To make digital transformation effective it needs to focus on the customer, each component of the transformation process needs to be aligned and employees must work together toward one common goal. While undergoing this process, it’s crucial to get a sense of the data each component brings in – this is where speech analytics can play a major part.
Jeff Gallino, CTO and Founder of CallMiner, discusses how speech analytics in the call center fits into the customer service ecosystem, and how it can provide an insight into all of the conversations going through the system.
Digital Journal: How important is customer service to businesses?
Jeff Gallino: Customer service should be at the heart of any organization. Its importance cannot be overstated and getting customer service right is a journey that starts right in the call center – making call center agents a very important piece in the puzzle of success, especially as customer churn continues to rise year after year.
DJ: How is digital technology altering the customer experience?
Gallino: AI-powered speech analytics is changing the game for agents and customers alike through automation. It allows for every voice of the customer to be heard – and in turn, these voices give invaluable insights in real-time to power every department of an organization, from sales and marketing, to product development and IT. The tech is a direct line to a wealth of knowledge that can guide an entire business strategy going forward, speeding time to insights to inform and drastically improve customer service performance. And on top of that, it equips agents with every tool needed to not only help customers quickly and effectively, but also to self-assess and improve. This is crucial because as digital self-service continues to rise, agent skills will still be needed.
DJ: What types of metrics can companies collect about customers?
Gallino: By implementing speech analytics, companies can move beyond collecting only spot-checked information based on a small sample of calls. Instead, valuable metrics that profoundly improve customer experience, like NPS and VOC, while positively impacting call center agent performance are provided to give businesses a cutting edge like never before.
For example, speech analytics can provide a clear view into what a call was about and why the customer was calling, how the customer felt during the call and their overall sentiment about services or products. It provides information on the context of each call to explain why they each vary in length so that companies can improve processes. And when calls contain extended periods of silence, it has the power to find out exactly what’s causing it.
DJ: How important is speech analytics?
Gallino: Speech analytics is an absolute key ingredient to a company’s recipe for customer service success. Without it, businesses are leaving their call recordings in the dark and in turn, are throwing away extremely useful insight that could improve customer retention, gain new business, create better awareness throughout the entire enterprise, develop better agents and so much more. It could very well be the difference between one brand and its competitor – helping organizations prevent the very high level of customer churn we are seeing today. Speech analytics doesn’t just transform customer service and CX – it impacts the bottom line and the entire business.
DJ: What types of information can be drawn out from speech analytics?
Gallino: Aside from collecting and analyzing crucial information about customers as mentioned in question #3, the tech takes it a step further with its ability to provide real-time feedback in order to arm agents with the guidance and insight they need to appropriately respond to all sorts of unique situations. For example, the tech can alert agents if a customer is open to an up-sell, or on the other hand, if they are considering a competitor – and how to respond to each scenario.
DJ: How good is current speech analytics software?
Gallino: Speech analytics software has come a long way since it began in this industry. One major advancement is the maturation of AI and how it can now understand, process, analyze and deliver contextual knowledge. This type of granular AI is getting humans out of the equation – but not out of the workplace! Instead, it’s transforming the role of analyst to advocate by automating mundane analysis tasks so that human employees can focus on what they want to do and what they are good at – making strategic, informed decisions and improving business.
A few other advancements that are pushing along the state of speech analytics is centered around emotional intelligence and speed to insight. The tech is now able to perform sentiment analysis around rate of speech, the amount of stress in speech, etc. in real-time so that agents can tailor their approach to every engagement immediately.
DJ: How is speech analytics software likely to develop?
Gallino: As the technology continues to advance, we can expect to see less human-curated AI (AI and machine learning that’s trained by humans) and more self-learning systems so that agents, analysts and other key members of the customer service sector can focus on solving the issues that only humans can tend to – and can entirely leave the tedious, monotonous tasks to automation.
We can also expect to see the emotional intelligence features of speech analytics tech become more powerful. For example, potential use cases could be in spotting fraud attempts in insurance call centers and detecting suicidal customers in debt/collections call centers – both of which are in the process of being researched and rolled out.
DJ: To what extent is machine learning assisting with this development?
Gallino: Many speech analytics solutions use both AI and machine learning to capture, transcribe and reveal insight from customer interactions. For example, machine learning helps create successful models for distinguishing sales calls from service calls, as well as the agent half of a transcript from the customer half.
Machine learning also plays a crucial role in the categorization of calls by allowing speech analytics tech to automatically tag interactions that contain certain language patterns, keywords, phrases or other characteristics – which allows employees to find, count and trend calls that contain these characteristics.
