Intelligent ways to monetize data
11 MINS READ
Information service providers (ISPs) monetize information by utilizing their knowledge and expertise to create valuable publications.
These publications cater to the demands of a wide range of consumers, including institutions, organizations, professionals, students, and the public. Generally, consumers can access these publications through paid subscriptions on digital content delivery platforms.
Most ISPs don’t take advantage of the data available to them because data privacy concerns have hindered the exploration of such information. Using this data to create new business opportunities is impacted by regulations surrounding personally identifiable information (PII). While data privacy laws like the EU’s General Data Protection Regulation (GDPR) are essential for protecting consumer information, they make it difficult for businesses to use the data they have collected.
Companies need to explore innovative and intelligent data monetization solutions to share this data with ecosystem partners and across geographies. We explore how to use fragmented data and monetize it.
We propose an AI-powered tool that analyzes anonymized data of content users and generates personas based on four criteria: subscriptions, consumption, behavior, and search. This tool creates a replicable way to recommend options for cross-selling and up-selling data. It also analyzes new publications using natural language processing (NLP) to estimate their potential user demand and popularity. With this estimated data, editorial and marketing teams can make intelligent, strategic decisions about the pricing for new publications.
Defining data monetization
Organizations are looking to leverage data to obtain quantifiable economic benefits.
We define data monetization as generating revenue from available data sources or real-time streamed data through the discovery, capture, storage, analysis, dissemination, and use of information. Companies can leverage information collected through business operations, individual social media or customer interactions, electronic devices, and sensors to gather valuable insights for future strategies.
Direct data monetization includes directly generating revenue from the sale of raw data produced from company analysis, data trade, and the creation of APIs. Indirect data monetization consists of the usage of collected data to reduce costs, improve productivity, develop new products or services, or discover potential markets.
The data monetization market
In 2020, the global data monetization market was valued at $2.1 billion and is projected to reach $15.4 billion by 2030.
This exponential growth trajectory represents an estimated 22.1% CAGR over a period of ten years. The growth in this sector is due to the consistent rise in enterprise data, technological advancements in big data and analytics solutions, and an increased focus on generating new revenue streams. Notably, the telecom industry has been reaping the fruits of the data monetization expansion. In 2021, North America was the largest region in terms of data monetization in the telecom market. Data monetization in the telecom market is segmented by components like tools and services; by data types like customer data, product data, financial data, and supplier data; by the size of the organization such as small and medium-sized enterprises (SMEs) or large enterprises; and by deployment types like on-premises, cloud. Corporations experiencing optimum growth and performance have adopted data monetization as an essential part of their strategy.
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.
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.
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.
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.