The rise of data-driven business models and their unprecedented growth has strongly increased the awareness around the value of data. Most people know they are the product in any free online service. Most people today are aware that their data is worth a lot of money.
But why is our data worth so much?
“It’s all about grabbing user attention and predicting behaviour.”
If an online service provider gets access to the magical mix of user attention and user data, they reap the benefits of advertising revenue. Accessing these two dimensions is so important that the majority of their services are offered for free. With user attention, they have the audience to present ads to. Complementing user attention with user data allows for the prediction of user behaviour. Simplified: If I present a user with this ad, what are the chances they’ll click on it? The more user data is collected, the better they can nudge users in clicking ads. Which means more revenue for them.
Now, how much is my data really worth?
This question has been answered in a variety of ways:
- The market capitalisation, divided by users
- The willingness to pay for online services
- Time spent online, to estimate the number of ads viewed
Each valuation method has its limitations. We perceived one method as underexplored. Dividing the revenue from advertising by Active users. This method seems more precise than the frequently used, market cap divided by users approach as most companies discussed also have other sources of revenue.
Here are the results:
The average user’s data value is $242.17 in 2020 alone!
Google has clearly found the most effective way of monetising users. Their strong growth in advertising revenue from 2016 onwards originates in the launch of YouTube ads. Which currently brings in $5 billion in ads revenue to Alphabet, Google’s parent organisation. However, this is still only a small slice of their $147 billion in total revenue from its Google Ads Platform.
Facebook, including Instagram, has been able to steadily increase the money made from users. Their acquisition of Instagram has greatly benefited their ability to target and reach users with advertised content. Their user base is close to reaching 3bn, and the data they collect is considered highly sensitive. Having access to a lot of social data: user messages (Whatsapp), the news and other content viewed, friends and groups interacted with (Facebook) and analysis of user images and faces (Instagram). Facebook is a champion in predicting user behaviours.
Amazon started off as a marketplace, this means that the majority of their revenue came from fees. Currently, Amazon has a strong presence in a range of markets: Cloud, streaming, logistics, … Having stepped into the advertising market at a later stage, their revenue from ads is now the stream with the largest growth, 64% growth in 2020. Their user attention collected through the marketplace and entertainment services, in combination with the purchasing data from their marketplace, put them in an easy position to monetise users. Predictions are made that they will be able to quadruple their advertising revenue by 2026.
Twitter’s two main revenue streams are advertising and data licensing. Both evolve around the monetisation of user data. Having been in the advertising space for a while they have managed to make about $10 for each user. However, the gaps with Google, Facebook and Amazon are still strongly visible.
Although an average of over 90% of Spotify’s revenue comes from premium subscribers, a growing proportion comes from advertising. Free users are monetised to about $3.50 to $4.50 a year.
Although Reddit has the lowest revenue per user from our list, their 430 million monthly active users will allow them to put more focus on this. Their latest valuation of $6 billion, shows that investors see potential in monetising these 430 million. The data used for targeting is presumed to be based on interests and groups.
Snapchat is facing some strong competition in the advertising space. This again highlights the monopolistic nature of platforms and data monetisation models. Although they have strong user attention (265 million, daily active users). Their targeting capabilities are less strong because they have fewer data.
We aimed to add Linkedin and Tiktok, however, as both companies are privately held, we could not find sufficient data to compare them in our study.
Also, it is important to note that these figures are global averages, a user’s data value strongly depends and their purchasing power and shopping behaviour.
We’ve had an era in which users were blindly exploited. It seems like the growing awareness among users is slowly changing this. Privacy-safe alternatives to search engines and social media platforms are increasingly being rolled out. The monopolistic tendencies of online platforms will make the change a long one. However, we believe there is nothing more powerful than user awareness and regulatory changes to disrupt a market. We’re looking forward to the transformation ahead.
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