Would you like to have a clearer understanding of your customers? If so, this week's featured SAS Author's Tip delivers the goods. SAS Press author Randy Collica is a Senior Solutions Architect for SAS and his book CRM Segmentation and Clustering: Using SAS Enterprise Miner contains a lot of how-to examples--so many that it was challenging selecting just one. With that being said, you can read industry experts' reviews of this book and a free chapter, and learn more about Randy on his author page.
The following excerpt is from SAS Press author Randy Collica's book CRM Segmentation and Clustering: Using SAS Enterprise Miner, Copyright © 2007, SAS Institute Inc., Cary, North Carolina, USA. ALL RIGHTS RESERVED. (please note that results may vary depending on your version of SAS software)
Using Text Mining in CRM Applications
It is rather difficult to demonstrate textual data in a CRM application without actually giving you data that should not be published. So, in lieu of doing this, I will describe a text application that I performed in my business and give you the thought and mining process. The business problem given to me was not really supposed to be a text mining exercise at all. I was consulting with an internal client about his sales segmentation. He had classified each of 250 or so top accounts in that industry into one of five possible groups. Four of these groups were attitudinal segments depicting the level of their technology and their feelings that this technology would help them have a competitive advantage. The fifth group was an unknown group where the marketing and sales did not know how to classify the account. When the project came to me, the marketing clients desired to know more about these segments from a product profile standpoint, which I was able to accomplish using the techniques given earlier in Chapter 10, “Product Affinity and Clustering of Product Affinities.”
What transpired after this profiling effort was a discussion that led to the usage of our call center database that housed unstructured notes and comments that our sales representatives had entered when communicating with customers. The desired goal in this project was to use the segmentation to create some specific campaigns with differing messaging and offers tailored to each segment group. That is when I had the idea of combining the structured and unstructured call center notes together along with the accounts that were already classified into the four segment groups. Leaving the fifth (unknown) group out of the data
mining, I was able to create a predictive classification model using the unstructured notes to classify the accounts into one of the four possible attitudinal segment groups. With a classification model in hand, I could then score the fifth (unknown) accounts into one of the four segments.