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Best practices to keep pace with changing payment behavior in the Post-Covid Era

Best practices to keep pace with changing payment behavior in the Post-Covid Era

Posted | Updated by Insights team:
Dr. Evangelo Damigos; PhD | Head of Digital Futures Research Desk
  • Competitive Differentiation
  • Post-Covid-19


Publication | Update: Oct 2020
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Peter Moedlhammer, Director, Product Management at ACI Worldwide, stresses on offering the right payment methods, providing reliable authentication and anti-fraud services, and making the checkout as seamless as possible, as key aspects in this fast-changing ecosystem

In 2019, we saw a few key trends that have shaped the ecommerce and payments landscape: there has been an increase in mobile commerce and payments, as well as cross-channel commerce. Furthermore, we have seen that the rise of alternative payment methods continued throughout 2019, creating a demand for merchants to offer more payment methods.

In 2020, the global pandemic has made it even more important to be able to quickly respond to the changing market requirements on various levels: a service provider needs to offer merchants the most relevant payment methods, have a robust anti-fraud environment, and provide a seamless checkout experience for the customer in order to drive conversion and increase sales.

Changing payment behavior

Payments have become increasingly digital for years now, and the pandemic has acutely accelerated this trend: in July 2020, YouGov surveyed 2081 UK adults about their shopping behaviour, and found that 63% of their participants have used more electronic payments as a result of COVID-19. 63% of participants used more card payments in general, and 80% used more contactless card payments. Mobile payments are also on the rise: 24% of participants indicated that they used more mobile wallets, such as PayPal and Apple Pay.

Not only are customers using digital payments in-store, they are also increasingly moving to ecommerce for their shopping. 2019 saw a 15% increase in ecommerce payments value compared to 2018, and COVID-19 is further driving people to buy more online, as 21% of the YouGov survey’s participants did more groceries online as a result of the pandemic.

Alongside more online shopping, consumers have adopted other shopping methods as well: according to the National Retail Federation, 50% of consumers have tried Buy Online, Pickup In Store (BOPIS) as a result of the pandemic, and 90% prefer to have the option of curb side delivery.

The increase of online shopping goes hand-in-hand with an increase in alternative payment methods, as we saw significant increases in the use of methods like PayPal, Klarna, ‘buy now, pay later’, and bank transfers to pay online. Offering the right payment methods is key to increasing conversion: according to our research, 59% of customers abandon if their preferred payment method is not offered.

Thus, it is up to the payment provider to offer a broad enough spectrum of payment methods for merchants to be effective.

The growing ecommerce industry also brings with it new fraud-related challenges: through data breaches and developing technology there has been a long-ongoing increase in account takeover fraud, and we saw that through the increase in popularity of click-and-collect as a shopping method, as this type of fraud was the fastest growing fraud trend in 2019. The rapidly changing shopping and payment landscape warrants a dynamic and multi-layered approach to fighting fraud.

Machine learning (ML) models can be a key aspect of a modern anti-fraud solution: as a merchant, you need to know who your shoppers are and many anti-fraud mechanisms cause a lot of friction for the customer. ML models can learn the difference between a good and a bad customer incrementally from historic data and thus provide a smoother experience for shoppers, while not compromising security, which makes it one of the most promising technologies out there.

When selling to European consumers, there is another challenge on the horizon: as of 31 December 2020, the PSD2/Strong Customer Authentication requirement will apply, which will make merchants liable for maintaining low fraud rates. It is the service provider’s task to enable the merchant to do this effectively and have a SCA-compliant solution in place that utilises 3DS 2.0. 3DS 2.0 aims to reduce a lot of the friction brought about by its predecessor, and it can be integrated with a variety of devices, thus driving conversion.

In sum, the digital landscape provides interesting opportunities as well as challenges for merchants. Service providers will have to support merchants by providing dynamic and multi-layered anti-fraud solutions.

Seamless checkout

Finally, we cannot stress enough how important it is for online conversion to offer a seamless experience to your customers. There are a number of things of which merchants and service providers need to be mindful. Firstly, the website needs to be responsive and fast: lacking this, the website will frustrate the customer and they will abandon. Secondly, a merchant needs to provide the option of a Guest check-out – having to create an account is a major reason for customers to abandon.

Thirdly, we have seen that enabling one-click payments through card on file and tokenization for recurring customers, as well as in-app payments can be highly beneficial for any online business. Finally, it can be incredibly useful to offer a customer an alternative payment method after their initial choice fails for some reason. Anything that you can do to retain the customer will be a good investment – improving the checkout to be more seamless can result in a 35.26% increase in conversion, according to research by the Baymard Institute.

In conclusion, during a time where the shopping and payment landscape changes faster than ever, it is key to increase conversion by offering the right payment methods, providing reliable authentication and anti-fraud services, and making the checkout as seamless as possible.

This article was published in The Paypers Payments Methods Report 2020, an extensive overview of what’s new in how people pay in the most relevant ecommerce markets.

Peter Moedlhammer, in his role as Director, Product Management at ACI, is responsible for defining, positioning, and launching the company’s Secure eCommerce payments solution. Bringing many years of product management experience in ecommerce and payments to ACI, Peter’s primary focus is on how merchants and merchant intermediaries interact with ACI’s ecommerce tools, platform, and value-added products (via the API) to create value and drive business growth globally.

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Objectives and Study Scope

This study has assimilated knowledge and insight from business and subject-matter experts, and from a broad spectrum of market initiatives. Building on this research, the objectives of this market research report is to provide actionable intelligence on opportunities alongside the market size of various segments, as well as fact-based information on key factors influencing the market- growth drivers, industry-specific challenges and other critical issues in terms of detailed analysis and impact.

The report in its entirety provides a comprehensive overview of the current global condition, as well as notable opportunities and challenges. The analysis reflects market size, latest trends, growth drivers, threats, opportunities, as well as key market segments. The study addresses market dynamics in several geographic segments along with market analysis for the current market environment and future scenario over the forecast period. The report also segments the market into various categories based on the product, end user, application, type, and region.
The report also studies various growth drivers and restraints impacting the  market, plus a comprehensive market and vendor landscape in addition to a SWOT analysis of the key players.  This analysis also examines the competitive landscape within each market. Market factors are assessed by examining barriers to entry and market opportunities. Strategies adopted by key players including recent developments, new product launches, merger and acquisitions, and other insightful updates are provided.

Research Process & Methodology

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We leverage extensive primary research, our contact database, knowledge of companies and industry relationships, patent and academic journal searches, and Institutes and University associate links to frame a strong visibility in the markets and technologies we cover.

We draw on available data sources and methods to profile developments. We use computerised data mining methods and analytical techniques, including cluster and regression modelling, to identify patterns from publicly available online information on enterprise web sites.
Historical, qualitative and quantitative information is obtained principally from confidential and proprietary sources, professional network, annual reports, investor relationship presentations, and expert interviews, about key factors, such as recent trends in industry performance and identify factors underlying those trends - drivers, restraints, opportunities, and challenges influencing the growth of the market, for both, the supply and demand sides.
In addition to our own desk research, various secondary sources, such as Hoovers, Dun & Bradstreet, Bloomberg BusinessWeek, Statista, are referred to identify key players in the industry, supply chain and market size, percentage shares, splits, and breakdowns into segments and subsegments with respect to individual growth trends, prospects, and contribution to the total market.

Research Portfolio Sources:

  • BBC Monitoring

  • BMI Research: Company Reports, Industry Reports, Special Reports, Industry Forecast Scenario

  • CIMB: Company Reports, Daily Market News, Economic Reports, Industry Reports, Strategy Reports, and Yearbooks

  • Dun & Bradstreet: Country Reports, Country Riskline Reports, Economic Indicators 5yr Forecast, and Industry Reports

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  • Ken Research: Industry Reports, Regional Industry Reports and Global Industry Reports

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  • OECD: Economic Outlook, Economic Surveys, Energy Prices and Taxes, Main Economic Indicators, Main Science and Technology Indicators, National Accounts, Quarterly International Trade Statistics

  • Oxford Economics: Global Industry Forecasts, Country Economic Forecasts, Industry Forecast Data, and Monthly Industry Briefings

  • Progressive Digital Media: Industry Snapshots, News, Company Profiles, Energy Business Review

  • Project Syndicate: News Commentary

  • Technavio: Global Market Assessment Reports, Regional Market Assessment Reports, and Market Assessment Country Reports

  • The Economist Intelligence Unit: Country Summaries, Industry Briefings, Industry Reports and Industry Statistics

Global Business Reviews, Research Papers, Commentary & Strategy Reports

  • World Bank

  • World Trade Organization

  • The Financial Times

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  • OneSource Business

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M&A and Risk Management | Regulation

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  • ISS Corporate Governance

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  • International Tax and Business Guides

  • CoreCompensation

  • CCH Research Network

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Forecast methodology

The future outlook “forecast” is based on a set of statistical methods such as regression analysis, industry specific drivers as well as analyst evaluations, as well as analysis of the trends that influence economic outcomes and business decision making.
The Global Economic Model is covering the political environment, the macroeconomic environment, market opportunities, policy towards free enterprise and competition, policy towards foreign investment, foreign trade and exchange controls, taxes, financing, the labour market and infrastructure. We aim update our market forecast to include the latest market developments and trends.

Forecasts, Data modelling and indicator normalisation

Review of independent forecasts for the main macroeconomic variables by the following organizations provide a holistic overview of the range of alternative opinions:

  • Cambridge Econometrics (CE)

  • The Centre for Economic and Business Research (CEBR)

  • Experian Economics (EE)

  • Oxford Economics (OE)

As a result, the reported forecasts derive from different forecasters and may not represent the view of any one forecaster over the whole of the forecast period. These projections provide an indication of what is, in our view most likely to happen, not what it will definitely happen.

Short- and medium-term forecasts are based on a “demand-side” forecasting framework, under the assumption that supply adjusts to meet demand either directly through changes in output or through the depletion of inventories.
Long-term projections rely on a supply-side framework, in which output is determined by the availability of labour and capital equipment and the growth in productivity.
Long-term growth prospects, are impacted by factors including the workforce capabilities, the openness of the economy to trade, the legal framework, fiscal policy, the degree of government regulation.

Direct contribution to GDP
The method for calculating the direct contribution of an industry to GDP, is to measure its ‘gross value added’ (GVA); that is, to calculate the difference between the industry’s total pre­tax revenue and its total bought­in costs (costs excluding wages and salaries).

Forecasts of GDP growth: GDP = CN+IN+GS+NEX

GDP growth estimates take into account:

  • Consumption, expressed as a function of income, wealth, prices and interest rates;

  • Investment as a function of the return on capital and changes in capacity utilization; Government spending as a function of intervention initiatives and state of the economy;

  • Net exports as a function of global economic conditions.

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Market Quantification
All relevant markets are quantified utilizing revenue figures for the forecast period. The Compound Annual Growth Rate (CAGR) within each segment is used to measure growth and to extrapolate data when figures are not publicly available.

Revenues

Our market segments reflect major categories and subcategories of the global market, followed by an analysis of statistical data covering national spending and international trade relations and patterns. Market values reflect revenues paid by the final customer / end user to vendors and service providers either directly or through distribution channels, excluding VAT. Local currencies are converted to USD using the yearly average exchange rates of local currencies to the USD for the respective year as provided by the IMF World Economic Outlook Database.

Industry Life Cycle Market Phase

Market phase is determined using factors in the Industry Life Cycle model. The adapted market phase definitions are as follows:

  • Nascent: New market need not yet determined; growth begins increasing toward end of cycle

  • Growth: Growth trajectory picks up; high growth rates

  • Mature: Typically fewer firms than growth phase, as dominant solutions continue to capture the majority of market share and market consolidation occurs, displaying lower growth rates that are typically on par with the general economy

  • Decline: Further market consolidation, rapidly declining growth rates

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The Global Economic Model
The Global Economic Model brings together macroeconomic and sectoral forecasts for quantifying the key relationships.

The model is a hybrid statistical model that uses macroeconomic variables and inter-industry linkages to forecast sectoral output. The model is used to forecast not just output, but prices, wages, employment and investment. The principal variables driving the industry model are the components of final demand, which directly or indirectly determine the demand facing each industry. However, other macroeconomic assumptions — in particular exchange rates, as well as world commodity prices — also enter into the equation, as well as other industry specific factors that have been or are expected to impact.

  • Vector Auto Regression (VAR) statistical models capturing the linear interdependencies among multiple time series, are best used for short-term forecasting, whereby shocks to demand will generate economic cycles that can be influenced by fiscal and monetary policy.

  • Dynamic-Stochastic Equilibrium (DSE) models replicate the behaviour of the economy by analyzing the interaction of economic variables, whereby output is determined by supply side factors, such as investment, demographics, labour participation and productivity.

  • Dynamic Econometric Error Correction (DEEC) modelling combines VAR and DSE models by estimating the speed at which a dependent variable returns to its equilibrium after a shock, as well as assessing the impact of a company, industry, new technology, regulation, or market change. DEEC modelling is best suited for forecasting.

Forecasts of GDP growth per capita based on these factors can then be combined with demographic projections to give forecasts for overall GDP growth.
Wherever possible, publicly available data from official sources are used for the latest available year. Qualitative indicators are normalised (on the basis of: Normalised x = (x - Min(x)) / (Max(x) - Min(x)) where Min(x) and Max(x) are, the lowest and highest values for any given indicator respectively) and then aggregated across categories to enable an overall comparison. The normalised value is then transformed into a positive number on a scale of 0 to 100. The weighting assigned to each indicator can be changed to reflect different assumptions about their relative importance.

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The principal explanatory variable in each industry’s output equation is the Total Demand variable, encompassing exogenous macroeconomic assumptions, consumer spending and investment, and intermediate demand for goods and services by sectors of the economy for use as inputs in the production of their own goods and services.

Elasticities
Elasticity measures the response of one economic variable to a change in another economic variable, whether the good or service is demanded as an input into a final product or whether it is the final product, and provides insight into the proportional impact of different economic actions and policy decisions.
Demand elasticities measure the change in the quantity demanded of a particular good or service as a result of changes to other economic variables, such as its own price, the price of competing or complementary goods and services, income levels, taxes.
Demand elasticities can be influenced by several factors. Each of these factors, along with the specific characteristics of the product, will interact to determine its overall responsiveness of demand to changes in prices and incomes.
The individual characteristics of a good or service will have an impact, but there are also a number of general factors that will typically affect the sensitivity of demand, such as the availability of substitutes, whereby the elasticity is typically higher the greater the number of available substitutes, as consumers can easily switch between different products.
The degree of necessity. Luxury products and habit forming ones, typically have a higher elasticity.
Proportion of the budget consumed by the item. Products that consume a large portion of the consumer’s budget tend to have greater elasticity.
Elasticities tend to be greater over the long run because consumers have more time to adjust their behaviour.
Finally, if the product or service is an input into a final product then the price elasticity will depend on the price elasticity of the final product, its cost share in the production costs, and the availability of substitutes for that good or service.

Prices
Prices are also forecast using an input-output framework. Input costs have two components; labour costs are driven by wages, while intermediate costs are computed as an input-output weighted aggregate of input sectors’ prices. Employment is a function of output and real sectoral wages, that are forecast as a function of whole economy growth in wages. Investment is forecast as a function of output and aggregate level business investment.

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