The acceptance rate conundrum



We recently spoke to a merchant that had pieced together their own solution for offering net 30 day payment terms for business buyers. When we had a closer look at their data, it turned out that they were only accepting the top 10% of their buyers. The remaining 90% of their buyers had to pay in advance for their purchases.


Though it is admirable that this merchant had created their own solution, the problem is that they were disappointing a large percentage of good buyers, buyers that have every intention of paying their invoices when they become due. This is not only a poor customer experience, but it also severely restricts revenue growth of the merchant.


The reason why this merchant was only accepting the top 10% of buyers is because they were afraid of non-payment, either because the business buyer would not be able to pay (poor creditworthiness) or never had any intention to pay (fraud).


This is a real conundrum: How do you avoid disappointing a large percentage of good business buyers by rejecting them for new payment terms, without taking on potentially crippling risk?


The difference between accepting 10% of buyers for net payment terms and accepting 70%+ of buyers, is that you need to have a deep network of data sources, combined with a smart credit engine to be able to quickly screen new buyers when they register on your site or when they select to pay with net terms during the checkout process.


You need to connect with multiple traditional and alternative data sources to increase the chance of finding credit information on the business buyer and you need the decision models to make sound decisions on whether to extend credit to a business buyer, and how much credit they should have access to.


You also need a solution for manually reviewing those business buyers where a clear cut decision to accept or reject them for net payment terms is not possible. In this case, you need human intervention to review the information available on the business buyer and to make the final decision to accept or reject.


This kind of solution, with the deep data network, the smart credit engine, and the expert team of reviewers, is hard to build and maintain in-house. It simply does not make sense for each merchant to reinvent this wheel for themselves.


As part of the Sprinque B2B payments platform, we have built a credit engine that can make automated credit decisions in real-time, with high business match and acceptance rates, across geographies and business legal forms. We are building this solution so B2B merchants, platforms and marketplaces don’t have to.


If you would like to learn more about our solution, please visit our website (www.sprinque.com) or contact us: sales@sprinque.com.

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