Big data is always described as an immensely valuable resource that can fuel any thriving enterprise, providing organizations with actionable insights, business opportunities and superior margins. Just like crude oil must be refined before it can be converted into a valuable and useful resource, however, data must be digested by artificial intelligence (AI) and machine learning (ML) before it is worth something. From leveraging it to improve the efficiency of an organization’s operations to harnessing it to create new revenue streams, business data can be monetized in a lot of different ways.
As Tim Sloane, VP of payments innovation at Mercator Advisory Group, explained, “data monetization is all about leveraging the data that you have through new channels.” Let’s have a look at a few concrete examples without wasting any time. Because time is money, my friend!
Selling Anonymized Customer Data to Third Parties
Customer data that is anonymized (i.e., deprived of any sensitive information) or synthetized (i.e., slightly modified so it is still 100% statistically relevant but impossible to trace back to the original customer) can be sold to other companies that need it in the form of analytic products. Aggregated, predigested data can be monetized since it may hold a value that goes beyond its original use and may create a new revenue stream. For example, a mall may want to know which type of food is preferred by video-game enthusiasts after they’ve made a purchase so that a specific fast-food booth can be placed in the same area as the gaming shops. Or a telecommunication company may sell customer geolocation data that can be used to plan more efficient “smart city” technology solutions.
Enhancing Marketing Efficiency
Reaching new prospects is necessary to providing a company with a constant flow of fresh customers. That’s the reason why marketing is almost always one of the most expensive items of expenditure in any modern enterprise’s budget. Machine learning can be used to make sense of a lot of marketing data, enhancing its efficiency and reducing the costs. Algorithms can be used to recommend further videos to watch or articles to read based on the individual preferences of the user, increasing the time spent on a website or platform, or grabbing the attention of more potential customers. The popularity of a piece of content can be forecasted through sentiment analysis, helping narrow down the type of content that you want to line up. (For more on AI in business, see How Artificial Intelligence Will Revolutionize the Sales Industry.)
Improved User Profiling
A full understanding of a company’s customers’ behavior is critical to squeeze more money out of them. Extracting actionable insights from user data is the bread and butter of big data analysis, and ML can take this process to the next level. Churn prediction models can be set to analyze customer behaviors and understand who are the people most likely to stop using your product after a short time. As appropriate action is taken to retain them (for example, through fully automatized CRM platforms), a lot of money is saved since the cost of acquisition is up to five times higher than the cost of retaining. Customer lifetime value (CLTV) models can also be used to determine which user personas are more likely to spend money on your products by extracting useful data from their habits. This helps companies focus their efforts only on those leads who can generate relevant revenue.
Insight and Advice as a Service
Companies often need to rely on the expertise of their oldest, most skilled employees to perform the most difficult tasks. An organization’s senior workforce is a critical asset whose knowledge and know-how is hardly transferable when these experienced workers eventually retire. However, some companies have employed artificial intelligence to digest countless pages of documentation that include user manuals, correspondence about daily operations, and reports written by the most skilled employees and former employees. The result was the creation of smart digital assistants that are able to provide useful insights in real time to new employees, quick analyses on material choices for manufacturing companies, and help every team member make any relevant decision on the spot. This helps employees be more productive by spending more time performing their jobs, and less time figuring out details.
Self-Service Analytics Platforms
Data can be turned into a monetizable asset even when a company is not proprietary of that data nor generates it. This complex business model is used to provide organizations who need to extract useful info from their strategic data with cloud-based, self-service analytics platforms. These platforms are powered by algorithms that aggregate, enrich and analyze their data for a variety of purposes — such as increasing the efficiency of machines in manufacturing implants and decreasing their costs by up to 68% — or enhance the management of complex systems, networks, power plants, etc. Often, these platforms combine the capabilities of ML with cutting-edge sensor data to improve their ability to predict and self-heal failures, automate and optimize operational tasks, and reduce downtimes by up to 40%. (Not everybody has implemented ML yet. Find out why in 4 Roadblocks That Are Stalling Adoption of Machine Learning.)
Avoid Advertising Fraud
Many companies who cannot afford in-house marketing teams must rely on third party vendors to provide them with new leads and prospects. However, in the age of digital fraud, not every seller is as transparent as it should be. To falsely inflate the number of customers reached, some less scrupulous advertising agencies sell false social profiles that provide false reviews, comments and interactions on social media, or bots which constantly download apps, software, and mobile/online games. However, these are not live users — not only will they never pay for any service, but they can also be confused with real people, and given their potentially large number, lead organizations into forming a false user persona. Bots and false profiles can be easily detected using machine learning because, you know, machines are more expert than us at detecting their own kind!
There should be a reason (probably more than one) if today, 68% of companies adopt machine learning to enhance processes. Those who understood the full potential of algorithm-powered data management and data governance saw their growth increase by 43% more than those who didn’t. A new market for data and insights has already been born, and machine learning is the “refinery” that is making this resource even more valuable and easy to monetize.