Chatbots Resurgence: Serious Games For Smart CRM
Serious Games and the rise of Speech Analytics in Contact Centers
Via: CRM Daily
Enterprises are rapidly learning how to use the output from speech analytics to benefit many operating areas including: sales, marketing, R&D, compliance, risk management, collections, fraud, back-office operations and, of course, the contact center.
A new report has analyzed this market and why speech analytics has rapidly developed into the fastest growing application in the history of contact centers.
The 2007 Speech Analytics Market Report by DMG Consulting LLC, is positioned as the definitive guide to this emerging market segment.
The report provides detailed information about the market, vendors, competitive landscape, technology products, functionality accuracy, ROI, pricing, market share, projections and implementation best practices
First introduced into the contact center in 2004, speech analytics is still considered an emerging application.
Contact centers implement speech analytics solutions to analyze customer conversations, while also providing benefit to the entire enterprise. The DMG report examines the technical and business opportunities and challenges involved in implementing these applications and also provides best practices and guidance for making these applications work for the enterprise.
The 2007 Speech Analytics Market Report is positioned as being essential for companies of all sizes looking to use innovative technology to gain a strategic advantage. The Report also explains how speech analytics leverages insights contained in unstructured customer conversations to improve profitability, reduce costs, enhance the customer experience and reduce corporate liability.
Via: Dovetail Software Blogs - The Voice of Customer Service and Support in CRM
Have you talked to any good chatbots lately? Neither have we, but the CRM industry is trying as hard as it can to turn that around. CRM, stationed in the very interface between companies and customers, obviously begs for automation wherever possible, humans being so costly, and so darned...unique.
The rise of speech analytics in call centers is not yet mirrored by speech synthesis: the chat bots are text-bound, chat-session hounds that do a good job at qualifying customers through multiple choices:
"Federman says Radiator.com was able to scrape the information from the chat session and deliver it to its contact center agents the second the call was connected, so the context of the online session was readily available to the customer service representative. “This approach increased sales conversion by 35 to 40 percent, according to executives...”
"Rather than try to force the chatbot to do more than it could handle, the company leveraged the chat bot's strength (gathering information) to create a much more personalized experience for the consumer once they made contact by phone."
So how well does the latest software do at that old human trick, pretending to be smart?
Results are mixed. John Ragsdale, formerly with Forrester and now with the Service & Support Professionals Association (SSPA), is reported in the same CRM News article above as saying that the situation is getting better with the advent of Natural Language Search engines that discern the intent of a searcher's question rather than the literal take.
Speech analytics is expected to grow as an important part of the contact center environment. With this growth, the industry should also expect to see future innovations that will continue to improve on product offerings and further applications throughout the enterprise.
Most chatbots rely on fairly simple tricks to appear lifelike.
Richard Wallace, creator of the top-ranked chatbot ALICE (Artificial Linguistic Internet Computer Entity), has handwritten a database of thousands of possible conversational gambits. Type a comment to ALICE, and it checks the phrase and its key words for a response coded to those words.
In contrast, Jabberwacky, another top-rated Internet bot produced by Rollo Carpenter, keeps track of everything people have said to it, and tries to reuse those statements by matching them to the writer’s input. Neither chatbot has long-term memory, so they respond only to the last sentence written.
Here's some more links to Chatbot-related sites: