Telecommunication companies are sitting on a veritable goldmine of data. How can they use it to increase revenue? And what role does AI play?



Telecom companies have an unusually deep pool of data to play in—arguably more so than any other industry. This rich data includes behavioral patterns, sensor data (IoT), Internet usage, location, daily travel patterns, app usage, purchase behaviors, media consumption, and more. Few industries have direct access to this much information on individual customers. And, aside from customer data, there’s also a vast amount of operational and network data available to telcos.

Telcos are masters at using this data in their own organization; improving customer service, honing marketing programs, optimizing network performance, and providing insights to decision-makers are common applications for such data. The list of potential applications is long and spans the entire organization, from sales and marketing to ops and network management.

As exciting as these opportunities are, data also presents opportunity to create entirely new revenue streams for telcos.



Forward-thinking telecom leaders are leveling up the usefulness of their data by monetizing it. In other words, they’re turning their data into new revenue streams. AI makes this process more efficient and effective, but it also presents unique opportunities for telcos to expand their services and revenue. One way is by using AI to find emerging patterns and trends that can be turned into new business offerings, and sometimes even new business units.

For example, let’s say a telecom company has location, sensor, and mobile data for 100,000 users in a high-growth city. New businesses coming in want to understand foot traffic and car traffic patterns. AI can be applied to process telco data in real-time, find these patterns, and use machine learning to model future patterns. Incoming commercial businesses pay for access to these models, using them to help select the most promising locations and layouts for their expansion plans. In this case, telecoms are essentially selling data as a service. New revenue.

AI is also adept at automating the data ingestion and harmonization process—making the delivery of data-driven services possible. New technologies are capable of processing notoriously challenging data sets, such as those with unstructured data, large data volumes, sensitive customer information, and even ‘dirty’ data (covered in more detail below).

As we can see, the technology is in place for telecoms to make the leap to data monetization with AI. There have been a few early adopters; what can we learn from them?



Telecoms who have started monetizing their data have primarily seen new revenue streams from three key channels: expanding service offerings to existing customers, creating new offerings for outside companies, and improving internal business practices. We’ve already covered the latter—using data to improve marketing, sales, support, and operations— and we know how AI can certainly take that to the next level. The first two channels—current customers and outside companies—are on the cutting edge; this is where telecoms can achieve high growth and gain a competitive advantage. Let’s look at each of these areas:

Monetizing Data via Current Customers

AI, data, and analytics are identifying new offerings for existing customers and finding ways to bring new subscribers over from the competition. This technology can also spot usage trends that telcos can use to create innovative upgrade and add-on features – some of which may be new to the industry. For example:

  • PRIVACY AND PROTECTION ADD-ON: Customers are increasingly dissatisfied with things like robocalling; knowing this, a telco could create an app or feature to manage nuisance calls. This differentiates them from their less-savvy competitors.
  • STORAGE OFFERING: AI and machine learning can detect heavy consumption patterns early in the game, allowing the telecom to offer additional cloud storage to customers with higher-than-usual media creation and consumption needs.
  • FLEXIBLE PLANS: By analyzing usage patterns, new customized levels of service can be created to accommodate select customer segments.

Monetizing Data via Outside Companies

Data can be monetized by creating revenue streams that target third parties. In addition to growing the number of revenue sources, such streams tend to be high margin. Here are just a few examples of how outside entities consume telecom data:

  • APP DEVELOPERS acquire aggregated data to improve user experience, prioritize features, and build relevant in-app purchase opportunities.
  • MUNICIPALITIES consume location and transportation data to improve traffic infrastructure settings, alter public transportation routes, and reduce road congestion.
  • STORES buy location data to select future retail sites and plan expansions.
  • ADVERTISERS use location data to personalize offers when customers are in the immediate area.



For data monetization to work, two critical components must be in place: a technical framework and a flow of high-quality data. It takes data management and munging to create the kind of data that can serve as a new stream of revenue and growth. But more than just a pure technical framework is needed. Telcos must also consider:

Compliance and Security

Telecoms have a host of regulatory, privacy, and ethical practices to consider before selling or sharing data. Being transparent about data collection and use, getting the proper legal consent from users, and thoroughly cleaning and anonymizing data is required. User data must be encrypted and securely stored.

Data Harmonization

The process of collecting, cleaning, and anonymizing data for internal or external purposes is admittedly daunting. Using AI to automate the data preparation process saves an enormous amount of time; a 2016 study estimated that nearly 80% of analysts’ time was spent either collecting (19%) or cleaning (60%) data prior to its use.

While these two areas add an additional challenge, AI and professional data services can help manage the time and costs involved.



With their uniquely abundant data sources, telecoms that embrace innovation are in a very favorable position to differentiate themselves in a highly competitive environment. This industry has a data gold mine at its collective fingertips, and AI is helping smart telecoms bring new revenue streams straight onto the top line.

Authored by Sandra Peterson, VP of Marketing at Absolutdata, and Richa Kapoor, Marketing Manager at Absolutdata