How Well Do You Know Your Customer?
Latest posts by Priya Rajan (see all)
- Are EMV Standards Enough to Stop In-store Fraud? - December 22, 2016
- When It Comes to Fraud, Where Is the Weakest Link? - September 14, 2016
- The Cost of Convenience: Why Merchants Bear the Brunt of Loss in Newer Retail Delivery Models - August 25, 2016
The digital revolution has paved newer ways for consumers to interact and transact within the commerce ecosystem. The transformation has not only redefined the shopping experience at traditional brick and mortar retail merchants like Macy’s, who offer virtual fitting room for online shoppers, but also led to the emergence of newer service models liker Uber and Amazon that wouldn’t have been possible without the acceleration of digital age. As we look at the history and evolution of ecommerce over the last two decades, it is obvious, that while different business models have come and gone, some successful and some not, the fact remains that the change itself has been continuous and meteoric.
Failure to gain traction has only led to refinement of the models and redefinition of the offerings to make them more appealing to consumers. For example, Webvan, an online grocery delivery business, filed for bankruptcy in 2001, merely two years after its inception while home delivery 2.0¹ which includes Instacart and Postmates have seen far more success.
The marketplace has steadily evolved to embrace card-not-present transactions and alternate payments methods. As generation Y widely adopts digital channels, it will become imperative for ecommerce merchants and the infrastructure they depend on to match the digital expectations of the new generation of shoppers.
Key trends impacting online payments
1) Click and collect is the new norm
According to the International Council of Shopping Centers’, nearly one-third of shoppers² during the last holiday season opted to purchase their products online and pick them up at the store. As omni-channel commerce becomes a key differentiator, leading retailers like Home Depot attribute increasing sales and greater customer satisfaction to congruence of online and physical stores.
2) Mobile wallets are here to stay
Innovations in mobile payments (Apple Pay, Android Pay, Samsung Pay), closed loop solutions (CurrentC, Starbucks) and mobile merchant payment solutions (Square) and machine to machine apps (bPay, MagicBand) are enabling customers and merchants to transact regardless of the location or channel. Mobile payments segment is expected to grow at a CAGR of 23.2% through 2020 with merchandise and money transfers being the top two areas in the segment³.
3) Customer centric technologies will be a competitive differentiator
Gartner study finds that 89% of companies surveyed plan to compete primarily on the basis of the customer experience in 2016. And yet friction during checkout and shopping cart abandonment is estimated to cost online retailers close to $4 trillion in lost revenues. As digitization of payment increases, issuers and acquirers are looking at ways to enhance their existing value-added services to end customers through customized offerings based on segmentation and behavior.
Regardless of the size of the organization or its capabilities, online retail is here to stay and competition is expected to heat up as newer, alternate payment methods gain traction. Growing businesses need to build better intelligence about the customer at the storefront not only to make the experience seamless and fluid to keep topline growing but also defend against fraudulent ones to protect bottom line.
Different types of credit card fraud, be it friendly fraud, chargeback fraud, payment fraud or gift card fraud or any other variations of these, are all ultimately caused by one major driver – lack of understanding about the customer. Once businesses have better understanding about their customers, they can better address known and unknown threats without creating friction.
Identifying known threats
Identifying and stopping known threats before they happen requires understanding of normal and abnormal behavior. These behaviors need to be identified at a granular level so that they don’t flag genuine transactions as fraudulent. Simple rules that evaluate conditions and result in black and white decisions tend to be more restrictive and result in more false positives and poor customer experience.
Behavioral profiles that are based on each data element (like customer, IP, Merchant device etc.,) instead of loose fitting cohorts (like age and demographics) lead to a better understanding of conventional behavior for that individual data point. And when these profiles are built on larger pool of data from multiple channels like online, offline and mobile, they offer richer, more comprehensive understanding of the customer across all points of sale. And with that knowledge, identifying every other anomaly that camouflages as fraud is more efficient and easier.
Identifying unknown threats
As ecommerce grows and evolves, identifying fraud needs to extend beyond just identifying known threats. Losses from organized fraud rings have far reaching financial implications than petty crimes committed by individual fraudsters. Fraudsters in Japan used credit cards created with stolen data to target 14000 convenience store cash machines in just under three hours to withdraw cash. Loss from the ATM scam is estimated to be a whopping $12.7 million. Spotting these unknown threats using archaic tools that rely on simple rules based engines is practically impossible. Sophisticated machine learning algorithms that use link analysis to draw connections among different data points and detect and stop the fraud as they happen in real time is far more practical and do-able.
Retail 2.0 has evolved from its infancy in brick and mortar department stores to shopping malls and now to the digital world. Consumers have embraced this shift with relative ease and the opportunity to disrupt is wide and growing. As merchants, platforms and payment providers adapt their businesses to this change, understanding customers and creating experiences to retain them is essential for survival.
Feedzai helps merchants, marketplaces and platforms manage this balancing act through machine learning based fraud detection platform built for big data. Feedzai is a comprehensive loss prevention platform that can scale to the needs of commerce. Whether transactions happen online or offline and at any point in the payment chain (issuers, acquirers, platforms, PSPs or merchants) Feedzai’s Technology That LearnsTM can detect and stop payment fraud and any other variations of fraud that results in loss.
To learn more about how Feedzai can help you, visit our fraud solutions for ecommerce page.