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When will CRM join the analytics revolution?

Companies have launched successful analytics programs in areas like fraud detection and drug development, but when will analytics become pervasive in CRM systems? The time is coming, says Denis Pombriant.

We are at the point where analytics is playing an increasing role in our lives and, surprisingly, CRM is not in the forefront of the analytics revolution. Yet. There are reasons for this; chief among them is that there have been lower-hanging opportunities for number crunching. 

For instance, analysis for things like fraud detection, new drug development and big retail efforts has taken precedence over CRM. In part, this stems from the audience for analytics.  Fraud detection, drug development and even assortment planning in retail all involve a small handful of people in an organization, and those few can certifiably be called rocket scientists.

By contrast, the challenge for analytics in CRM is that the tools need to be easy enough for everyman (or woman) to use. Truth be told, the ways that rocket scientists and everyperson use analytics are different for good reasons. The rocket scientists might go back to the well again and again asking what-if questions, changing the inquiry ever so slightly each time. In contrast, when the sales vice president wants to know how the company is doing year over year, it’s the same analysis applied to a different data set.

The difference is important and necessary. The rocket scientists aim to optimize processes whose variables change from time to time. The sales VP wants standardized metrics. That’s oversimplifying by a lot but good enough for this work. 

Perhaps more important, the flow of data into the organization and the extreme importance of analyzing it correctly has favored other departments. Consider fraud detection. An avalanche of transaction data arrives at the doorsteps of banks and other financial institutions daily. The opportunity for loss is open-ended. Compare this with optimizing markdowns in a retail chain and you have a qualitative and quantitative difference.

But this is all changing rapidly. The quick assimilation of social media has deposited the same mountain of data on the doorstep of the front office. The difference now is that the front office has less feel for analytics than R&D and the back office. But the need is certainly real. A recent Harvard Business School study of 2,100 companies showed

•         Three-quarters (75%) of the companies in the survey said they did not know where their most valuable customers were talking about them.

•         Nearly one-third (31%) do not measure effectiveness of social media.

•         Less than one-quarter (23%) are using social media analytic tools.

•         A fraction (7%) of participating companies are able to integrate social media into their marketing activities.

While more than half were getting on the social media train, a much smaller group was analyzing the data generated by these applications.

The Harvard study found that the vast majority of companies surveyed had no idea what their customers were saying about them or where it was being said. Talk about unlimited downside.  I did a short study last year of customer antipathy and discovered that even really good companies had lots of detractors. Not-so-good companies have detractors coming out of the woodwork, and we’re not just talking about people who are upset. We’re talking about people who are upset enough to sponsor blogs and post to them regularly. It’s almost a fetish. Think your company is immune? Guess again. My own study even showed that every Ivy League college has hundreds of thousands of detractors.

Understanding what makes customers happy and identifying who is not are twin pillars of the modern social-driven enterprise’s outreach, and analytics is essential to the effort. We’re always in a kind of arms race with technology. We ratchet up based on demand and what we see our competition doing. Today the competition is well on its way to nailing social media, and its sites are now harnessing analytics. That’s where we all need to be, because as soon as we’re done with analytics there will be something else. I can already see it.


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