Real-time and predictive analytics can be a powerful tool in delivering exceptional customer experience. By making use of these types of analytics, contact centre agents are able to smooth out any pain points of the customer journey while simultaneously providing an empathetic experience and reducing wait time.
In this month’s edition of the CX Today round table, we welcome:
- D. Daniel Ziv, VP Speech and Text Analytics – Global Product Strategy, Verint
- Sharon Einstein, Vice President and General Manager – Customer Engagement Analytics, NICE Nexidia
- Esteban Kolsky, Chief Evangelist, CX, SAP
- Martin Taylor, Deputy CEO and Co-Founder, Content Guru
- Dawn Herbert, Solutions Consultant, Odigo CX Solutions
- Leslie Blanke, VP of Strategy, UJET
- Frank Sherlock, VP International, CallMiner
Experts from seven companies share their thoughts on whether real-time and predictive analytics are a valuable addition to contact centres and what human agents can learn from these analytics. Here's what they had to say.
How can real-time and predictive analytics impact the customer journey?
Ziv: “The role of real-time and predictive analytics is to anticipate and provide the right answers to customers. This can be done via self-service journeys and in the agent-assisted journey, but both need to be informed by data via recent customer interactions.
D. Daniel Ziv
“For many years, self-service had a bad reputation; it was a gatekeeper that the customer had to get past. Today, real-time and predictive analytics infused with IVRs and IVAs are reinventing self-serve to be more relevant, intuitive and effective.
“With agent-assisted journeys, the goal is to leverage real-time analytics to anticipate issues and concerns and to help the agent better serve the customer. Verint’s approach leverages and infuses triggers from three unique sources to guide employees in real time: Linguistic — recognizing specific words, or a positive or negative sentiment, Acoustic — recognizing sound triggers, such as long silences and interruptions, and Application — adding critical context and recognizing actions from the employee’s desktop, such as adherence to processes.
"Employees are notified of recommended actions to take in a unified Work Assist application, so they can immediately guide the call to a better outcome. Predictive analytics helps build these models to be more effective over time.”
Einstein: “AI predictive analytics is especially useful when mapping customer journeys because the AI finds patterns and begins to predict not only where a specific journey can be improved, but also offer prescriptive next-best-action for mitigating negative journeys or further improving on existing customer experiences. In real time, AI powered analysis offers in the moment guidance to agents actively engaged with customers to improve customer satisfaction and issue resolution, to mitigate regulatory or churn risks, to assist with script and behavioral adherence, and more.”
Kolsky: “Customers interact with organizations in many different ways, and rarely are two interactions the exact same. Using analytics, organizations can spot those changes in real time, and adapt to the customer’s desired interaction instantly. For example, a user of a mobile banking app may use it one day to simply check a balance, but the next day review all recent transactions and maybe dispute a charge.
"So the bank has to look at the two different interactions, how the customer took their journey in two different ways, and then apply analytics to understand the amount of effort it took the deliver both journeys, how many systems were involved, and ways to optimize the interactions. Using analytics, whether the customer goes one way or the other, both directions are optimized and require less time and fewer resources. Analytics is how the company understands the customer journey to deliver an optimized process and have the right resources in place.”
Martin Taylor
Taylor: “Implementing real-time and predictive analytics brings many benefits to the customer journey. Powerful predictive analytics enable organizations to accurately forecast customers’ needs even, in some cases before those customers know themselves. This empowers contact centers to utilize proactive communications based on these predictions, and to personalize and automate inbound interactions based on customer preferences. The result is that customers are kept happy, thereby minimizing the amount of unplanned inbound contact and improving customer loyalty.
“Predictive analytics can also help organizations with workforce management (WFM), by anticipating when to expect lower or higher levels of customer contact and automatically adjust employee scheduling accordingly. McKinsey estimates that companies have already applied this technology to cut employee costs by up to $5 million per organization.
“Real-time analytics provide live feedback to agents and supervisors on the success of calls. These can incorporate NLP (Natural Language Processing) to parse meaning from speech and use key words to suggest useful information to customer service agents. This type of technology also monitors stress levels using sentiment analysis, and examines script adherence to ensure compliance, all while a call is in progress.”
Herbert: “Real-time predictive analytics is revolutionising customer journeys. By aggregating and analysing customer data, predictive analytics yields valuable predictions about future customer behaviour. These insights enable customer service agents to proactively anticipate customers’ needs, and this ability translates into real benefits for customers, agents and organisations.
“Consider the case of a customer who lacks the technical skills to articulate their issue. The potential for frustration and poor experience is high. Predictive analytics can help an agent discern the customer’s needs and resolve the issue based on similar issues that this customer and other customers have experienced, transforming a difficult situation into a smooth, value-filled customer journey.
“The ability to anticipate customers’ needs transforms journeys into highly contextual, personalised experiences that drive brand loyalty, increase customer satisfaction and achieve rewarding experiences. In a customer journey, predictive analytics lights the path.”
Blanke: "Because the customer journey has historically been pre-defined with a one-size-fits-all approach and with a limited set of inputs, the experience is often inefficient and robotic with customers frequently repeating themselves and leaving the conversation frustrated. Real-time and predictive analytics provide more subtle insights to dynamically influence and intelligently automate and guide the journey - resulting in a more efficient and personalized experience.
"For one, the customer journey lens widens and we pivot to providing more proactive support rather than reactive by looking at real-time data such as web and app activity to determine the ideal time to prompt engagement. Combined with historical and user profile data, we can begin to predict intent to provide self-service capabilities, intelligently route, and dynamically tailor our message with more relevant and personalized content.
"Intent and sentiment monitoring provide live feedback during the conversation to help the agent quickly identify the issue, equip them with answers, and recognize when the conversation is going off-track in order to escalate before issues arise. Real-time and predictive analytics on their own are not enough though, you need automation that is keyed off the data to take action in real-time and orchestrate a more personalized and more contextual customer journey."
Sherlock: "Today’s customers engage with organisations across a range of communication channels, from social media and email to chat and voice. It’s also no surprise that customers no longer take a linear path to solve their product or service issues. They engage on multiple devices and across channels, while pausing and resuming their journey along the way.
"For businesses, it’s critical to provide smooth, integrated customer experiences across every channel. But understanding customer needs, even as they jump between channels, is easier said than done.
"Predictive analytics enables brands to anticipate the next channel a customer might move to based on past history, making it possible to route customers to the right customer service agent. For example, if a customer often moves from live chat to the phone, organisations can ensure they provide the proper department phone number in the chat when it’s clear the customer is ready to move channels. Further, real-time analytics can help agents improve outcomes during interactions, regardless of channel, to enhance the customer journey, such as identifying cross- or up-sell opportunities while during the conversation.
"Solutions that combine the power of predictive and real-time analytics to make it possible to weave together customer interactions across every touchpoint during a journey."
Will real-time and predictive analytics end up over-riding live agent empathy or compliment it?
Ziv: "There is something incredibly and inherently valuable about human-to-human interaction. Humans are quite good at being empathetic – and today something that exposes machine limitations and weaknesses. To this end, I don’t believe that we’ll see real-time and predictive analytics trumping live agent empathy anytime soon. But machines can support a key role in enabling our super-human empathy trait to shine through.
