The Case for Building a Customer Intelligence System : “A Little Rebellion is a Good Thing”


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VoC Fusion is really the first conference at which I’ve presented a fully developed vision of what a Customer Intelligence System (CIS) designed to capture, understand, disseminate, and act on Voice of Customer data might look like. I believe that a well designed CIS is far more than just an Enterprise Feedback Management system. A CIS includes technology to store a wide-range of VoC data (including Social Media, Online Survey, Offline Survey, Online Feedback, Offline Comment Cards, and Call-Center), to process that data intelligently using advanced text analytics, to integrate the data with behavioral inputs, and to report out on that data using powerful data visualization tools like Tableau or Spotfire. It’s not just technology, either. An even bigger piece of a real Customer Intelligence System is the creation of a set of processes around this technology. Key processes include the standardization of intake, the creation of robust taxonomy and segmentation classifications and their standardization at the enterprise level, aggressive sampling to support behavioral integration and targeting precision, and the creation of virtuous cycles around survey design, analysis, and feedback.

It’s far from a trivial amount of work, but done thoroughly, it will transform the enterprise capability to understand customer drivers of choice and decision.

The Customer Intelligence System is not meant to be an evolutionary step in VoC. It’s revolution.

When Revolution is Necessary

Why revolution? Revolution is not to be taken lightly. Thomas Jefferson (from whom I’ve borrowed my sub-title) aside, revolution is risky and expensive. But sometimes it’s just plain necessary. In a set of pieces earlier this year, I laid out my case for why the current state of Voice of Customer in the enterprise is unacceptable. In today’s post, I’m going to summarize that case.

Each and every channel that captures key Voice of Customer data is seriously under-served in most enterprises today and there are gaping holes in the intersections between those channels as well as the distribution of information collected within those channels to the rest of the organization.

Existing Research Channels are a Mess

In Online Survey, most enterprise efforts are static, too site-focused, too long, under-sampled, and too concerned with top-line metrics like NPS (Net Promoter Score). Online surveys are an amazingly flexible and inexpensive way to understand your customers, so it’s a particularly sad that they are so poorly utilized.

In Social Media, the state of measurement is even worse – hardly worthy of the name. Social media metrics are a hodge-podge of the inconsequential and the mistaken. Social Media samples are mis-understood and poorly governed. The classifications of social media data are shallow and largely irrelevant. And the technologies used to generate social media reporting are deficient in their ability to effectively categorize the information.

It doesn’t get much better in Call-Center. Call-Center is the place that Voice of Customer data goes to die. No area of the enterprise is more siloed, more focused on their immediate operational concerns, and less integrated into the broader enterprise reporting and data streams. If you’re lucky enough to get a report of most-common Call-Center call-types, consider yourself in the vanguard of Call-Center reporting at the enterprise level.

Lack of Coordination Cross-Channel

The problems don’t end “in-channel”; indeed, they get worse (as is generally true), when you remove the silos and take a broader look at how the individual programs fit together to create an enterprise-wide view of customer.

At most enterprises, there is very little infrastructure (either technical or organization) for welding these inputs together into a coherent picture of customer attitudes. This begins at the most basic level in terms of setting research programs and standardizing core inputs.

At almost no enterprise is the research program across online survey, offline survey, social media, feedback mechanisms and call-center standardized and set in a consistent fashion. It’s not unusual even for obviously related items like online and offline surveys to be run by completely unrelated and uncoordinated teams. Often, when I ask the online survey teams for their offline survey data, I just get blank stares. With Social Media and Call-Center, it’s almost universally true that there is no coordination of research.

This has significant impact on the enterprise ability to really understand the customer. Every channel has limitations when it comes to research. By failing to standardize key questions and segmentations, you lose comparability across channels. If you ask questions differently or categorize answers differently, you can’t compare segmentations across survey instrument. By failing to coordinate research across all the available channels, organizations leave gaping holes in their understanding of the customer and the customer journey.

Errors in Online Sampling Drive Constant Errors in High-Level VoC Metrics

In VoC, the analysis of the data nearly always requires careful stratification and segmentation. For years now, I’ve been pointing out how audience research in Social Media and Online Surveys IS NOT the same as traditional survey research. The sampling is different, less random, and more complex. As a result, top-line metrics like NPS and Brand Sentiment are extremely prone to error. So error-prone, in fact, that it’s a serious mistake to use these numbers at the site-wide or social-channel level.

All those warnings don’t seem to have done much good. I hear endless examples of organizations whose overweening VoC focus is on their NPS score. And at VoC Fusion, you’re talking about the high-end of the market – companies with a true Enterprise Feedback Management (EFM) solution! Even if you’re doing the sampling right, NPS is, at best, a simple reading of your overall customer state relationship. Without deeper analysis of the real drivers of choice, it isn’t actionable and it produces no understanding of what drives the underlying customer relationship and experience.

If the best that a VoC program can achieve is to get senior decision-makers to follow their NPS scores, then we should invest the money spent on customer research into something genuinely useful – like better snacks in our meetings.

Technology for Text Classification is Poorly Adopted and Inadequate

Of course, one of the reasons for this heavy focus on simple top-line metrics like NPS is that the tools we use to work with text heavy data sources are terribly inadequate.

In Call-Center, the key data is often trapped in non-digital form. Many call-centers still use systems that make it challenging or impossible to extract even the basic operator actions or call information. This IS getting better. Current generation call-systems are much, much more open and call digitalization is an emerging and increasingly practical option.

In Social Media, we’re still struggling with technologies that do a very poor job of analyzing and classifying free form text data. Given that social media is 90% free-form text data, that’s a problem. If you’re using keyword-based systems for parsing and classifying Social Media data, it’s just not possible to do the job well. I can’t tell you how delighted (and surprised) I was to learn that E&Y has a strong document text analytics technology (via acquisition), and uses Crimson Hexagon for Social Media analysis. Very few of our clients are in anything like such good shape.

Dashboarding and Reporting is almost Non-Existent

As bad as all these problems are, no part of the overall use of Voice-of-Customer research is as broken as the reporting piece. There simply are not tools or efforts to combine this customer attitudes data across research channel in a consistent way and to distribute the resulting picture of customer attitudes out to the broader organization.

EFM tools are unable to do this. Most survey tools have very poor dashboarding and are completely siloed. There are some Social Media tools with strong dashboarding capabilities, but those tools are generally unsuited to real research and are locked into their proprietary, social-only data structures.

Even within these limitations, the state of enterprise customer reporting is remarkably poor. Very little survey data ever sees the broader light of day beyond the research teams that own them. And the enterprise that tracks anything about customer attitudes at the executive level except a few high-level metrics like NPS or Brand Sentiment is vanishingly rare.

Enterprise VoC programs just aren’t very good.

A Call to Arms

I’ll admit to being a perfectionist when it comes to measurement. Heaven knows I’ve complained often enough about the state of enterprise measurement when it comes to digital behavioral data. There is much, much work to be done before we get that right. Yet what we have achieved in the behavioral space is considerably better than the corresponding state of practice around VoC. Our technologies are better. Our efforts are more integrated. Our data is more standardized. And our efforts to socialize our data far more mature. If the state of customer behavior analysis is far from ideal, neither is it completely broken.

No so in Voice of Customer. The state of enterprise Voice of Customer is so bad, so broken in almost every important respect, that fundamental change is necessary. Change in technology. Change in ownership. Change in funding. Change in process. Change in focus.Enterprise’s need to fundamentally re-think their whole approach to Voice-of Customer research.

The value of Voice of Customer programs at the typical enterprise is dangerously close to zero. Yet what data is more important, more valuable or as easily understood and acted upon as Voice of Customer? The opportunity is immense. The potential competitive advantage real. The current state abysmal.

It’s time for a change.

Republished with author's permission from original post.

Gary Angel
Gary is the CEO of Digital Mortar. DM is the leading platform for in-store customer journey analytics. It provides near real-time reporting and analysis of how stores performed including full in-store funnel analysis, segmented customer journey analysis, staff evaluation and optimization, and compliance reporting. Prior to founding Digital Mortar, Gary led Ernst & Young's Digital Analytics practice. His previous company, Semphonic, was acquired by EY in 2013.


  1. Great article!

    I couldn’t agree more with the idea that revolution is necessary (and in some cases happening) within VoC Programs.

    I would add Market Research to the mix of data that should be included or layered after the channels you mentioned. The opportunity to better understand the customer is real, but there is also an opportunity to project behavior based on that data when combined with economic and market research, providing a tremendous competitive advantage. The tools and technology need to catch up and take a giant leap forward, but after reading your article, I am encouraged that we are on the right track in recognizing the challenges and the requirements.

    Getting the right customer intelligence balance from all channels is key. Being able to predict future behavior, taking the right actions, measuring and tracking them to a successful conclusion (in the customers’ eyes) is equally important in an enterprise VoC implementation. It sounds like an enterprise data management software solution opportunity to me!

  2. Great article and strategy for integrating all the VoC data across channels with super analytical capabilities. Technology exists today with an easy to implement suite from Medallia (



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