TechBytes with Jeff Gallino, CTO at CallMiner


Tell us about your role and the team/technology you handle at CallMiner.

As Co-Founder and CTO at CallMiner, I oversee all aspects of the team’s vision and development of our SaaS-based customer engagement and speech analytics platform and product suite, Eureka. All seven of our product solutions—Analyze, Coach, Capture, Alert, Visualize, Redact and API—work interchangeably by leveraging Artificial Intelligence (AI) and Machine Learning (ML) to analyze both ends of each customer interaction and drive customer experience (CX), regardless of the communication channel.

What runs the CallMiner data engine?

The core data processing engine that fuels our entire cloud-based customer engagement analytics platform is built using sophisticated microservices architecture. To put it simply, this allows for faster, more reliable information that supports the capacity to scale across even the largest enterprises. Our speech analytics isn’t a one-size-fits-all solution, but instead a technology that’s designed to run on an organization’s unique metadata and become more sophisticated or “smarter” with each interaction.

Do you leverage AI ML algorithms at CallMiner? If yes, tell us more about your AI research and analytics.

AI and ML are critical components of Eureka—starting with transcription, the very foundation of the platform. We use an advanced combination of deep neural networks and ML to optimize accuracy. On any given day, our platform captures and analyses over 2 million interactions and 1 billion words, whether that be from calls, chats, emails, or social media for our customers. To make sense of all that information, both in real-time and post-interaction, we leverage ML and conversational AI to categorize and tag each conversation— as well as measure acoustics including tempo, stress, agitation, silence and more—to truly understand overall conversation context. Using that data, we’re then able to generate uniquely informed insights that our customers can put into action, based on their business’s objectives.

What is the current technology driving Data Analytics for Sales and Customer Service?

In today’s increasingly data-driven world, the competitive advantage for Sales and customer service relies heavily on an organization’s ability to personalize their offerings and analyse customer trends and usage patterns. Customization optimizes loyalty and encourages new business—which is why these departments are turning to Big Data. However, not only does this require capturing and storing mass amounts of information—but also being able to quickly turn that information into action, which wouldn’t be possible without Automation and Machine Learning. These aren’t just industry buzzwords anymore, but rather necessary elements in ensuring customer satisfaction and reducing churn. By automating the engagement analysis process and integrating a technology that learns over time, Marketing and Sales teams alike can achieve a holistic view of the customer’s perception of whichever brand, product, campaign or service they choose.

Read the rest of the interview with Jeff here.