Monetizing Data: A New Source Of Value In Payments

monetizing data new source value payments

In today’s business world, leading companies are using modernized analytics to monetize the data itself. Providers of payments are already experts at generating customer insights from data. 

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Payments providers, nowadays, have a treasure trove of data in just one click. They use it to generate insights into consumer purchasing behavior, and connecting these insights with an analysis of emerging macro trends. Because of these, payments firms can offer better service to customers from fraud detection to spending insights. Furthermore, they can move forward by capturing emerging opportunities to extract value by means of knowing how to transform data into actual revenue, either internally or through different third parties. According to the top marketing management agencies, these opportunities extend further than the margins of traditional payments firms and require them to take advantage of distinct data sets and apply innovated analytical strategies. 

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If payments providers are able to interpret the data correctly, they can experience a range of benefits, from providing service to previously unserved customer segments up to starting new revenue sources via product cross-selling. Taking these benefits will involve undergoing strategic steps such as building partnerships and alliances to make the data stronger or exploring how to forecast new business trends such as peer-to-peer and mobile payments take hold. Payment providers have distinctive power over their position to attain emerging opportunities because they have insights into merchants as well as consumers, and can link the distance between them by giving incentives to influence consumers’ decisions when choosing merchants. This can be a great business idea for moms

What Are The Sources Of Data? 

All businesses and banks have two types of customer data: line-of-business (LOB) data owned by a specific part of the business, and common data, which I divided into two groups: enterprise-level data and supplemental data. 

Enterprise-level data contains similar elements as LOB data such as customer preferences, needs assessments and many more. It’s just that it spans the organization. 

Supplemental data on the other hand, comes from raw data derived from external sources like social media, weather data, and digital IDs to value-added analysis that are taken through projective modelling, sentiment analysis and so on. 

Payments firms can capture the most valuable insight via adding supplemental data to their recent internal data. Combining internal and external data can also create great value and can help data owners generate value. 

Next Steps For Payments Providers 

According to A + Digital, data monetization launching process requires an effort. Payments providers can start by taking some practical steps: 

• Identify and prioritize use cases and hypothesis for existing data. Concentrate on those areas where data is already available and uncontaminated, and where the organization has the fuel to deliver its capabilities. 

• Make sure that the appropriate stakeholders are personally involved and committed. Both frontline business and back-end technology providers require a great team effort, especially in a multidisciplinary environment. 

• Have nontraditional data sources to make the value proposition stronger for identified use cases. These include web behavior, mobile data session, social media, public blogs, and weather forecasts. To capture more insights you should use data platforms, partnerships and internal sources. 

• Develop external skills like involvement of global community of data scientists through giving them public or sanitized data sets and run hackathons or by tournaments to generate new ideas, strategies, and methods. Just like how Prudential Financial and Santander sponsored competitions of data-science hackathon platform on Kaggle. 

How To Capture Value From Your Customer Data? 

• Continue experimenting. Run and develop algorithms to prove or disprove a hypothesis. Let the machine to its job and apply analysis with experience. Encourage continuous improvement and learning to develop new systems. 

• Be a culture sensitive. Engaging with too much advanced analytics requires radical shift in mind-set. You should be careful with sudden and unplanned shifting. Study and analyze if you need to continue or discontinue a certain method. 

• Create a more flexible process and plan that can accommodate immediately to unpredictable market changes. Urgency should not bring chaos to your data sources and technology.

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