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Using Customer Data to Help Prevent False Declines

Using Customer Data to Help Prevent False Declines

Customers expect merchants to recognize them as more and more business is conducted online. According to a study conducted by McKinsey in November. 71% of customers anticipate personalized interactions with businesses, and 76% “get frustrated when this does not happen.” Marketing messages, email campaigns, and product recommendations are all examples of personalization. However, there is nothing more important to a personalized shopping experience than recognizing loyal customers when they place an order.

Sadly, even businesses that invest in personalized marketing occasionally fail to complete this last step before the sale. Their fraud screening tools flag an order as possible fraud rather than recognizing it as legitimate, and the order is rejected. Over 5,000 adults who took part in the most recent ClearSale State of Consumer Attitudes, Fraud, and CX report said that since the pandemic started, these false positives, or false declines, have happened more often.

Rising Rates of False Declines in E-commerce

Between March 2020 and March 2021, 15% of survey respondents in the United States, Canada, Mexico, the United Kingdom, and Australia reported experiencing online payment fraud. However, while attempting to purchase something online, 25% of respondents reported that their order was declined. Almost half of those respondents reported that during the survey period, they experienced more declines than in 2019.

Error-based declines were significantly more common among Millennial and Generation Z customers: During the survey period, 48% of 18- to 24-year-olds and 34% of 25- to 39-year-olds reported at least one decline. Only 16% of respondents from Generation X and Baby Boomers experienced a decline. Why did decline rates differ between generations? It could be that older customers have more historical data to work with or that they are less likely to buy things like tickets to events and online games that are more susceptible to fraud.

When these declines occur, many customers are unforgiving, regardless of the reason. According to the survey, 65% of all online shoppers said they would not provide additional information if a merchant rejected their order and then asked for additional information to approve it. To put it another way, the majority of customers will simply opt-out if the merchant does not immediately recognize them. In the same survey, almost half of the respondents stated that they will never shop with a merchant again. That amounts to a loss of both the customer’s lifetime value. The marketing budget that was used to attract them to the store in the first place.

Understanding Why False Declines Happen

Despite the abundance of data available to screen orders for fraud, it is important to comprehend why false declines occur given the high stakes for customer experience and retention. There are two main problems. The kind of data used by a retailer’s fraud control program and what happens after that data is used to screen an order

Let’s start with the kind of data. The built-in fraud screening tools of many e-commerce platforms apply straightforward rules to orders and flag those that do not comply. A straightforward fraud tool, for instance, might run an order through the Address Verification Service (AVS) and verify the card number and CVV code. The program’s rules may flag the order as a possible fraudulent activity if one digit of the ZIP code is incorrect or the customer is placing an order from a new billing address because they have moved.

Naturally, flagging an order or giving it a risk score that is higher than average is not the same as declining the order. The decisions made based on those outcomes are crucial here. Based on the scenarios we just considered, a merchant will generate some false declines. If their fraud control program is set to reject orders above a certain risk threshold or any order with fraud flags. There is a possibility of customer churn with each decline.

Using Customer Data for Better Fraud Screening & CX

Merchants have the ability to alter the data they use and how they use it in order to prevent fraud and reject legitimate orders at the same time. Accessing real-time and historical customer data, including markers like device data, behavioral biometrics, and geolocation, as well as the customer’s overall history of online interactions and interactions with the merchant, is the most crucial step.

A one-digit address mismatch may still raise a small red flag with this data, but other data indicating that the customer recently changed their address may lessen that red flag. The historical data of the first-time online shopper might indicate that they are using an email address they have had for ten years and a delivery address they have lived at for twenty years, making it less likely that they are a fraudster.

Before making a decision, each order with a risk score above a certain threshold is set. This is because “edge cases” that the fraud algorithm cannot resolve still require human expertise. The merchant can also feed the results of the manual reviews into the machine learning program, allowing their fraud program to get better over time at identifying positive and negative customer behavior.

The key to building and maintaining long-term relationships and avoiding customer churn is to recognize customers at the checkout when they have committed their time and money to your business. You can approve more orders and keep customers returning to your store by working with more real-time and historical customer data and manually reviewing suspicious orders.

Using Customer Data to Help Prevent False Declines
Using Customer Data to Help Prevent False Declines

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