Updated: Feb 16, 2018
A few years ago, at an eBusiness & Channel Strategy Forum, a senior finance executive and I took a moment to chat customer research trends while in between sessions. As an economic sociologist and student of behavioral economics, I really enjoyed the nerdy conversation but got hung up on a term I hadn’t heard before: “directional data”. Investing in the right solutions, the executive reasoned, could produce such clarity that the typical analytical hassle would be eliminated. This silver bullet solution, he argued, arms businesses with a panacea to disruption. While it was nice to hear a senior leader talk so enthusiastically about data, I struggled with his apparent lack of love for data interpretation. Since then, I have increasingly noticed that even among some of the most ardent corporate advocates, appreciation for analysis seems to be an afterthought to the dazzle of big data – though no fault of their own. Uber, Alphabet, and Amazon are consistently exalted as companies that “get” their customers largely on account of their “secret sauce” algorithms’ competitive access to data. Everywhere you look, this salvation through-data-alone is preached by eager “thought leaders” and solutions providers alike. Praise and worship be to the data, but rarely do we invite the analysts to testify their contribution to the business case. This form of results-driven evangelization risks an oversimplification of the discipline and overemphasizes measurability rather than true customer understanding.
Today’s digitally-enhanced economy continues to separate experienced management from expertise in new, essential competencies required to address growing customer expectations and behavioral shifts. Accordingly, industry leaders are faced with the challenge of uncertain decision-making in an evolving world inundated by ubiquitous data. Newer interaction platforms and diagnostic solutions across the customer journey have given rise to a hyper-skilled, technical workforce, one whose iconoclastic mindset and subject matter expertise test traditional managerial roles and corporate structures. Uberization is no longer a novel approach to customer experience strategy and disruptive market-entry of start-ups provide myriad organizational alternatives. Executives, who once built careers around managing complex, multi-tiered teams, now see mitigation of those very same operations disadvantageous. Even the well-intended integration of systems inevitably defies once “motherhood-and-apple pie” beliefs in traditional hierarchal structures. In many cases these relationships are exposed as largely political rather than critical to overall success. In the face of this existentialist crisis, management increasingly looks to “data” for relevancy and leadership opportunities.
In pitching to potential enterprise clients, certain digital evangelicals sometimes overstate the extent to which receptive management can overcome uncertainty by distilling data (using their tool). This claim is then corroborated by showing exponential growth of unlikely brands whose use of data in innovative ways has enabled better understanding of customer pain points to improve operations. By overselling cookie-cutter solutions without telling the full story we underrepresent the analytical support needed to usher customer feedback into management discussions. Several industry voices tout the advantages of such “insights,” however, data-driven approaches are equally a coping mechanism in response to lack of trust. Because of the previously-mentioned experience dichotomy presented by innovation, many executives draw upon previous backgrounds in corporate finance, accounting, engineering, business administration, or economics; and ardently champion data on its familiar promise of measurability and accountability. After all, as the saying goes “what can be measured can be managed.” Unfortunately, today’s big data, by its very nature of comprehensiveness, is not easily manipulated through existing statistical or financial models. Not from lack of effort as researchers, B-schools, and corporations are tirelessly endeavoring to define such actionable economic concepts.
Though it is widely accepted as a crucial competitive advantage, many companies struggle to maintain comprehensive and effective internal programs needed to understand today's customer perspective. Because of the shear volume and ubiquity of data, analytics management must also sometimes be a little creative in nature. Corporate risk concerns with such approaches can be legitimate but also hamper innovation by insisting upon more conservative treatment of data or limiting the scope of research. Predictive modeling and artificial intelligence, on the other hand, lure executives with the prospect of premonition like modern-day Oracles. Similar convictions can also lead to “The data speaks for itself” mentalities implying there is little need for interpretation at all. It should be noted that sans an effective analytical environment, the data itself dictates the relationship. By validating old thinking or driving teams to inaccurate conclusions, data programs that fail to prove value and relevancy undermine customer research as a respected corporate function.
Indeed, relevant data when interpreted correctly can prove extremely persuasive if used effectively. Infographics and other forms of data visualization, for example, allow complex information to be condensed, shared, and consumed by wider business audiences. Regardless of format, data’s objective nature can facilitate productive discussion across organizations. For this to be realized it not enough to simply collect, report, and discuss data. Dynamic data requires a high degree of curated interpretation to be truly valuable. As such, organizations must correspondingly develop comprehensive governance models and appropriate analytical protocols through which to unravel data productively. Even the most highly-qualified analyst teams cannot be expected to develop this in a vacuum. Without focused scope and qualified interpretation, analysis risks misrepresenting key trends and undermining trust in data-driven leadership itself.
Perhaps, rather than presenting data as a form of corporate alchemy and trying to “own” insights, leaders might instead view themselves as patrons of analysis and customer expertise. Leading by example to inspire appreciation for analytical processes, managers can also play an increasingly transformative role by balancing what the customer data shows with their own experience by framing insights with context. Further providing institutional knowledge and leveraging organizational relationships, this approach can ensure that analytics is given the right corporate forum to share feedback on the overall customer experience. Such leadership will more effectively align research goals with realistic enterprise expectations. Similarly, leaders must also enable stakeholders to better understand the implications and limitations of corporate comfort-zones, legacy systems, existing FTE competencies, or established business processes in analyzing and addressing customer concerns. That said, in respecting the Socratic method of analysis, we must also acknowledge the limitations of knowledge itself. In such an uncertain business environment as today’s, leaders who can effectively focus build trust between management and analyst teams also indirectly connect consumers to executives. Ultimately, while “data” provides the proliferation of catchy buzzwords, it is analytics that is the true language of understanding customer experiences. For disruption to turn into innovation, executives must now open the c-suite to native-speakers of customer expertise and allow sound reasoning – not data – to drive corporate strategy.