Acquiring is Dead. Long Live Acquiring.
Data-driven services can help merchant acquirers add value to their core capabilities. However, to succeed, they need to be armed with the necessary data governance capabilities & know-how.
There is an emergence of bullish outlooks for payments acquiring globally. This is evidenced by the merchant acquirer Price to Earning valuations which are now in line with top technology company valuations, including Apple, Facebook, and Google — highlighting investors’ high expectations for future growth and profitability. (See Figure 1 for select company valuations).
Figure 1: Payment acquirers are outpacing their industry peers
Traditionally, the acquirer’s primary product had been merchant processing and services and were generally treated as a cost centre within a bank.
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Figure 2: Foundational core capabilities of traditional acquirer
However, as the payment industry undergoes secular growth, acquirers will need to focus on adding value to their foundational capabilities that resonate with their merchant base as well as to achieve the rich valuations that the market is placing on them.
Data-driven, value-added services offer exciting possibilities to help acquirers add value to their core capabilities.
Increased focus on SMEs
Small and medium enterprises (SMEs) are more lucrative than crowded enterprise segments — according to BCG estimates, this segment will contribute to 70–80% of global net acquiring revenues by 2023 (See figure below).
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Figure 3: SMEs will driving acquiring revenues (Source: BCG)
However, it is not an easy path to profitability for SME acquiring which varies significantly by industry vertical. Furthermore, pricing power is heavily skewed toward ISVs (Independent Software Vendors) in some verticals (e.g., EdTech, food & beverages, etc.). With advanced data analytics, an acquirer can run segment profitability analytics during planning processes to ensure healthy P&L (Profit & Loss) while servicing this segment.
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Figure 4: Profitability analysis will be “make or break” factor in serving SMEs
large enough dataset to drive any meaningful analytics. This is where an acquirer, by virtue of its transaction visibility across multiple merchants, can build a sticky service using their aggregated view to gain deeper insights than an individual small business could ever do. For example, examining the behaviour and performance of a certain merchant population, can help individual merchants benchmark against competitors. Another use case for aggregated data is producing market studies available for purchase by third parties such as marketing companies.
Data Analytics as a service (DAaaS) Platform
Many acquirers are now working on a single payments hub that integrates data, pricing, and functionality across all channels for their merchant base. Such platforms can be bolstered with “data analytics on demand” like service.
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Figure 5: Typical analytical capabilities across business functions
With a platform that can act as repository of clean, model-ready datasets, it can become a great enabler for merchants large and small alike. Apart from that, acquirers can monetise such data by opening to 3rd parties to build value-added services, hence triggering a platform network effect.
For example, consider Cardlytics. It uses advanced analytic technologies to help banks take advantage of their consumer purchasing data. It delivers card-linked, location-specific advertising to consumers through their mobile and online banking applications. Consumers see ads for nearby retailers and get cashback rewards from their bank for shopping at those retailers using their credit card.
Regulatory Considerations
Monetising data will require companies to build a deep understanding of fragmented privacy and compliance requirements from the very beginning and use them as design principles in shaping new products and services. It becomes paramount to either develop in-house skills of proper data governance or work with a partner that has experience handling sensitive payment and financial data. Furthermore, moves toward open banking and similar initiatives can also be seen as a call to action, given that they make access to payments data easier for third-party players that can threaten an acquirer’s window of opportunity. Armed with the necessary data governance, know-how, and capabilities, acquirers could then consider offering customers rewards or benefits for allowing their personal data to be used for commercial purposes. Incumbent banks are uniquely positioned to pursue emerging data monetisation opportunities because they have insights into merchants (acquiring) as well as consumers (issuing) and can bridge the gap between them by providing incentives to influence consumers’ choice of merchants.
Organisational Considerations
To unlock real value, acquirers need to learn how to work productively with analytics specialists — or develop in-house capabilities to provide similar services for themselves. This will include, but is not limited to data scientists, data engineers, data architects, and digital product managers. And beyond technology, business leaders need to become more analytics-savvy to be able to work seamlessly with technology teams to deliver business outcomes. Finally, the use of advanced analytics such as AI and machine learning will require building new capabilities across the organisation, especially around data governance, DataOps and AnalyticsOps.
Conclusion
Incumbent APAC banks and acquirers are well poised to revitalise existing capabilities with data-driven outcomes. Increasing valuation multiples suggest that acquirers have robust growth expectations and that strong drivers of additional revenue, such as expanding value-added services, are improving economics within these firms. Advanced data analytics is an area that cannot be ignored. A “start small, scale fast” approach can be deployed for flexibility, learning, and course correction as the business develops. Partnerships with experienced data management players in payments could be a winning factor.
Figure 1: Payment acquirers are outpacing their industry peersTraditionally, the acquirer’s primary product had been merchant processing and services and were generally treated as a cost centre within a bank.
Figure 2: Foundational core capabilities of traditional acquirerHowever, as the payment industry undergoes secular growth, acquirers will need to focus on adding value to their foundational capabilities that resonate with their merchant base as well as to achieve the rich valuations that the market is placing on them.
Data-driven, value-added services offer exciting possibilities to help acquirers add value to their core capabilities.
Increased focus on SMEs
Small and medium enterprises (SMEs) are more lucrative than crowded enterprise segments — according to BCG estimates, this segment will contribute to 70–80% of global net acquiring revenues by 2023 (See figure below).
Figure 3: SMEs will driving acquiring revenues (Source: BCG)However, it is not an easy path to profitability for SME acquiring which varies significantly by industry vertical. Furthermore, pricing power is heavily skewed toward ISVs (Independent Software Vendors) in some verticals (e.g., EdTech, food & beverages, etc.). With advanced data analytics, an acquirer can run segment profitability analytics during planning processes to ensure healthy P&L (Profit & Loss) while servicing this segment.
Figure 4: Profitability analysis will be “make or break” factor in serving SMEslarge enough dataset to drive any meaningful analytics. This is where an acquirer, by virtue of its transaction visibility across multiple merchants, can build a sticky service using their aggregated view to gain deeper insights than an individual small business could ever do. For example, examining the behaviour and performance of a certain merchant population, can help individual merchants benchmark against competitors. Another use case for aggregated data is producing market studies available for purchase by third parties such as marketing companies.
Data Analytics as a service (DAaaS) Platform
Many acquirers are now working on a single payments hub that integrates data, pricing, and functionality across all channels for their merchant base. Such platforms can be bolstered with “data analytics on demand” like service.
Figure 5: Typical analytical capabilities across business functionsWith a platform that can act as repository of clean, model-ready datasets, it can become a great enabler for merchants large and small alike. Apart from that, acquirers can monetise such data by opening to 3rd parties to build value-added services, hence triggering a platform network effect.
For example, consider Cardlytics. It uses advanced analytic technologies to help banks take advantage of their consumer purchasing data. It delivers card-linked, location-specific advertising to consumers through their mobile and online banking applications. Consumers see ads for nearby retailers and get cashback rewards from their bank for shopping at those retailers using their credit card.
Regulatory Considerations
Monetising data will require companies to build a deep understanding of fragmented privacy and compliance requirements from the very beginning and use them as design principles in shaping new products and services. It becomes paramount to either develop in-house skills of proper data governance or work with a partner that has experience handling sensitive payment and financial data. Furthermore, moves toward open banking and similar initiatives can also be seen as a call to action, given that they make access to payments data easier for third-party players that can threaten an acquirer’s window of opportunity. Armed with the necessary data governance, know-how, and capabilities, acquirers could then consider offering customers rewards or benefits for allowing their personal data to be used for commercial purposes. Incumbent banks are uniquely positioned to pursue emerging data monetisation opportunities because they have insights into merchants (acquiring) as well as consumers (issuing) and can bridge the gap between them by providing incentives to influence consumers’ choice of merchants.
Organisational Considerations
To unlock real value, acquirers need to learn how to work productively with analytics specialists — or develop in-house capabilities to provide similar services for themselves. This will include, but is not limited to data scientists, data engineers, data architects, and digital product managers. And beyond technology, business leaders need to become more analytics-savvy to be able to work seamlessly with technology teams to deliver business outcomes. Finally, the use of advanced analytics such as AI and machine learning will require building new capabilities across the organisation, especially around data governance, DataOps and AnalyticsOps.
Conclusion
Incumbent APAC banks and acquirers are well poised to revitalise existing capabilities with data-driven outcomes. Increasing valuation multiples suggest that acquirers have robust growth expectations and that strong drivers of additional revenue, such as expanding value-added services, are improving economics within these firms. Advanced data analytics is an area that cannot be ignored. A “start small, scale fast” approach can be deployed for flexibility, learning, and course correction as the business develops. Partnerships with experienced data management players in payments could be a winning factor.
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