Thought Leaders in Big Data: Interview with Sid Banerjee, CEO of Clarabridge (Part 4)

Sramana Mitra: In other words, you have identified a specific usage of words and expressions, whether it is in chat or social media. You see certain types of expressions and you have been able to algorithmically qualify that in order to predict certain behavior.

Sid Banerjee: Yes and no. We do that in run time. We don’t do it a priori and then apply it to the data. Each customer is different. The correlating words are going to be different from day one to day two. That correlation occurs with Clarabridge in real-time against that data.

SM: How does that help customers?

SB: Let’s say I am a leading hotelier and all of a sudden I see that my survey scores in a particular market are spiking negative or my social media connections are going negative. I can do a root cause analysis, which is the same type of analysis that is a part of this correlation analytic that we have. It will then highlight the attributes, the words and the demographics that tend to have a high correlation to that spike.

SM: The real application here is diagnosis and figuring out the root cause and solving the problem as opposed to predicting where there may be problems ahead of time.

SB: Yes, but let’s be clear. Predicting and correlating are equally useful, and I would argue that in many cases correlation is more derivative of business value – when you are trying to fix a problem or making sure the problem doesn’t occur again. Predicting is very good in marketing. When I am about to spend $1 million on a new campaign, I want to target [a group] to make sure they buy the most. But when you are in the world of customer experience  –  a widely variable and temperamental world – you can’t really predict.

SM: So your focus is more on the customer experience analytics and identifying problems and helping people to diagnose and solve problems.

SB: That is correct. If you look at our webpage, we talk about Clarabridge’s solution basis being something we describe as “ICE” (Intelligent Customer Experience). This includes applying intelligent algorithms to all of the different sources of customer’s interaction feedback to be able to maintain high quality experiences, with the goal of them translating to higher satisfaction, loyalty, and profitability.

SM: So far we have primarily talked about retail. Is there a substantial difference in the other verticals or is the general value proposition still the same?

SB: The general value proposition is consistent. That is one of the reasons why we can stay in different industries. Think about airlines, hotels, banks, consumer electronics, etc. The common problem we are trying to solve is when a customer applies, uses, or receives some form of support from a vendor or provider, we want to make sure they are not driven away by experiences that can ultimately produce turn, and we want to make sure we are amplifying the programs and services that drive loyalty.

SM: But your workflow and the value you are providing are more or less the same.

SB: Exactly. That is why we have been able to scale the business so quickly. We are applying a customer-centric problem solution paradigm to any business that has lots of customers and a lot of data coming in from lots of different sources, which is otherwise hard to collect, manage, and analyze without a technology like ours.

This segment is part 4 in the series : Thought Leaders in Big Data: Interview with Sid Banerjee, CEO of Clarabridge
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