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Impact of Information Management through Text Analysis – A Case Study Approach for Classroom Teaching Source: Tech Talk Abstract: This is an article that demonstrates the actual potential of different technologies of information management. Especially this captures the impact of Emergent technologies. This case illustrates how unstructured data may be mined to capture useful information for different stake- holders across industries. Keywords: Information systems, Web technology Extended Abstract: Information management is an integral part of any successful business. This is one of the key aspect that is typically focussed by the discipline of Management Information Systems. This case study on JetBlue is just to stimulate some thoughts on how information assets may be used by enterprises to create business value. Research on Information Technology and Systems typically depicts the different ways MIS may be used to create value through the effective management of Information Assets within an enterprise. JetBlue recently experienced unprecedented levels of customer discontent after an ice storm that resulted in numerous flight cancellations and planes stranded. JetBlue received 15,000 e-mails per day from customers during the period, up from its usual daily volume of 400. The volume was so much larger than usual that JetBlue had no simple way to read everything its customers were saying. So, obviously, the senior executives of JetBlue had no clue on how to address this challenge. Management of Information Assets is one of the top priorities in Information Systems research. Fortunately, JetBlue had recently contracted with Attensity, a leading vendor of text analytics software, and was able to use the software to analyse all of the e-mail it had received within two days. According to a JetBlue research analyst, Attensity Analyze for Voice of the Customer enabled JetBlue to rapidly extract customer sentiments, preferences, and requests it couldn’t find any other way. This tool uses a proprietary technology to automatically identify facts, opinions, requests, trends, and trouble spots from the unstructured text of survey responses, service notes, e-mail messages, Web forums, blog entries, news articles, and other communications. So how can these actually create value for JetBlue?

Case Study on Analytics

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Page 1: Case Study on Analytics

Impact of Information Management through

Text Analysis – A Case Study Approach for

Classroom Teaching Source: Tech Talk

Abstract: This is an article that demonstrates the actual potential of different technologies of

information management. Especially this captures the impact of Emergent technologies. This case

illustrates how unstructured data may be mined to capture useful information for different stake-

holders across industries.

Keywords: Information systems, Web technology

Extended Abstract:

Information management is an integral part of any successful business. This is one of the key aspect

that is typically focussed by the discipline of Management Information Systems. This case study on

JetBlue is just to stimulate some thoughts on how information assets may be used by enterprises to

create business value. Research on Information Technology and Systems typically depicts the

different ways MIS may be used to create value through the effective management of Information

Assets within an enterprise.

JetBlue recently experienced unprecedented levels of customer discontent after an ice storm that

resulted in numerous flight cancellations and planes stranded. JetBlue received 15,000 e-mails per

day from customers during the period, up from its usual daily volume of 400. The volume was so

much larger than usual that JetBlue had no simple way to read everything its customers were saying.

So, obviously, the senior executives of JetBlue had no clue on how to address this challenge.

Management of Information Assets is one of the top priorities in Information Systems research.

Fortunately, JetBlue had recently contracted with Attensity, a leading vendor of text analytics

software, and was able to use the software to analyse all of the e-mail it had received within two

days. According to a JetBlue research analyst, Attensity Analyze for Voice of the Customer enabled

JetBlue to rapidly extract customer sentiments, preferences, and requests it couldn’t find any other

way.

This tool uses a proprietary technology to automatically identify facts, opinions, requests, trends,

and trouble spots from the unstructured text of survey responses, service notes, e-mail messages,

Web forums, blog entries, news articles, and other communications. So how can these actually

create value for JetBlue?

Page 2: Case Study on Analytics

The technology which was provided by Attensity for JetBlue was able to accurately and

automatically identify the many different “voices” customers use to express their feedback (such as

a negative voice, positive voice, or conditional voice), which helped jetBlue pinpoint key events and

relationships, such as intent to buy, intent to leave, or even customer “wishful” events. It can reveal

specific product and service issues, reactions to marketing and PR efforts, and even buying signals

for related or unrelated offerings. This again improved JetBlue’s position in being able to cross sell

and up sell valuable offers and thus enhance the value propostion using Attensity technology.

Attensity’s software integrated with JetBlue’s other customer analysis tools, such as Attensity Net

Promoter metrics, which classifies customers into groups that are generating positive, negative, or

no feedback about the company. Using Attensity’s text analytics in tandem with these tools, JetBlue

developed a customer bill of rights that addressed the major issues of the customers. Indeed, after

the implementation of this solution at JetBlue, the customers were delighted and there was a

positive recall of the services rendered by JetBlue.

Key references

1. Gruhl, D., Chavet, L., Gibson, D., Meyer, J., Pattanayak, P., Tomkins, A., & Zien, J. (2004). How

to build a WebFountain: An architecture for very large-scale text analytics. IBM Systems

Journal, 43(1), 64-77.

2. Endert, A., Fiaux, P., & North, C. (2012, May). Semantic interaction for visual text analytics. In

Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 473-

482). ACM.

3. Hu, X., & Liu, H. (2012). Text analytics in social media. In Mining text data (pp. 385-414).

Springer US.

4. Kar, A. K., & Pani, A. K. (2011). A model for pricing emergent technology based on perceived

business impact value. International Journal of Technology Marketing, 6(3), 241-258.

5. Kutas, M., & Hillyard, S. A. (1980). Reading senseless sentences: Brain potentials reflect

semantic incongruity. Science, 207(4427), 203-205.

Page 3: Case Study on Analytics

6. Kar, A. K., Pani, A. K., & De, S. K. (2010). A study on using business intelligence for improving

marketing efforts. Business Intelligence Journal, 3(2), 141-150.

7. Berners-Lee, T., Hendler, J., & Lassila, O. (2001). The semantic web. Scientific american,

284(5), 28-37.

8. Khatwani, G., Anand, O., & Kar, A. K. (2014). Evaluating Internet Information Search

Channels Using Hybrid MCDM Technique. In Swarm, Evolutionary, and Memetic Computing

(pp. 123-133). Springer International Publishing.

9. Endert, A., Fiaux, P., & North, C. (2012, May). Semantic interaction for visual text analytics. In

Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 473-

482). ACM.

10. Kar, A. K. (2014). Integrating websites with social media–An approach for group decision

support. Journal of Decision Systems, (ahead-of-print), 1-15.

11. Hu, X., & Liu, H. (2012). Text analytics in social media. In Mining text data (pp. 385-414).

Springer US.

12. Angell, R., Boyer, S., Cooper, J., Hennessy, R., Kanungo, T., Kreulen, J., ... & Weintraub, H.

(2005). U.S. Patent Application 11/281,291.

13. Basole, R. C., Seuss, C. D., & Rouse, W. B. (2013). IT innovation adoption by enterprises:

Knowledge discovery through text analytics. Decision Support Systems, 54(2), 1044-1054.

14. Bedathur, S., Berberich, K., Dittrich, J., Mamoulis, N., & Weikum, G. (2010). Interesting-

phrase mining for ad-hoc text analytics. Proceedings of the VLDB Endowment, 3(1-2), 1348-

1357.

15. Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business Intelligence and Analytics: From Big

Data to Big Impact. MIS quarterly, 36(4), 1165-1188.