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Data monetization is the process of turning data into cash flow. In today’s digital age, data is the new goldmine for businesses of all sizes and industries. The global data sphere will reach 175 zettabytes by 2025, with businesses expected to generate more than 90% of this data.
Data monetization refers to turning data into a profitable asset for a business. From customer information to internal metrics, data can provide valuable insights into the performance of a business and inform strategic decision-making. However, many businesses need help to turn this data into a revenue stream.
This blog post will explore how businesses can monetize their data, including creating data-driven products and services and providing data-based solutions to other businesses. In preparing a business for data monetization, it is essential to understand the data’s value and how to use it. Additionally, businesses should ensure they have the necessary infrastructure and resources to collect, analyze, and utilize data effectively to generate revenue.
Real-world data monetization examples include Google and Facebook. They use data to target advertising to specific demographics, while companies like Netflix and Amazon use data to make personalized customer recommendations. Other data monetization examples include healthcare companies using data to develop customized treatment plans and insurance companies using data to adjust pricing based on risk.
By understanding the different ways businesses can participate in data commercialization, preparing the business for data monetization, and learning from real-world examples, businesses can unlock the potential of their data and take their business to the next level. Now let’s examine the different ways we can monetize our data.
What Are the Different Ways of Monetizing Your Data?
When selling data sets, it’s crucial to ensure that the data is clean, accurate, and formatted in a way easily usable by others. Additionally, you’ll need a system to deliver the data to customers and provide support if they have any issues. Let’s us first look at some real life companies and how they monetize their datasets.
Examples of Data Monetization
There are several ways to turn data into revenue, including selling it to third-party companies, licensing it for research and analysis, and charging for premium services or features.
| Company Name | How They Monetize Their Data |
| Nielsen | Provides data and analytics on consumer behavior and media consumption to businesses in various industries |
| CME group | Offers traditional and alternative data for the financial sector. |
| Dunn and Bradstreet | Sell data is used to augment an organization’s existing client account data or provide access to new client information to improve sales and marketing efforts for every industry. |
| Microsoft | Generates revenue through its Windows operating systems and productivity and collaboration tools, such as Office and Teams. The company also offers cloud computing services through its Azure platform, which includes a range of storage, computing, and data analytics services. Microsoft’s AI services include machine learning and computer vision tools, which businesses can use to build intelligent applications. |
| Amazon | Generates revenue from various sources, including e-commerce, advertising, and cloud computing. The company’s e-commerce platform provides a marketplace for third-party sellers and its products and services. Amazon’s advertising business allows advertisers to target customers based on their browsing and purchase history and demographic information. Additionally, Amazon Web Services (AWS) provides a suite of cloud computing services, including storage, computing power, and data analytics tools, to businesses and organizations. |
Generally, there is a myriad of ways in which an organization can monetize its data. But data monetization, no matter how it’s done, always falls within one of two categories:
Internal Data Monetization
Internal data monetization refers to using your data to improve the performance and efficiency of your business. Here are some examples of internal data monetization:
- Customer Experience. By analyzing customer data, you can better understand their needs and preferences and improve the overall customer experience. For example, website data shows that customers are dropping off at a distinct point in the purchase funnel. More customers can complete their purchases through experimentation and optimization of the purchase process, which results in more revenue and a more streamlined customer experience.
- Product Improvement and Development. By analyzing product performance and customer feedback data, you can identify areas for improvement and make changes to your products to meet customer needs better. For example, a ready-mix concrete company can use industry data to improve their mixes, potentially producing a less expensive but higher-quality product for their customers.
- Supply chain management. You can optimize your supply chain and improve efficiency by analyzing inventory, logistics, and other supply chain metrics. For instance, using data to understand how your supplier works with your distributors and identifying potential bottlenecks that prevent the effective movement of goods. Improving those bottlenecks improves efficiency, resulting in positive effects throughout the rest of the supply chain.
- HR and business management. You can improve employee engagement and retention by analyzing employee performance, turnover, and other HR metrics and making data-driven decisions on staffing and other business management issues.
External Data Monetization
External data monetization refers to selling or sharing your data with other businesses or organizations. This can include:
- Sharing data with other organizations. Companies can leverage their data assets and gain a competitive advantage in their industry. By sharing data with partners, affiliates, or other businesses in the same industry, companies can gain valuable insights and access to new market opportunities. This can be done through various methods such as data exchange agreements, sharing platforms, or even joint ventures or strategic partnerships.
- Selling insights to third-party businesses. Companies can monetize their data by selling insights to third parties. This includes providing custom reports, analysis, or other data-driven insights. These insights can inform business decisions such as product development, marketing, or sales strategy. Additionally, businesses can use these insights to identify new opportunities for growth or expansion. Companies can offer these insights as a standalone service or as a part of a larger consulting package.
- Offering premium products to members. Companies can also offer premium products to members. The products can include exclusive access to data or analytics for paying customers. The analytics can be advanced detailed reports or access to proprietary data sets. These products can generate additional revenue for the company while providing customers with valuable insights to help them make better business decisions. Additionally, these premium products can be a retention tool for customers and help build loyalty.
- Creating a subscription service for data and analytics. This can include offering a monthly or annual subscription to access data or analytics tools. These subscriptions can generate recurring revenue for the company while providing customers with ongoing access to valuable data and insights that can help them make better business decisions. Additionally, creating a subscription service can help to ensure that customers are receiving up-to-date information and can help to build a loyal customer base. Subscription services can also be tailored to meet the specific needs of different customer segments, allowing companies to offer different levels of access and pricing options.
Three Ways for Monetizing Data Analytics
There are many different ways to monetize data analytics:
- Business optimization: Data analytics can identify areas of inefficiency or underperformance within a business. By analyzing data on key performance indicators (KPIs), such as revenue, cost, and customer satisfaction, businesses can identify areas for improvement and make changes to optimize their operations. This can lead to cost savings, increased efficiency, and improved customer satisfaction.
- Addressing business challenges: Data analytics can also identify new opportunities and challenges for a business. By analyzing industry trends, competitor performance, and other factors, businesses can gain valuable insights into the market and use this information to inform their strategic decisions. This can help businesses identify new product or service offerings, enter new markets, or develop new business models.
- Using insights to improve future data collection: Data analytics can inform a business’s marketing and sales efforts. By analyzing customer behavior and preferences, businesses can identify the most valuable data to collect and use to target their marketing and sales efforts more effectively. This can lead to improved customer engagement and increased sales.
Overall, data analytics can be monetized in many different ways, and the best approach will depend on the specific business and its goals. By understanding the value of their data analytics, businesses can develop a strategy to maximize their return on investment.
Early Data Monetization Use Cases
The concept of monetizing data isn’t new but has gained increased notoriety in recent years due to the sheer amount of data that our world produces. While access to more data has resulted in organizations coming up with more creative ways to monetize their data, some early data monetization examples include:
1. Business Values
Data monetization can provide significant business value by unlocking new revenue streams, reducing costs, and gaining a competitive advantage. By leveraging data to inform decision-making and improve performance, businesses can create new revenue opportunities, optimize operations, and gain valuable insights. This can lead to increased sales and revenue, improved customer satisfaction, and a competitive advantage over other businesses. For instance, Service Level Agreements (SLAs) are agreements between a service provider and a customer that define the level of service that the customer can expect. Similarly, Quality of Service (QoS) documentation outlines a benchmark for customer service. By analyzing data on SLAs and QoS, businesses can identify areas where their performance is not meeting the expectations of their customers. This can include data on service availability, response times, and other key performance indicators. By identifying these areas of underperformance, businesses can take steps to improve their performance and ensure that they are meeting the expectations of their customers.
2. Feasibility
Feasibility of data monetization is essential to evaluate the potential return on investment, the costs associated with collecting, processing, and storing data, and the revenue generated from monetizing it. Businesses should assess the target market and demand for their products or services, as well as the competitive landscape, and ensure they comply with regulatory and legal requirements for selling data.
In monetizing data effectively, it’s also essential to have the right technology infrastructure, resources, and a team with the necessary skills to manage and analyze the data.
- Threat/anomaly detection: Protect your business from potential security breaches by proactively identifying and mitigating potential threats with data analytics tools. Continuously monitor your data for potential threats and take action to prevent them before they cause damage.
- Energy efficiency optimization: Save money and reduce your carbon footprint by identifying opportunities to improve energy efficiency with data-driven solutions. By analyzing energy usage data, pinpoint areas for improvement and implement energy-saving practices that will impact your bottom line.
- Peak data rate tracking: Optimize network performance and improve overall service quality by identifying data usage patterns with data analytics tools. Plan for future capacity needs and take steps to improve network performance, such as upgrading equipment or adjusting usage patterns.
Conclusion
Data monetization is a powerful strategy that allows businesses to turn their data into valuable assets. From creating data-driven products and services to providing data-based solutions to other organizations, there are many different ways to monetize your data. By understanding the different strategies and preparing your business accordingly, you can turn your data into a revenue stream and explore new business opportunities. This post provided various data monetization examples, demonstrating that monetizing data can be a successful strategy for many businesses. In a nutshell, Data monetization is a powerful tool that can help companies to unlock new revenue streams and gain a competitive advantage. By following the tips outlined in this post and evaluating the feasibility of different monetization strategies, companies can turn their data into valuable assets.
Ready to monetize your data? Get in touch with us to learn more about how we can help. Whether you’re looking to create data products, share insights with other organizations, or improve the performance of your business, we have the expertise to help you succeed. Contact us today to schedule a demo.
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