The Lifetime Impact of Ecommerce False Declines, Generation by Generation

False declines are a hazard of ecommerce fraud prevention, but they’re not inevitable. In fact, businesses that want to maximize their customer lifetime value will do their best to eliminate false declines. That’s because what customers do after their good order is rejected can cost a business far more than fraud. The total cost of the damage depends on the customer’s generation, sometimes in ways that may feel counterintuitive.

These declines are also becoming more common and costly. The actual cost of false declines globally comes to roughly $443 billion annually, far outweighing actual credit card fraud, where annual losses amount to roughly $48 billion. A recent survey on consumer attitudes on ecommerce, CX, and fraud showed that 27% of respondents were declined at least once in 2023, up from 25% in 2022. At the same time, overall consumer patience with declines is eroding a little more each year. In 2021, 40% of respondents said they would boycott an online store after a false decline. In 2022, 41% agreed. In 2023, 42% said they would boycott. As we’ll discuss, the tendency to boycott varies quite a bit by age group.

Why false declines are still happening

It may seem strange that false declines are a worsening problem at a time when there are so many technology advances to support customer recognition for personalization, login verification, and other domains. But many businesses still rely on the rules-based fraud filters that are built into their ecommerce platforms.

These filters look for simple indicators of potential fraud, such as shipping and billing address mismatches, card verification number errors, and external data sources such as lists of customers to reject. Depending on the filters’ settings, they may automatically flag or reject orders originating from specific regions or over certain transaction amounts.

That’s not to say that fraud filters are unhelpful; they can be a valuable way to flag orders for investigation. But unless flagged orders then undergo a contextual review by a fraud analyst, they’re going to be automatically rejected. The business won’t know if they were actually good orders unless they’re analyzing their past orders or hearing from rejected customers and will go on losing order revenue and customer lifetime value.

Impact on Customer Lifetime Value

A simple formula can help you estimate how much you’re losing in lifetime value every time a customer abandons your brand because of a decline. If your average customer spends $50 a month on your site, then you lose $600 per year if they don’t come back after a decline–for every year that they will be shopping online.

So, for a baby boomer in their 60s or early 70s, you’d lose out on up to 20 years of revenue for that customer, or up to $12,000. Gen Xers might shop online for another 30 years, representing a loss of $18,000. For Millennials, the shopping horizon stretches another 40 years, or $24,000 of lost value. And for the youngest adults, Gen Z, the loss could reach $30,000 over five decades.

These are general estimates, but consumer behavior varies by age group. Let’s look in more detail at how false declines can impact each cohort.

Baby boomers

In the most recent consumer attitudes survey, the oldest consumers have the least patience for declines among all the age groups. Forty-four percent won’t shop with a store again after a decline. And although these consumers don’t have as many years of shopping ahead of them as younger customers, they are valuable in many ways.

Boston Consulting Group found that baby boomers account for 30% of consumer spending in the U.S. and “have immense buying power, spend more than members of other age groups do on individual purchases, exhibit strong brand loyalty, are resilient through economic ups and downs, and wield surprising influence over younger consumers.” Baby boomers are the least likely to complain on social media after a decline (31%), but they may spread word of mouth through conversations and other channels with friends, family, and neighbors.

Gen X

These middle-aged consumers are less likely than younger shoppers to complain on social media about declines (34%), and they’re slightly less likely than baby boomers and millennials to boycott a store after a decline (41%). That may be because they’re often supporting their kids and their parents financially and logistically and don’t have time to complain or change merchants.

However, losing out on relationships with members of this “sandwich generation” means missing out on purchases from the biggest-spending group in the U.S. according to the World Economic forum–an average of just over $83,000 per year. If these consumers are also making purchasing decisions for family members in other generations, that can affect their brand preferences as well.


This group of younger adults was the first to grow up online, and they’re also the most likely to complain on social media after a decline (40%). Combine that negative word of mouth with the fact that 43% will also boycott a store after a decline and those declines can result in widespread, long-term erosion of lifetime value.

Gen Z

Gen Z doesn’t have quite the spending power of older generations, but it does have the most shopping years ahead of it, and the most potential for income and spending growth. These youngest adults are also moving into the workforce, earning more, and already starting to spend more over time. After a decline, 40% will never shop with that store again, and 35% will complain on social media–spreading the word to their Gen Z peers.

Solving the False Decline Problem

Preventing false declines and lost customer value starts with discovering the extent of the problem. Because most fraud filters don’t review rejected orders, it’s up to brands to conduct their own post-processing audits. Analyzing batches of rejected orders with machine learning can reveal the percentage of declined orders that would have been good.

Identifying the false decline rate allows you to set a benchmark and realistic targets for reducing those declines. This may require multiple steps including adjusting fraud filter rules to accommodate your customers’ specific needs, such as order amount, to flag rather than automatically reject those orders. Another critical prevention step is using some form of machine learning to analyze incoming orders to help identify good orders and fraud–this can help resolve some of the flags raised by filters. After that step, contextual analysis of suspicious orders can separate actual fraud from unusual but good orders–and provide more data for the machine learning algorithm to improve its order assessments.

Finally, continue to monitor your false decline rate using post-processing audits. It’s also wise to monitor your social and customer service channels for customer complaints about declines, and be prepared to respond quickly to address those issues and protect your brand reputation.

Putting a false decline reduction strategy in place requires planning, buy-in, and ongoing work, but there are immediate and future payoffs: higher order approval rates in the near term and increased customer value over their lifetimes, plus a better brand reputation among all age groups on social media.


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