What Data Scientists Must Learn About Customers

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2012 has been coined t

2012 has been coined the year of the customer by experts in a number of industries.

Because of this, companies are striving to become more customer-centric in their approaches to business.

But companies that hope to improve business outcomes by tightening relationships with consumers must gain a deeper understanding about their customers from the multiple channels customers use to interact with them.

While social, web, mobile, and other sources of customer data can help companies develop richer views of their customers, data analysts who work with this data need to develop a greater understanding about what makes customers tick.

For instance, data scientists are adept at problem solving and doing root cause analysis to get at the heart of a business challenge or a goal that a company is striving to achieve. When it comes to tackling customer-focused strategies such as figuring out approaches to increase the Net Promoter Score (customers that are likely to recommend a company’s products to others) or sales of a particular product in a certain region, customer survey results and other forms of customer feedback can be useful guides.

Still, data scientists must do more than gather and act on common sources of customer data (contact, transactional, marketing information). It’s also critical for analysts to understand the drivers behind customer behavior as well as customers’ attitudes, needs, and preferences. Listening to what customers have to say, what makes them upset or happy, and examining the data (when customers place orders, why they left a web page) can reveal useful insights that decision makers can act on.

Needless to say, face-to-face discussions with customers are invaluable. And while data scientists aren’t in customer-facing roles, there are multiple ways they can connect with customers regularly.

For instance, data scientists can and should participate in customer forums, customer conferences, customer feedback sessions, roundtables, and other events to gain a richer understanding of what a company’s customers and prospects are looking for, what their sources of aggravation are, etc.

Still, there are certain things that customers often don’t share with companies through solicited surveys and other feedback vehicles that can help data scientists better understand customers more fully. For instance, sentiment analytics that are applied to social media mentions about a company can help data scientists determine if there’s an early-stage product or service issue that’s percolating and needs to be addressed.

Data scientists can also leverage emotion detection technologies. These can be used to gather and act on customer sentiment following contact center interactions to help identify problems with products that are causing customer angst and potentially defection as well as to act on suggestions for improving a company’s processes, including call center support.

By working with customer-facing supervisors and staff, data analysts can gain a better understanding of what business leaders are looking to achieve with their customer strategies. Just as companies need to gain 360-degree, multidimensional views of their customers, so, too, do the data scientists who are trying to make sense of all of this information.

Next steps: For more information on this topic, check out our complimentary “5-Minute Guide to CRM Analytics.”

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