The Voice-Based Data Abyss
What business issue is keeping you up at night? If you are like most execs, business intelligence is near the top of the list. What I mean is how you manage your expanding universe of mobile, fixed-line, and social media data (customer data, customer service interactions, financial data, etc.) with a data analytic strategy designed to help keep clients happy, maintain compliance, and uncover meaningful sales and marketing trends, which can add to your company’s bottom line.
Like it or not, 'All companies are data businesses now' , and businesses successfully leveraging their corporate data today will see hundreds of billions of productivity benefits over their competitors who are not using their data as effectively as possible, according to International Institute for Analytics. The goal for businesses is to focus heavily on data that is readily accessible, visually presented, and rapidly actionable. For those that have been early adopters of voice analytics and/or Big Data, they are probably shaking their heads and saying, ‘that sounds great but that's not what I’ve experienced’.
For the sake of this article, I would like to focus on the data generated by the front line interactions between companies and their customers, the contact center. According to Gartner, an estimated 420 billion words per day are spoken in the world's contact centers. (For context, Twitter generates about 2.7 billion words per day.) Further, they estimate less than 1 percent of those 420 billion words are analyzed. If you're like me, I found that piece of information startling. Imagine the productivity and customer insight gains that are lost, and the potential risk awaiting you in that corpus of data that goes unexamined.
The goal for businesses is to focus heavily on data that is readily accessible, visually presented, and rapidly actionable
In the past, the major obstacles for analyzing voice data had been the lack of large scale speech-to-text transcription engines, processing power, and access to analyst and scientist skill sets. Today, we are seeing the convergence of open standards for communications, programming interfaces, voice recording interfaces, and media formats coupled with the aggregated networking, processing and storage resources of Cloud infrastructure from the likes of Microsoft, Amazon, and Google. These advances are being leveraged by application companies to create real-time and near real-time voice analytics solutions for call centers and enterprises.
These new application companies can deliver actionable data in as little as a day versus the months or more of the old world proprietary competition. This timely capability has the potential to unlock an enormous amount of otherwise-unanalyzed corporate intelligence. Further, since you are not required to build large scale architecture and hire dedicated resources to maintain and operate the application, these solutions are available for pennies on the dollar, with most companies offering short-term trials to prove the value before you buy. And to top it all off, you are in complete control of your data, meaning you own it. Therefore, you are not locked into you a single vendor as the market evolves.
For illustration, here is a Use Case that has wide-spread applicability to contact centers. The customer environment includes a VoIP switch with enriched call metadata from a CTI component, and the company has an existing proprietary call recording system. The company is interested in exporting 100 percent of their recorded files with metadata to a cloud-based speech-to-text transcription solution and also conducting root-cause analysis on customer interactions. The challenges are two-fold: 1) It can be difficult and time consuming to export call recording files from the existing recording system in real-time along with optimizing the file format to 'stereo' to improve transcription results. 2) With regard to solution design, it can be quite challenging to maintain the existing recording system, replicate the desired IP traffic to a premise-based 'collection' application that supports real-time and the stereo format requirements, and export the media/metadata to a cloud-based analytics application company that presents same day, browser-based interactive display of root cause analysis. In other words, the data has to be extracted, transformed and then ported to the cloud. This takes time, money and resources.
So, while the capabilities do exist today to analyze your voice-based customer data to transform it into meaningful business intelligence, there are still many road blocks in the way related to closed proprietary platforms, non-conforming standards, non web-based environments, etc.
Once these roadblocks are overcome, businesses can efficiently and cost effectively, enjoy the value derived from a treasure trove of customer data, which is currently sitting idle and untapped. Additionally, there are countless other applications for voice-based data analytics waiting to be taking advantage of, including fraud prevention with voice biometrics, workforce optimization, automated quality assurance, automated voice of the customer, voice search and word spotting, and end-to-end call analytics.
So, if you want to be on the right side of the data analytics equation in the coming years, research those companies that are building solutions on open standards and leveraging the power of the Internet and public cloud infrastructure to deliver productivity, business intelligence and other benefits to their customers.
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