Each day, the SAS Customer Contact Center participates in hundreds of interactions with customers, prospective customers, educators, students and the media. While the team responds to inbound calls, web forms, social media requests and emails, the live-chat sessions that occur on the corporate website make up the majority of these interactions.
The information contained in these chat transcripts can be a useful way to get feedback from customers and prospects. As a result, the contact center frequently asked by departments across the company what customers are saying about the company and its products – and what types of questions are asked.
Chat transcripts are a source for measuring the relative happiness of those engaged with SAS. Using sentiment analysis, this information can help paint a more accurate picture of the health of customer relationships.
The live-chat feature includes an exit survey that provides some data including the visitor’s overall satisfaction with the chat agent and with SAS. While 13 percent of chat visitors complete the exit survey (which is above the industry average), that means thousands of chat sessions only have the transcript as a record of participant sentiment.
Analyzing chat transcripts often required the contact center to pore through the text to identify trends within the chat transcripts. With other, more pressing priorities, the manual review only provided some anecdotal information.
Performing more formal analytics using text information gets tricky due to the nature of text data. Text, unlike tabular data in databases or spreadsheets, is unstructured. There are no columns that dictate what bits of data go where. And, words can be assembled in nearly infinite combinations.
For the SAS team, however, the information contained within these transcripts were a valuable asset. Using text analytics, the team could start to uncover and understand trends and connections across thousands of chat sessions.
SAS turned to SAS Text Miner to conduct a more thorough analysis of the chat transcripts. The contact center worked with subject-matter experts across SAS to feed this text information into the analytics engine. The team used a variety of dimensions in the analysis:
- Volume of the chat transcripts across different topics.
- Web pages where the chat session originated.
- Location of the customer.
- Contact center agent who responded.
- Duration of the chat session.
- Products or initiatives mentioned within the text.
In addition, North Carolina State University’s Institute for Advanced Analytics began to use the chat data for a text analytics project focused on sentiment analysis. This partnership between the university and SAS helped students learn how to uncover trends in positive and negative sentiment across topics.
After applying SAS text analytics to the chat data, the SAS contact center better understood the volume and type of inquiries and how they were being addressed. Often, the analysis could point areas on the corporate website that needed updates or improvements by tracking URLs for web pages that were the launch point for a chat.
Information from chat sessions also helped tune SAS’ strategy. After the announcement of Windows 10, the contact center received customer questions about the operating system, including some negative sentiment about a perceived lack of support. Based on this feedback, SAS released a statement to customers assuring them that Windows 10 was an integral part of the product roadmap.
The project with NC State University has also provided an opportunity for SAS and soon-to-be analytics professionals to continue and expand on the analysis of chat transcripts. They continue to look at the sentiment data and how it changes across different categories (products in use, duration of chat) to see if there are any trends to explore further.
Today, sentiment analysis feeds the training process for new chat agents and enables managers to highlight examples where an agent was able to turn a negative chat session into a positive resolution.
SAS Sentiment Analysis and SAS Text Analytics, combined with SAS Customer Intelligence solutions such as SAS Marketing Automation and SAS Real Time Decision Manager, allow marketing organizations like SAS to understand sentiment or emotion within text strings (chat, email, social, even voice to text) and use that information to inform sales, service, support and marketing efforts.
If you’d like to learn more about how to use SAS Sentiment Analysis to explore sentiment in electronic chat text, register for our SAS Sentiment Analysis course. And, the book, Text Mining and Analysis: Practical Methods, Examples, and Case Studies Using SAS, offers insights into SAS Text Miner capabilities and more.
Editor’s note: This post is part of a series excerpted from Adele Sweetwood’s book, The Analytical Marketer: How to Transform Your Marketing Organization. Each post is a real-world case study of how to improve your customers’ experience and optimize your marketing campaigns.