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A compendium of articles from McKinsey & Company and Russell Reynolds Associates















Executive summary | 5


Disruptive change has a long history of taking the disrupted by surprise. Incumbent companies almost always underestimate both the pace of change and the magnitude.

Will network operators be the next to find their business models overturned?

Already, technology players are rushing sleek new devices and services to market that offer connectivity as an add-on, a monthly fee that keeps the stream of books, movies, and data flowing. If that model prevails, network operators will become the back end for consumer-focused companies that engage and delight former telecom customers.

It doesn’t have to unfold this way. Advanced analytics and machine learning offer network operators a way to manage and lead with certainty, to draw accurate insights about their customers, their networks, and their employees from the mountains of unstructured data they collect each day.

This compendium contains articles from McKinsey & Company and Russell Reynolds Associates that highlight the potential of advanced analytics as well as the leadership and organizational challenges that network operators would need to overcome in order

to realize that potential. Five insights emerge from this collection:

1) The technology is transformative. Network operators that want to take advantage of advanced analytics must be willing to upend the status quo. Organizations that are not able to tolerate the level of change required are risking their survival as the industry changes around them.

2) The impact is widespread. Advanced analytics doesn’t just help the marketing department make better decisions (although it will do that). Powerful algorithms take the guesswork out of decision making in all parts of the organization, from finance to talent management, from network maintenance to managing customer churn.

3) The right data is already in place. Network operators generate vast stores of data every day. Yes, it’s messy but the algorithms used in advanced analytics are well suited to breaking it down and extracting the value. Companies that insist on perfecting the data first will never get started.

4) The leadership profile of telecom executives may be one of the biggest obstacles to change. Research shows that telecom executives are typically more risk averse, more attached to the status quo, and more driven by conformity than transformation leaders from other industries.

5) The second big obstacle is everyone else. Transformative change means culture change. New roles will emerge. Budgets will fluctuate. Many in the organization will resist change in both explicit and implicit ways.

–  –  –

Although analytics has become the latest buzzword, the field emerged as a scientific discipline in the late 1990s as advances in computing enabled data scientists to build computers capable of self-learning. They could then leverage that ability with algorithms able to tease out predictive patterns from massive data sets, even when the data was imperfect.

These powerful algorithms could comb through massive sets of data, theoretically opening the way for companies to understand and predict customer behavior and to build tools that could make customized product recommendations, optimize networks, and isolate microgroups to be addressed with tailored campaigns and services. It would take another 20 years, however, before the discipline could truly come into its own.

Today, network operators face ever-increasing complexity in their global marketplaces and operations and competition from new quarters, creating an urgent need for advanced analytics. At the same time, three technological factors have converged to put the full

power of analytics in reach of organizations able to appreciate and seize this opportunity:

The explosion in the volume of available data. With the rapid increase in sensors, mobile technology, and digitization, almost every action and interaction an individual undertakes generates data, from turning on the lights in the morning to relaxing in front of the television in the evening. Taking the metro or driving through a toll on the morning commute, swiping a credit card at a café, withdrawing cash from an ATM, sending texts, making phone calls, playing online games, or simply downloading GPS-enabled apps—all these contribute to a continuous flow of new data. IDC estimates that from 2013 to 2020, the amount of data in the digital universe will grow by a factor of ten—from 4.4 trillion gigabytes to 44 trillion gigabytes. The volume of data more than doubles every two years. Just as important, organizations now have access to services that enable them to store big unstructured data sets, including e-mails, audio files, and text documents. Most companies, however, use only a small percentage of the data available to them. Take the case of an offshore oilrig equipped with 30,000 sensors for capturing data. Today, less than 1 percent of the data generated by those sensors is being used to make decisions, and that same percentage holds true in other industries. Moreover, of that 1 percent, most is used simply to detect anomalies or for real-time control.

The availability of cheap computer power. That vast trove of big data has been building for years. But now we have the computing horsepower to make use of it. Calculations that would have once required a roomful of servers can now be accomplished on a laptop.

New algorithms. Finally, new algorithms have come onto the market that enable users to take advantage of all that data and all that computing power. Previously, organizations that may have invested billions of dollars in computer technology were still using regression analysis to make sense of their data. While a marvel of their time, regressions were developed more than 200 years ago by German mathematician Carl Gauss, long before the advent of computers or even handheld calculators. Today’s algorithms offer several advantages: they can work with larger data sets, even data sets that are incomplete or imperfect, and they can infer missing data by the combination of many other data points.

For businesses, one of the most important aspects of advanced analytics is the so-called machine learning. With advanced analytics, computers can automatically adapt and learn as they encounter variables, without the intervention of data scientists. The more data and the more variables they encounter, the more precise they become. IBM’s Watson computer, for example, relied on a self-generated scoring system to differentiate among hundreds of potential answers when it crushed the world’s best Jeopardy! players in 2011.

The more Watson played, the better it became.

Background | 9




By Pallav Jain, Gloria Macias-Lizaso, and Guido Frisiani of McKinsey & Company The age of advanced analytics will bring with it dramatic paradigm shifts for network operators, who will need to fundamentally rethink how they manage their organizations.

Indeed, the industry will see more change in the next 5 years than in the previous 15.

Processes and functions will become dynamic rather than static. Instead of actions governed by complex sets of fixed rules that must be periodically updated, algorithms will continuously search for and update the optimal course of action in any given situation.

The more they update, the more precise the algorithms will become. With the ability to analyze large and disparate data sets instantaneously, the network operators of the near future will have the tools to become leaner and more agile, able to make complex decisions quickly and for optimal outcomes.


Advanced analytics has the potential to fundamentally alter the customer relationship.

Network operators have long been multichannel, operating on all platforms, including digital. They can now take that one step further. With advanced analytics, the network operator of the near future will be able to interact with a mass market on an almost one-to-one basis. With the ability to unlock data that was previously too massive, too unstructured, and too difficult to collect, network operators will be able to identify those most likely to become customers with a high degree of certainty.

This up-to-the-minute, customer-specific data will enable the company to precisely calibrate promotions, campaigns, and service interventions along the entire customer journey—from joining through usage to termination. At each step, the company will be able to use advanced analytics to maximize outcomes, provide an outstanding customer experience regardless of channel, and deepen the relationship, turning the customer into a brand advocate.

Advanced analytics will have impact at each step of the journey:

1) As the customer in the “I join” phase begins to research handsets on the company website, the network operator will deploy algorithms in the background to determine the most suitable product for her specific profile, leveraging internal and external data. Once the customer makes a choice, the company can offer her the option to pick it up at the closest store. The company will then use its advanced analytics to optimize the store experience, perhaps by having a precise view of high-traffic periods and adjusting staffing levels to minimize queuing.

When the customer picks up her handset, the company’s algorithms will guide the store in tailoring add-on offers, based on the customer’s personal profile and history.

–  –  –

a high level of dropped calls, she might receive a text message apologizing for the glitch and offering the next call free of charge. The company can also use the dropped-call data to optimize its network, decreasing the likelihood of such problems in the future.

3) As the customer in the “I pay” phase settles her monthly bill, the network operator will use analytics to enhance the relationship, even through difficult periods. If the customer slips into a pattern of late payments, for example, the company can predict with reasonable accuracy if she is likely to default on the bill or just needs some support as she navigates through a temporary personal issue. Recurring data charges on the monthly bill can alert the network operator to offer a more appropriate plan—before the customer bolts to a different provider.

–  –  –

4) In the “I engage” phase, the company may field questions, complaints, or requests for upgrades. The analytics-enabled network operator will field these queries with equal ease online, on the phone, or in the store and will use its analytics capability to optimize the experience. If a customer dials into the call center, for example, algorithms working in the background will comb through the caller’s social media, call history, usage, and other data in milliseconds to produce an instantaneous, detailed portrait of the caller. Based on preferences, interests, and personality type, she will then be matched up with a call representative who shares those interests and traits, based on data gleaned from surveys all representatives will take.

The company can also head off operational problems before they reach crisis proportions. All calls will be recorded and stored in a database that will in turn be connected to other customer databases. Using speech analytics and machine learning, the company will monitor conversations for patterns that could indicate either additional selling opportunities or challenges to address before they result in a loss of business. One network operator using such a technology discovered it was receiving 35,000 calls per month from customers who were unable to reset their voice mail passwords. A quick investigation uncovered a glitch that kept resetting passwords for some customers. The company was able to fix the problem before it had serious impact.

5/6) In the final two phases, “I renew/I change” and “I leave,” analytics can again help ensure an optimal outcome. With its sophisticated algorithms, the network operator will anticipate the customer’s needs and offer a solution before she heads off to another provider. Similarly, the company will know with a high degree of likelihood which customers would be interested in upgrading their service, contracting for additional services, or increasing their data plans, allowing the company to provide relevant offers at the moment the customer is most receptive. When customers do leave, the company can use its advanced analytics to detect patterns that help it uncover network flaws and unmet needs.

It can then reinforce its infrastructure or tweak its menu of offerings as needed, reducing customer churn.


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