Are You Leveraging the Three C’s to Build Decision Intelligence?


By Camille Nicita, President & CEO, Gongos, Inc.

Anyone challenged with change management inside of an organization is familiar with the three C’s of capability, capacity, and competency.  As we talk with some of these leaders, we find that these key dynamics apply to the organization’s ability to build decision intelligence—their ability to not only gain but also apply wisdom that inspires great consumer-minded decision making.

But first let’s take a step back. The idea of putting the customer first has been with us for decades, but how many companies truly live up to this?  Given that consumer trust in brands is in decline, room for improvement clearly exists—and that improvement isn’t about merely gathering more consumer input. While the influx of new data streams could make it easier to understand consumers, predict behavior and shape experiences to drive loyalty, it’s actually become more difficult. Why?  Because to achieve results, customer-centricity must be a strategy, requiring alignment across all areas of the business to deliver against consumer needs.

High-performing organizations know that to transform customer-centricity from a buzz-word to a strategy, they must leverage the three C’s to grow their decision intelligence.  Only then are they in a position to enable opportunities for customer-centric growth in a world with increasingly complex inputs.

To help illustrate this, let’s use the metaphor of a Smart TV.  Over 580 million of the world population have one, but most people are only using a small fraction of its intelligence to add value to their lives. The situation that organizations find themselves in today is not so unlike this—many are grappling to truly find ways to optimize all of the intelligence within their midst.

Many organizations have the capability to gain consumer wisdom. From insights and trends teams, to CRM and social listening teams, the ability to draw on consumer input is not new. However, if capacity and 3 Cs of DIcompetency have not been fully developed, capabilities are only operating at a fraction of their true potential. Furthermore, capabilities in the form of individual talents, and even technology, are the most apt to change over time to keep pace with external forces and business demands.

Consider the Smart TV, its introduction has the potential to fundamentally change users’ relationship with entertainment, access to online content and networking of other devices. However, it’s abundance of features and functionalities, are often overwhelming to users because they find they lack both the capacity (time, resources, and expectation of added value) and competency (what’s my optimal path to learn and become proficient with this new thing over time?) Similar to the world of insights and analytics, multiple new inputs (i.e. data) exist to better understand consumers. Yet, unless we build our capacity and competency to harness them for decision-making, we’ll fall short on our capabilities to continually evolve an organization’s path to growth.
The overabundance of data, red-lining brains, and organizational silos are the modern organization’s equivalent of a pool’s inability to contain twelve feet of water when it’s only built to hold ten. In both cases, there is a capacity issue that, if not addressed, has the potential to wreak havoc. Today’s organizations must invest in their capacity to receive both new and traditional forms of consumer input, and allow room to refresh and/or replace knowledge assets over time.

Smart TV users must first have the desire to wrap their heads around all of the new options, and then dedicate time and headspace to connect features and functionality. In an organization, that desire translates to acknowledging that a variety of data sets exist, but to connect them in meaningful ways they must either add capacity or condense what’s there. Collaboration among typically siloed teams united with a problem solving mentality is one way to reduce redundancies, as is quickly getting to the data that matters, relegating “noise” to the cutting room floor.

Historically, when faced with a problem to be solved, many organizations’ natural inclination was to create new data assets. Today, with so much focus on the “get” of data, many decision makers are underutilizing consumer and enterprise knowledge that already exists. The discipline of auditing the current knowledge estate before investing in new information can validate or negate hypotheses, and strategically fills in gaps to continually sharpen decision making. But this is merely the ‘gain’ portion of the equation. ‘Applying’ consumer wisdom is where real differentiation lives. Organizations must inhabit the knowledge by making it consumable, immersive and accessible across teams, so that it becomes part of the heuristics that inspire and inform consumer-minded strategies.

Competence in Smart TVs can be built in a variety of ways, including (but not limited to) studying the owner’s manual, sourcing the internet and message boards, or contacting the manufacturer. But truly proficient users get that way through a disciplined approach to continually adapt new features over time. In an organization, this more systematic approach is the engine, continually fueled by consumer wisdom to inform strategy and drive growth.

Each year, global corporations spend nearly $350MM on insight communities. While the idea of 24/7 input is alluring, a community’s full potential to co-create with consumers and inform strategy is often not realized. Why? Because this tool’s success relies heavily on organizational capacity (resources, time, engagement of stakeholders) and competency (development of an insights agenda, connecting dots to existing assets, socializing results to inspire action). One CPG company, however, took full advantage of its community by investing not only in consumer engagement, but in leveraging the platform’s capabilities to build an internal ecosystem. It established a cadence of two-way communication with category managers and external customer teams to compound and socialize learning, and develop action plans. Customer team engagement enabled access to behavioral data, allowing primary data to be married with real-world behavior. The platform fosters organizational-partner alignment on consumer-minded decisions, ultimately reinforcing its category captain status.

Building capacity can come in the form of breaking down siloes to better leverage existing data that drive decisions. This was the case with a company with a historically strong manufacturer-focused orientation. It spent millions of dollars on advanced analytics with an intent to advance data-driven decisions, yet product designers couldn’t easily interpret the results, leading to frustration and inactivity. Worse yet, ROI was literally slipping away with every new product concept with this clunky way of distributing input. While analytics is a highly valued and integral part of program development, department language barriers were impacting decision makers’ ability to receive critical cues and act on them accordingly. Expanding capacity first required grasping how end-users would most efficiently assimilate the knowledge. From there, cross-functional collaboration produced a more common vernacular, and the creation of highly consumable output that not only motivates product designers to leverage it, but to adopt it and cultivate new consumer wisdom over time.

A financial services company found that overseas market volatility came with a reduction in domestic sales. Instead of assuming this was a direct correlation of events, the company first extracted information from its enterprise data warehouse to pinpoint where sales were dipping. Much to its surprise, the data within its very grasp indicated that female Millennials were attriting, not its general customer base. Further primary research revealed that a recent ad campaign was off message to these key Millennials; not the global volatility which on the surface appeared the perpetrator. Further probing illustrated missing emotional influencers. This missing context was so essential to their Millennial customers that the company engaged in a socialization strategy across its internal teams and advertising agency to build empathic learning for all decision makers. Had this systematic approach to problem-solving not been activated, assumptions (and decisions) would have been made based on an incomplete and inaccurate portrayal of reality

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