How Well Do You Know Your Customers?
By Don Duncan, Sales Engineer, NuData
I find it intriguing how the online presence of a digital user compares to that of a brick-and-mortar experience. A recent Globe and Mail report on how retailers are fighting for digital authenticity got me thinking.
As vendors see how the power of online reviews can translate to multi-channel sales, they are looking at validating reviewer identity. Ensuring the authenticity and integrity of a company’s reviews is a surprisingly common challenge. This problem is derived from one the biggest pains facing the eCommerce and mCommerce world: how do you truly validate the identity of legitimate users behind a computer, app or mobile device?
In a physical store, you may not know the customer’s name, but you understand whether or not there is a living, breathing person in front of you (learn more about the evolution of authentication in our e-book here). As you engage with them over time, you start to build a level of familiarity from their mannerisms, what they look like, and their usual means of engagement – what they normally buy or what time they usually come into the store, etc. In the online world, this personal rapport is missing. E-tailers are trying to identify ways to take these real-life identification concepts and apply them to the digital world. Online, authentication is partly a matter of how to tie a user’s digital data back to their historical reputation.
Taking these identification concepts into the digital world is an area where passive behavioral biometrics excels. The days of identifying the user from their device have become a dated idea in the IoT space where many individuals own and interact with multiple devices. Usernames and passwords no longer have the same ability to authenticate and validate users. The threat of synthetic users is becoming more and more common, and merchants must be able to distinguish between a user, someone impersonating a user, and automation.
Using a multi-layered approach of integrating device intelligence, active and passive biometric analysis, and behavioral analytics is the key to actually understanding the user behind the device. So even if Johnny comes into the corner store wearing a hoodie that he normally wouldn’t wear, the shop owner can still identify it’s him based on his behavior – just as we can now similarly recognize users online. If the reviewer behaves a bit differently online, we will still know it is a legitimate user based on hundreds of behavioral, biometric and device analytics.
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