Big data monetization in telecoms has been a focus in recent years. However, telcos’ interest has fluctuated over time due to the difficulties of delivering and selling such a vast range of products, as well as extremely fluctuating income potential based on sector.

Telcos’ willingness to pursue data monetization tactics has also been greatly influenced by the success of other emerging telco products, particularly IoT, due to the link between many telecom data and analytics products and IoT solutions. In many industries, IoT data monetization is the primary strategy, although in others, telecom operators can address prospects independently of IoT services.

We investigate the major data monetization approaches and use cases across ten sectors. We divide them into two categories: those where data monetization methods are closely tied to IoT and others that are more independent.

1. Agriculture

The majority of activity in the agriculture industry comes from large multinational telcos with established IoT solutions. However, not all of the largest telecoms have reported exploring such projects, with case studies most likely coming from companies with a substantial presence in developing regions or large multinational enterprise customers in developing nations.

The majority of opportunities are related to IoT and sensors, and they include a combination of connectivity services, payload data storage, and analytics. For more complicated and specialized use cases, telcos are more likely to play a connectivity-only role. For example, NTT Docomo provides hardware and analytics for crop management, whereas many other telcos choose to partner with specialist platform providers.

2. Manufacturing

Much of the speculation about potential telecom activity in this vertical revolves around the supply of 5G services to enable Industry 4.0 capabilities. The most noticeable telco manufacturing solutions are frequently tied to long-standing ties with a certain industry, such as Vodafone and T-Systems solutions for the car industry. Barriers for telcos to overcome include delivering 5G capabilities quickly enough to satisfy manufacturers and enable the replacement of LTE, creating flexibility in their offers, and making access easier through on-demand provisioning, among other things.

The table indicates that there is little financial value for data/analytics in the vertical, but this is due to the prevalence of IoT use cases in which data analytics will not be sold separately. Telcos who choose to focus aggressively on 5G and edge computing for manufacturing are more likely to capitalize on data/analytics prospects, with predictive maintenance and the provision of analytics for autonomous vehicles on the factory floor looking particularly attractive.

Telcos offer asset management, supply chain analytics, and transportation/logistics solutions to other verticals in addition to manufacturing. These are thus included in the part that discusses horizontal solutions for all verticals.

3. Retail

Historically, this was one of the first verticals that telcos targeted for customer movement within products. The difficulty of locating a suitable individual in a retail organization, as well as the possibility of non-standard criteria from each retail customer, impeded product development. However, larger telecoms with data/analytics ambitions already have a reasonably developed retail product offering.

Ongoing opportunities fall into three categories:

• Customer movement insight products: These tend to be the most feasible project as they are more mature and use telco data, for example for store placement calculations.
• Customer insight products: Related projects use customer insight (demographic, sociographic) rather than geolocation data. For example, the open data platform described above could be accessed by retailers, hoteliers or other types of customers in this vertical.

• IoT/small cell opportunities: There are additional data/analytics opportunities that use small cell, video and CCTV data to track customers in small spaces or within a shopping mall – however, these are considered of lower feasibility because they require rollouts of these capabilities and potentially IoT related products such as sensors. These opportunities are subdivided into those that require specialist analytics and those that require additional AI capabilities such as facial recognition. All of these use cases require a sustained focus on the retail sector and its needs, plus enough rollouts of small cells, wifi, beacons etc to make a business case for adding data/analytics on top.

4. Transportation

Like other verticals, the majority of the most accessible financial opportunities come from consumer mobility insights offered to passenger transportation firms such as trains and buses. This is a somewhat developed use case for telcos. Much of the remaining opportunity is in mature fleet management markets with few chances to incorporate data/analytics. Finally, the connected vehicle sector offers several potentially viable ways to incorporate data/analytics into IoT implementations.

5. Finance

The feasibility of providing services for retail and investment banks and other companies within the financial services sector is divided broadly into three categories:

• Services live today: anecdotally, location-based card authentication (i.e. alerting a bank when a customer travels to a different country, which improves fraud management) is one of the highest revenue services for telcos today. There are additional services alerting retail banks to potentially fraudulent behaviours, but these seem less popular. Services using customer movement insight such as identification of where to open a bank branch are also popular, although the financial benefit is not seen in the table below as it is categorised with other similar services for other high street retailers.

• Possible services not yet on the market: customer movement insight could also be used for optimizing the location of bank ATMs and telco data could be added to specialist analytics for operating them, however, example services have not yet been seen from telcos, so it is possible that there is limited demand.

• Specialist services: data and analytics services on high speed, complex customer and market data which offer less attractive opportunities for telco services, but is not completely infeasible. For example, there are cases of telcos adding customer movement insight data to improve bank trading decisions and risk management. There are also examples of telcos, such as CenturyLink, who have purchased analytics companies because they host financial data, although it is not clear how much financial return this has delivered for them.

6. Insurance

External data is used by insurers to monitor risks, calculate actuaries, and make underwriting decisions. There are compelling motivations for insurers to incorporate new data sources; yet, there are legislative constraints (since corporations require data on individuals) and the data must be reliable and up to date.

There has been minimal telco activity in this arena, with the exception of the occasional anecdotal evidence that they are collaborating with specialist actuarial firms. As a result, the financial value ascribed to data providing is mostly for niche items that do not require PII, while probability ratings are low due to operators’ reduced sales focus.

Telematics products for insurers, particularly usage-based insurance, are one area where telcos have made significant investments. Analytics generates driver scores for pricing and risk management purposes. Tier 1 telcos such as Verizon, Telefónica, Telstra, and Orange offer data monetization products in this field; some develop the analytics themselves, while others collaborate.

7. Healthcare

Building new revenue in the healthcare vertical needs telcos to have a long-term plan and a thorough grasp of the industry. In terms of data and analytics, practically all telco activities involve data transfer and storage. However, depending on the use case, they may require a combination of specialized platforms, apps, and smart devices, which may allow for the integration of A3. As the market evolves, alternative strategies for investment (build or purchase) along the value chain emerge, allowing telcos to develop A3 capabilities.

Opportunities are divided into several categories:

• Telemedicine use cases provide smart devices that generate payload data. The data requires transportation and storage, also providing opportunities for the development of analytics to generate alerts or provide historical trends.

• The management of electronic health records, medical images, electronic prescriptions and insurance claims. These require data transport, storage and then specific platforms for exchanging information between different parties.

• Solutions for the pharmaceutical and life science industries including collaboration platforms for clinical trials.

8. Real estate and construction

This vertical provides numerous prospects for consumer movement insight products. Anecdotally, deal sizes are smaller than in, for example, retail, but location mapping is beneficial for a variety of uses. To be successful, use cases require a significant amount of external data as well as open data from government platforms.

Potential opportunities include:

• Use of customer movement insight to understand demographics, behaviors and requirements of a local community to improve development and investment decisions for both retail and commercial real estate companies

• Use of the data for pricing, marketing and sales decisions within estate agents and brokers

• Use of indoor data from small cell deployments within shopping malls to understand customer movement in order to position advertising, adapt opening hours according to foot traffic and change layouts to drive traffic to, say, food courts.

9. Telecom, media and technology

The provision of insight to entertainment/sporting venues is a relatively frequent use case today, utilizing customer movement insight and sensor data. There is also room for analytics like customer segmentation and behavior. Projects in which telcos have reported participation typically have a strong consulting component, making this best suited to operators with a consultant staff.

Telcos face significant challenges in pursuing other opportunities related to content consumption patterns. Telcos may have knowledge from their set-top boxes and other platforms that content providers would find useful, but this is a mature market that is used to consuming various types of data, and it does not appear to be a popular use case.

10. Utilities

This market is divided into products for consumers, which appear to be increasingly difficult for telecoms to offer. An examination of telco websites reveals that, with a few exceptions, most have shifted away from a variety of smart home goods and toward an emphasis on security. (STL has previously stated that the smart home is not a viable product in and of itself, and that telcos should instead focus on tackling specific household challenges such as security, entertainment, or energy conservation.

Products for utilities are mature, and major telcos have had success. Telcos provide grid and smart meter monitoring and management services, as well as security, communication networking solutions, drone management, and fleet management. There are three basic types of goods in which consumer movement insight data could be provided alongside analytical solutions using IoT payload data:

• Grid distribution, monitoring and control: the largest telcos offer descriptive and diagnostic analytics on data about electricity, water and gas networks. 5G will offer new opportunities for real-time prescriptive activity using digital twins. Meanwhile, shifting the energy market from fossil fuels to renewables will require matching demand to supply (when the sun shines and the wind blows), as opposed to the current environment of matching supply to demand whenever it occurs, which will in turn require very advanced analytics and automation across all levels of the energy market.

• Smart metering control and management: currently a mature market, with opportunities to add prescriptive analytics that enable better management of problems. This area will also evolve significantly over the coming decades towards smart “just in time” energy usage in homes and businesses.

• Site and network planning: Opportunities for customer movement insight data to be added to give information about the population to enable new installations (pylons, sub-stations, water facilities, green-energy installations etc).

Real-World Examples of Successful Data Monetization

These examples showcase how businesses across various industries can leverage data to generate revenue and improve customer experiences.

Management Consulting Insights

Management consulting firm, Positive Insights, specializes in measuring and forecasting business performance for its clients.

Positive Insights uses data-driven insights to make better-informed decisions that drive revenue. They were able to use Zuar’s data pipeline solution, Zuar Runner, to automate the flow of data from various disparate systems to efficiently centralize and streamline their data processes.

Additionally, by implementing Zuar Portal, they were able to monetize their data by providing data as a service to their clients. This enabled their clients to easily access actionable insights through a convenient data portal that was fully customized to match Positive Insights’ branding.

Through both direct and indirect data monetization, Positive Insights is able to capitalize on the value of its data assets, creating new revenue streams and enhancing its business strategies with data-driven insights.

E-commerce and Personalization

E-commerce companies, such as Amazon and Shopify, use customer data to personalize their platforms, leading to increased revenue through improved customer experiences. By analyzing customer behavior, preferences, and purchase history, these companies can provide tailored product recommendations, customized content, and targeted promotions.

This personalized approach not only enhances customer engagement but also drives customer loyalty and sales, showcasing the power of data monetization in the FMCG e-commerce industry.

Telecommunications and Targeted Advertising

Telecommunications companies, such as Verizon and AT&T, monetize data by using it to deliver targeted advertising, generating additional revenue streams.

These companies accumulate large amounts of data from their clients, including call records, location data, and browsing habits. By leveraging this data, they can provide targeted ads based on customer interests and behavior, leading to increased ad revenue and customer satisfaction.

This example demonstrates how internal data monetization and data monetization capability can be applied in the telecommunications industry to unlock new opportunities and drive growth.