Intelligent ways to monetize data

The benefits of AI implementation

In the US alone, 22% of businesses use AI as part of their regular operations

Companies that leverage AI and machine learning experience 43% more growth on an average than their competitors who either do not use AI or haven’t integrated the technologies well.

While implementing an AI tool will not solve all data monetization issues, it can help businesses use fragmented data effectively while staying GDPR-compliant. Companies using this tool will need to train their employees or hire a knowledgeable third-party team.

Companies can utilize AI for purposes like searching for and analyzing patterns, behaviors, and emerging trends to provide end-users options for new business ventures. For example, unearthing hidden patterns would enable a company to shape the persona of a user. It can chalk out product journey maps or that of specific types of accounts like subscription account journey versus trial account journey. Companies can estimate the lifetime values of their customers, individual products, or publications. These newly developed capabilities and insights would help the companies determine the optimal price point of a publication or simulate consumption patterns of their various offerings.

Large corporations can use AI to automate data ingestion and harmonization and streamline the delivery of data-driven services.  AI can also process large, complex, and unstructured data sets, as well as sensitive information, enabling businesses to make use of otherwise inaccessible data.

Telecoms, AI, and data monetization

Using AI, telco industry leaders can expect three potential revenue streams from data monetization.

Expansion of service offerings to existing customers

Brand loyalty is an essential component of any business. Companies can utilize AI to identify new services they could offer to their existing customers. By expanding their available services, companies gain an edge over their competitors.

Most telco industry leaders have used AI to develop innovative upgrades and additional features for consumers. Some of these upgrades include:

• Privacy and protection features: Creating a feature that relieves customers from the nuisance of robocalls and spam calls 

• Supplementary storage offerings: Tracking the amount of data customers consume, and if they exhibit an above-average threshold of data consumption, offering them additional cloud storage

• Flexible plans: Monitoring usage patterns to help companies create new service plans or custom options

Creation of new offerings for external companies

Organizations can also consider catering to third parties. Providing data to external companies offers a high-profit margin, and the possibilities for data collection and distribution will continue to increase.

Options for data monetization for third parties include:

• Data for app developers: Aggregating app data to help developers improve UX, prioritize features, and create relevant in-app purchase opportunities

• Data for municipalities: Collecting location and transportation information to change public transportation routes, reduce road congestion, and improve traffic infrastructure

• Data for retailers: Providing location data to help store owners purchase their first storefront or expand their reach

• Data for advertisers:  Gathering location data so that marketers can craft personalized offers for customers in a specific location

Improvement of internal business practices

Using collected data to improve internal business practices is an excellent way to enhance or maintain brand reputation. Customers will show more willingness to work with companies that have a positive reputation and stand out from their competitors.

Business divisions that benefit from utilizing AI-collected data include marketing, sales, support, and general operations.

Implementing an effective AI-driven data monetization strategy

For effective AI-driven data monetization, companies need to consider some critical components.

Technical framework

Before companies can begin monetizing their data, they must create the technical framework for data collection and aggregation. This framework will vary depending on whether the corporation decides to parse information from old data, incoming data, or both.

The flow of high-quality data

Monetizing data is impossible without high-quality data. Before companies start collecting data, they must ensure a consistent source of accurate information.

Compliance and security

Businesses must abide by data privacy ethics when sharing or selling data to avoid fines and legal repercussions. Companies implement AI to collect and analyze data; they must also be transparent about collection and use, receive legal consent from consumers, and thoroughly clean and anonymize data. AI ensures compliance by encrypting and safely storing user data.

Data harmonization

Collecting data is only a piece of the puzzle. Cleaning and anonymizing data for internal or external purposes are essential but time-consuming. Data scientists spend approximately 60% of their time cleaning and organizing data. Utilizing AI to clean data will give analysts more time to use the data. Thus, companies can strategize data monetization by banking on AI.

User expertise

An AI tool is only as strong as the user behind it. To maximize the effect of AI for data monetization, companies either need a well-trained, knowledgeable staff or a third-party partner that can provide the necessary expertise.

Intelligent data monetization solutions are vital when data privacy regulations prevent the disclosure of PII data outside of an organization or a country. Building a platform that ensures compliance while enabling mass personalization from fractured data will be game-changing for subscription-based content delivery business models.