Exploring the different levels of data monetization, we unveil how companies leverage indirect and direct methods to generate profit and enhance customer experiences.
SwissCognitive Guest Blogger: Arek Skuza – “The Levels of Data Monetization”
Data monetization is becoming an increasingly popular way for businesses to optimize their profits while providing customers with tailored services. By capitalizing on the wealth of data that already exists in their databases, companies are able to increase revenue and understand customer preferences for internal and external stakeholders better than ever before. In this article, we’ll explore the different levels of data monetization and how businesses leverage data to generate profit.
First, it’s important to understand what data monetization is and how it works. Data monetization involves taking the data collected from customers or potential customers and turning it into actionable insights that can be used to improve products or services for the customer or create quantifiable economic benefits for the business. Data monetization can be done through a variety of methods, such as marketing or selling the data, creating new services based on the data, or using the data to improve existing customer experiences. The graphic below will be used as the basis of this article to highlight the different levels of data monetization best.
Indirect data monetization is the lowest and most common form of data monetization. It is the act of putting data in front of users to help drive decisions. For example, Peloton, an American exercise equipment company, uses indirect data analytics to offer consumers data about their workouts and how they relate to other customers. This data keeps customers engaged and increases retention. During the COVID-19 pandemic, Peloton was able to retain customers by offering them analytics about other customers’ performances. Therefore, users were able to compete with other users from the safety of their own homes. Indirect data monetization is commonly used to make internal decisions within a company as well.
Indirect data analytics leverages the power of behavioral analytics. Behavioral analytics is a more advanced level of monetizing data that uses machine learning to create actionable insights from customer behavior. Behavioral analytics involves studying customer actions and reactions in order to gain insight into their motivations, preferences, and behaviors. This insight helps businesses understand customer behavior so they can optimize the experience for them.
Other Companies That Leverage Indirect Data Monetization
A few more companies that use indirect data monetization include Robinhood, an online stock trading platform; DoorDash, a food delivery service; and Airbnb, a home rental company. All these companies leverage data to better understand customer preferences and provide them with personalized experiences. For example, Robinhood notifies consumers when a specific stock drops or jumps in value. Again, this notification provides value to the customer because they are able to make better decisions regarding market trends. Indirect analytics is also the easiest level of data analytics to apply to all customers. The algorithms associated with this level are practical to apply to all customers that use a certain application or service. The insights are personalized to a certain extent but not enough to charge customers extra. The main revenue stream generated by data monetization for companies comes from direct monetization.
Direct monetization is mainly prevalent in the business-to-business (B2B) landscape. It is where companies charge other companies for access to data. This data comes from consumers or external data sources. As the graphic above shows, direct data monetization is more of a transaction than indirect data monetization. Traditionally, direct monetization looked like raw data drops. The company that purchased the raw data would then use its tools to process it and generate insights. While this type of direct monetization still exists, the focus is now on companies that offer customization as a service. This service includes customizing the raw data to better fit the goals and functionality of the company purchasing it. In fact, companies offer tiers of offering that range based on levels of customization and access to certain areas of data.
Tiered SaaS Offering
The most basic level of direct monetization is the tiered SaaS offering. This offering is a freemium, a product that is free to use but charges a fee for additional features and services such as customization. This form of direct monetization offers high-value insights, killer features like drilling, ad-hoc analysis, alerts, and custom actions. The tiered SaaS offering is the most popular and most commonly used form of direct monetization.
The next level of direct monetization is the diamond level. This level is the smallest but most valuable form of direct monetization. It offers customization as a service and involves single tenancy. This level is so specific that it is difficult to be applied to all customers wanting it. Therefore, companies often charge a high price for it. The diamond level allows customers to choose their customizations and data integrations so that they get the insights they require.
Which Level Should Your Company Use?
While both indirect and direct data monetization, along with their sub-levels, offer valuable insights and new revenue streams, it’s difficult to determine which one best fits your company and its goals. However, the decision is easier than you might think. The answer to which level your company should use is both of them! The strongest data monetization strategy used by successful companies is one that combines both indirect and direct data monetization techniques.
Even if the most valuable customers demand diamond-level direct monetization or even a tiered SaaS offering, there is still value in offering data monetization to the masses through indirect data monetization. Likewise, if your company offers indirect analytics to all customers, there will always be a group that demands more advanced and customized direct analytics. Therefore, it is wise to offer both levels to ensure your company offers the most value possible.
Data monetization is a great way for companies to make money out of their data and increase the value they offer customers. There are two main levels of data monetization: indirect monetization and direct monetization. Indirect monetization offers efficient insights that can be applied to all customers in order to improve customer experience, while direct monetization allows companies to charge a premium for customizations and data integrations to other businesses. Companies are advised to use both levels of data monetization in order to maximize their profit potential. By combining the two, companies can gain valuable insights from all customers while maintaining flexibility in terms of pricing and customization. With the right combination of indirect and direct analytics, companies can make money out of their data while also providing an invaluable customer experience.
About the Author:
Arek Skuza is an experienced technology leader, with over 10 years experience in project management and working with technical cross-functional and cross-organizational teams. He has expertise in AI strategy design & implementation, Robotic Process Automation implementation, Machine Learning projects leading, Artificial Intelligence-powered product launch management and Go-to-Market strategies & Data Monetization strategies. Arek has consulted for major companies such as Shell Energy, Discovery Networks and IKEA, helping to monetize & leverage data to drive sales, engagement, retention & referrals.