We’ve come a long way, baby. . .
In the past three decades, the exponential growth in technology’s capabilities have given us the power to integrate multiple sources, predict behaviors, and deliver insights at a speed we only dreamt of when I was starting out. CMB Chairman and co-founder, Dr. John Martin, was an early cheerleader of the value of using multiple methods and multiple sources, so the promise of bringing disparate data sources into a unified view of customers and the marketplace is this researcher’s dream come true.
While integrating data to help make smarter decisions has always been a best practice, it is the advances in technology that have allowed for an even greater and easier integration. Below are some recent examples we’ve implemented at CMB:
- In segmentation studies, we include needs/attitude-based survey data, internal CRM behaviors, and third-party appended data into the modeling to create more useful segments. Our clients have found that our perceptual data is a necessary complement to their internal data because it helps explain the “why’s” to the “what’s” that the internal behavioral/demographic data tell them.
- For our brand tracking clients, we often combine web analytics (e.g., Google search data, social media sentiment analysis, client’s web traffic statistics) and internal data (e.g., inquiries, loyalty applications) with our tracking results to help tell a much more nuanced story of the brand’s progress. Additionally, we use dashboards to tie that data together in one place, providing a real-time view of the brand.
- Our customer experience clients now provide us with internal data from call center reports (detailing the types of complaints received) and internal performance metrics to complement our satisfaction tracking.
. . .but we’ve got a ways to go.
While many organizations are leveraging technology to integrate data for specific decision areas, I see a number of stumbling blocks. Many companies are still failing to develop an enterprise-wide, unified view of the marketplace—and the barriers often have little to do with the data or tech themselves:
- Organizational siloes make it very challenging for different functional areas to come together and create a common platform for this type of unified view.
- Moreover, the politics of who owns what—and more importantly, who pays for what—oftentimes means efforts like this never get off the ground.
So, while it seems like technology is helping make all sorts of different data “play together,” we as humans haven’t mastered the same challenge!
How do organizations overcome these challenges to take advantage of this possibility? Like most challenges, the solution starts with senior leadership. If the C-suite makes it a priority for the organization to become customer-centric and stresses that data is a big part of getting there, that goes far to pave the way for the different personalities and siloes to come together. Starting small is another way to tackle this problem. Look for opportunities in which teams can collaborate, even if it’s something as simple as looking at subsequent purchase behaviors from customers six months after they complete a satisfaction questionnaire in order to develop/refine the predictive power of your customer experience tracking. Starting small can create a more positive beginning to the partnership, building the trust and communication necessary to attack the bigger challenges down the road.
Mark is a Vice President at CMB, and while he recognizes that technology has absolutely transformed all aspects of his professional and personal life, he sees meaning in the fact that he prefers his music playlists generated by humans, not algorithms. Long live the DJ!
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Will big data destroy primary research? I’ve read dozens (hundreds?) of articles that argue both sides, but despite a lot of speculation, I see very little attention paid to what may be the real killer of a lot of primary research: the “need for speed.” The increasing velocity of business (compressed product development cycles, social media and the desire for real-time marketing) often means not having the 6-10 weeks a typical quantitative research project needs. For many, the availability of big data is seen as “good enough” given the time constraints. So big data may be an accomplice, but tightened timelines are what researchers really need to address.
