Communications service providers (CSPs) have had varying degrees of success with their digital transformation efforts. And with the new digital transformations of many of their customers due the pandemic, operators’ own digital transformations are even more critical and they must find ways to overcome even greater obstacles and meet significantly increased demands.
In the past, the CEOs at many CSPs have tended to express their transformation goals with familiar directives such as “do more with less,” “reduce costs,” and “improve the customer experience.” The difficulty with following through on these pat marching orders is that while they sound simple enough, each requires a substantial and often complex behind-the-scenes effort.
That’s why digital transformation is a journey rather than a wholesale change that occurs at a moment in time. That journey consists of many steps, which will differ from company to company. The most important step, however, is the obvious yet typically overlooked first step: identifying the number one problem that the company’s top management cares about and then building a quick win that represents success.
Putting a unique stake in the ground
Taking that first step of identifying their transformational goals has proven to be a sticking point for many CSPs. While every CSP generally has an objective to “become a digital service provider (DSP),” each has different value propositions it wants to offer the market. That means that each CSP-to-DSP transformation journey will be different.
Because business goals are apt to vary widely from provider to provider, the data they’ll monetize and the type of analytics they’ll need to run are also different. Changes to their operations and processes will take different forms, depending on what they’re trying to achieve.
The DSP hopeful must look deep within itself and make some hard decisions about what it plans to bring to the table. Only after it figures this out, and defines it as explicitly as possible, will it be able to successfully transform its internal processes and operations - in part using analytics and automation - so that it’s able to profitably deliver its core value to the market.
Sample value propositions
Below are a few real-world examples of unique value propositions of different CSPs and how they’re impacting the digital transformation steps each operator takes, which data they monetize, and how they apply AI and analytics to that data.
"The best network"
This U.S. wireless operator is laser focused on having the highest-quality network, a goal that’s baked into its business mission and marketing campaigns. This operator believes that consistently having the highest-quality connections and the broadest coverage will continually attract and retain the most customers. With these goals in mind, the operator’s AI and analytics strategy begins with mining, analyzing, and automating the data that will allow it to consistently achieve the fewest dropped calls. Its transformation steps will heavily involve applying predictive analytics with direct linkage to the automation framework within network operations to head off issues off before they affect a customer’s experience (e.g., foresight).
Over the years, the perception of customer experiences has grown more complex than voice coverage maps and dropped calls. Experiences can be a cumulation of many factors - the response time of a smart phone application, the quality of a streaming video session, the number of times a game stops running, for example. As a result, the measure of “best” is changing for this operator and requires analytics and AI-assisted operations that can take on real-time service management and network optimization that accounts for high levels of variability.
"Good-enough reliability at the lowest price”
This U.S. mobile network operator seeks to compete as the lowest-cost wireless operator. The MNO needs to figure out operational processes that will keep customer interactions to a minimum, so it can keep its own costs down and continue to afford to sell connectivity at rock-bottom prices. It needs access to different data sets than the “best network” MNO - those that reveal opportunities for squeezing as much cost as possible out of its operations.
In the case of this “good-enough” approach, it will involve an understanding of what’s happening now (e.g., insight) and historically (e.g., hindsight) so that the operator can react in near-time to improve service levels. Descriptive analytics provide “good enough” insight into how elements of the network are performing or details into service availability and network response time. These are the basics required for delivering service assurance in any telco business. Key performance indicators (KPIs) such as those that track and illustrate network load/congestion issues, signal or packet loss, and identification of reduced call quality are all highly useful for problem resolution.
“The best customer experience”
This telco focuses on delivering the best possible experiences to customers. Delivering on that mission involves actions like continually enriching a customer self-service model and learning how each customer prefers to interact with the company and communicating in kind. Subscribers want communication done their own way, such as a chatbot experience or text messaging with an automated call back versus sitting on the phone waiting for a customer care representative to respond. This company will mine customer data to learn about the customer’s preferences based on past behaviors and then offer content or deals that might be of interest to that customer.
In this case, the telco focused on providing “the best customer experience” wants to deliver both an optimized network experience and an intimate understanding of the customer themselves. The analytics strategy here is one that has full visibility into each subscriber’s journey through the CSP’s network and their quality of experience. Customer experience (CX) metrics such as customer loyalty (e.g., to measure brand awareness), customer journey indicators (e.g., online behavior, content interests, etc.), and a real-time Net Promoter score (e.g., derived through quality of experience scoring) are useful in gauging the level of engagement with subscribers.
“Full-service electronic marketplace provider”
This provider in the Asian market has a multifaceted business model, with plans to be in many different businesses in a walled-garden ecosystem similar to Apple. It doesn’t strive to be the best at any one of those markets, but to be the most cost-effective on-ramp to reaching those services. As the owner and operator of the “front door” into these various businesses, it knows it will likely pick up some customers across these businesses because it will be the most convenient choice.
In this case, the provider is looking to try a combination of approaches using analytics and AI techniques to reduce the cost of operations and gain enough insight into their subcribers’ CX to keep pace with their competitors. This business model will likely have a significant reliance on AI-assisted operations to drive costs down and keep engagement high through technology such as chatbots and online help. Analytics can be mined in retrospect to understand the usage trends of groups of subscribers in real-time (e.g., dynamic micro-segmentation) to identify groups of subscribers with like interests that can be marketed to, in order to drive increases in potential revenue, such as upselling new rate plans, handsets and suggesting add-on products.
Slow and steady wins the race
As these examples illustrate, digital transformation success requires the provider to first determine the unique value it wants to deliver. From there, it can begin to build out its processes with a step-by-step roadmap that phases in change over time and to pursue its transformation goals holistically across the company.
While showing incremental successes is important, it’s best to choose solid progress over speed as a general approach. Transformation tentacles reach everywhere throughout the CSP organization, so all the ramifications of business changes must be carefully considered. Choosing incremental change, then, is the path most likely to land transforming companies exactly where they want to be.
