This one is the one before last of the Big Integration blog post republishing before I close it for good. This interview with Larry Freed, CEO of ForeSee Results, was very interesting in the sense that Larry gave very good examples of the added value of integrating both behavioral and attitudinal analyses in Web Analytics. Still too many companies either don’t do both, or fail to really integrate them.
Today I am very happy to welcome Larry S. Freed, President & CEO of well-known ForeSee Results, one of the leaders in attitudinal analysis, often called Voice of Customer analysis. Larry was kind enough to find some time in his very busy schedule to answer a few questions about how behavioral and attitudinal analyses can be integrated to offer extra added value.
JW – Could you tell us about how online attitudinal analysis has evolved in the last 2, 3 years, and where it is now (adoption, evolution, etc.)?
LF – We had our first beta client right after September 11, 2001, so we’ve been doing this work for seven or eight years now. I think five years ago, even two or three years ago, analyzing attitudinal input or voice-of-customer feedback was a nice-to-have. Only the really forward-looking, advanced companies did it. What we see now, especially with the weak economy and fierce competition for share of a smaller and smaller wallet is that attitudinal analysis is a must-have. There are a lot of academic studies showing that customer satisfaction is predictive of future financial performance, and companies really can’t afford to ignore it anymore. So we see more and more companies starting not only to collect and pay attention to attitudinal data, but when they can apply science to it, they are also using it to measure success and drive decision making. There is huge demand for actionable analytics that actually help companies make decisions rather than just sitting there in a spreadsheet, showing what already happened. Adoption of attitudinal analysis is fast and growing every day. The companies that aren’t doing it will be left behind.
JW – We are interested in data integration, can you tell us how ForeSee Results can make its data available? Any API, or export schema?
LF – We make our data available through an FTP file transmission or clients can download data through our online portal. We can provide all the schema that people will need– file export schemas are available.
We’ve actually created an exchange mechanism to take other sources of data in, be it behavioral or financial data; we can import those other sources into the ForeSee Results portal and marry those sources with our online satisfaction data.
JW – We would like to know if it’s possible to integrate ForeSee Results data with applications such as WebTrends or Omniture.
LF – Yes, we do integrate with companies like Web Trends, Omniture, Coremetrics and other clickstream analytics tools by providing a common linkage point at a respondent level between ForeSee’s system and the clickstream analytics’ system. We’ve done this numerous times across various platforms.
JW – Is it possible to somehow link respondents to what they actually did on the site ? If yes, how do you technically accomplish that?
LF – Absolutely, we have the ability to link either by integrating with the clickstream analytic tool, with a company’s internal behavioral/financial applications, or through passing of parameters from the web site to the ForeSee system.
JW – Without naming the client, could you give us an example of a company that actually linked their behavioral and attitudinal data? What was the most interesting finding that could not have been possible with only doing the survey?
LF – There are a lot of great examples, and it’s been really instructive to see the kinds of things people can learn by linking behavioral and attitudinal data. It can be really simple—we had one client who was able to look specifically at browsers who abandoned a cart to find out what they were likely to do next—whether they were planning to buy from that same company in a store, buy from a competitor, return to purchase later, etc. This company was really able to put the cost of an abandoned cart into perspective when they saw why people were abandoning. Another great example is a company we worked with that was able to understand the value of improving satisfaction by linking satisfaction to purchase data. We know that satisfaction is proven to predict future financial performance, but integration actually allowed this company to prove that out and show how revenue increased as satisfaction increased. A third example is a company that was able to segment satisfaction and likely future behaviors based on referring site and keywords. They were able to see which referring sites resulted in the best and worst quality visitors. They could actually see that visitors who came to the site via one keyword were more likely to buy than those that came through another key word. This is invaluable information when it comes to marketing decisions and resource allocation.
JW – How do you see a solution like yours evolve in the near future in terms of better integrating its data with other enterprise customer data systems ?
LF – We’re actually just coming off a period where I feel we’ve made tremendous strides in integration. But the industry is always evolving, and our goal is always to make it as easy as possible to extract data from the clickstream tools.
JW – Thanks a whole lot, Larry.