We had the privilege of speaking with this renowned researcher on the trends in Conversational Selling and evolving role Live Chat is playing in this field. Read on and listen in to excerpts of the interview below:
RB: You coined the term “Conversation Commerce” back in 2011, but the name Chat Analytics is new. What are the similarities and differences between Conversational Commerce and Chat Analytics?
Dan: Sure, and let us put as the intermediate phase the term Speech Analytics. We’ve been looking at this category as part of the Interaction Analytics because conversations between people that made calls in their contact centers have become multi-channel and unstructured. We decided to apply analytic technology 10 years ago.
Like Speech Analytics, Chat Analytics are pattern recognition-based. It’s actually easier to do than its predecessor because organizations capture conversations. It’s pretty exciting to watch.
We’ve also started using the term Conversational Analytics to represent the combined resources found in capturing and analyzing all customer conversations, both text and speech.
RB: Give us an idea of who is involved in the market currently, who are the players that you are seeing in the trends in that?
Dan: The call recording leaders certainly took the leading role and some of the acquisitions that they made. Then there are a number of innovative startups which is where I put your company. They are looking at addressing some of the hot burden issues that happen and want to do some either more in-depth analysis of chat.
RB: Doing a deeper dive on chat analytics requires more resources. What are the potential benefits for a contact center to do so?
Dan: What we are framing with this type of strategic initiative is creating a world where customers and prospects expend less effort to accomplish the goals that are trying carry out, such as searching for a particular item or seeking recommendations for a product to buy. A lot of it can be aided by computer understanding. Conversational and chat analytics can help both the customer and call center agent do a better job of communicating.
RB: So, with that in mind, I was hoping you could talk about the evolution of the term “awful words spotting.” Is it possible to measure the emotions in chat?
Dan: I get asked when will our computers understand sarcasm. The answer, maybe never.
Now, in some cases, it isn’t different from Speech Analytics, where the conversation was transcribed. That’s not enough to detect things like emotion, and certainly intent, from what the chat input is.
RB: This leads to the next question on emotional intelligence, which is another new thing in automated conversations. What does the role of Artificial Intelligence play in this, and what is the balance between automation versus using humans with emotional intelligence software?
Dan: One of the tough areas of this field is determining what are humans good at, what are machines good at. In the ideal situation, there are some solution architectures where the intelligence is like the little angel or devil on the chat agent’s shoulder. When you use the automated resource in sort of a background role, you’re getting the best of both worlds to fulfill what the customer wants.