Application of artificial intelligence techniques for root cause analysis of customer support calls

Dell Technologies seeks to use the advancements in the field of artificial intelligence to improve its products and services. This thesis aims to implement artificial intelligence techniques in the context of Dell's Client Solutions Division, specifically to analyze the root cause of customer calls so actions can be taken to remedy them. This improves the customer experience while reducing the volume of calls, and hence costs, to Dell. This thesis evaluated the external vendor landscape for text analytics, developed an internal proof-of-concept model using open source algorithms, and explored other applications for artificial intelligence within Dell. The external technologies were not a good fit for this use-case at this time. The internal model achieved an accuracy of 72%, which was above the acceptable internal threshold of 65%, thus making it viable to replace manual analytics with an artificial intelligence model. Other applications were identified in the Client Solutions division as well as in the Support and Services, Supply Chain, and Sales and Marketing divisions. Our recommendations include developing a production model from the internal proof-of-concept model, improving the quality of the call logs, and exploring the use of artificial intelligence across the business. Towards that end, the specific recommendations are: (i) to build division-based teams focused on deploying artificial intelligence technologies, (ii) to test speech analytics, and (iii) to develop a Dell-wide Center of Excellence. The division-based teams are estimated to incur an annual cost $1.5M per team while the Center of Excellence is estimated to cost $1.8M annually.

Department dc:contributor.department

Leaders for Global Operations Program at MIT

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2017

Author dc:creator

• Parthasarathy, Sailashri, 1982-

Advisor dc:contributor.advisor

• Tauhid Zaman and Bruce Cameron.

• Sloan School of Management.
• Institute for Data, Systems, and Society.
• Engineering Systems Division.
• Leaders for Global Operations Program.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/111276

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/111276