E-commerce merchants must embrace local payment methods in order to unlock significant growth, suggests a new report from market research firm Juniper Research.
As efforts to extend high-speed internet to rural areas in emerging markets continue, Juniper Research has found that this creates a huge revenue opportunity – thanks to access to large populations with increasing amounts of disposable income.
Juniper also says that purchasing digital goods will become more accessible through the growing popularity of mobile money solutions like M-Pesa, the pan-African mobile wallet facilitating digital payments through phones without requiring a bank account.
“To capitalise on this revenue growth, e-commerce merchants must optimise their platforms to support local payment methods, maximising accessibility for a growing user base,” explained research author Lorien Carter.
In many cases, customers are put off by other currencies that they are less familiar with, as it takes time to calculate the exchange rate. To combat this, merchants can offer customers local payment types they are familiar with, in their preferred currency. Businesses also rely on partners and third-party integrations to help them offer these services; meaning they don’t have to keep up with changing consumer demands, as the partner firm will cover this.
Aside from better conversion rates at checkout, merchants also benefit from higher authorisation rates, caused by local acquiring – because transaction occurrs in a familiar environment for the issuing bank,
reducing the chance of falling foul of regulatory rules.
The research also suggests that payments for digital subscription services will grow the fastest in and around India over the next five years. It says that this growth will be caused by the wider range of pricing plans being offered to users in India by popular music subscription services like Spotify, and an expansion of available languages.
To develop new service differentiation points, vendors must aim to localise their offerings, and invest in machine learning that enables hyper-personalised recommendations to capture diverse audience preferences.
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