As one of my colleagues pointed out in her recent blog, “140 years later, what do we know about customer service?”, the invention of the telephone in 1876 — and the subsequent invention of the switchboard in 1894 – ushered in the era of modern telecommunications and provided consumers with a way to more easily voice their concerns to companies with whom they do business. Ever since, companies have been looking for better ways to handle customer care.
One of the biggest challenges for communications service providers (CSPs) has been the lack of specific information about the customer who’s calling with an issue; their service, their location, their equipment. However, given recent advances, this no longer needs to be a challenge.
Knowing the unknown
Thanks to a variety of different analytics applications, when customers call the help desk of their local CSP, customer service representatives (CSRs) are now able to access quite a bit of client-specific information, including the type of access point found in the home, the settings of that device, what network equipment they connect to for service and more. Using this information, CSRs can provide detailed instructions on how to resolve a customer’s issue.
To ensure that best practices are followed and that all agents have a consistent approach to problem resolution, CSRs typically use guided troubleshooting processes – sometimes called workflows – which provide step-by-step instructions.
Typically, the steps included in a workflow are fixed, with a pre-defined sequence based upon a series of educated guesses. In reality, the most appropriate actions will differ from call to call, depending on the customer context. Until recently, however, workflows were not able to adapt to changing contexts; it was seen as too much effort to have a workflow respond to various options.
Dynamic Intelligent Workflows: Selecting the next-best action for improved problem resolution
Using machine learning – which collects information on each successfully (and unsuccessfully) completed workflow – and adapting the sequence for every customer’s unique situation, Dynamic Intelligent Workflows can predict the optimal sequence of tasks that should be taken to resolve specific issues.
Algorithms developed by Nokia Bell Labs use all of the information available – workflow history, customer information and network status – to prescribe specific workflows to agents using a recommendation engine that selects the next-best action (NBA) that has the highest probability of resolving a customer issue in the shortest time.
Instead of the fixed sequence that characterized workflows in the past, Dynamic Intelligent Workflows start with a common set of introductory steps. Then, based on the available data – some of which is collected in near real time –quickly diverge into customized paths.
As a result, not only do all workflows get continuously optimized, but each individual workflow has the highest probability to resolve a customer issue in the shortest time. This enables faster response times, reduced support costs and a better customer experience. It also simplifies the workflow design since special cases based on context do not need to be hard coded into the workflows.
Don’t forget self-care
It’s also important to provide customer care using a variety of different channels, including self-service, email, live chat and more, which I encourage you to read more about in the blog: “Omni-channel is the channel”.
I mention this because one of the benefits of Dynamic Intelligent Workflows – which are available in Release 7.0 of the Nokia Service Management Platform (SMP) – is that they can be used in a number of different channels apart from agent-assisted care, including self-care and field technician applications. The Nokia SMP is the cornerstone of many successful omni-channel customer care solutions for home, mobile, small cell and enterprise devices and services across the globe.
Does your call center have challenges with static workflows that cannot adapt to personalized context? Are your workflow engineers using their domain expertise to create sequences to resolve service issues? Then talk to an expert within Nokia who has solved these issues for customers around the world.
Share your thoughts on this topic by replying below – or join the Twitter discussion with @nokianetworks using #CEM, #CSPCX, #analytics