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How Interaction Analytics Help Contact Centers Win


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Whether it’s baseball, football, or tennis, sports center around strategy. Those strategies are malleable and can be changed mid-game to adapt to new circumstances. Nowadays, through technology and other resources, management teams gather more information and data to edge out their competition. Operational analytics enables them to optimize training programs, and strategic analytics helps identify the best players. Having access to these analytics is no longer optional for sports teams — they’re necessary to become a champion.
Contact centers are similar to sports teams in terms of infrastructure. Without data, companies will struggle to keep pace with their consumers’ growing demands. Leveraging technology to gain data and insights through interaction analytics empowers contact centers to make data-driven adjustments on the fly. Similar to the movie Moneyball (more to come on this below) contact centers can also use interaction analytics to identify the best agents for their team and win the game of customer experience.
Interaction Analytics at a Scale
Contact centers process an incredible amount of information every day. Some have already tried capturing that data through tools like customer surveys but end up missing the real-time details present in conversations. How a customer feels at the moment describes much more than an after-service survey alone. Unfortunately, accessing these unstructured conversations and doing something useful with them has always been challenging — especially at scale.
Interaction analytics allow contact centers to dissect conversations in chat or live-person calls and derive meaningful insights that management can deploy immediately to improve the customer experience. Contact centers can mine the treasure trove of conversational data and leverage extracted information to better serve customers.
To accomplish this, interaction analytics systems gather conversations and related customer and interaction metadata. Natural language processing (NLP) parses dialogs and structures conversations by topics or other logical category groupings. Natural language understanding (NLU) augments this analysis by using a combination of rules and AI to derive attributes such as a customer’s emotional state or intent, an agent’s empathy, the level of effort expressed, and more. This analysis produces nuanced insights about the customer and the experience overall.
The game-changer is that contact centers can run interaction analytics at scale. Now, millions of audio and digital conversations can pass through an engine and deliver reliable insights, improve agent performance, and enhance the customer experience. With more finely tuned players and an improved understanding of the playing field, contact centers have a higher advantage of becoming champions of the customer experience game.
Interaction Analytics and the Customer Experience
2020 demonstrated how quickly the playing field could alter contact centers. COVID-19 initially led to a massive increase in call volume, which overwhelmed unprepared centers as they shifted to remote work models. Digital channels proliferated, with customers reaching out through email, social media, and chat. Without the right technology and business acumen to mine these new resources, contact centers could miss out on valuable insights present in these conversations.
Those insights set champions apart. Remember—sports teams not only want to win the game; they want to put on a show for fans. It’s the experience that makes the difference, and the same goes for contact centers. Interaction analytics deployed across every communication channel generates information companies can use to show their customers to drive tangible results.
For example, contact centers want to understand the reasons customers contact them, especially when there’s an unexpected spike or surge. Driver analysis can help management teams better understand emerging trends, patterns over time, and why customers choose a particular multichannel interaction over others. That information can guide improvements in their agents’ knowledge base, build better self-service resources (deflecting calls to lower-cost service channels), and optimize user experiences.
Issues that increase customer effort like long hold times, ineffective chatbot engagements, and unresolved issues, spoil the experience. Effort analysis can unpack why these challenges occur and assess other potential upstream process issues like unclear billing practices, ineffective self-service tools, or product and shipping defects that impact overall satisfaction. Smoothing or even eliminating friction points in the customer journey ultimately leads to long term loyalty and repeat business.
Interaction Analytics and Agent Performance
Interaction analytics help you assess your playing field, but also hold the key to strengthening your players. In the film Moneyball, Billy Beane and his team of statisticians deployed data to build the best team possible from limited resources. Their data-driven decision process redefined the game of baseball.
In a contact center, quality of service (QoS) is the backbone metric of team-building success. How well agents serve customers and contribute to the overall experience sets apart star players from the competition. While changes in the playing field affect some agents, yours can adapt and provide excellent customer service regardless of their environment.
The work-from-home shift spurred by the pandemic tested this theory, as monitoring and assessing QoS became more important than ever. Interaction analytics help construct a consistent QoS score across all service channels to find where agents struggle, how to change their customer approach, and improve their game overall. An analytics engine can identify technical and QoS challenges and measure customer satisfaction based on a change in customer experience.
An interaction analytics engine with world-class natural language understanding facilitates ad-hoc discovery, root cause analysis, trend analysis, script adherence, regulatory compliance, emotion analysis, sentiment analysis, intent detection, and effort evaluation. These types of analyses reveal the strongest agents and why they’re star performers, enabling team leaders to build and retain top-notch service teams.
Going forward, contact centers can count on customer circumstances changing frequently—and must adapt their strategies to succeed. Interaction analytics captures data at scale and delivers insights to build the best team and strategies for winning. Contact centers can use analytics to optimize their operations and create customer experiences that stand above the competition. Champions aren’t born; they’re made from data. Put interaction analytics to work and win.