Features

Although I missed SAS Global Forum, SAS kindly arranged for me to talk to some of their clients about analytics. Here I present my discussion with Eric Webster, vice president of Marketing at State Farm.

Eric Webster

Eric Webster joined State Farm in July 2000 as assistant vice president, Customer insights, Marketing. He was promoted to his current position in April 2007. Before coming to State Farm, he was vice president of FCB Direct in Chicago, and previously was senior database marketing manager for IBM, responsible for marketing analytics for IBM worldwide. Eric has BS in Finance and an MS in Math from the U. of Illinois, Urbana-Champaign.

Gregory Piatetsky-Shapiro: What are 2-3 most important things you do with analytics / data mining?

Eric Webster: One of the major applications is direct marketing campaigns.

Our analytics enable us to understand customer purchase cycles - how likely are they to leave us, what the customer is likely to buy, what is the next product to offer.

We also give tools to our agents to enable them to use analytics results without having to conduct analyses themselves.

Another major area where we focus is actuarial - where data mining is central.

Making sure that we are matching the right price with the right risk is the core of what insurance is, and that is an analytic exercise.

Understanding who is most likely to get into an accident, and generating the right price for them, and at the same time helping people who are more at risk to lower their risk.

For example, we can look at teen drivers, who often have problematic driving records, and provide discounts for them if they take safety courses.

Another major area is what we call risk management. Our portfolio of risks at State Farm includes thinking about risk:

• what happens when the next hurricane comes along
• what will that mean in terms of potential financial loss
• how many claims reps we will need to send to any particular area, given, for example, that a force 3 hurricane will make landfall in this particular spot.

GPS: When I worked with insurance data, one of the problems was that interactions with customers were very infrequent - sometimes once a year. It was difficult to build good models using such slowly changing data. How do you deal with this problem:

Eric Webster: It is an issue. But insurance itself is a slowly changing dimension [so slow changes in data are not a problem if the change in risk is also slow. GPS].

Most people experience changes in their needs for insurance based on life events such as getting married, having children, buying a car, and moving, which do not happen frequently.

GPS: Where do you currently see the highest ROI from analytics?

Eric Webster: Any advances we make in matching price and risk is huge.

If we can lower the prices for certain groups of people with good driving records, that increases their customer satisfaction and increases our ability to keep customers.

From a pure marketing perspective we use analytics to predict what is going to happen in our marketing campaigns. We let the analytics drive the campaign - we don't mail to people who are not likely to respond, etc. I cannot quote numbers, but we have seriously increased the ROI over what we were doing before we were doing data mining.

GPS: Where and how analytics need improvement to meet your current needs?

Eric Webster: In terms of improvements we would like in analytics tools is to make them easier to use - have the tools make intelligent defaults for us.

For example, if I use a neural net node in SAS Enterprise Miner, I would like it to choose its parameters and algorithms adaptively - have it train itself on the data for a given problem.

GPS: More automation?

Eric Webster: Yes, and have the tools help us make the right choices. So I don't have to always have PhD statisticians running the tools. Which convergence criteria should I use? Should I use step-wise regression variable selection or backwards/forwards selection? GLM or logistic regression?

Can we tell the tool - here is a subset of the data - go play with it and tell me which methods and algorithms should I use.

[GPS: there are tools which have higher degree of automation, for example KXEN;
SAS also offered to discuss their plans for increasing automation, which may be a good topic for discussion]

GPS: Do you use text analytics?

Eric Webster: we experiment with it, but it is not currently in production.

We get a lot of comments written on our website, so we look at tools that can do "triage", to identify which ones which humans should look at. This one should go to a claim rep, this should go to the marketing dept, this should go to the website people. It is not in production now, but it is something we are looking at.

GPS: What role can analytics play in the current financial crisis?

Eric Webster: From our perspective it is important to get fundamentals right. Make sure that we keep a sharp eye on our costs, making sure that we are as process efficient as we can be, and the way we do that is largely thru analytics - where are the areas where we can improve, can we tweak the models and increase the ROI. The current crisis demonstrates the need that we stay on top our game, and analytics is at the core of that.

GPS: You may have a Wall Street Journal article (Jan 26, 2009) that listed the top 200 jobs.
#1 job was a mathematician, #2 was actuary, and #3 was a statistician.
Actuary has a reputation of being a boring job. Can you tell us what actuaries actually do and why their job is so great?

Eric Webster: My degree is in math, so I can relate to all of these. Some people think, "Can there be anything more boring than insurance?!?" But when you start to dig into what it is all about - you see that there is a lot of interesting work to be done, there is a never ending demand for analytics, and insurance is one of very few places where data mining and analytics turn immediately into company fundamentals. This is what actuaries and statisticians do at State Farm. It is a very exciting profession for people who like data mining and numbers, since people really care about what you produce.

Insurance is nothing but management of information. It is pooling of risk, and whomever can manipulate information the best has a significant competitive advantage, so I agree that a mathematician, actuary, or a statistician is a safe and rewarding job.