The data whisperer -- Why you need one, and why it's not your data scientist
Data scientists can do no wrong, at least in the eyes of the companies that are clamoring to hire them. Every company wants them so they can use data to better understand and predict customer behavior. It’s why Glassdoor named data scientists as 2016’s hottest job.
However, what many organizations don’t realize is that a data scientist only sees part of the equation. Identifying and understanding patterns and using algorithms are a great start, but they aren’t the silver bullet businesses are looking for to instantly turn their data into insights that improve the customer experience.
Companies must look beyond the aggregate data sets and get to the reasoning behind human behavior, or to the "why" part of customer actions. This is where the data whisperer comes into action. While data scientists can provide information and predictions based on data that is present, only the data whisperer can fill in the gaps to provide insights into individual thought processes and offer real world factors that impact decision making. When the scientist and the whisperer work together, science and art combine to provide the complete view of the customer.
A data scientist’s role is to analyze large sets of data by using math, statistics and algorithms to identify patterns and, ultimately, make predictions based on past customer behavior. The data scientist often resides outside of a business unit and can direct strategies based on large trends in data sets. While this information is imperative for someone like a CMO to understand initiatives such as campaign performance, the series of data outputs that identify trends don’t pinpoint why individuals exhibit certain behaviors, nor do they provide insights about interacting with a specific person. If these large scale outputs are viewed as an exact science, companies risk making mistakes because there are always outliers, and individual human behavior is, by its very definition, unpredictable.
The data whisperer is a human-focused analyst, or customer service expert, who has a fundamental understanding of the business, market drivers and customers. While the whisperer may use some of the same modeling techniques as the data scientist, that person is tasked with combining these insights with with the human element, or cognitive science, to show what created those patterns. Unlike the data scientist, the whisperer has the experience and skills to make inferences about individuals within the data set and determine the underlying reasons for a specific action. The whisperer views information through a cognitive lens, adds market and business context, and mixes in human behavior constructs, which allows companies to finally understand why specific customers exhibit certain traits or perform particular actions.
The subtleties and nuances in behavior that the whisperer identifies are precisely what allow companies to be more in tune with customers and be prepared to work with them as individuals, rather than as part of a broader pool. With this information, organizations can then adjust strategies to cater to both the broader data set and the outliers in order to address individual needs and wants. The combination of the whisperer and the scientist gives companies the data insights they need to form a complete picture of the individual customer.
When implemented as part of a tandem data strategy, the data scientist and data whisperer give companies both the output analysis through pattern recognition and the input analysis of the “why” behind the behavior. For example, aggregate data may help predict customer churn by indicating that a certain type of customer may discontinue service or leave. This information can help inform the conversation to encourage the customer to stay, but knowing that a customer is “at risk” doesn’t necessarily equip employees on the front line to successfully engage with each individual. The whisperer’s touch adds the cognitive and affective elements that can provide a glimpse of the frequently hidden behavior drivers. This is what allows companies to connect with customers at a human level, which is the only place where intended behavior can change.
As more and more customer data is collected, companies are charged with making sense of it all in order to understand customers, adjust strategies, and provide a more personalized customer experience. While hiring a data scientist may seem like a quick fix, the skills of the data scientist must be balanced with the insight of the data whisperer to truly gain the complete view of customer behavior. The combination of these two forces is incredibly powerful and, when art and science meet, companies can make better, more informed decisions about their customers.
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Matt Matsui, Senior Vice President of Products, Markets, & Organizational Strategy at Calabrio, oversees company-wide go-to-market efforts. Matt joined Calabrio with more than 25 years of experience leading product and marketing organizations for a broad range of companies, including ACNielsen, Cognos, Fair Isaac and numerous early stage analytics firms. Through this experience, Matt has developed a keen sense for growing markets with a heavy emphasis on big data and business analytics.