How to Monetizing Data Through Machine Learning
Big Data is a resource that can be used to fuel any business. It provides actionable insight and monetary value. Superior margins and business opportunities Just as crude oil needs to be refined before it can become a valuable and usable resource, so data must be processed by artificial intelligence (AI), and machine learning before it is useful. Using it to increase efficiency in an organization’s operations or to generate new revenue streams from it, Business data can be monetized in many different ways.
Tim Sloane is VP of payments innovation at Mercator Advisory Group. He explained that data monetization involves leveraging data through new channels. Let’s take a look at some concrete examples, without wasting time. My friend, time is money!
Selling anonymized customer data to third parties
Customer data is anonymized (i.e. deprived of sensitive information) or synthesized. This means that it has been (slightly modified to be statistically relevant at 100% but it is impossible to trace the origin customer back) This information can be sold to other companies in the form of analytic products.
Predigested aggregate data can be monetized as it may have a greater value than its original purpose and could create a new revenue stream. A mall might want to know what type of food video-game fans prefer after making a purchase. This could allow them to place a fast-food stand in the same location as the gaming shops. A telecommunication business may offer geolocation customer data that can be used for planning more efficient “smart cities” technology solutions.
Enhancing Marketing Efficiency
To ensure a steady flow of new customers, it is important to reach out to new prospects. Marketing is therefore almost always the most costly item of expenditure in an enterprise’s budget. Machine Learning is a way to get a lot out of marketing data. This can improve its efficiency and reduce costs. Algorithms are used to suggest additional videos or articles be viewed based on individual preferences. This increases the time spent on a site or platform and grabs the attention of more customers. sentiment analysis can predict the popularity of a piece of content. This will help you narrow down the content you are interested in.
Improved User Profiling
To extract more from customers, it is crucial to have a complete understanding of their behavior. Big data analysis is all about extracting actionable insights from user data. ML can help make this process to the next stage. You can use churn prediction models to analyze customer behavior and determine who is most likely to discontinue using your product. If the right actions are taken to keep them, such as through fully automated CRM platforms, you can save a lot of money.
The cost of acquisition is often five times greater than the cost to retain. You can also use customer lifetime value (CLTV), models to identify which user persons are most likely to spend money on your products. This is done by extracting valuable data from their behavior. This allows companies to focus their efforts on leads that can generate revenue.
Insight and Advice as a Service
For the most challenging tasks, companies often have to rely on their most senior employees. The knowledge and experience of a company’s senior workers are invaluable assets. However, this knowledge is not transferable once they retire. Some companies use artificial intelligence for the processing of countless pages worth of documentation. This includes user manuals, correspondence regarding daily operations, as well as reports from former employees. Smart digital assistants were created.
They can provide valuable insights to new employees in real-time, quickly analyze material choices for manufacturing firms, and assist every member of the team with any pertinent decision. Employees are more productive because they spend more time doing their job and less time trying to figure out details.
Self-Service Analytics Platforms
Even if a company does not own the data or generate it, data can still be made into a monetizable asset. This business model allows organizations to access useful information from strategic data using cloud-based, self-service analytics platforms. These platforms use algorithms to aggregate, enrich, and analyze data.
This can be used for many purposes, including increasing the efficiency of machines manufacturing implants and decreasing their cost by up to 68%. It also helps improve the management of complex networks and power plants. These platforms often combine the capabilities and data of ML with cutting-edge sensor data to increase their ability to predict and heal failures, automate and optimize operations, and reduce downtimes up to 40%. (Not everyone has yet implemented ML.
Avoid Advertising Fraud
Many companies cannot afford to hire in-house marketing staff and must rely on third-party vendors for new leads or prospects. In the digital age, every seller may not be as transparent as they should be. Some less careful advertising agencies sell fake social profiles to inflate customer reach. These social profiles provide false reviews, comments, interactions, and interactions on social networks or bots that download software and mobile/online games constantly.
These are not real users. They will not pay for any services, and they can be misidentified with real people. This could lead to organizations creating a false user persona due to their potentially large number. False profiles and bots can easily be detected by machine learning, as machines are better at detecting their kind than we are.
It should not be surprising that 68% of companies use machine learning to improve their processes. The growth of companies that understood the potential of algorithm-powered data management, and data governance increased by 43% compared to those that did not. The machine-learning market has opened up a new avenue for data and insights. This makes it even more valuable and easier to monetize.