Has Customer Centricity Been Lacking Empathy All Along? Correlation & Causation: The New Norm in Decision Making

Introduction: The Need for a Multi-Disciplined Approach to Consumer-Centric Decisions

In an effort to maintain and grow market share, organizations aspire to and define “customer centricity” in a number of ways.  In 1999, Peppers and Rogers defined one of its major characteristics as being the focus on each customer’s interests and interactions with the organization in order to deliver targeted, personal messages. While many share this view of aligning products to meet target-customer needs, others focus their efforts on delivering a great customer experience (American Management Association, 2010).

Another more recent school of thought (Fader, 2012) suggests that the latter type of consumer reciprocity does not necessarily equate to consumer centricity.  Under this theory, consumer-centric companies understand the various kinds of customers they have and strategize accordingly, all the while narrowing in on the customers that will provide the most value over their lifetime. Admittedly, Fader’s view is weighted toward profitability, marketplace advantage and data-driven decision making.

However, decisions based on hard data are not enough. If organizations are to achieve a truer state of consumer centricity that comprises both points of view, they must also understand and incorporate the why behind the increasing amount of data they have at their disposal, namely the who, what, where and when. In doing so, organizations will not only enhance the reciprocal relationship they have with consumers, but also improve business performance.  Most companies acknowledge that this approach to consumer centricity will take discipline. A more holistic consumer-centric approach will require us to continue to view what is best for consumers as our center of gravity, even if that means sacrificing near-term performance.

The Conundrum: What’s Happening in Many Organizations Today

In many organizations, great work is underway by both analytics and market research teams.  However, these teams frequently operate independently—as if mutually exclusive—and do not inform the work of one another.

As consumer data becomes more voluminous inside organizations, so too does the pressure to use this information to drive business outcomes.  As a result, analytics departments are challenged with leveraging “hard” (behavioral and quantitative) data to uncover substantive knowledge about consumers and their pathways. They often use data as a tool to create business opportunities using descriptive, predictive, and prescriptive modeling.  (Harris and Davenport, 2007)

At the same time, because this hard data (i.e. transactions, usage) is collected and available in near real-time, it often becomes the go-to option for frontline decision makers.  Not only do they have direct access to it, but they possess an inherent affinity towards it, versus “soft”/qualitative/primary data.  In the here and now of day-to-day responsibilities, leveraging hard data can be an efficient way to drive tactical, short-term strategies. However, this data can often make situations appear black and white, which can impart a false sense of security in the decision-making process.  Consequentially, data can become the “fall guy” when resulting actions do not perform as intended (i.e., “we made that decision based on the best data available at the time”).

In parallel to analytics and front line teams putting their faith into hard data, traditional market research teams are relied upon to address why consumers behave the way they do.  This function acknowledges that consumer behaviors are more colorful and complex than any model is able to represent.  By going directly to the source, researchers uncover why individuals act the way that they do. While these insights are also viewed as essential to bottom-line strategies, they are largely not available “on demand” like those gleaned with hard data (see Figure 1).

Figure 1.

This approach (who, what, where, when vs. why) can result in an incomplete, potentially conflicting picture—one that does not address all facets of a consumer’s story.  Organizations must adopt a new model that seeks to understand consumers from multiple, integrated perspectives—one where analytics and market research work in tandem.  This shift, which will require organizational discipline to operationalize fully, will drive a higher level of consumer centricity and ultimately guide bottom-line growth strategies.

If we were to draw inspiration externally, multiple fields—Technology, Science, and Medicine—innovate by taking a page from nature (Vanacek, 2013; Ratner, Hoffman, Schoen & Lemons, 2013). By leveraging organically perfected designs, they discover new ways to adapt and manage complexity in order to achieve a desired end state. Throughout time, dissimilar organisms discovered mutually beneficial interaction, sometimes for survival. In fact, today many professionals in these very disciplines study biomimicry to inspire innovation. If we are to see analytics and market research as two historically dissimilar “organisms,” then data proliferation has become the catalyst for symbiosis. Tomorrow’s approaches will require not only the collection of primary data to surround why consumers behave the way they do, but also the integration of existing data assets to describe, predict and prescribe who will do what, when, where and how much.

Collaborative efforts will enable the use of learnings (or more accurately, gaps in knowledge) from analytics to guide primary research initiatives; and insights from primary research will be used to refine and focus analytic efforts. To achieve this ‘ask,’ market research and analytics must not only coalesce to achieve a greater level of customer centricity, but to advance their respective disciplines and propel organizations forward.

The Evolution of Consumer Understanding

How did we get here and why?

As the foundational discipline of extracting behavioral and attitudinal understanding within organizations, market research has undergone tremendous evolution since its inception in the early 1920s (Vermaak, 2014; DeVault, 2015).  Yet one thing has remained constant—a strong focus on understanding causation. By interfacing directly with their subjects, researchers uncover why individuals think and act as they do.  This depth of inquiry ensures the consumer is “heard,” often resulting in greater organizational EQ (emotional quotient).

On a nearly parallel path, the analytics discipline was born—and although the general field of analytics originated during the dawn of the Computer Age (in the early 1930s), its commercialization and application to business was initiated to support fact-based decision making and business planning several decades later (van Rijmenam, 2015).  By the turn of the century, computing power, data storage and low-cost/high-speed processing had arrived. This led to the exponential growth of Enterprise Data Warehouses filled with non-primary, hard data. Business analytics was then charged with not only uncovering trends and looking back—but with uncovering correlations to predict and help to optimize future outcomes (see Figure 2).

Figure 2.

Relative to primary research, the growing need for analytics skillsets and outputs happened seemingly overnight.  The business-focused analytics function, therefore, was not conceived of as complementary to the market research function, and largely not set up for purposeful collaboration.

Where do we go from here?

On their own, both analytics and market research are necessary disciplines that inform decision making and add organizational value, both short- and long-term. However, these two approaches need to work together to bring about greater “decision intelligence” that will drive more balanced and comprehensive consumer-minded action—the “now what?”

Barriers (i.e. organizational, skillset, etc.) often exist between the disciplines.  In fact, it is rare to see an analytical model informing research, and vice versa.  Yet, neither discipline can realize consumer empathy alone.  Specifically, analytics efforts lack causation and the human characteristics brought to bear through market research; and market research lacks correlation and immediacy without data analytics.  To achieve this “ask,” market research and analytics must coalesce to achieve a greater level of centricity, and to advance their respective disciplines and propel organizations forward.

UnitedHealthcare: Case Study

UnitedHealthcare’s core values of integrity, compassion, relationships, innovation, and performance result in the consumer being at the center of the decision-making process. For acquisition marketing within the Medicare & Retirement Insurance Solutions business, UnitedHealthcare has historically targeted consumers through mass media (broadcast, digital and print) and market channels such as television, direct mail, telemarketing, paid search, magazines, and newspapers.  Moreover, as in most traditional marketing organizations, these acquisition-marketing activities are supported by data analytics teams that focus on modeling efforts and market research teams that focus on self-reported consumer wants, needs, and behaviors.  Both of these teams independently answer relevant and meaningful questions related to consumer understanding and conversion activities.  The focus of this case study is to develop a “blueprint” for linking how consumers behave to why they behave the way they do, in order to meet consumers where they are.  This “blueprint” focuses on personalizing and differentiating the consumer experience by integrating hard measurement of behavior (correlation) with softer motivations (causation).

The approach: linking analytics to insights

This 10-month initiative encompassed a four-phase approach that went well beyond integrating analytical toolboxes and market research functions.  It set out to encourage teams to continue to partner in informing approaches that will more closely emulate the consumer journey.

Phase I: A one-month “audit” of current analytic models and market research work was conducted to identify the degree of integration between these nearly parallel work streams.

This encompassed multiple in-person and virtual stakeholder interviews—from the C-suite to the front line. A second layer of immersion included delving into UnitedHealthcare processes and bodies of work across both analytics and market research teams. In this phase, C-suite priorities, as well as potential barriers to success were also identified.

Phase II: A proprietary framework for linking analytics and research intelligence was developed for the purpose of being applied to their marketing strategy.

This framework sought to operationalize how each discipline can inform the other in an ongoing and cyclical way. Its goal is to inform a common language and focus to drive more targeted, customer-centric marketing, and establish a foundation for a disciplined and purposeful approach centered on the consumer.

Phase III: In this phase, modeling initiatives that proved to be most impactful for driving conversion were identified and prioritized.

The most impactful model was determined by assessing its ability to uncover the most granular view of how marketing touch points affect sales.

Phase IV: To demonstrate the feasibility of more broadly operationalizing this framework, proof of concept work was conducted by the team over six months. The four steps of this phase included (see Figure 3):

1. Conducting primary market research among current customers to determine self-reported proactive and reactive behaviors, and the impact of them, during the purchase process.
2. Assessing and comparing differences between self-reported and analytically prescribed outcomes.
3. Running cluster analyses to determine exclusive subgroups of consumers based on research findings.
4. Profiling consumer subgroups from hard behavioral and demographic data in order to create algorithms to link research findings to analytic datasets. This will enable the fine-tuning of existing analytics models by closing the gap between self-reported and modeled outcomes.

Figure 3.

The outcomes and the impact

Systematically exploring the human element—as it relates to data—revealed several soft data points as having been excluded from existing analytical models. In this case, primary research served as a diagnostic tool for highlighting areas where analytic results could be enhanced to better inform business knowledge. Furthermore, these learnings allow for the mapping of a cadence of primary research initiatives to confirm and/or course correct in order to remain grounded in the dynamics of consumers’ journeys.

An additional outcome of this work was the identification of several common subgroups of customers—identifiable by their motivations, the actions they took, their demographics and other distinct characteristics. This discovery will help to empower a shift toward tailored marketing activities that acknowledge the multiple influences and informational requirements of consumers.

Lastly, refining models, data collection, and analytics initiatives spurred a conversation around the marketing and conversion process, which will help to align the marketer’s path with the consumer path.

Overall, this convergence initiative sheds light on the position that neither approach (hard data nor soft data) fully depicts a customer’s journey.  On one side, this work validates the general hypothesis that what consumers convey through primary measures may not necessarily be what actually motivated them to action, while on the other side, predictive analytics and modeling initiatives are unable to capture the “whys” behind consumer actions.  It is in the combining of the two that organizations are able to develop a ‘truer’ picture of a consumer’s pathway to purchase.

Lessons learned

As evidenced by this work, transforming the way an organization goes about embodying customer centricity in their ongoing actions requires discipline, but is achievable.  This case study reveals that converging complementary, yet independent “insight” disciplines requires adjustment across three areas.

Mindset
First, an organization must be in agreement that consumer-minded decision making will prevail in both its short- and long-term thinking. Beyond this, it must adopt a willingness to change and adapt. It is important to be clear that there are no correct or incorrect approaches, but that each will bring to bear additional and unique perspectives currently unfulfilled by the other. Lastly, an organization must submit to strong cross-functional collaboration not just among analytics and market research teams, but among leadership and front-line teams.

Skillset
If convergence is to fulfill on its promise of increasing organizational empathy, it is necessary to have dedicated resources. At minimum, an internal or third-party “integrator” who can both comprehend and translate consolidated outcomes is needed not only to bridge the gap between the two, but also communicate back to decision makers. In the future, organizations must get better at identifying and developing “polymath-types” who are capable of thriving in a dual-perspective environment.

Ownership
When bringing together functional areas within an organization, a leader must be designated. It’s necessary for this person to be granted the authority to drive the iterative and incremental collaborative process, but to also be an influencer of change within the organization. Additionally, it is important that this person be both purposeful and forward-focused, operating with an imperative to continually bring clarity to the organization around the importance of consumer empathy and a link to the business challenge at hand.

Conclusion:  Implications for the Future of Consumer Centricity

Consumers do not think or act in silos and organizations therefore must pursue integrated approaches that link causation (who, what, where, when) with correlation (why) to deliver consumer-centric experiences.  The continued emergence of “big data” and advanced analytical techniques present the opportunity and the need for decision-making approaches that leverage market research in combination with analytics to map and enhance the consumer journey.  Leveraging these opportunities will improve the overall consumer experience through further channel integration and data alignment.

This more “outside in” and holistic way of thinking represents the new norm in building a reciprocal relationship between company and consumer.  It requires a purposeful and multi-disciplinary model that continually evolves to include emerging information steams and that prevents the formation of insights silos.  Without an integrated approach, incomplete market intelligence (i.e. solely hard or soft data) may limit the ability to optimize the consumer journey and business outcomes (VanPraet, 2013).

Just as is in nature, evolution more rapidly occurs when a species identifies new ways to adapt and thrive.  The proliferation of data could very well be the market research industry’s environmental trigger to adopt methods that drive a more empathic connection with consumers.

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Editor’s note: this paper was presented at the 2015 ESOMAR Congress, co-authored by Justin Smith, Associate Director, Marketing Performance and Optimization, UnitedHealthcare; Sarah Phillips, Vice President, Client Consulting, Gongos, Inc.; Camille Nicita, President & CEO, Gongos, Inc.; Susan Scarlet, Vice President, Strategic Branding, Gongos, Inc.