Most companies like to analyze data. It gives them a sense of security: "We understand our business and our customers because the metrics on the reports says so." The reality is somewhat different, however. The analysis of operational customer data alone explores only one of several dimensions and can lead to incorrect conclusions. Other data sources need to be included, such as informal interaction data (that is, what was said and how it was said during customer-agent conversations) and more-formal survey data, both of which collate data directly from the customer (with or without their knowledge) for analysis.

I am currently writing a case study on an organization that embraced this broader concept of data analytics with startling results. It identified several areas for process improvement and highlighted discrepancies between what its traditional operational analysis was telling it compared with the reality of the situation. One example is highlighted below:

Survey analysis revealed that customer dissatisfaction in specific locations fluctuated at different times of the year. The question the company asked was: "Was this due to those customers genuinely receiving a poorer level of field service and, if so, which aspects of the service need to be delivered differently, or were they just more difficult to please?" The company mined the associated operational service data for insight and found an interesting correlation: customers became more tolerant of a lower level of service during extreme weather conditions. For example, if there was flooding or heavy snow, customers were much more tolerant of engineers being late and service levels slipping.

From an operational insight perspective, the company could see a reduction in service efficiency in those areas at the same time and would normally have diverted extra resources to those regions to fund field engineer overtime to bring the service levels back up. Instead, with this insight, it increased the number of call center agents and got them to proactively confirm adjusted engineer arrival times (via call, SMS and so forth). This reduced inbound calls and the need for engineer overtime to meet slipping operational service levels. It also improved customer satisfaction. The proverbial win-win. Nice.

So, is this company alone or have you found some interesting insights since taking a more holistic approach to data? I'd love to know.

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Isn't that what Tom Seibel did (blindly followed analysis) right before he predicted the iminent demise of SaaS ? You bring up a good point.

I would say, every comany irrespective of its size and turnover needs to spend a good amount of time evaluating its data and talking to its customers.
Customer retention is the best way of staying in business.
Remember, in B2B & B2C segment, referred customers are the most loyal customers.

Cheers

I would agree companies do like to analyze data. Netflix is the first company that comes to mind when thinking about that. Data mining is and has become big research within companies. I can think of my last job that had the dollar amount calculated each time the phone ring when someone called our 800 number. That dollar amount represented a Significant potion to the deptments revenue.