Have you ever wondered how companies like Instagram or Snapchat make money? How do these companies that offer you products that you can use for free without a limit make their money? The simple answer is data.
Data to the digital economy is as important as oil was to the industrial economy. Data is now leveraged and used in business by thousands, if not millions, of companies in the modern age.
But how is data collected?
Data is generated each time a user uses the internet through something known as the digital footprint. The digital footprint is basically all of the actions taken by a user when they were online. The digital footprint tracks everything from the profiles someone views on social media to items the user placed on a digital shopping cart on platforms like Amazon.
How do companies monetize the data?
While you don’t pay for most of the services you use, you end up leaving hundreds of small data points that together create a profile of you. This profile of you is then used by some of the biggest firms of the world to generate an income. For most methods of data monetization, it needs to go through some form of refinement. Let’s look at what behavioral profiling truly is:
Behavioral Profiling: As mentioned above, companies can use the data to create a user profile. This profile includes information such as likes, comments, time spent searching for products, sports interests, music interests, and much more. This is done by companies such as Meta and Google.
These companies can then use this profile they create to generate an income through the following methods:
Targeted advertising:
Targeted advertising is one of the biggest sources of income for social media companies. These companies can use the data they collected on the user to display specific ads to specific users based on the user profile that was created for them. This results in ads specifically targeting consumers who may have an interest in a product rather than a general ad. This results in more revenue for both the advertiser company and the social media company because targeted ads are more likely to generate a click and a purchase from the user than a general target ad. The social media company also charges a greater fee for the advertisement due to the more targeted nature of the ad allowing the social media company to leverage the data collected and also allows the advertiser to gain a better return on the advertisement.
Data Brokers
Data brokers are also known as data aggregators.
Traditionally, data brokers buy the data from apps that collect it.
They then enhance this data by adding other forms of information such as social profile, financial information and publicly available records.
How do brokers make money?
Brokers can charge clients for specific parts of the data.
This includes data on the demographics of a region, credit records for a region, purchase trends for a region and much more. This allows several business decisions to be made such as expansions into new regions.
Business Decisions Companies use customer data to make operational decisions, from inventory and pricing to expansion plans and product development.
What’s analyzed:
Buying behavior
Customer preferences
Peak usage times and seasonal patterns
Monetary Impacts:
Allows for firms to improve the accuracy of their inventory resulting in the production being as close to demand as possible reducing potential wastage. This also improves conversion rates by allowing firms to produce what firms in the regions want allowing for a greater percentage of the produced and stocked item sold.
Data also enables dynamic pricing. Dynamic pricing is when the price of a good or service changes according to the time of the day or the day based on factors such as the peak time for demand, any public holidays or other factors dependent on the firm allowing firms to maximise times with high demand to generate more revenue and profit.
How does gathering data help the user?
It may seem like gathering user data would only be beneficial to the firm but this isn’t the case. There are several ways the gathering and usage of data can be an advantage to consumers as well.
Hyper-Personalized Recommendations
Algorithms can be trained on user data and identify the users tastes and preferences.
These algorithms can then be used to provide users with personalized recommendations based on factors such as time of day or preferences.
An example of this can be found in Netflix. Netflix uses an algorithm that provides recommendations for users that match with previous movies watched and the preferred genre which reduces the time the user needs to spend scrolling.
Access to free services.
The data collection for monetization results in the provision of several free services which offer massive utility to the user.
Examples of this are google services such as Gmail, docs, slides and free access to social media platforms and other such services such as games on the App Store.
These platforms use user collected data to target them with advertisements which offers the user access to a lot of different applications and websites offering a benefit to both users and companies.
3. Lower consumer prices through greater data driven efficiency for firms.
Companies use sales data to avoid overproduction, lowering storage and waste costs.
Airlines and ride-hailing services use dynamic pricing to balance supply and demand.
Subscription services adjust pricing tiers based on user behavior and engagement data.
These are examples of actions that save the user money and increase their overall satisfaction by dynamically adjusting to the users needs using data.
4. Smarter AI assistants
Siri, Alexa, and Google Assistant remember commands, favorite settings, and patterns.
Smart assistants adjust responses based on context like time, location, or prior queries.
Automation tools become more useful with repeated use and learning.
Dictation and voice search improve over time as they adapt to your voice and speaking and can also adjust to accents.
5. Better Fraud detection
Location tracking helps verify identity:If you're in Dubai and someone tries to log into your account from Brazil, the system can block or flag it.
Spending patterns are monitored:Banks know your usual purchase behavior. If a large or strange purchase suddenly appears, they can alert you or freeze the card.
Device recognition adds security:Logging in from a familiar phone or laptop is considered safe. A new device might trigger extra verification steps.
Login habits are tracked:If you usually log in once a day and someone suddenly logs in five times in an hour, it can be flagged as suspicious.
AI watches for unusual actions:Platforms use algorithms to notice strange activity, like rapid transactions, password resets, or logging in from multiple places at once.
Two-factor authentication (2FA) is powered by data:Services use your contact info and habits to add a second layer of protection.
Final Remarks
As we navigate the modern digital landscape, it becomes increasingly clear that data has evolved into a cornerstone of the global economy. Our personal information is now the most valuable commodity enabling greater sources of income for tech giants like Google, Amazon and Meta while causing our lives to be more and more integrated into a digital economy as time passes.
As an exchange for gathering and monetizing our data, users are offered a better customer experience with greater optionality and utility to the user and greater access to new forms of using the internet at almost no cost to the user. However, there is always a concern about the misuse of data where companies could act in a way that is not beneficial for the user or violates the privacy of the user. This includes selling data to third parties without the consent of the user, collecting very personal details or collecting too much data about the user. There is a risk of data leaks and influencing and manipulating people based on their data and their interests such as for scams or influencing politician opinions for elections.
As we explore further, we will also look at how companies are being regulated to maintain privacy and how companies are adapting to that. Stay tuned for the next article.
