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Learn, Anticipate and Simplify the Customer Experience across Channels with Predictive Analytics

Posted in Analytics, Customer Experience, Customer Insights, Experience Design, and Service Design

We are at the intersection of three maturing technologies: mobile devices, cloud services and big data analytics. Experience Makers collect, correlate and analyze data from their customer interactions across channels to learn, anticipate and simplify the experiences they create for their customers.

In the Forbes.com post, Using Big Data and Machine Learning to Enrich Customer Experiences, Kurt Marko shares:

“The best conversations are continuous, naturally picking up from where they left off, but they are also interactive, efficient, and mutually beneficial, providing tangible benefits in new and useful information. In an omnichannel world, customers change devices at a whim and expect the ability to start a task at one point in time, pick it up later and maintain continuity throughout the event. Transforming the customer experience into a natural conversation improves brand strength and engagement and eliminates support frustrations, resulting in happy, loyal customers.”

Kurt explains that Experience Makers support processes and tasks that have high volume, span multiple channels and are of high value to both the business and customer. And they are pragmatic – identifying the areas where changes provide the greatest value like identify and prioritize channel pairs like Web and phone support or Web and interactive chat that customers are most likely to use together that logical fits in an omnichannel journey. Kurt explains:

“Too often organizations fixate on internal processes and end up making things easy for support agents or in-house bureaucracy without actually improving the customer experience. Instead, examine the customer experience problem from the outside in, as the customer experiences the process. An effective, structured technique for such outside-in analysis entails building customer personas.”

Experience Makers look at transaction data and metrics, for example, to gain insight into the support experience. They look for indicators of process inefficiency, customer frustration and cross-channel breakdowns or gaps. Identify the attempted user tasks and measure their rates of success and transfer. Then look for data identifying problems such as Web-to-call agent transfers that are clues to user frustration and problem escalation.

Experience Makers use machine learning to mine data and proactively anticipate customers’ needs and prevent problems before they happen. Using predictive analysis to provide context for the customer experience and statistical modeling and forecasting to anticipate needs and enchant them.

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