The holiday season is accompanied with intense stress despite ecommerce sales continuing to witness major increases. Less time is available for customers to review orders due to escalating demands. The 2015 Thanksgiving season in-store shopping statistics were literally eclipsed by online shopping numbers. This hike in online purchases reminds us of the serious threat posed by card-not-present (CNP) fraud. While merchants welcome new customers, one cannot rule out the threat of new consumers that remain unknown in nature. Ecommerce retailers need to keep their eyes open for patterns of fraud, expected to materialize after Thanksgiving and the New Years.
The 2016 holiday season has estimates of a stunning $9 billion rise in holiday season ecommerce revenue comparison to last year, growing from $69B to $78B. Average order values will also increase by around $25 from the beginning to the end of the annual holiday season. Despite this elevation in spending, experts believe holiday online purchases are 55% less probable of being fraud. However, this does not cancel the necessity to evaluate and analyze the past with the goal of refining our current methods. Chargebacks are extremely costly and an argument otherwise is quite hard to comprehend.
Using past data correctly
November renders a massive increase in consumers using international cards to make their purchases. To define the safe or threatening nature of each order, merchants are now able to take advantage of reviewing past data. While it may seem strange, the days of Black Friday, Cyber Monday and New Year’s Eve actually pose a lesser risk compared to other days of the calendar. Due to a lack of understanding on the part of merchants, majority of all rejected orders during the 2015 holiday season essentially lacked any legitimacy concerns.
As a result, relying solely on past data would be an unwise practice, especially as our times continue to change. Take Donald Trump’s election as president, for example. With each passing year, ecommerce sales have been increasing in ratio in comparison to in-person purchases. To cope with the new environment, fraudsters have evolved their tactics. The proxies they use are more complex, such as mobile versions. Another increasing malicious fraud trend includes account takeovers. To ensure customer security, merchants are encouraged to welcome machine learning algorithms, enjoying the ability of real time rules refining.
High value found in returning customers
Knowing if a consumer is interested in your goods or services for the first time, or is a returning customer, is crucial. Returning customers are less likely to be involved in any form of fraud in comparison to new cases. As a result, returning customers should enjoy far better approaches by retailers. The ability to make such a distinction is crucial during the holiday season when large orders are involved.
The truth is, however, that distinguishing returning customers from the new is no easy task. This brings into necessity the employment of a complex, yet powerful, linking system able to identify customers according to different fields. This includes device fingerprinting, email domains, IP addresses and products. However, a must to keep in mind is the sensitive nature of ecommerce merchants collecting such data from their customers. Many may consider such a practice a violation of their privacy. In the meantime, this method will decrease the possibility of false decline rates and facilitate the need to identify returning customers.
Data, data, and more data
January is a month of chargeback arguments and merchants need advance preparations. Successful merchants are those able to access more historic data about their consumers. The more data, the more capable merchants are in corroborating returning customers’ identity. A log of well-kept customer data, for example, from the month of September will help ease and clarify disputes erupting each year in February. Such screenshots are best stored as PDFs, parallel to third party sources such as Emailage, Facebook or White Pages, all used to enrich a merchant’s data bank.
How to respond
Merchants can find a more vigorous understanding of their customers through elastic linking data technology provided by Riskified, a leading e-commerce fraud prevention company pioneering the chargeback guarantee. Merchants are also capable of accessing past customer shopping information through the company’s database, especially necessary when dealing with new customers. This is especially useful in decreasing the number of order declines.
“Not only do we approve 66% of orders retailers plan to decline, but we also guarantee every order we approve to provide assurance and peace of mind,” explained Riskified CEO Eido Gal in an interview.
With more small and large businesses embracing methods to immaculate records these days, further evidence is useful to help resolve chargeback arguments. This type of service is best provided through partners for third party fraud prevention purposes.
Improving shopping experience is always a major goal for ecommerce merchants. While smart data methods can provide significant leverage for merchants, the consumer must never feel intruded, tracked, followed or any such dangerous impression. Asking proper questions and seeking the right data is key in how to tread on this fine line. Considering the powerful nature of smart data collection technology, it is wise to take simple steps to begin with and time will tell when to implement more complex methods.
All in all, executing correct methods, treating new and returning customers accordingly, and seeking the correct source of support will decrease money going down the chargeback drain. And most importantly, allowing consumers and merchants rightfully enjoy their holiday season.
The post How Smart Data Can Help eCommerce Merchants Save Money Over the Holidays appeared first on SmallBizTechnology.
Ramon Ray, Editor & Technology Evangelist, Smallbiztechnology.com
http://www.twitter.com/ramonray | http://www.facebook.com/smallbiztechnology firstname.lastname@example.org
Small Business Technology Tour (Fall 2011)
* Mountain View * Salt Lake City * Boston * Chicago * Atlanta