Though voice conversations and customer service calls may seem like things of the past, speech technology and data analytics continue to usher such interactions quickly into the future—no flux capacitor necessary.
While many believe that online and social avenues are quickly becoming the customers’ choice contact channels, one recent Gartner study proves that 90 percent of all customer conversations still take place via phone despite the increasing popularity of digital service opportunities. Thus, the need for speech analytics remains and, as technology evolves, such tools have the potential to revolutionize the customer experience, allowing companies to dive deeper into this granular insight more than ever.
Speech technology has added an entirely new dimension to the analytics spectrum. While companies once relied upon transactional data for analytics and strategy development, speech analytics allows businesses to tap into behavioral data, as the conversations between the company and the customer represent the largest, non-monetized data asset of any company.
More and more companies are beginning to adopt the full customer experience perspective, in fact, which ties quality management, analytics, and voice of the customer together in ways that allow leaders to determine how agents are affecting customers, and vice versa. By gaining increased insight into what’s driving customer satisfaction, businesses can then improve front-end processes in an effort to achieve long-term success. The cloud ushered in an era of innovation that allows companies to experiment with analytics within different channels without breaking their budget. Lacking excessive hardware or maintenance, the cloud’s flexibility makes speech technology more affordable across the board, thereby enabling agility—the key to identifying the best analytics approach for any given brand.
While quality assurance and compliance often serve as the launching point for most speech analytics strategies, as such initiatives allow companies to organize and analyze customer feedback, there are numerous incremental business cases that have emerged alongside these most basic tactics:
- Escalation management -- Leaders can gain visibility into escalated calls, thereby revealing issues with both people and processes that need to be addressed. By analyzing such calls, companies can quickly discover what triggered the call and learn how the agent responded. Contact centers can also catch instances where agents may need training.
- First-contact resolution -- Repeat calls from the same customer can be costly, and often reflect negative customer experience and sentiment. Speech analytics affords companies the opportunity to identify examples of customer dissatisfaction, establish tell-tale behavioral patterns, determine why such issues aren’t being addressed initially, and train employees accordingly so they may respond effectively and efficiently if the need arises.
- Customer retention -- By using speech analytics to understand and establish language patterns, companies can easily identify at-risk customers via keyword terms. Leaders can then train agents to detect and respond to said terms in an effort to curb dissatisfaction and improve performance as they work to sustain loyalty, trust, and retention.
Keyword spotting, in particular, helps marketers identify the phrases and words associated with calls that are more likely to convert. By monitoring and identifying such terms, agents can then use said knowledge to reflect such trends in conversation and positively impact future sales and support calls. Though seemingly simple, this technique can empower agents to respond appropriately in the moment as they come to understand customer behavioral patterns. But, as Art Schoeller, vice president and principal analyst at Forrester Research, emphasizes, this tactic requires the expertise of an analyst who truly understands the speech tools at hand. False positives and other issues may arise when interpreting the results, leading to mistrust. Therefore, companies must be sure to employ someone who can dive deep and effectively decipher behavioral trends with the precision this skill requires.
Ultimately, companies must move away from basic quality monitoring, expanding their scope to include developing the complete picture of their inbound calls in order to make better marketing decisions, improving ROI, and enhancing the customer experience. Because inbound calls typically convert at much higher rates, marketers need the data and intelligence necessary to drive more of these calls to their business. However, many companies aren’t doing enough when it comes to integrating the necessary speech technologies. Most stop at call monitoring, which only provides minimal value, as it doesn’t make business processes smarter or more efficient. Technology must be used to help businesses analyze and act upon customer interactions.
Real-time analytics feeds into this need by allowing leaders to make decisions quickly, often immediately. Using real-time conversational data in combination with real-time decisioning and algorithms will enable any given brand’s speech analytics strategy to operate at peak performance levels. Real-time analytics allows agents to detect behaviors that may impact customer relations and the bottom line, such as churn. Data will dictate how to respond, as the workflow to follow remains critical. Should the agent offer the customer something special, or simply apologize? Should they escalate the case, keep troubleshooting, or follow up on an alternative channel? Often times, agents need only have the right article from the knowledge base appear on their screen. Thus, by blending behavioral science (the interaction) and data science (customer history), agents can react with speed and agility as they work to curb tensions in the moment, instead of waiting until after the call happens to develop and implement best practices.
However, as Don Peppers, founding partner of Peppers & Rogers Group, highlights, companies must recognize and understand the difference between speech and voice analytics. While speech analytics mostly relies upon speech-to-text technology that then uses text analytics to parse the words used and sentence structure in order to detect complaints, moods, and sentiment, voice analytics focuses on tone. When communicating with others, much can be interpreted from non-verbal signaling. Thus, companies must be sure to embrace the difference in order to add an even deeper layer of granularity to their strategy.
“Our voices betray our emotions in ways that simple words don’t always do,” Peppers says. “If I say ‘I’m just wild about this product’ in two different tones of voice, the same words could mean radically different things. And while a good speech analytics program will pick up these different meanings from the context of other words surrounding them, a voice analytics program will be more likely to detect the real meaning from the tone of voice itself.”
In a sense, voice data appears three-dimensional as compared to survey and transactional data’s two-dimensional nature, for voice remains multi-faceted and nuanced. However, many companies still hesitate to embrace this basic form of communication, for its breadth has always seemed daunting.
Changing the mindset from recordings being a necessary evil, to being the richest data asset in existence is core. The industry has been talking about using the ‘voice of the customer’ for more than a decade, yet it hasn’t. Starting with accurate, complete, and timely data, however, will pave the way for success. While companies always boast about their voice of the customer strategies, few truly put the customers’ actual voice to good use. Brands must return to the contact center’s roots and reconnect with the most basic of customer service interactions so they may finally reintroduce the ‘voice’ to their VOC initiatives.
Where We’re Going, We Don’t Need Roads
Moving forward, the speech analytics space will likely grow increasingly simplified. Overall, companies will need to focus on simplifying the technology and making the applications more intuitive in order to get more analyzed data into more people’s hands so they can make better, faster decisions about customers and service—not just in the contact center, but across the organization. However, adoption will continue to lag if only large organizations can afford the tools and staff needed to maintain this technology. Thus, innovative solutions that allow businesses of all sizes to analyze more interactions and share insight with more people in an intuitive way are the future.
Speech analytics will also need to move beyond siloed channels and trendspotting toward outcome-based analysis that informs improvements around best practices, behavior modeling, and customer preference. Analytics teams will need to become cross-functional as such capabilities much reach outside the contact center to enhance the entire business. Ultimately, analytics is more than speech, for today’s tools can analyze far more than just the interaction at hand. Advanced tools also enable companies to assess how agents use said tools during those interactions, allowing leaders to pinpoint problems and improve agent performance in an effort to boost customer satisfaction and reduce handle times. By keeping the customer at the heart of all initiatives, and embracing the available abundance of insight, companies can accomplish anything.