It’s hard to imagine in the midst of the holiday shopping chaos, but the post-season letdown is coming.
From Black Friday on, the holiday period provides retailers across the globe with more potential customers than they can often handle. So much traffic in fact, that while missing out on a sale is always painful, missing out during holiday is muted by sheer volume.
But what happens when all that traffic dries up and goes back to normal levels? Or even worse, lower than normal, as consumers just don’t have so much money to go round, having blown the budget over the festive period.
Suddenly missing out on a few sales is no longer an option; and it is more important than ever that retailers optimize the orders they do get. And that of course includes the step of accepting more orders in the first place.
So how can retailers go about doing this? Depending on their tech stack, it’s very possible retailers already have tools at their disposal to enable this — at least in part. Consider, for instance, your payment and fraud-prevention tools.
Each step of the process has the potential to inadvertently screen out good orders. Checkout, payments and even ecommerce platforms sometimes include rules that can block geographies or disrupt orders with apparent CVV or AVS irregularities.
Traditional, static fraud rules lead to false positives
Traditional fraud prevention tools come with static rules that produce false positives, meaning good orders are not shipped to legitimate customers. They depend on logic that reinforces mistakes that label good orders as fraudulent.
The fraud prevention flaws stem from thinking of enterprise fraud management as a way to throttle out bad orders. But what about flipping that on its head and turning to newer fraud models — models that combine big data, machine learning and domain expertise to assess orders and in effect look for every possible reason to accept an order?
It makes sense, given that refusing legitimate orders from honest customers is a bigger problem than incorrectly sending products to fraudsters posing as honest customers. In Europe, one in five online shoppers told 451 Research that they’d been wrongly denied an order because of a merchant’s fraud suspicions. And two in five say they will likely never shop with that merchant again.
So it is time for retailers to use the fraud tools at their disposal but in a new light — to approve more orders, not just squash fraud.
And this doesn’t need to be at the risk of increased chargebacks and greater fraud pressure. Retailers can, in fact, accept these additional orders without also having to accept the liability for them — and this is where liability shift solutions such as guaranteed fraud protection and 3-D Secure come into play.
Both approaches work in different ways, but achieve the same goal of shifting the financial liability away from the retailer.
Most of us are familiar with 3-D Secure. 3-D Secure is an additional step in the checkout process during which the consumer is challenged to re-enter details from their card/bank account or answer a challenge-and-response-type question.
If the response is successful, the transaction is approved and the liability is shifted from the retailer to the card issuer. The problem is, it’s not a great customer experience, interrupting the flow and causing friction during checkout. Typical 3-D Secure drop-off rates are shown in the chart below.
Source: Adyen’s Impact of 3-D Secure on conversion rates per country
Now, on initial look, it appears that Great Britain is positively benefited solely by 3-D Secure. But Ayden noted that its conversion figures reflect increased authorizations from issuers as well as 3-D Secure related drop off. In other words, the UK’s 3 percent conversion rate boost is actually achieved by the increase in authorizations.
Making the most of an increase in authorized transactions
In the end, that’s great news. It shows that removing some of the overly restrictive payment processing rules in the first place results in accepting so many additional transactions into the funnel that even the drop off from 3-D Secure doesn’t negate the positive impact.
So what if you could let these additional orders in and not have the additional friction of 3-D Secure? In other words, what if you could still be protected by a liability shift, while still providing a frictionless checkout experience?
This is where guaranteed fraud prevention solutions come into play. These solutions can take all orders and pass them to a big data, machine learning model that will append rafts of further third-party data and compare against historical and modelled data, to look for every which way an order can be approved — not declined.
Exceptional orders may also go into manual review where a dedicated team of domain experts can then add human intuition to search for reasons to approve these orders. These experts represent a specialist resource with the remit to look for reasons to approve. Every approved order is guaranteed against chargebacks should it turn out to be fraudulent.
And the key benefit? All this happens without the end-user journey being interrupted. All the user sees is their successful order and the goods as they arrive. And the retailers? They can focus on their core business of selling great products, accepting more orders and delighting customers, without the fear of fraud.
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