There’s been a bit of a gap since I last posted, I ‘m blaming that on Christmas holidays, nasty colds, training courses and starting an MSc. Anyway I thought I’d start again, where I left off before the Christmas holidays: text mining!
How do companies use text mining?
It’s a fact that companies have clicked that text mining is a very useful tool and demand for these tools has increased in the last few years. Especially, in the media, retail, insurance, travel and hospitality business sectors. Companies are using text mining tools for “brand management, reputation management…competitive intelligence...and customer experience management” (Grimes 2009, p.1).
Why do companies find text mining useful?
A couple of reasons really have contributed to the increasing popularity of text mining in the commercial sector. Firstly there's been a rapid increase of unstructured data available to organisations via the internet and through internal data collation exercises. Secondly companies have increasingly adopted a ‘Return on Investment’ mentality for all areas of their business including how they handle their data and lastly, and perhaps most significantly the technology behind text mining and business intelligence tools has simply gotten a lot better (Clarabridge n.d.a, p.2).
As well as this, there’s also a drive in some sectors to reduce customer churn. Churn management is the concept of "identifying those customers who are intending to move their custom to a competing service provider” (Hadden et al. 2005, p.2902). Organisations are recognising that retaining customers ensures their long-term sustainability, for example, T-Mobile Austria implemented text analytics software in 2006 and achieved a 20% reduction in the number of customers leaving the organisation to go elsewhere (Kim et al., 2004;Portrait Software, 2009).
Companies are also placing a focus on customer experience management which is “the practice of listening to customers, analyzing what they are saying to make better business decisions and measuring the impact of those decisions to drive organizational performance and loyalty” (Clarabridge, n.d.b). Companies are looking managing every aspect of the customer’s journey from the service delivery, to promoting the organisations brand, to building better products and services. It helps to improve churn rates and cost savings, and increase profits in the long-term (ibid).
Another focal point, is that companies are trying to gage their customers sentiments about them and their products or services. Sentiment analysis “provides companies with a means to estimate the extent of product acceptance and to determine strategies to improve product quality. It also facilitates policy makers or politicians to analyse public sentiments with respect to policies, public services or political issues” (Prabowo and Thelwall 2009, p.143)
Some common uses of text mining?
I’ve already mentioned several ways that companies are using these tools, I’ve just picked a few examples that spell out the benefits of using text mining.
Example 1
A case study of Cincinnati Zoo demonstrates the benefits of CEM. The zoo had several non-integrated systems for different functions of the zoo, which made it hard to track customer-spending habits. The implementation of CEM software in 2010 has helped the zoo to integrate its systems to track customer-spending habits. The information helped to launch a customer loyalty scheme to decrease churn rates and an estimated 50,000 new visitors are expected in the first year after its launch (Brunelli, 2011).
Example 2
The benefits of using text mining to conduct sentiment analysis were demonstrated by a successful campaign by Mindshare UK to recruit new customers for First Direct, a retail bank. Mindshare mined existing customer’s comments about the bank using sentiment analysis techniques and used both positive and negative comments in an advertising campaign. The success of the campaign saw First Direct win more customers and Mindshare win a highly coveted marketing award (Twentyman, 2011).
A quick summary
Facts are facts; the information age has enabled organisations to evolve from being ‘data poor’ to ‘data rich’. But some organisations are still ‘knowledge poor’, lacking the resources to deal with the flood of data now available. Text mining techniques are evolving and offer organisations in the commercial sector, a variety of tools and techniques to manage and enhance this flood of data. Tools like customer experience management and sentiment analysis make the process, of turning information into actionable knowledge, a lot easier (Choudhary, 2009; Gopal et al,2011).
References
BRUNELLI, M., 2011. Cincinnati Zoo monkeys around with business analytics software. SearchBusinessAnalytics.com, [online] Available at: [cited 22 March 2011].
CHOUDHARY, A.K., OLUIKPE, P.I., HARDING, J.A. and CARILLO, P.M., 2009. The needs and benefits of text mining applications on Post-Project Reviews. Computers in Industry[online]. 60 [cited 12 March 2011] pp.728-740.
CLARABRIDGE, (n.d.a), Text Mining’s Moment: The three trends triggering commercial adaptation. clarabridge.com, [online] Available at: [cited 1 March 2011].
CLARABRIDGE, n.d.b. Customer Experience Success through Text Analytics. clarabridge.com, [online] Available at: [cited 1 March 2011].
GOPAL, R., MARSDEN, J.R., and VANTHIENEN, J., 2011., Information Mining – Reflections on recent advancements and the road ahead in data, text and media mining, Decision Support Systems[online]. Article in Press [cited 25 March 2011].
GRIMES, S., 2009. Text Analytics 2009: User Perspectives on Solutions and Providers, An Alta Plana research study sponsored by Clarabridge. clarabridge.com, [online] [cited 1 March 2011].
HADDEN, J., TIWARI, A., ROY, R. and RUTA, D., 2007. Computer assisted customer churn management: State-of-the-art and future trends. Computers & Operations Research[online]. 34 [cited 17 March 2011] pp.2902-2917.
HEARST, M., 2003. What is text mining?. Berkeley, [online] Available at: [cited 2 March 2011].
KIM, M., PARK, M. and JEONG, D., 2004. The effects of customer satisfaction and switching barrier on customer loyalty in Korean mobile telecommunications services. Telecommunications Policy. 28 pp.145–159. cited by HADDEN, J., TIWARI, A.,
PORTRAIT SOFTWARE, 2009. A Portrait Software case study, Portrait Software, [online] Available at: [cited 20 March 2011].
PRABOWO, R. and THELWALL, M., 2009. Sentiment Analysis: A combined approach. Journal of Informetrics[online]. 3 (2) [cited 11 March 2011] pp. 143-157.
THE 451 GROUP, 2005. Text-aware Applications: The Endgame for Unstructured Data Analysis. cited by CLARABRIDGE, 2008. Text Mining’s Moment: The three trends triggering commercial adaptation. clarabridge.com, [online] Available at: [cited 1 March 2011].
TWENTYMAN, J., 2011. Sentiment analysis at work: a sentimental education for the data rich. Search data management, [online] Available at: [cited 15 March 2011].
