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5 Great Data Monetization Examples & Models

5 Great Data Monetization Examples & Models

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Lucy Kelly

Marketing Manager

5 Great Data Monetization Examples & Models

There are few opportunities for generating revenue as powerful as data monetization. The data monetization the market is anticipated to surge to US $15.5 billion by the end of this decade - which is a compound annual growth rate of 22.1%.

Many companies have already begun to realize the value of their data and are driving top-line growth by selling their data assets to other companies which need it. If you’re considering monetizing data, these 5 examples of data monetization models adopted by big-name brands will help you decide on which model would work best for your business.

Data Monetization Examples

  1. Automotive data monetization - Uber
  2. Customer data monetization - Eskimi
  3. Healthcare data monetization - Fitbit
  4. Patient data monetization - Flatiron Health
  5. Payment data monetization - eBay

Example 1 - Uber's Data Monetization Model

Uber in Context

Uber, founded in 2009, has transformed the transportation industry by introducing a disruptive model that connects riders with drivers through a mobile app. The ride-hailing giant has expanded its services globally, offering not only traditional rides but also food delivery and freight services. Becoming synonymous with the gig economy and the future of transportation, Uber has not only revolutionized transportation but also amassed a vast reservoir of data from its operations, laying the groundwork for innovative data monetization strategies.

Data Monetization Strategies which Uber Implemented

Reduced Cost with Data-driven Ride Optimization:

Uber’s primary data monetization models comes from how it uses internal data to reduce company costs. Uber leverages geospatial data to optimize ride routes, estimate travel times, and enhance overall efficiency. Through continuous analysis of historical and real-time data, Uber's algorithms predict demand patterns, allowing for more accurate driver deployment, reduced wait times for riders and lower costs for the company.

Surge Pricing and Dynamic Pricing:

Uber utilizes data-driven pricing models to implement surge pricing during peak demand periods. By dynamically adjusting prices based on factors such as demand, traffic, and weather conditions, Uber maximizes revenue while incentivizing drivers to meet increased demand.

Uber Eats and Uber Freight:

In addition to ride-hailing, Uber has expanded its services to include food delivery (Uber Eats) and freight transportation (Uber Freight). The data collected from these services enables Uber to optimize delivery routes, predict food preferences, and streamline freight logistics, creating additional revenue streams.

Advertising and Partnerships:

Uber monetizes data by partnering with businesses to offer targeted advertising to riders. By understanding rider preferences and behaviors, Uber can deliver personalized promotions and advertisements, creating a win-win situation for both riders and advertisers.

Uber’s Challenges with Data Monetization and Solutions

Data Privacy and Security:

Uber faces significant challenges in maintaining the privacy and security of the vast amount of sensitive data it collects. The company has implemented robust encryption protocols, strict access controls, and compliance measures to safeguard user information.

Regulatory Compliance:

Uber operates in diverse regulatory environments worldwide, necessitating compliance with various data protection laws. The company has invested in legal and regulatory teams to navigate these complexities, ensuring that data monetization strategies adhere to local regulations.

User Trust and Transparency:

Building and maintaining user trust is crucial. Uber addresses this challenge by being transparent about its data usage policies and providing users with control over their data through opt-in features and clear communication regarding how their data is utilized.

Results and Impact of Uber’s Data Monetization Initiatives

Increased Revenue Streams:

Uber's data monetization strategies have led to diversified revenue streams beyond traditional ride-hailing. Uber Eats and Uber Freight, fueled by data-driven optimizations, have contributed significantly to the company's overall revenue.

Enhanced User Experience:

Data-driven insights enable Uber to continually enhance the user experience by providing personalized recommendations, efficient routing, and tailored promotions. This, in turn, contributes to higher customer satisfaction and loyalty.

Adaptability and Innovation:

Uber's ability to adapt and innovate based on data insights has allowed it to stay ahead in a rapidly evolving industry. By leveraging data for predictive modeling and strategic decision-making, Uber remains at the forefront of technological advancements in transportation and related services.

Uber is a great case study of data monetization done smartly, with benefits reaching beyond the company itself. Through strategic implementation and addressing challenges head-on, Uber has not only transformed the transportation industry but also demonstrated the potential of data as a valuable asset in driving innovation and business growth.

Example 2 - Eskimi’s Data Monetization Model

Eskimi in Context

Eskimi, founded in 2010, is a global programmatic advertising platform that focuses on delivering innovative solutions for digital marketing. Operating in the competitive landscape of ad tech, Eskimi has positioned itself as a leader in providing targeted advertising solutions, utilizing data-driven insights to enhance ad delivery and campaign effectiveness.

Successful Data Monetization through a DaCo Subsidiary

Introduction of Redmob:

Eskimi's foray into data monetization was marked by the launch of Redmob, a subsidiary dedicated to selling audience data. Launching a "DataCo" is a means of data monetizing when a a separate corporate entity whose sole objective is to manage and extract value from its parent company's data. Recognizing the growing importance of data in the digital advertising ecosystem, Eskimi strategically leveraged its extensive user data to create a new revenue stream through Redmob.

Building a Comprehensive Data Repository:

Eskimi's success in data monetization stems from its ability to aggregate and analyze vast amounts of user data. Redmob operates by tapping into Eskimi's extensive database, encompassing user demographics, preferences, and behavioral patterns gathered from the Eskimi platform.

Data Monetization Strategies which Redmob Implemented

Audience Segmentation:

Redmob employs advanced audience segmentation techniques to categorize users based on various attributes such as demographics, interests, and online behavior. This allows advertisers to target specific audience segments with tailored and relevant ads, increasing the likelihood of engagement.

Programmatic Advertising:

Eskimi and Redmob employ programmatic advertising, using automated algorithms to buy and sell ad space in real-time. This model enables advertisers to reach their target audience efficiently, while Redmob maximizes the value of its audience data through dynamic and data-driven ad placements.

Data Monetization Partnerships:

Redmob has formed strategic partnerships with advertisers, agencies, and other stakeholders in the digital advertising ecosystem. By offering access to its rich audience data, Redmob creates mutually beneficial relationships, enabling advertisers to enhance their targeting capabilities and Eskimi to generate revenue through data licensing.

Results and Impact of Eskimi’s Data Monetization Initiatives

Diversification of Revenue Streams:

The launch of Redmob has allowed Eskimi to diversify its revenue streams beyond traditional advertising services. By capitalizing on the value of its audience data, Eskimi has created a sustainable and scalable business model that complements its core offerings.

Increased Advertiser ROI:

Advertisers leveraging Redmob's audience data benefit from improved targeting precision, resulting in higher return on investment (ROI). The granular insights derived from Eskimi's data enable advertisers to craft more personalized and effective campaigns, reaching audiences more likely to convert.

Competitive Edge in AdTech:

Eskimi's data monetization strategies, facilitated by Redmob, have provided the company with a competitive edge in the dynamic ad tech industry. The ability to offer precise audience targeting and data-driven advertising solutions positions Eskimi as a preferred partner for advertisers seeking impactful and measurable results.

Eskimi's successful data monetization model through Redmob exemplifies the strategic utilization of user data in the digital advertising ecosystem. By leveraging its data assets, Eskimi not only diversifies its revenue streams but also enhances its position as an innovative and influential player in the competitive landscape of adtech - itself an industry where data is instrumental.

Example 3 - Fitbit’s Data Monetization Model

Fitbit in context

Fitbit, founded in 2007, is a leading player in the wearables market, specializing in fitness trackers and smartwatches. With millions of users globally, Fitbit has become synonymous with health and wellness tracking. In the evolving landscape of data monetization, Fitbit stands out as a company that not only collects vast amounts of health-related data but also strategically leverages it to create value for both users and third-party partners.

Data Monetization Strategies which Fitbit Implemented

Health and Wellness Insights Platform:

Fitbit's data monetization strategy revolves around transforming raw health and fitness data into actionable insights. Fitbit offers a Health and Wellness Insights Platform that provides anonymized, aggregated data to healthcare providers, insurers, and researchers. This platform allows external entities to access valuable health metrics while maintaining user privacy.

Collaboration with Healthcare Stakeholders:

Fitbit actively collaborates with healthcare providers, insurers, and employers to integrate its data into their wellness programs. By forging partnerships, Fitbit expands its reach and offers its wealth of health-related data to entities looking to enhance their offerings with personalized and data-driven insights.

Fitbit’s Challenges with Data Monetization and Solutions

Privacy Concerns:

One of the major challenges Fitbit faced was addressing privacy concerns associated with health data. Fitbit implemented robust data anonymization and aggregation techniques, ensuring that individual user identities are protected while still providing valuable insights at a collective level.

Data Security:

With the sensitivity of health data, ensuring the security of user information is paramount. Fitbit invested heavily in cybersecurity measures, employing encryption protocols and stringent access controls to safeguard user data from unauthorized access and potential breaches.

Regulatory Compliance:

Fitbit operates in a highly regulated space concerning health data. To overcome regulatory challenges, Fitbit has established compliance teams and protocols to ensure that its data monetization efforts align with healthcare data privacy laws and regulations.

Results and Impact of Fitbit’s Data Monetization Initiatives

Improved User Engagement:

Fitbit's data monetization initiatives have led to the development of more personalized and relevant features for users. By analyzing aggregated data trends, Fitbit enhances its products and services, resulting in increased user engagement and satisfaction.

Enabling Healthcare Innovation:

The insights provided by Fitbit's data are contributing to advancements in healthcare research and innovation. Researchers and healthcare providers can leverage the anonymized data to identify health trends, design more effective interventions, and contribute to the broader understanding of population health.

New Revenue Streams:

Fitbit's data monetization model has created new revenue streams by licensing its anonymized and aggregated data to healthcare partners. This diversification allows Fitbit to continue evolving as a company and remain financially resilient in the rapidly changing wearables market.

Fitbit's data monetization model showcases how a company in the wearables industry can manage and monetize health data responsibly and ethically. By addressing challenges related to privacy and security, Fitbit has not only enhanced its products and user experience but has also played a pivotal role in advancing healthcare research and providing valuable insights to external stakeholders.

Example 4 - Flatiron Health's Data Monetization Model

Flatiron Health in Context

Flatiron Health, founded in 2012, is a healthcare technology company dedicated to transforming cancer care through data-driven solutions. Its primary line of business involves developing oncology-specific software and platforms to streamline and enhance the entire cancer care ecosystem. At the core of Flatiron Health's success is its robust data-driven business model, which not only supports cancer research but also serves as a foundation for innovative data monetization strategies.

Leveraging Data Assets for Monetization

Comprehensive Oncology Data Sets:

Flatiron Health has built an extensive repository of real-world, de-identified oncology data from sources like electronic health records (EHRs) and other healthcare systems. Recognizing the inherent value in this vast dataset, Flatiron Health strategically leverages its oncology-specific data to drive advancements in cancer research and patient care, while also exploring avenues for data monetization.

Collaboration with Research Partners:

Flatiron Health monetizes its data by collaborating with research institutions, pharmaceutical companies, and other healthcare stakeholders. These partnerships involve providing access to Flatiron's rich datasets for clinical research, drug development, and insights generation, generating revenue through data licensing agreements.

Strategies and Techniques to Maximize Data Value

Advanced Analytics and Machine Learning:

Flatiron Health employs advanced analytics and machine learning techniques to extract meaningful insights from its oncology data. By utilizing these technologies, Flatiron can identify patterns, trends, and correlations within the data, offering valuable information to its partners and clients.

Tailored Data Products and Services:

Flatiron Health has developed tailored data products and services that cater to the specific needs of its partners. Whether it's supporting clinical trials, improving treatment protocols, or aiding in drug development, Flatiron's data monetization strategies involve offering customized solutions that align with the goals of its collaborators.

Strategic Commercialization:

Flatiron Health strategically commercializes its data through licensing agreements and collaborations with pharmaceutical companies. By providing access to its comprehensive datasets, Flatiron facilitates the development of targeted therapies, personalized medicine, and evidence-based clinical practices, thereby maximizing the commercial value of its data.

Results and Impact of Flatiron Health’s Data Monetization Initiatives

Advancements in Cancer Research:

Through data monetization, Flatiron Health has played a pivotal role in advancing cancer research. The insights derived from its datasets contribute to the understanding of disease patterns, treatment effectiveness, and patient outcomes, leading to more informed and targeted approaches in oncology.

Revenue Diversification:

Data monetization has allowed Flatiron Health to diversify its revenue streams beyond its core software and technology offerings. By capitalizing on the value of its oncology data, the company generates additional income through licensing agreements and collaborations, ensuring financial sustainability and growth.

Improved Patient Outcomes:

The utilization of Flatiron Health's data in research and drug development translates into improved patient outcomes. The insights derived from the data contribute to the development of more effective treatments, personalized care plans, and advancements in the overall quality of cancer care.

To sum up, Flatiron Health's data monetization model not only drives innovation in oncology but also showcases the potential for utilizing healthcare data responsibly. By strategically commercializing their data assets, Flatiron Health has not only diversified its revenue streams but has also played a crucial role in advancing cancer research and improving patient outcomes.

Example 5 - eBay's Data Monetization Model

eBay in Context

Founded in 1995, eBay has established itself as a global e-commerce giant, providing a platform for individuals and businesses to buy and sell a diverse range of products. As one of the pioneers in online marketplaces, eBay operates in a dynamic industry where data plays a crucial role in enhancing user experience, optimizing operations, and unlocking additional revenue streams.

Innovative Methods Employed by eBay for Data Monetization

Terapeak Product Research Tool:

eBay took an innovative approach to data monetization by leveraging its proprietary Terapeak product research tool. This tool provides users with access to years of real-world sales data, enabling sellers to make informed decisions about their inventory, pricing, and marketing strategies. By monetizing this valuable dataset, eBay not only adds an additional revenue stream but also enhances the value proposition for its user base.

Real-world Sales Data Insights:

Terapeak's real-world sales data insights go beyond typical market research, offering sellers a competitive edge by understanding trends, demand patterns, and pricing dynamics. eBay effectively monetizes this data by providing premium features and analytics within Terapeak, creating a subscription-based revenue model.

Partnerships and Collaborations

Strategic Data Partnerships:

eBay recognized the potential of collaborating with external partners to amplify the impact of its data monetization efforts. The company forged partnerships with retailers, manufacturers, and industry analysts, offering them access to aggregated and anonymized sales data through Terapeak. These collaborations not only contributed to additional revenue but also expanded the reach of eBay's data insights beyond its platform.

API Integrations:

eBay facilitated data monetization through API integrations with third-party applications and platforms. By allowing developers to access and incorporate eBay's data into their tools, eBay created a mutually beneficial ecosystem where developers could enhance their products, and eBay could monetize its data through licensing agreements.

Results and Impact of eBay’s Data Monetization Initiatives

Data Monetization as a Value Addition:

eBay's success demonstrates that data monetization can be a value-added service for users. By providing sellers with actionable insights through Terapeak, eBay not only enhances user satisfaction but also generates revenue by offering premium features that leverage its proprietary data.

Strategic Partnerships Enhance Reach:

Collaborations with external partners significantly contributed to eBay's success in data monetization. The strategic alliances expanded the audience for eBay's data, creating new opportunities for revenue generation and establishing eBay as a key player in the broader e-commerce data ecosystem.

Balancing Privacy and Monetization:

eBay's success in data monetization is underpinned by a careful balance between leveraging valuable data and respecting user privacy. By providing aggregated and anonymized insights, eBay ensures that individual user identities are protected, addressing concerns related to data privacy and compliance.

As we've seen, eBay's data monetization model, driven by the innovative use of its Terapeak product research tool and strategic partnerships, exemplifies how e-commerce platforms can extract value from their datasets. The key takeaways emphasize the importance of viewing data monetization as a value addition for users, the significance of strategic collaborations, and the need to maintain a balance between data utilization and privacy.

Inspired by these data monetization examples? Chat to our team to learn about how we can help grow your data monetization initiatives.