By Jason Tan of Sift Science
This year, Cyber Monday heralded the biggest ever day for e-commerce sales. Yes, ever, in all of history. More than $3.1 billion was spent at online retailers that day, with shoppers using desktop and mobile devices alike to buy, buy, buy. Holiday cheer indeed!
(Photo courtesy of Jason Tan)
Now that the big day has come and gone, we wanted to get a closer look at fraud trends on this busy shopping day. So we took a sample of 1 million items from orders that were placed on Cyber Monday. We classified all of the orders above a commonly used risk threshold as “fraud,” since this is the point when e-commerce businesses usually block orders from going through. Here’s what we found:
Overall, Orders Were Up
We were pleased to see that our customers did good business on Cyber Monday: the number of orders created on November 30 was 30% higher compared to the rest of that month. There’s been some chatter about Cyber Monday becoming less meaningful as retailers offer special online deals at different points in the shopping season, but it seems that day hasn’t arrived yet. For all intents and purposes, the first Monday after Thanksgiving is still a boon for online merchants.
However, Fraud Relative To Orders Was Down
You may have heard it said before: fraud isn’t necessarily seasonal, because it’s more like a full-time job. More fraudsters don’t come out of the woodwork just because it’s the holidays. Our data confirms this hypothesis. The amount of fraudulent orders attempted on our customers’ websites didn’t increase on Cyber Monday.
That means that if you look at the relatively consistent volume of fraudulent orders as a proportion of total orders (of which there was a significant increase), you actually get a lower fraud rate on Cyber Monday than on a typical day.
Still, that doesn’t mean holiday fraud detection is any less challenging for retailers. Due to the increase in orders of all types, there’s just more for a fraud team to manage and review. Using a fraud-prevention tool that enables merchants to automate as much fraud management as possible can free up everyone’s time to focus on giving legitimate customers the best experience possible. After all, our findings suggest that e-commerce sites could potentially worry less about blocking fraud during these times of high volume, and focus more on making sure they’re reducing friction at checkout for users who they know aren’t risky.
Thieves Exploit The Ease Of Buying Digital Goods
When we looked at individual categories of items that fraudsters attempted to steal on Cyber Monday, two types stood out: gift cards and gaming. What do these two have in common? A large proportion of sales are made up of virtual goods, a target that’s ripe for what’s known as “fast fraud.”
Why? First of all, criminals don’t have to worry about re-shipping physical items, so there’s a lower barrier for this type of fraud. Also, digital gifts don’t require a shipping address during the ordering process, so it can be harder for businesses to tell the legitimate customers from the fraudsters.
Finally, customers buying digital goods expect their purchases to arrive immediately, so there’s basically no time for manual review. As the popularity of digital goods continues to grow, the businesses that offer them will continue to need real-time fraud-detection tools to help them weed out the fraudsters in the blink of an eye.
Popular Brands Draw Both Shoppers And Fraudsters
We also looked through the data to see if fraudsters have an affinity for a particular brand name, particularly when comparing two legendary rivals. Here’s some of what we found:
- Playstation or X-box? Devices and gift cards related to these platforms are about equally likely to be targets of fraud.
- Coke or Pepsi? Coke wins with both legitimate shoppers and fraudsters. A higher volume of Coca-Cola products than Pepsi products were sold on Cyber Monday – and Coke also had a higher fraud rate.
- Nike or Adidas? Fraudsters appear equally likely to steal both brands of shoe.
- Apple or Samsung? Samsung products popped up more often when it came to attempted fraud, but accessories related to the Apple Watch – like docks and stands – were also popular.
Jason Tan (@jasontan) is the Co-Founder and CEO of Sift Science, a San Francisco technology company that fights online fraud with large-scale machine learning. He previously served as CTO of BuzzLabs, a machine learning startup acquired by InterActiveCorp. Prior to that, he was an early engineer at two Seattle startups, Zillow and Optify. Jason graduated magna cum laude from the University of Washington in 2006 with a Computer Engineering degree.
The views, opinions and positions expressed within this guest post are those of the authors alone and do not represent those of CBS Small Business Pulse or the CBS Corporation. The accuracy, completeness and validity of any statements made within this article are verified solely by the authors.