Editor’s note: This was originally posted on Document Strategy’s website here.
Experts anticipate the amount of data collected globally to break the 40-zettabyte mark by the year 2020 at the latest. That is about five times the number of grains of sand on the planet—an impressive figure, and it underscores the colossal scale of the data flood. More importantly, the amount of data is nearly doubling every year.
This data revolution has triggered a far-reaching transformation in many areas of our lives. That applies not least to the customer journey. Consumers have increasingly more opportunities to find out about products and services, compare them to one another, and make purchasing decisions based on an unprecedented level of market transparency. They exchange views across a wide variety of channels, provide feedback, and also read the reviews of others.
Companies collect this customer activity data, at times, with unconstrained giddiness. Their hope is to extract key insights about future customer behavior and respond accordingly. Up to now, successes have been few and far between because information is often processed in a poorly coordinated, decentralized, and untargeted manner.
In the age of big data and the emergence of do-it-yourself programs, the role of market research is changing. While the Internet of things (IoT) is becoming an increasingly abundant source of data, the idea that market researchers could become superfluous is certainly not on the mark. Quite to the contrary, the numerous sources of useful data are giving these professionals the opportunity to redefine their role in the company. Market researchers have to be proactive in developing new methods and approaches. In close collaboration with data warehousing, they should assume the key role in transforming big data into smart data. They need to become consultants who filter salient information out of the mountains of data and present tangible courses of action.
Market researchers will, therefore, not only have to evaluate traditional surveys but also integrate and analyze other related data sources. They need to integrate feedback from social media and complaint management as well as operational data along the customer journey. A comprehensive picture will begin to emerge once there is a fusion of customer satisfaction tracking data, social media monitoring, online communities, transaction and sales data, and customer relationship management (CRM) databases.
To maximize customer lifetime value, companies have to know exactly how customers experience their products and brands at any given point in time and at every relevant touchpoint in real time. Successful market players use customer feedback actively, effectively, and in a timely manner. They do this in two ways: First, organizations use the feedback to perform continuous process optimization that benefits customers at every touchpoint. Second, companies use the feedback to actively manage the individual customer relationship: To solve existing customer issues and to manage their future experiences along all touchpoints throughout the entire customer lifecycle.
In the new era of big data, customer feedback analysis should be derived from structured and unstructured data and should be sharpened by using the breadth and depth of other data sources available. This means that data can no longer be hoarded in different departments and left to fester. The opposite has become a decisive factor for success: knowledge—regardless of where it comes from—is not evaluated in isolation but, rather, is interconnected with other insights centrally in the data warehousing environment. Today’s solutions can help to break down data silos and perform structured synthesis processes for relevant data using automated analytics. This makes it possible to integrate structured and unstructured data, such as open comments, call center discussions, emails from customers, or social media posts as well as transaction data into the analysis. That way, companies gain a much clearer overview of their customer segments and of each individual customer. When there is no individual data, predictive modeling techniques make it possible to foresee future behavior of customers for whom little data is available. Companies can then use those predictions to effectively manage their touchpoints, ensuring customer experiences are leading to the right business outcomes. To put it simply, purely backward-looking customer satisfaction measurement is increasingly being replaced by future-oriented customer experience management (CXM) with measurable targets as a proof of value to the companies’ business performance, such as stronger customer loyalty with a longer customer life cycle, a higher “share of wallet,” or increased new customer acquisition due to a rising recommendation rate.
The time is ripe to start turning the big data buzzword into a customer centric initiative. Today’s technology solutions allow you to gather feedback, collate data from a multitude of streams and sources, analyze it and disseminate it to those who need to take action in real time. And market researchers can help you turn the analysis into insights by identifying stories that help you drive change and action that improves customer experience and ultimately your bottom line. Technology and the role of the market researcher are not in conflict – they are symbiotic drivers of success for your big data strategies.