Contributed by Alex Shevchenko, CEO at Guavus, a Thales company.
5G mobile network spending nearly doubled last year to $8.1 billion, according to Gartner. At the same time, however, revenues and profits have remained flat, leaving most operators struggling to recoup their substantial expenditures.
To recover investments and begin realizing the huge profit potential of 5G, operators need to move beyond their consumer subscription comfort zones and push innovation into the enterprise market. For example, one relatively untapped telco growth area is in advanced data analytics for vertical services companies.
You may already be using AI and analytics to better support and understand your consumer customers. But how can you apply them to deliver new enterprise vertical services and create new revenue opportunities?
A sweet spot is to capture insights from your own mobile network data, combine them with your vertical customer data, then package and sell the analytics as a new service to customers in government, transportation, manufacturing and other industries. Real-time services that trigger immediate actions and decisions are particularly attractive to enterprises looking for ways to avoid downtime and delays that could impact their business.
The big question is: how can you harness the power of your data to build and sell new services to your vertical customers?
7 Customized Vertical Services Built on AI-Based Analytics
The analytics services packages you can create are limited only by your imagination and your ability to partner with an expert in the target vertical. Below are seven examples based on our work with operators deploying AI-based analytics to enterprises in leading verticals.
> Government and Public Safety: The world’s governments often need to prepare for huge influxes of people when there’s a big event, such as the Olympics or the World Cup soccer competition. Host cities need to beef up transportation flow management while the venue, such as a sports stadium, must monitor crowds and flows for public safety and security. Geolocation data from user smart phones provide a macro view of population densities; this data is tied in with non-telco data, such as camera data captured in corridors that may be paired with facial recognition technology. This data and analytics could be used to not only react to anomalies but to predict and manage them with much higher levels of efficiency.
> IoT: It pays to take services beyond basic network connectivity for transporting IoT data. You can offer some simple telemetry services as well as smart maintenance services based on the predictive capabilities of the underlying analytics. Such services use analytics and ML to detect patterns or spot anomalies from connected sensors and other devices.
> Drone Management: Many operators today are passively participating in this emerging market, providing their network and data if requested. However, if you could use your data to predict an outage in a drone’s path, the delivery could automatically redirect to another carrier, making your and your vertical customer’s services far more reliable.
> Automotive: Mobile network data combined with connected car data and analytics can help car rental and manufacturing companies learn the habits, likes, and dislikes of their customers. These companies can also learn how their customers’ experiences are impacted by various vehicle features and attributes, such as chatbots and edge caching of content to provide interaction between systems and those in a vehicle. All this insight allows the car companies to better tailor their offerings toward well-defined categories of customers.
> Supply Chain Fleet Management: Predictive fleet management analytics services could target supply chains that rely on competitive delivery windows. These services could anticipate outages or delays and automatically reroute deliveries or tap alternate carrier sources. Also, your data combined with other data sources and ML enables the creation of continually optimized digital territory maps to make sure drivers are always guided down the most efficient delivery routes.
> Railway Efficiencies: Big data analytics and machine learning (ML) might be used to tighten train schedule controls such that a railroad could operate many more trains at the same time without having to expand its track infrastructure for far greater efficiencies.
> Manufacturing: Big data analytics for predictive maintenance can be packaged to help a manufacturer improve factory floor productivity, product safety, and quality management. By anticipating an equipment failure, for example, the manufacturer can take steps to minimize or eliminate the impact on operations, such as by scheduling a repair during off-hours. More broadly, 5G enables Industry 4.0 capabilities, which increase the overall use of automation for self-monitoring and diagnosis in manufacturing environments by integrating machine-to-machine (M2M) and Internet of Things (IoT) data and communications.
Analytics can also improve the timeliness and accuracy of production lines through “smart” production scheduling. For example, it’s important how candy manufacturers schedule the production of sweets of different colors because color residue left in equipment could bleed into their ingredients. If that should happen, the business suffers downtime while cleaning the production line. Smart scheduling can automatically line up blocks of same-color items to be scheduled after one another to help ensure product quality and efficient use of time and facilities.
Keeping Data Private and Secure
In highly regulated industries, it greatly simplifies security and privacy constraints if each of your vertical-market customers, and possibly each of their applications or use cases, is supported on their own 5G network slice. Using network virtualization, 5G-standard network slicing creates closed user groups within your connectivity infrastructure.
Each slice is partitioned off from others for privacy. Each can also be assigned unique network resources that apply to each customer as a whole or to each customer’s individual applications, each on a separate network slice, to meet the specific service-level agreement (SLA) requirements of the various applications.
Private 5G networks you supply to manufacturing organizations, for example, enable the Industry 4.0 communications necessary for automating and scheduling various processes. Each job or process might be assigned its own network slice with the bandwidth and resource allocations that it requires.
Getting Started with Key Partnerships
To expand your vertical customer offerings with analytics, you’ll need partners with experience both in the operator environment and in each industry that you wish to target.
Today, communications service providers (CSPs) are regularly brought into cross-industry projects in “pull” mode. A car fleet management company, a transport operator, a government entity, a smart city management body, or other industry player taps a mobile operator to deliver basic transport connectivity to serve all the project’s data needs.
However, there’s no reason that CSPs shouldn’t take a “push” approach on such projects with their own enterprise customers by creating analytics services on top of their connectivity offers and selling the package proactively. By combining their mobile data with vertical project data, they can upsell their B2B customers with additional business value and play a more important role in the B2B partner ecosystem. CSPs are uniquely positioned to take advantage of this powerful 1+1=3 synergetic effect.
These types of opportunities can be difficult for CSPs just starting out to identify, and they can find it challenging to make the paradigm shifts necessary to succeed. That’s why
partnerships and industry collaborations can be very helpful in jumpstarting innovation and creativity.
Look for collaboration not only with suppliers of technology such as analytics and cloud, but with adjacent industry suppliers. For example, supply chain management can include support from not only from logistics companies, but also from the logistics industry associations, component suppliers, and other business-to-business-to-consumer entities that bundle the end product with services for individual buyers.
Generating New Value
Operators are sitting on highly valuable mobile data that can add great extra value to industry data. By combining both for analytics and, often, ML, they can offer creative new offerings to vertical-market companies.
Success belongs to those operators that successfully anticipate and meet enterprise 5G requirements with new business value, such as lowering their customers’ costs, helping them avoid downtime and delays, and improving their customers’ experiences. These moves will pay far bigger dividends than continuing to focus competitive efforts on network speed, coverage, and data plan pricing.
Alex Shevchenko is CEO at Guavus, a Thales company and pioneer in AI-driven analytics for communications service providers. He has been working in the IT, telecom and IoT industries for more than 20 years. He joined Guavus from Thales’ Digital Identity and Security organization (formerly Gemalto) where he led commercial and technical teams around the world for 15 years, with a great track record of commercial success and innovation. Prior to Thales, he was SVP of Telecom Sales at Gemalto. Alex graduated from Moscow Institute of Physics and Technology with degrees in applied math and physics, and studied management at Cambridge Judge Business School.