Are opinions on social media a lead indicator for CSAT? Absolutely yes. With millions of opinions expressed on social media sites such as Twitter/ Facebook everyday it has become a reality today. For example as a company we analyse tweets about several companies through 247tweetview (http://www.247tweetview.com) and when we compare the sentiments expressed by consumers with the end customer CSAT for our clients there is a clear pattern. Companies do not need to wait for a survey to be done/ analyse it and then figure out their CSAT scores are good or bad. The moment of truth is happening every minute/ every day.

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Customer Satisfaction Surveys have become the default to measure how content customers are. By nature, CSATs tend to be reactive; they measure the satisfaction of customers long after the customers and the business have completed their interactions.

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According to industry sources, an aggregate view of customer contacts and channel mix in the United States of America across major industry verticals (The US Contact Center Operational Review, 2nd Edition, 2008) shows that 89% of all contacts are served by the phone channel either through a live agent (65%) or through automated solutions (14%) such as Interactive Voice Response (IVR) or speech recognition.  This data only considers “customer-company” interactions and excludes web based self-service and peer-to-peer interactions through social media/community forums/blogs whose growing impact in providing resolutions to customer service issues has not been satisfactorily estimated till date.

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The power of N=1, R=G has become very relevant in the Web 2.0 world, where a lot of end customer data is accessible and can be used to customize the experience for every individual visiting an online site, as opposed to the traditional retail model. The R=G part is an ecosystem.

In brick-and-mortar stores, traditionally, a lot of analysis was done to segment and drive the right traffic to the store, followed by advanced merchandising and pricing methods to drive up sales. However, in the new 1-to-1 online world, we are more advanced in how every movement of the visitor to a store can tracked.

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Look at the numbers from ATT’s point of view. They have some 60 million customers, and the average customer calls with a question or complaint six to eight times per year. The average call costs about $1 per minute to handle, and lasts four to five minutes. That means that ATT, according to industry averages, must spend around $2 billion per year on call centers.

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It seems like every subscription business (wireless carriers, cable/internet providers….) is worried about one thing in this down economy – customer retention. Analytics teams in these companies are focused on building “customer attrition” models. Typical attrition models take structured attributes and historical behavior of customers to segment them based on their “propensity” to attrite. The end result of these attrition models is simply a statistical prediction of a customers’ likelihood to remain loyal and/or leave. By applying the model to current customers, a prediction of future loyalty is obtained and customers are separated into high-risk and low-risk groups. Retention campaigns can be targeted to the high-risk groups.

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Most of the analysis and outputs from CSAT surveys are focused on what needs to change at the contact center. Whether it is an improvement in agent performance or the type of training or “bringing up a center” to the network average, the call center agent directly come under the microscope. However a quick analysis of the extent of problems/ poor customer experience will show that nearly 50% of the problems originate outside the contact center.

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I was recently talking to a few customers about their opinion on CSAT surveys. (QXXM6MP3PJG6) One of the interesting view points that came out was the actual number of CSAT surveys that were being done and the impact of that on CSAT. The typical average collection of survey data was around 1.5% to 2% of the interactions; however important action items or decisions were being taken on the basis of this small sample. (Also many of the action items from all of these surveys center around agents performance, which is still only part of the story)

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While I was thinking about how we at 24/7 Customer are helping our customers to serve their end consumers using the N=1, R=G model, I started looking at how we are using it for our own business. Being a global company gives us a lot of opportunity to leverage both those formulae internally, and there are several areas where they have been applied. A couple that stood out for me are in the areas of product innovation and process improvement.

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