need help; it can even anticipate issues before they arise. It’s like having a full-time data scientist analyzing your customer engagement 24/7, quantifying all those smaller bits of data and spotting the patterns beneath. Machine learning’s strong predictive analytics abilities can then help you make the most informed choices about which customers to engage, or how to head off a potential issue even before it occurs.

The tremendous amount of demographic, usage and engagement data you’re currently collecting has valuable information waiting to be put to work for you.  See how machine learning can easily turn your data into useful, actionable insights.

  Machine Learning for Customer Success, Wise.io

Topics: Machine Learning, Customer Success