Using behavioral data to improve customer satisfaction

Why cross-channel analytics are crucial to empowering business teams with a behavioral view of your customer.

By Emily Drevets
October 4, 2016
Bird's eye view. Bird's eye view. (source: Pixabay)

Today’s customer can choose to interact with you in many ways—over the phone, in your stores, on the web, through social media, or likely all of the above. The data from these interactions provides a new level of insight into customer satisfaction, more so than traditional loyalty metrics.

Behavioral data is exciting because unlike loyalty metrics, it shows you what the customer does, instead of telling you what they think. It can also shed light on the context of their actions, helping you see areas where you can improve immediately.

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By using information from every touchpoint, you create a picture of customer behavior that moves like they do—in real-time and in all dimensions. Not only is this level of detail necessary for you to have a clear picture of customer happiness, you need it to satisfy the current demands of business.

Ryan Garrett of Teradata works with data teams to improve customer satisfaction. He says that though the way customers interact with companies has changed:

“The goal of the interaction hasn’t. Customers still want a simple experience. Except now, that means companies both understanding who you are as well as your intentions. It’s the modern version of calling a phone company and the support representative knowing about the recent issues you’ve experienced.”

According to Garrett, overlooking one of the channels in which your customers interact with you is a mistake since without the full picture, it’s difficult to understand how events affect customer satisfaction with products and services.

Garrett gives the example of in-store visits for telecom companies, which are typically viewed as positive events. A very different picture should emerge here, however, when the customer shows up after two calls to a center with the problem still unresolved. Cross-channel analytics reveal the more likely conclusion of this scenario where analysis of only one channel would have fallen short. “Once you tie those cross-channel pieces together,” Garrett says, “you can more accurately infer the customer’s degree of satisfaction or dissatisfaction and respond accordingly.”

In some cases, analytics teams have the relevant behavioral data, but are analyzing it in a way that is inactionable—you may learn of an increasing number of customer problems without any detailed information on what types of problems they are, for example. Rather than leading to more efficient business, this abets a notorious enemy of work—information overload.

In another pitfall, analysts sometimes build complicated models that confuse the business teams that need to use them. As a result, nothing changes. To solve this problem, Garrett says, “You need to provide non-engineering users with tools to understand the data behind the events so they can take action to simplify customers’ lives.” This allows both teams to play to their strengths and navigates the gap between the two stakeholders.

Understanding cross-channel customer behavior will take time, resources, and experience (read: mistakes). You might need to update your organization’s strategy and analytics structure, or make deep changes to how you’re organizing analytics processes or gathering and preparing data.

Developing the most accurate picture of your customer, however, is invaluable. With it, business teams can make better decisions, resulting in smarter customer interactions wherever and whenever they happen.

This post is a collaboration between O’Reilly and Teradata. See our statement of editorial independence.

Post topics: Data science