Operations in the telecommunications industry is often said to be one of the most complex aspects of the business to run, and the most successful telcos tend to be those that outperform at this task. It requires a simultaneous, coordinated, and dynamic approach across business units, each of which alone would be a giant business to run. In recent years, artificial intelligence has had the potential to simplify the task by optimizing various functions that make up operations. Telcos are only just beginning to utilize that promise, with operators finding success with AI solutions that help optimize service operations journeys, such as the in-store customer experience, call center use, and deployment of employees in stores, call centers, and the field.
The intensely challenging economic landscape that telcos have had to navigate in recent years makes the prospect of investment in new solutions daunting. The value at stake, though, is potentially quite significant. Leading telcos have already begun to deploy AI in their field and service operations. So too have upstart digital attackers entering the landscape as networks become increasingly software defined and cloud based. Remaining competitive will necessitate keeping up with both the technology and the front-runners.
Why now is the time to deploy AI
Field and service operations account for 60 to 70 percent of most telcos’ operating budgets, so applying AI can offer real and rapid benefits. The industry has already faced a decade-plus of increasing cost pressure, and the returns on necessary infrastructure investments are barely outpacing the cost of capital. Now the sector must cope with the pandemic-related changes to how people work and shop, which have caused demand to surpass all expectations. At the same time, staffing telco operations functions has become increasingly difficult, with labor shortages and new coronavirus variants further complicating the process. Holding on to workers is also harder than ever, especially in the United States, where 40 percent of employees say they’re likely to leave their current jobs within the next three to six months.1
To stay ahead, operators will need to make critical investment decisions around customer and employee experience. At the same time, they need to offer efficient and effective processes to keep costs down while increasing retention of both customers and employees. These are the very areas where front-runner telcos are deploying AI solutions and finding success. As the following use cases illustrate, those solutions fall into several categories: smart scheduling and forecasting; store-of-the-future experiences enabled by machine learning–driven personalization and other basic operational efficiency; self-healing in which problems are either preempted or solved automatically; and smart coaching.
Enhancing the retail customer experience
A critical area in which AI tools can help enhance operations is the retail setting, where store-of-the-future technologies and tools along with smart scheduling and forecasting can assist in breaking through the bottlenecks that plague the current retail experience. Getting a phone line activated can take up to an hour on average, making the retail setting a prime opportunity for upselling. In the United States, for example, some 40 to 50 percent of phone sales happen in a retail setting, and 70 percent of those transactions involve the purchase of an accessory such as a protective screen cover, phone case, or headphones. Yet customers are left to sit idly while their phone line is set up and their purchase completed.
AI tools can put that time to better use. In addition to personalized ads and offers targeted to the customers in a store at a given time, analytics-driven integration of telcos’ online and physical retail functions could solve the problem of devices and accessories being out of stock or unavailable at a particular location. Better use of analytics could allow retail stores to ship items to customers’ homes if something is out of stock at a particular site, much the way fashion retailers have begun to. In that case, telcos could offer a fully customizable supply of accessories at all its locations, and satisfy a larger share of its customers. (For more on personalization in the store of the future, see “The future of shopping: Technology everywhere” on McKinsey.com.)
Making this a reality, however, requires that a retail outlet has sufficient staff on hand to help customers with their decision journey and purchases. This is where smart scheduling can help. Customers’ ability to get what they need when they want it correlates closely to overall customer acquisition and retention rates, so having enough staff on duty is critical. Forecasting staffing needs in the retail setting, however, remains difficult. Existing tools don’t offer enough precision to anticipate a telco’s retail hiring needs. A hot new phone release or upcoming holiday shopping are predictable enough, but foreseeing rush times that don’t seem to be connected to anything is trickier. A spreadsheet alone is not powerful enough to understand the forces at work and make adequate predictions. Also, such forecasting functions are typically siloed in disparate systems, preventing the scheduling process from being made dynamic and operating in real time.
AI tools such as machine learning can eliminate much of the guesswork and manual processes that most operators currently use to forecast retail staffing needs and schedule them appropriately. Done well, these tools can dramatically reduce the problem of overstaffing and understaffing. By building predictive models that augment historical internal data with information such as demographic, income, and search trend data, telcos can forecast staffing needs with up to 80 percent accuracy at the retail level.
Implementation of smart scheduling enabled one telco to realize improvements in cost savings, service levels, and sales. With more than 10,000 retail employees across 1,500 locations, the company had struggled to avoid understaffing that resulted in overtime costs as well as overstaffing that left employees with too much downtime.
The company had multiple workforce management teams using a combination of spreadsheets and third-party tools to try to forecast demand and schedule employees. In addition to being slow, the process wasn’t accurate enough. The company combined internal data with external information such as demographics and online search trends to build dashboards on top of its core AI models for forecasting and schedule optimization, with an initial pilot ready in about three months. These dashboards provided unprecedented transparency and visibility to workforce schedulers, such as previously hidden peaks and troughs in demand for and availability of labor, allowing much greater precision in scheduling for retail staff. Over time, the company saw 10 to 20 percent cost savings through better hiring and scheduling, as well as a 10 to 20 percent increase in sales through improved response to customer demand. Additionally, it saw utilization of retail staff increase by 5 to 10 percent, by redeploying idle time.
Improving operations in the contact center
As AI applications become increasingly sophisticated, leading telcos look not only to reduce customer need to call or message regarding problems that could be prevented or solved in other ways. They also want to ensure upsell opportunities that could result from a contact are maximized.