Exceptional customer experiences (CXs) are critical to short- and long-term business success. Because of this, it’s instrumental to have a detailed understanding of what occurs during each customer interaction and along the customer journey. In fact, these insights have shifted from nice-to-have to need-to-have.
Now, more than ever, service-centric businesses must prioritize integrated quality monitoring (QM) and analytics applications within their contact center: the mecca of customer interactions and the foundation for raising the quality of CXs.
Here’s the gist. With speech and text analytics applications that convert phone conversations and written communications (i.e., emails, free-form feedback surveys, social media interactions) into transcripts, insights, and actions, contact centers can better identify coaching and training opportunities for agents.
These applications can equally discover where and why competitors are winning over them. Plus, they can learn which products and services are meeting expectations and which are not.
Are agents following protocol, or do we have training gaps that we need to address? Is a new product generating a lot of inbound calls from customers? Are negative words or phrases regularly coming up during conversations, and if so, why?
With combined QM and analytics capabilities, organizations can easily answer these questions and more. They provide the full view of customer interactions, helping teams make data-backed decisions to improve experiences, helping to drive revenue and scale the business.
It is not just the business and customers who benefit from these applications. Contact center agents themselves will be able to pinpoint areas of improvement that, when acted upon, help to shape and advance their customer relationships and their careers.
Robust QM and analytics applications, working together, create a win-win-win scenario for leaders...
These tools are not meant to solely mine for interactions that need improvement; they are used to look for opportunities to further help and support teams.
For example, through QM, a contact center manager might notice an agent has had one too many difficult interactions in a row with customers and can step in to tell them to take a mental health break. That way, the agent does not continue taking calls with other customers when they are already worn out or frustrated leading to second-rate interactions.
Robust QM and analytics applications, working together, create a win-win-win scenario for leaders looking to increase competitiveness, keep customers happy, and ensure the workforce is fulfilled and engaged.
So why, then, are organizations still not prioritizing these applications within their contact centers? Why does the average organization evaluate only 2% of all customer interactions, and how can they ensure those 2% of interactions can represent the other 98%?
Perceived Barriers to Adoption
The simple and honest answer is analytics can be intimidating. There is so much data to gather, quality check, and analyze, plus disparate systems and data silos running wild in most organizations. Research from 451 Research from 451 Research reported that 39% of highly data-driven organizations have more than 50 data silos.
Deploying contact center analytics solutions, figuring out what should be tracked, how to track it, what the reports will look like, and who will be contributing to them is understandably overwhelming and cumbersome. The perception alone of analytics being difficult to deploy and execute can halt progress altogether.
The cost of QM and analytics applications also can be a barrier for contact center leaders whose budgets are already often squeezed whether in times of economic prosperity or uncertainty.
Beyond the technology itself, to effectively deploy a QM and analytics program, organizations have historically needed experienced analysts to oversee the process. Depending on the size of the business and the number of contact centers, the number of analysts needed can be significant.
However, leaders need to look at the payoff rather than initial investment. When properly implemented and supported by talent with the right skill sets, analytics solutions can pay for themselves in three-to-six months via operational savings or revenue increases found through root-cause analysis and improvements.
Equally important, today’s analytics solutions should be intuitive enough that the ask for analyst headcount isn’t so great.
Leaders are often unsure of where to start, how they can prove value in the near term, and if they can guarantee ROI over the long haul with new, robust solutions. These doubts result in either inaction or taking on too much too soon, and ultimately fall short of any meaningful results—but it does not have to be that way.
The ‘Three Ps’ of Analytics-Fueled QM
There is good news: leaders looking to accurately and cost effectively analyze customer interactions and agent activity can do so with the support of modern applications. They address perceived barriers, capture data across the enterprise, pull out predictive and prescriptive insights, and help tell the full story of what customers are experiencing—all 100% of them.
...we’re all familiar with the “this call will be monitored for quality purposes” prompt—but it is time to make that phrase truly count.
When organizations embrace technology that can enhance and introduce new quality and analytics capabilities in the contact center, everyone benefits. Here are three areas where these solutions make a substantial impact:
1. Protocols. Analyzing interactions between customers and agents allows contact center managers and other leaders to identify areas of improvement across the enterprise, especially as it relates to whether protocols are being accurately followed or not. This improves both the customer experience and helps to reduce stressful situations for agents.
The AAA Northeast team, for example, used analytics to discover that the calls members made from highways were receiving the lowest quality score of all calls and, more importantly, took substantially more time for agents to resolve.
Given these calls are typically tied to safety and emergency situations, AAA leaders quickly set out to resolve this issue.
Through a combination of speech and desktop analytics, they found agents were inconsistent in how they interacted with members stranded on highways and often asked irrelevant questions about members’ locations because they didn’t know how to use AAA’s GPS tool.
Leaders were then able to revise the required call flow and implemented new training for the GPS tool, which led to a decrease in average call time by 14 seconds for all calls and by 53 seconds for highway calls.
2. Phrases. Contact centers should be monitoring and analyzing 100% of calls, interactions, and other desktop activity, rather than relying on one-off monitoring and a small subset of interactions to try to piece together themes.
With the right QM and analytics applications in place, they can better understand when and why agents and customers use critical words or phrases and learn about other related phrases they can track to optimize their analytics.
A primary example of this is the classic “I want to cancel my service” phrase. Contact centers should be monitoring for interactions like this as well as other related phrases, including “can I speak to a manager”, “I want to opt-out”, or “I want to downgrade my service.”
These are all trigger phrases that allow leaders to dive deeper and understand the context for why a customer’s experience was subpar. They can then course correct as needed, whether by agent coaching or evaluating the causes for the customer reach-out in the first place.
3. Predictive evaluations. Monitoring for key phrases ties directly into predictive evaluations, which essentially allows organizations to select interactions with low predictive scores, helping them find the most impactful communications to analyze.
A solution with artificial intelligence (AI) capabilities, for example, can learn from human-completed evaluations as well as speech analytics data to predict evaluation scores on 100% of all customer interactions. This helps organizations anticipate customer needs, resolve issues faster, and deliver more personalized interactions.
Most organizations today have some level of QM—we’re all familiar with the “this call will be monitored for quality purposes” prompt—but it is time to make that phrase truly count.
Contact centers must uplevel those capabilities with robust analytics applications, gain deeper contextual data and insights, and get the most out of each customer interaction.