6 Styles of Customer Analytics

Different types of analysis suit different customer strategies.

Organizations that limit their thinking on customer analytics to customer acquisition, growth and retention could be missing out.

“Some of the most innovative and significant customer experiences are driven by different types of customer analysis,” said Gareth Herschel, research director at Gartner, ahead of the Gartner Business Intelligence & Analytics Summit 2016, October 10-11, in Munich, Germany.

Mr. Hershel outlined six styles of customer analytics to consider for three key objectives. 

Objective: Enable Exceptional Experiences and Advocacy

For organizations seeking to generate social media “buzz”, to create “customers for life”, and possibly to redefine their industry, there are two styles to consider.

Style 1. People Make the Difference

Most exceptional customer experiences are driven by exceptional employee behavior. Customer analysis can help by conferring understanding of customers’ preferences and behaviors, so that organizations can match them to the right employees in the right circumstances.

Style 2. Deep Listening

Customers don’t always know what they want. Analytics technology provides insight into their wants and needs. Analysis of social media and other customer-created content, as well as monitoring of customers’ purchasing and usage data, can yield ideas for new products to develop and new ways to package existing products.

Design a Data and Analytics Strategy

Advance your organization's strategy by communicating the business value of data and analytics.

Download eBook

Objective: Set Expectations and Minimize Problems

Sometimes the objective isn’t to delight customers but to stop annoying them. Two further styles can help to reduce or prevent customer dissatisfaction.

Style 3. Sharing Insights

Organizations fails to use much of the customer data they collect, but they could achieve a variety of benefits from sharing some of it with their customers. Sharing data can establish a company as a trusted element in a customer’s decision-making process, and can even lead to opportunities to monetize data. The data may require little analysis, as the emphasis is on openness and sharing, not on providing deep insights.

Style 4. Ensuring “It Just Works”

An organization’s value proposition is often built on a few key attributes, such as product reliability, low cost and service consistency. When these fundamentals go wrong, it can be a long and costly process to regain a customer’s trust, especially if his or her needs are met by a competitor in the meantime. Organizations can apply analytics in a variety of ways to determine the key factors for customer satisfaction. In some cases, doing this requires feedback from customers, but in others organizations can use event-monitoring systems to identify issues before they become visible.

Objective: Do the Usual Things, but Better Every Time

The final two styles should improve the customer relationship the more consistently they are used.

Style 5. Massive Customization

In an increasingly digital world, it’s already surprisingly easy to deliver customized products. It should get even easier as the traditional 4Ps of marketing (price, product, promotion, and place) can be adjusted to suit customers better, which should lead to it becoming commonplace to create and deliver customized products on the basis of analysis.

Style 6. Changing Behaviors

The most obvious use of analytics is to encourage changes in customers’ behavior. One can strive to understand and change behavior at any phase in the customer relationship, and in any context, but most efforts focus on acquisition, cross-selling and retention. In some cases, though, the greatest benefit comes from analysis that is informed by an understanding of customer psychology.

Further information is available to Gartner clients in the report Six Styles of Customer Analytics by Gareth Herschel, et al.

Get Smarter

Follow #Gartner

Attend a Gartner event

Explore Gartner Conferences

Complete Your Data and Analytics Strategy With a Clear Value Proposition

As a data and analytics leader, one of the most important things to articulate in your strategy is the value proposition. Learn how to create a modern, actionable D&A strategy that creates common ground amount stakeholders.

Read Free Gartner Research


Get actionable advice in 60 minutes from the world's most respected experts. Keep pace with the latest issues that impact business.

Start Watching