The Puzzle of Big Data in eCommerce Marketing

It may feel to some marketers that too big a data pool means too much data to narrow down to a tailored message to consumers.  This can cause marketers to shy away from using too big a data pool in their analytics. In fact, the opposite is true.  The bigger the data, the more focused the targeting can be.

Ecommerce sites and companies have begun more and more over the last couple of years to incorporate big data into their marketing analytics in efforts to acquire as much granular intelligence on their customers habits.  This data is in turn analyzed and turned into insights that can provide powerful results if properly acted on.  Many companies will create segments from this data to narrow down targeting to specific genders, geos, ages, time of day etc.  However, as precise as these targets can be to groups of customers, they are still casting a wide net as they do not narrow all the way down to one specifically personalized customer.

A Columbia Business school study found that 26% of marketers don’t use real time advanced data to customize user experience whereas 75% of customers buy from brands that personalize the shopping experience.  With this in mind, it’s abundantly clear that marketers need to be using their big data to make their shopping experience a one to one interaction with each customer.

The company online bicycle Bikeberry.com used big data to tailor discounts to individual customers based on data from multiple sources including browser activity, session time, number of logins, purchase behavior and more to create 5 different offers, free shipping discounts of 5% off, 10% off, 15% off, and $30 off new products.  They then sent each individual customer a tailored offer based on their behavior and projected likelihood of buying based on their large sample of data points.  The company saw a 133% lift in sales using this data for personal targeting.  Because of using so many points of reference, they were able to avoid using segmented group discounts and narrow the right discounts to the right customers, saving big dollars thanks to big data.