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In today’s world of instant gratification, consumers want – and expect – immediate answers to their questions. Quite often, that help comes in the form of a live chat session with a customer service agent.

The logs from these chats provide a unique analysis opportunity. Like a call center transcript, there is two-way dialogue with a question/answer pattern similar to a tennis match, and a “politeness overlay” that can make sentiment analysis tricky. Yet people tend to use an abbreviated style of conversation in chats that is more akin to a tweet or a text message – short and sweet, with fewer words and less formal phrasing than you might see in a call transcript.

So how can we effectively mine these chats in order to:

  • Understand what questions are driving customers to this service channel?
  • Identify key paths to issue resolution (or nonresolution)?
  • Increase agent performance and customer satisfaction?

At SAS, we asked ourselves these very questions when faced with hundreds of thousands of chat logs from sas.com. These inbound chats span multiple years and are in support of customers in more than 35 countries.

Goals for analyzing chat logs

For us, the first objective was simply to understand what topics were occurring across the all the chats. How can we characterize our customer inquiries? Are people looking for information on SAS events or conferences? Are they asking about training and education, or seeking technical support? And if so, for which products? What URLs and resources are most often promoted by the agents? Do these things vary by country, time of year, or even time of day?

Our goals influenced both how we structured the data and what techniques we applied to analyze it. Initially, we consolidated the full chat log into a single text field. This allowed us to use SAS® Text Miner on the entire conversation to identify key topics and clusters which naturally occur in the data. Once we discovered and explored these topics, we were able to re-organize them into classification hierarchies (i.e., taxonomies) that aligned well with our business objectives – one taxonomy for products, and another for inquiry types. Using SAS Contextual Analysis, we then created definitions for these categories using keywords, Boolean expressions and powerful linguistic operators.

Using SAS® Visual Analytics, we are able to easily explore results and surface interactive reports to interested internal parties.  Below are just a few samples from our live chat reports:

Frequency and Duration of Chats by Inquiry Type

Frequency and Duration of Chats by Inquiry Type

 

Faceted Search – Quickly Inspect a Subset of Chats That Meet Certain Criteria

Faceted Search – Quickly Inspect a Subset of Chats That Meet Certain Criteria

 

Chats About Our Statistics-Specific Products Over Time

Chats About Our Statistics-Specific Products Over Time

How can you use this information?

Through these explorations, we are continually learning more about our customers and how they want to do business with us.  In the future, our line of questioning might naturally also evolve to exploring the path of the conversation, answering questions like:

  • What are the most frequently asked question-and-answer pairs which yield a successful resolution to the chat?
  • What is the most effective sequence of questions agents should ask to troubleshoot a technical issue?
  • Does customer sentiment change throughout the course of the conversation?

To support these analyses, we would instead structure the data at an utterance level, where each individual speaker’s comment is stored in its own record, with sequence and speaker IDs.  This is the approach one of our Technology-sector clients is taking. Their volume of inbound service requests is increasing faster than they can scale, so by identifying the most effective question-and-answer pairs they hope to make these “troubleshooting tips and FAQs” readily available on less costly channels, such as the website and mobile app.

Another client, a major financial services provider, is analyzing chat topics as well as sentiment at the beginning and end of a chat session to contribute to a “risk” score for that individual.  The idea is to use sentiment to help predict an outcome variable such as attrition/churn, or likelihood to file a formal complaint with regulators.

Learn more about SAS Text Analytics and how you can apply it to your organization’s unstructured data (chat or otherwise!).

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About Author

Christina Engelhardt

Sr Solutions Architect

Christina is a Sales Consultant at SAS Institute specializing in Text Analytics and Social Media Analysis. Her background is in primary market research and social media/Voice of Customer analysis, spanning all industries and all phases of a project lifecycle. Prior to joining SAS in 2010, she managed the Business Analyst group at Cymfony (a Kantar Media company, now merged with Visible Technologies).

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