Jeff oversees research, language development, and future product direction. Gallino was President and CEO during CallMiner’s first five years. Jeff has more than 25 years of experience delivering complex software and hardware solutions to enterprise and government customers. Prior to founding CallMiner, Jeff worked at ThinkEngine Networks, Grant Thornton Consulting, and served for 11 years in the US Air Force.
CallMiner’s market leading cloud-based speech analytics solution automatically analyzes contacts across all communication channels: calls, chat, email, and social. They offer real-time monitoring and post-call analytics, delivering actionable insights to contact center staff, business analysts, and executives.
Tell us about yourself and your journey to start CallMiner.
I founded CallMiner in 2002 after realizing no one was leveraging speech recognition and analytics together to analyze large call sets. I had experience with speech recognition from my job at the time and put the analytics piece together after attending a financial analyst conference and listening to analysts bemoan the time it took to listen and classify all the quarterly earnings calls. There were no vendor solutions for both transcribing and automating the review process, and I realized then what a competitive advantage the two together could provide for companies that had large call volumes to review.
What were the traditional methods for call centers to get feedback about their customer interactions? How does it compare against CallMiner technology?
Traditional methods have involved a random selection of a portion of calls from each agent for the month, typically only 1-2% of their total call volume.
Aside from being a labor intensive manual process, this (traditional) approach to measuring an agent’s performance produces a completely inaccurate reflection of the agent’s performance because it’s such a small sample set. Inaccurate feedback leads to lack of improvement in performance and customer experience. This also drives agent attrition, one of the largest challenges contact centers face.
CallMiner Eureka automates the process and reviews 100% of calls to provide an unbiased and comprehensive view of agent activity, which means turnaround time to performance improvements is substantially faster. In addition, there is a treasure-trove of other business intelligence and customer insights that can be extracted from customer conversations that typically go untapped through the existing agent quality monitoring processes.
What is Automated Call Scoring and what kind of data can one hope to capture from customer support calls, texts, emails and social?
Automated call scoring is an AI-fueled score based on a combination of customizable call characteristics such as:
- Expressions of dissatisfaction and empathy
- Escalations and sales objection handling
- Metrics such as silence and acoustic agitation
- Call attributes such as whether a sale occurred or not
Scoring can be customized and leveraged for any number of business objectives including agent quality or performance, customer satisfaction, compliance risk, customer churn risk, and fraud prediction.
What are the challenges of speech to text, and how do you try to overcome these?
Speech to text is an evolving technology that depends on audio quality for accurate transcription. Using a telephony infrastructure and/or recording technology that allows access to high-quality audio, ideally speaker separated, will produce the best results. However, the key to gaining insights from speech analytics is a solution that understands the audio transcription at the contextual level. Categories and semantic building blocks within Eureka can recognize word phrases even with transcription inaccuracies to provide meaningful data without a transcript that is 100% accurate. Scale is another challenge. Processing a small number of calls is easy with modern tools, but we really had to solve some interesting challenges to scale to simultaneously process 100,000+ calls in real time. Before any really interesting ROIs can be tackled, you have to have this kind of scale.
How can call centers leverage CallMiner’s technology to improve the productivity of their agents?
Automated scoring of 100% of interactions can help agents understand where they need improvements in customer handling and where they may be wasting valuable time. Objective scoring on every call provides standardized, data-driven coaching for targeted review sessions with supervisors and opportunities for self-improvement in between coaching sessions by having direct access to daily feedback on performance. Specifically, analytics can identify abnormal instances of silence that represent optimizations for call handling. Real-time alerting can guide agents to next best action in context to the conversation, driving to rapid resolution.
What kind of performance metrics does Eureka Coach provide to establish how effective customer support has been? How does the technology calculate emotion?
Coach provides agents and supervisors access to automated scoring on any number of performance attributes for 100% of customer interactions to understand performance trends. Automated scoring in CallMiner Eureka can be customized to any number of key indicators that are most important to the company and the specific agent group.
Top level metrics could include an overall agent performance score, customer satisfaction, sales effectiveness, compliance and call handling efficiency. Elements of these scores may measure presence of language such as empathy when dissatisfaction is expressed or understandability issues, combined with acoustic metrics such as acoustic emotion or agitation, call duration, percent silence, and other metadata values such as sales closed or not, and dollar value of sale.
Tell us about the real-time redaction feature and how CallMiner ensures the privacy of customers.
Real-time redaction grabs audio and call metadata while the call is occurring, recognizes and targets PCI data and other sensitive data such as social security, account, and credit card numbers within call audio and transcription and removes the data for customer security. Redacted audio and transcripts can be safely held to meet compliance and regulatory requirements without the threat of internal or external sensitive data exposure.
How does Eureka Alert help call centers take immediate action in real time?
Eureka Alert provides real-time alerting to agents, supervisors, or managers to allow for action to be taken in context with what is occurring on the call. Alerts can trigger in response to lack of language – such as a reminder to an agent to read a specific disclosure statement – or in response to something the customer states – such as a mention of a competitor promotion to guide the agent to offer a competing promotion. Alerts can also trigger messaging such as emails to senior management for sensitive issues, or integrate into a supervisor’s desktop monitoring application when a call is escalating and may require intervention.
Providing real-time reminders and suggestions gives agents the opportunity to quickly address sales objections, cancellation threats, competitor mentions, and special offers based on customer buying or churn signals. By offering the right product or solution at the right time based on what the customer is sharing with the agent, companies can reduce customer attrition and improve upsell rates as well.
Which industries have exhibited higher traction? To what extent can one customize the solutions to suit industry and business goals?
We have customers that span the range of industries as our solution can be customized to suit any industry and goals.
We have seen financial services lead the way in adoption of speech and engagement analytics but have also seen recent uptick in more healthcare, travel and tourism, insurance, and retail companies adopting speech analytics for their customer service and claims departments.
Tell us about CallMiner’s technology offering for the healthcare industry? What is the future impact that you are moving toward?
Healthcare is moving toward a strategic focus on patient experience both within the medical facility and throughout the billing process. Healthcare organizations have realized that a drop in satisfaction in either process can taint the whole experience so they must understand the full patient experience even after care may have ended. By leveraging speech analytics, healthcare payers and providers can understand where patients are being unnecessarily routed or put on hold, what’s driving the majority of their calls, and how to better staff their contact centers. Additional impacts can include patient safety – ensuring proper triaging or surgery preparation procedures for healthcare providers – or customer retention for healthcare payers during open enrollment.
What can you share with us about future plans?
CallMiner is focused on further developing our AI-driven functionality with enhanced discovery and predictive modeling tools. By using a combination of AI, NLP, and Machine Learning, CallMiner aims to provide more automation in the analytical process such as automatically correlating events on interactions to outcomes, and simplifying predictive model development and automated interaction classifications.
Thank you, Jeff! That was fun and hope to see you back on AiThority soon.