4 Types of Analytics to Boost Engagement Center Performance
December 1st, 2016
With the rise of social media and mobile channels, our attention spans are shorter than ever, and the business competition for customer attention is fierce. For many brands, the plan is to create the experiences customers demand. But how can they meet these demands? By embracing the power of data analytics, companies can use their engagement centers to actively listen to their customers and optimize accordingly.
Engagement centers can capture real-time and historical data about customers: identity mapping, time and duration of calls, context of contacts, etc. This data capture drives active listening (i.e. analytics) so companies can carry out meaningful conversations with customers about how to improve their overall experience. Analytic solutions identify “journey gaps,” opportunities to improve engagement center service strategies, technologies and agent performance.
Here are four types of engagement center analytics that optimize performance:
Omni-channel analytics identify patterns in a customer’s behavior so companies can cater their support efforts in ways that drive sales and customer satisfaction. With customers that are increasingly multi-modal, omni-channel capabilities are absolutely critical.
For example, in today’s market it is not uncommon for a customer to call a company for support while browsing its website. A customer that is browsing an airline’s website to see if she can alter her flight itinerary might also be calling into the service desk. The context of her call would typically be lost between channels, however, in an omni-channel service environment, analytics inform the Interactive Voice Response system (IVR) that the caller is also searching the website for information on “ticket changes.” The IVR can respond with a prompt for ticket changes.
Customers often opt to speak to a live agent if they find an IVR system difficult to use. Using IVR data analytics, companies can optimize the IVR flows, identify the bottlenecks and determine areas of improvement to reduce customer effort and improve overall experience. IVR data analysis provides valuable insights, like:
- What percentage of customers opted out of the IVR call flow by hitting zero to talk to an agent?
- At which prompt(s) did most customers opt out of the IVR to talk to an agent?
- Why were certain calls transferred between departments?
- How many customers who stated specific reasons for calling were misrouted to the wrong queue, and subsequently transferred?
- How many calls were handled by the IVR itself without requiring agent assistance?
- Which types of calls were handled by the IVR without the customer having to speak to an agent? And what was the quality of those IVR only calls when compared to agent handled calls?
These insights help businesses improve IVR efficiency and measure metrics like IVR containment, First Call Resolution, agent Transfer Analysis and drop offs. Even modest improvements in these areas will dramatically increase efficiency and lower cost per resolution. Agents will get fewer calls that could have been addressed in the IVR.
Performance analytics guide the efficiency of service teams with insights that are timely and valid. Modern performance management platforms consolidate and analyze large quantities of data across multiple systems to create more reliable metrics that identify real improvement opportunities.
With real-time and historical data views, engagement center managers get detailed insight into channel efficiency using true performance metrics, like First Contact Resolution and sales conversions. Alerts and workflows encourage faster action, unified case treatment and wider adoption of best practices across departments. Managers can monitor the productivity of individual agents, agent teams and customer engagement initiatives so they can manage resources and handle interactions more effectively.
Real-time capabilities share complete customer information in a timely manner, and personalize it in the appropriate format for the agent involved. This context is invaluable in helping agents become proactive problem solvers on behalf of their customers. If the agents need assistance, they receive guidance on how to handle issues as they happen, a much more efficient strategy than sending calls down an escalation path.
With this form of analytics, engagement centers use pattern recognition to predict future behaviors of individual customers and customer segments. From detecting fraud to improving marketing campaigns, organizations are using predictive analytics to be proactive in their treatment of customers and to maintain a competitive advantage.
Predictive analytics also releases businesses from leaning on unreliable surveys. If a customer has a series of frustrating service experiences, predictive analytics can perform sentiment analysis to determine the probability of the customer absconding to a competitor or transitioning into a state of inactivity. If the likelihood is high, calls from this particular customer can be given high priority so they’ll be directed to better agents. Predictive analytics can also provide sales agents the context they need to be more efficient and productive salesmen.
Engagement center analytics can help businesses nurture a symbiotic relationship with their customers, helping them beat out competitors and cement their position in the marketplace. When companies use analytical insights to pursue customers, it shapes an effortless and personalized customer journey. Perceptive of these efforts, customers will reward brands with a longer lifetime value and will promote their brands to friends, family and social media followers.
The Aceyus blog is co-authored by: Ben Vesta, Vice President of Product Development and Kortney Parkman, Marketing Specialist.