The Undiscovered Gold Mine of UC Data
Today’s world is exploding with applications for business communications, from messaging apps like HipChat and FB Messenger to established technologies like VoIP and web and video conferencing solutions. These applications produce vast amounts of data— everything from standard call metadata to the content of our phone calls, video conferences, and texts—that until now has been an unmined cache of valuable business information. We spend a lot of time figuring out how to make the most of the data within our CRM applications; why haven’t we done the same for our communications data?
Part of the answer is that we haven’t had good and accessible tools to do so before now. But that’s changing as recent advancements in Big Data and AI are bringing us closer to being able to draw actionable insights from such data. To see what’s been brewing, let’s walk through two areas where technology advancements are poised to have a big impact on business: customer success and marketing automation.
Customer sentiment analysis is the most common use of new Big Data capabilities. It’s especially useful for enterprises that have large customers bases to serve—such as big banks and healthcare organizations. Sentiment analysis technology will track a support call, transcribe it, and then conduct a transcript analysis that assigns an emotional “score” to the call—happy, frustrated, angry, etc. Humana, for example, leverages a DARPA-backed AI bot, Cogito, to inform its support agents about the emotional state of a specific customer, and advises them when to change support tactics.
These new capabilities are a huge leap forward in efficiency for both training and policy adherence. Traditionally, managers listened in on calls to gauge how their service reps were handling customers—and if their interactions violated any privacy policies—but this model doesn’t scale well for obvious reasons. Now, by automating transcription and analysis, management can rapidly get insight into which agents need more training.
While attempts to deliver these tools have been going on for decades, huge leaps in computing power have now made them possible, and cloud-hosted APIs have made them accessible. Today a company can tap into sentiment analysis tools from IBM Watson, Google Cloud Platform, or Amazon AWS without having developer expertise or deep pockets for infrastructure costs. On the supplier side, vendors are incentivized to keep costs down because this data is a valuable currency in our increasingly digital economy. This is certainly the case for Amazon’s Echo Dot, the voice assistant powered by natural language processing (NLP). NLP is fueled by gathering as much data as possible to detect patterns and statistically map huge data sets; the more data that is input, the more accurate the output.
The democratization of advanced capabilities has given rise to startups, Voicebase among them, that package these capabilities to give companies the tools to feed data—in this case voice—into their systems and assess the the emotional states of calls, in real time and with customized output options. ExecVision is another interesting startup that uses AI to extract intelligence from call recordings, but with more focus on sales acceleration. However, these sentiment analysis and NLP technologies rely heavily on Big Data to train their AI to become more accurate; and the advent of affordable and cloud-based UC and contact center services will surely inject tremendous value into this space.
Behavioral UC data—how users interact with various communication channels, coupled with the content of those communications—is another valuable data set. For example, I’m someone who responds very quickly if I get an SMS alert that my credit card has been flagged for fraudulent activity. My behavior indicates that I care about protecting my credit score. A marketer who analyzed this behavior would be able to pinpoint me—and other users with similar behavior—as good candidates for the marketing of a credit protection product. Although such communications-channel behaviors matter, they’re not currently being tracked. As a UC provider, RingCentral has the logs and data, but we don’t take the next step to analyze patterns of behavioral data.
That’s where new and emerging marketing automation software solutions come in. The one from Marketo is designed to analyze large amounts of behavioral data and cull insights that help its users create accurate customer profiles. Pi, a marketing tech startup from former Google execs, ingests social network streams and then constructs marketing content that’s tailored to specific user profiles. Technologies such as these aren’t just driving marketing and advertising campaigns— they’re aggregating the data of customers who use their platform. Any good source of user engagement data, including UC data from contact centers, could feed into similar marketing platforms.
This has promising appeal for marketers everywhere. You’ve probably heard the advertising adage that marketing departments waste half of their advertising dollars—they just don’t know which half. The Holy Grail of marketing is to create a customer profile with enough depth to create specific target segments, which allows marketers to maximize their yield. Data locked in a UC system—what the context and content of a conversation is, how long it lasts, when and with whom it occurs, and most interestingly, which trigger words invoke what sentiments from a customer—can become important targeting data when it’s fed into other business-critical systems to build a stronger and smarter profile of company’s customers.
The booming growth of APIs, coupled with advancements in AI and Big Data, has resulted in easier accessibility to data and insightful analysis, creating greater opportunities for businesses to boost their bottom lines. In fact, for a typical Fortune 1000 company, just a 10% increase in data accessibility will result in more than $65 million in additional net income. That’s no mere pocket change.
Today more than ever, businesses that understand their customers best have a competitive advantage. The way to get there is through data, and businesses—and the technology companies that support them—have a lot to gain by taking a closer look at the riches hidden within UC data.
By David Lee, VP of Product Management at RingCentral