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Monetizing data: A new source of value in payments

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Summary

This article examines the value of data monetization in the payments industry and how payments providers can use data to generate insights and extract value. It discusses the sources of data, the uses of data monetization, and the challenges payments providers must overcome in order to capture emerging opportunities. It looks at the successes of early adopters, the opportunities for monetizing data, pricing models, and the four primary challenges payments providers face. It also suggests practical steps for payments providers to launch a data monetization effort.

Q&As

What data sources are available to payments providers?
Payments providers have access to line-of-business (LOB) data owned by a particular part of the business, enterprise-level data, and supplemental data such as raw data derived from external sources and value-added analytics.

What are the opportunities for data monetization in the payments industry?
Opportunities for data monetization in the payments industry include coupling consumers with preferred merchants, channels, and potentially products; geo-referring transactions to identify a customer’s location; and understanding the dynamics of local markets at a sub-postal code level.

What type of customers are typically targeted for data monetization?
The target customers for data monetization are usually merchants, who are charged a fee for a service provided. Cardholders are more difficult to monetize, but could be offered additional services with a “freemium” pricing scheme. Banks could also become valuable end customers.

What are the primary challenges to monetizing data?
The primary challenges to monetizing data include a lack of business focus, ownership, and accountability; difficulty in attracting the right talent; significant regulatory and reputational barriers; and the need to overhaul existing organizational structures.

What steps can payments providers take to begin a data monetization effort?
Payments providers can begin a data monetization effort by defining and prioritizing use cases and hypotheses for existing data, ensuring the appropriate stakeholders are involved and committed, drawing on nontraditional data sources, using partnerships, data platforms, and internal resources to capture customer insights, running algorithms to prove or disprove hypotheses, trying new technologies, algorithms, and data sources, and developing a flexible process and plan.

AI Comments

👍 This article provides an in-depth analysis of the potential value that payments providers can gain from monetizing data. It also outlines the steps that can be taken to maximize the value of this data.

👎 This article does not provide enough insight into how payments providers can safeguard customer data while still monetizing it. It also fails to discuss the potential risks associated with monetizing data.

AI Discussion

Me: It's about monetizing data and how payments providers can take advantage of this to unlock additional value. It looks at the different sources of data, how the business is developing, and the challenges payments providers might face.

Friend: Interesting. What are some of the implications of this article?

Me: Well, payments providers have a unique opportunity to capture emerging data monetization opportunities due to their access to customer and merchant data. However, there are some challenges they must overcome like business focus, ownership, and accountability. They also need to consider data privacy and compliance regulations and overhaul their existing organizational structures. To launch a data monetization effort, payments providers need to define and prioritize use cases, draw on external skills, and experiment with new technologies, algorithms, and data sources.

Action items

Develop a strategy for data monetization that takes into account the challenges and opportunities outlined in the article.
Identify potential use cases and hypotheses for existing data and prioritize them based on the data available and the organization's capabilities.
Invest in the necessary talent and resources to develop an attractive environment for hiring, developing, and retaining the necessary data scientists, data engineers, and digital product managers.

Technical terms

Gateways: A gateway is a computer or network device that serves as an access point to another network.
Treasure trove: A treasure trove is a large collection of valuable items or information.
Fraud detection: Fraud detection is the process of identifying suspicious activity that may indicate fraudulent behavior.
Cross-selling: Cross-selling is a sales technique in which a salesperson attempts to sell additional products or services to an existing customer.
PSD2: The Payment Services Directive 2 (PSD2) is an EU directive that regulates payment services and payment service providers throughout the European Union.
Data lake: A data lake is a large repository of data stored in its native format, usually object blobs or files.
Sentiment analysis: Sentiment analysis is the process of analyzing text to determine the sentiment it conveys, such as positive, negative, or neutral.
Predictive modelling: Predictive modelling is the process of using data to create a model that can predict future outcomes.
Freemium: Freemium is a pricing model in which a basic version of a product or service is offered for free, while additional features are available for a fee.
DevOps: DevOps is a set of practices that combines software development and operations to shorten the development life cycle and provide continuous delivery with high software quality.