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Originally published 22 February 2010
Unstructured text mining is an area which is seeing a sudden spurt in adoptions for business applications. The spurt in adoption is triggered by heightened awareness about text mining and the reduced price points at which text mining tools are available today. Text mining is being applied to answer business questions and to optimize day-to-day operational efficiencies as well as improve long-term strategic decisions. The objective of this article is to demystify the text mining process and examine its ROI by exploring practical real-world instances where text mining has been successfully applied in three industries:
It’s been estimated that warranties cost automotive companies more than $35 billion in the U.S. annually. Considering this tough environment, it is imperative that auto companies explore all opportunities for reducing costs. Optimizing warranty cost is a very important lever in the cost equation for automobile manufacturers. If one is able to get even a marginal improvement in money spent in warranty cost, it can have a multiplier effect on the overall bottom line. One of the most underutilized dimensions of optimizing warranty cost is input from service technicians’ comments. From those comments, the text mining process can surface nuggets of component defect insights yielding interventions for preventing them in future.
In order to optimize the warranty process, it is very important to formulate some of the business questions, which are currently unanswered based on technicians’ comments. Here are a few indicative business questions:
A typical text mining solution to answer the above questions incorporates four kinds of unstructured input about the vehicle from internal or external sources. Once the input is received, it is fed to the text mining process which produces three outputs. One is a list of keywords, the other is a higher abstraction of keywords into key vehicle defect themes and the third is a list of instances where certain high-risk keywords were encountered such as “oil leakage.” etc.
Once we know the answers to these questions through a structured text mining process, automobile companies can take four follow-up actions which will reduce warranty-related cost erosion, optimize dealer inventory for spare parts and help suppliers deliver quality components:
Now that we have understood the application of text mining in the auto industry, let’s explore its application in the healthcare industry.
Most countries typically spend anywhere between 3-10% of their GDP on healthcare. The healthcare industry is a huge spender on technology and, with the proliferation of hospital management systems and low-cost devices to log patient statistics, there is a sudden increase in the breadth and depth of patient data. By mining the comments of doctors’ diagnosis transcripts, outputs can yield information that benefits the healthcare industry in numerous ways, such as:
The components of such a successful text mining solution can be found in Figure 2.
Now that we have an understanding of some mission-critical text mining applications for prognostic interventions in the healthcare industry, let’s examine the applications for the credit card industry.
With the proliferation of credit cards, companies need to do the difficult balancing act of identifying which card features (i.e., line of credit, billing cycle, outlet points and coverage) are resonating with customers and, at the same time, minimize the number of defaults/recovery related interventions. Text mining can help optimize both the collection process as well as the customer experience optimization process.
In a diverse set of industries ranging from credit cards to auto to healthcare and beyond, the text mining process is slowly being adopted to mine gigabytes of unstructured data. In this tough economic environment, as the pressure to optimize the efficiency of business processes increases, using unstructured text mining techniques on previously ignored data such as comments from technicians, doctors and call center representatives can provide competitive differentiation. This competitive advantage can be in terms of optimizing internal business processes and managing external customer-facing experiences which, in turn, can have a multiplier effect on the overall bottom line. As Marcel Proust said, “The real voyage of discovery consists not in seeking new landscapes, but in having new eyes.” Unstructured data has always been lying around, but never “discovered.” All it takes are “new eyes” within the organization to look at the same unstructured data to gain new bottom-line impacting insights.
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