Data monetization is a revenue strategy for mobile apps and other businesses that involves collecting, analyzing, and selling data generated by users of the app.

This data can include information such as user behavior, preferences, and demographics, which can be valuable to other businesses that are looking to target their marketing efforts more effectively.

Data monetization revenue strategy can take different forms depending on the business model and the data collected. Here are a few examples of how apps use data monetization as a revenue strategy:

1. Targeted advertising: Apps can use user data to sell targeted advertising to businesses. For example, an app that collects data on user preferences and behavior can offer advertising that is more relevant to the user’s interests.
2. Market research: Apps can sell user data to businesses for market research purposes. For example, an app that collects data on user demographics and behavior can offer this information to businesses looking to better understand their target audience.
3. Premium services: Some apps offer premium services or subscriptions that include access to additional features or data. For example, a fitness app that offers personalized workout plans based on user data can charge a premium for this service.
4. Data licensing: Apps can also sell user data to other businesses for a fee. For example, an app that collects data on user behavior and preferences can offer this information to other businesses for a fee.

Overall, data monetization is a strategy that can help mobile apps generate additional revenue while providing users with a more personalized experience. However, it’s important for businesses to be transparent about how they collect and use user data to maintain user trust and comply with data privacy regulations.

Examples of mobile apps that use data monetization revenue strategy

There are several mobile apps that use data monetization as a revenue strategy. Here are some examples:

1. Facebook: Facebook collects user data and uses it to sell targeted advertising to businesses.
2. Google Maps: Google Maps uses user location data to provide personalized recommendations and advertisements to users.
3. Uber: Uber collects data on user locations, ride preferences, and payment information to offer targeted promotions and discounts to users.
4. Waze: Waze uses user data to provide location-based advertising to users.
5. LinkedIn: LinkedIn collects user data to offer targeted advertising to businesses and promote its premium subscription service.
6. Shazam: Shazam uses user data to offer personalized recommendations and advertisements to users.
7. Pandora: Pandora uses user data to offer personalized music recommendations and targeted advertising to users.
8. Twitter: Twitter collects user data to offer targeted advertising to businesses and promote its promoted tweet and promoted account services.
9. Instagram: Instagram collects user data to offer targeted advertising to businesses and promote its sponsored post and promoted account services.
10. Snapchat: Snapchat uses user data to offer targeted advertising to businesses and promote its sponsored filter and lens services.

How big is this industry?

According to a report by Grand View Research, the global data monetization market size was valued at $1.3 billion in 2020 and is expected to grow at a compound annual growth rate (CAGR) of 21.1% from 2021 to 2028. The report identifies several factors driving this growth, including the increasing volume of digital data generated by businesses and consumers, the growing demand for personalized marketing and advertising, and the rise of advanced analytics tools and artificial intelligence.

Another report by MarketsandMarkets estimates that the global data monetization market size will grow from $2.3 billion in 2020 to $6.1 billion by 2025, at a CAGR of 20.5% during the forecast period. This report identifies similar drivers of growth, including the increasing adoption of cloud-based platforms, the growth of mobile and social media, and the need for businesses to generate additional revenue streams.

Overall, it is clear that data monetization has become an important revenue strategy for many businesses, and the industry is expected to continue to grow as data becomes an increasingly valuable asset in the digital economy.

Types of data monetisation

There are various types of data monetization that businesses can use to generate revenue from the data they collect. Here are some common types of data monetization:

1. Data as a Service (DaaS): DaaS is a model where businesses sell access to their data to other businesses or consumers. The data is often collected, analyzed, and packaged in a way that is valuable to the buyer, such as customer behavior data or market research.
2. Targeted advertising: This involves using data to deliver ads to consumers who are more likely to be interested in the product or service being advertised. For example, a fitness app that collects data on users’ exercise habits can sell ad space to companies that sell workout gear or supplements.
3. Personalization: This involves using data to personalize the user experience and offer tailored recommendations. For example, a music streaming app can use data on a user’s listening habits to offer personalized playlists and artist recommendations.
4. Selling data to third parties: This involves selling user data to third-party companies that can use the information for various purposes, such as market research or targeted advertising.
5. Subscription-based services: This involves offering additional features or data to users for a fee. For example, a financial app can offer premium services such as access to market research or investment advice.
6. Aggregated data: This involves selling data in the form of aggregate or anonymized data sets. This type of data can be used for various purposes, such as research or analysis.
7. In-app purchases: This involves offering users the ability to purchase additional data, such as additional game levels or data on their social media accounts.

Overall, data monetization can take many forms, and the specific strategy a business uses will depend on the type of data collected and the needs of the target audience.

What is Direct vs indirect data monetisation?

Direct and indirect data monetization are two different approaches to generating revenue from data.

Direct data monetization involves selling the data itself for a fee. This may involve selling access to raw data or selling the data in a packaged form, such as a report or analysis. Direct data monetization can be a highly effective strategy for businesses that have access to unique or proprietary data that is in high demand. For example, a company that collects data on social media trends can sell access to this data to marketing firms or researchers.

Indirect data monetization involves using the data to generate revenue in other ways, such as through advertising or personalized recommendations. In this model, the data itself is not sold directly, but rather is used to create value for the user or advertiser. For example, a fitness app may use data on users’ workout habits to offer personalized workout recommendations, while also selling advertising to companies that sell workout gear or supplements.

Both direct and indirect data monetization can be effective strategies for generating revenue from data. The specific approach a business uses will depend on factors such as the type of data collected, the target audience, and the competitive landscape.