Paul R. Cherrington Web Analytics Blog post #1 I have worked in a call center for 7 years as a phone representative with DIRECTV for 4 years and Morgan Stanley for 3 years. While at DIRECTV, I was responsible for new activations, technical support, and retention of existing customers. I was part of a very large call center system that included 7 call centers across the United States and 2 call centers in the Philippines with thousands of phone representatives. During my time at Morgan Stanley, we had 3 call centers and about 60 representatives. Our approach to hold time was to have high net worth clients wait the shortest amount of time. Hold time was avoided if possible and phone reps were placed where they wanted to be rather than where their talents for communication were. In researching through the University of Utah’s database, I came across an article analyzing how call centers are using analytics to increase performance and retention. Assurant Solutions sells credit insurance and debt protection products and had a retention rate of 16% when clients called in to cancel their service. Prior to using analytics, Assurant used intuition to place employees where they thought they were best and did not analyze results. After consulting with actuaries and mathematicians, they changed how they thought approached retention of clients. Rather than focus on short hold times, and ease of system, they focused on employee’s ability to retain customers and judged success by results rather than intuition. They found that customers who were retained were more likely to remain customers in the long term rather than their existing customer base. To analyze performance, they looked at every interaction a rep had going back four or five years. By going through all of this data, they found out which area the rep excelled at in retention and redirected their efforts there. They did not try to make the employee stronger in other areas but let them concentrate on what they are good at. They also used the data to predict how long a rep would be on the phone and figured that information to forecast staffing needs. Another long held intuition that proved not to be true was customers would hang up and go elsewhere if their call was not immediately answered. Rather than get the right person on the phone with them, they would get any available person on the phone and the client was less likely to be retained as a result. By waiting a little bit longer they were able to improve their efforts and customer satisfaction didn’t suffer as much as originally thought. Having worked several years at a call center, several of these concepts are revelations that could have saved a lot of heartache and headaches. I was placed based on what shift I could work rather than where my natural abilities were. I would be interested to see what the data would have said about my performance because I know what my intuition would have said but that has been proven to be often incorrect. Also, when I worked at DIRECTV, the number one desire was to keep hold time down rather than a focus on results. We would throw money at the problems to get them to stay rather than get the right people talking to them. Each rep had a $100 credit limit they could offer to each customer on each phone call. The average rep might take 65 calls per day and make it possible for $6500 to be used to retain customers. Obviously it never got that far but the possibility was there for extreme amounts of money to be wasted. Had employees been correctly evaluated, they might have been placed in an area that they enjoyed more we would have had less turnover than 212% during my first year employed there. In conclusion, it has been several years since I worked at DIRECTV and in fairness to them, perhaps they have employed some of these techniques to improve performance and decrease inefficiencies. I would have no way to know but they do have very capable people working for them. However the article mentions that many top companies, Fortune 50 level, are still hesitant to employ analytics as a means of increasing performance. It would seem that many of them achieved their tremendous success by recognizing a competitive advantage and becoming an early adopter. Its hard to argue with the results that Assurant Solutions has obtained but I think that many of these companies will invest heavily in data mining and analytics as a means of finding out ways to improve, become more efficient and better process the information that they are being given. References: MIT Sloan Management review article – December 15, 2010 – Cameron Hurst Interviewed by Michael S. Hopkins and Leslie Brokaw – Matchmaking with Math: How Analytics Beats Intuition to Win Customers