The Convergence Mindset: A New Partnership Between Insights and Analytics
By Camille Nicita, President & CEO & Susan Scarlet, Vice President, Strategic Branding, Gongos, Inc.
As consumer data becomes more voluminous inside organizations, so too does the pressure to leverage it to drive business outcomes. From performance metrics to real-time intelligence, data helps us take on stubborn business challenges that are otherwise unconquerable. Likewise, approaches that uncover why consumers behave as they do are just as important to bottom-line strategies. Without empathic knowledge, organizations fail to build their EQ.
Yet, organizations continue to earmark investments for data analytics and consumer insights separately. Beyond the redundancy of dollars, this contributes to operational silos. Worse yet, these groups are often not
informing one another. Joining these work streams will enable organizations to better leverage existing assets, while forging a symbiotic relationship between the two disciplines.
This new kind of relationship—a type of convergence—will unlock the interplay between the what and the why, giving rise to new levels of consumer intelligence.
Sounds easy, right? While possible, key organizational stakeholders acknowledge that internal and external barriers to this relationship exist:
Fortune 500 leaders of marketing and analytics admit four factors limit the likelihood of partnership—and the imagination—between analytics and insights today: mindset, skill set, structure, and bandwidth.
Structurally speaking, both groups are set up to answer relevant and meaningful questions, but analytics tends to lean in with a performance/tools mindset, and market research with a rational/methodological mindset. Top that with data scientists’ affinity to capture the what and researcher’s faculty to explore the why – and you can see how getting these two sides in the same room can feel like a dance of north-seeking magnets.
Performance-wise, it is often the lingering manufacturer mindset that gets in the way of these two teams working in tandem. In order to give way to hybrid approaches, something must give. Each side must learn to appreciate what the other brings to the table while finding a common language to propel an organization’s decision intelligence. In doing so, redundancies will be lifted, and bandwidth on both sides will open up.
Marketplace Barriers (…And White Space)
A current snapshot of the marketplace reveals a deficit of sorts. Simply speaking, the categories of the “big” data players fall into three camps: those who do math all day long – the analytics services companies; those who customize business intelligence tools and write code in their sleep; and the other guys who don’t sleep much at all – consulting on big business while leaving a hefty bill at the door. In this all-too-real scenario, white space does exist. It is in this white space where opportunity lies.
Multidisciplinary partners who thrive in this white space see the world of tools and platforms agnostically. These purveyors of intelligence are decision scientists who operate with a business-first ethos. Once exposed to the business challenge, they take stock of the situation, extract only what’s meaningful, and create new purpose-driven data at a pace that’s in lock-step with the organization.
The Tenets of Convergence
Under a convergence mindset, the researcher in the room will not operate under the assumption that new data needs to be created. Nor will the data analyst contend that “the answer is in there somewhere if we look long and hard enough.” They are, first and foremost, problem solvers who are unbiased about the potential sources of information and insights that will address the business challenge.
Rule #1: Adopt a relentless focus on solving the business challenge.
Before investing in huge infrastructural outlays, more advanced analytical tools, or armies of analysts, find out if you can first leverage what you’ve got. And, we’re not just talking about enterprise data, but also delving deep into previous insights work to reveal how past research can be triangulated—and enlivened—to shed light on both latent factors and new possibilities.
Rule #2: Extract value from the investments you’ve already made.
Analytics teams are tasked with leveraging big data to predict outcomes based on trends and patterns, yet this work often lacks contextual factors, such as the emotional drivers that influence them. While data alone captures a substantial side of the story, it is devoid of human context. Worse yet, it results in dehumanized decision-making. When both disciplines find a way to inspire one another, it will bring organizations closer to consumers and their worlds in which they make decisions.
Rule #3: Activate on a multilayered portrayal of consumers.
Using these tenets, a confluence of mindsets and skill sets will emerge—one that honors the very disciplines that already exist inside your organization. While easier to adopt in mid-market companies, this approach has not gotten the attention it deserves from the executive leadership of larger organizations, or from the volume, velocity and variety-focused analytics community. It’s time to ask yourself and your peers “why?”
Convergence entails a systematic, purposeful and disciplined approach to solving problems by bringing together information from any combination of sources. And, when this is in play, it will allow organizations to better make decisions based not only on intelligence, but on a stronger empathic bond with consumers. And, every smart organization knows – when the consumer wins, you win.
As published in Marketing Insights.