Every now and then, I publish a guest post from someone who is an industry thought leader and an intelligent commentator. I have the distinct honor of providing you with an interview conducted by Seth Grimes with Sid Banerjee, the CEO of analytics provider Clarabridge. Seth, a thought leader in the analytics space, covers BI, text analytics, and sentiment analysis as a consultant, independent industry analyst, and writer. Seth organizes the twice-yearly Sentiment Analysis Symposium, and is founding chair of the Text Analytics Summit series.
You can follow him on Twitter at @SethGrimes.
Clarabridge launched in early 2006 as a text mining platform, providing "smart solutions to complex business problems ... backed by reliable analytics". The company's promise was to "unlock the full power of your information assets by transforming unstructured content into rich, structured data". Within a few years, however, Clarabridge (wisely) shifted its focus to high-value business applications centering on the then-new field of Customer Experience Management.
The transformation was timely; interaction-focused Customer Relationship Management was not providing the insights that businesses needed (and need) in order to boost customer satisfaction and loyalty. And now, seven years after Clarabridge's start, while smart solutions and reliable analytics are still too-infrequently encountered, Clarabridge continues to impress, including its recent Intelligent Customer Experience rebranding.
It's not my purpose today to describe Clarabridge's solution set. You can learn for yourself about the recent Clarabridge 6.0 release — it consists of three components: Analyze, Collaborate, and Engage — via the company's product page or the Clarabridge 6.0 press announcement. Instead, I thought I'd explore the meaning of Intelligent Customer Experience (ICE). What better way to do that than through a Q&A with Clarabridge co-founder and CEO Sid Banerjee? So here goes:
Let's start with a definition. What's Intelligent Customer Experience?
ICE is the merging of "intelligence" with "customer experience". Customer experience management solutions have traditionally been associated with transactional platforms — such as workforce management systems that provide feedback with agent interactions, or survey platforms that provide feedback on customer/company transactions, or social media monitoring platforms that provide a means for organizing and responding to social conversations.
So businesses have all this disparate customer-experience raw material. How do they generate intelligence from it, to make progress toward Intelligent Customer Experience?
ICE is really about integrating all the content, from all the sources, and applying intelligence to the multichannel content. Apply text and sentiment analytics, a mix of descriptive, statistical, machine learning, and ontological approaches to organize, quantify, track, and distill insights from many customers, from many sources, into actionable insights and actions. And ICE is about applying these interdisciplinary algorithms and approaches in a way that produces business friendly, usable output useful to non-technical, customer-centric organizations.
Functional examples? What's the business advantage in ICE?
Examples of ICE include:
Intelligence that can differentiate between incidental shifts in customer feedback, and material issues that are likely to drive down satisfaction loyalty and profitability
Intelligence that can catch quickly moving problems and issues when they are small, so they can be addressed before they become big and costly
Intelligence that can look at all the data, from all customer listening posts across sales, marketing support, and social channels, and determine who within an organization needs to be notified proactively across an enterprise so that operational decisions can be made to fix an issue or change a program
Intelligence that can scalably and accurately triage a wide range of customer feedback sources to differentiate between useful and non-useful content, analytically useful content to be monitored, and tactically immediate feedback that requires direct response to a customer.
And what sort of business problems does ICE respond to?
ICE lets organizations address important questions such as:
What are the material changes in my customers' experiences adversely affecting efficiency, loyalty, or profitability?
Which customers require immediate response, regardless of where they are and how they are expressing themselves?
How can I ensure that strategic and operational insights from my customers are delivered to the right people, across an organization, in an actionable format, to allow the a timely response and resolution?
So in sum, Intelligent Customer Experience is...
For a shorter answer, how about this: ICE is the intelligent merging of content from all platforms containing customer interactions and insights, the application of advanced text/sentiment, statistical, machine learning, and alerting algorithms against the multichannel content, and the delivery of business critical insights in the right form factor, to the right people, to ensure insight and issue resolution.