Someone new is on your website. They’re on the product page for women’s scarfs.

You can certainly glean information and make some predictions from that single data point; based on the product, she’s probably middle aged and in an upper income bracket.

Or maybe she’s not her at all. It’s her son looking for a gift and for the next year, his inbox overflows with products targeted at his mother. 

Eroneous conclusions based on incomplete data take place within ecommerce all the time.

As revealed in Econsultancy’s upcoming research, in partnership with SDL, retailers place the highest value on customer purchase history and current digital behavior when making strategic predictions about personalization and shopping habits.

These attributes are certainly useful when recommending products as the consumer is shopping, but they are insufficient for building loyalty for the long term, especially if the shopper is new (or relatively new) to the site.

There simply isn’t enough information to tell whether or not the consumer is buying for himself/herself or as a gift—and that can be especially problematic during the Christmas season and other key retail drive times, because shoppers can easily be miscategorized for future communications.


In other words, Mary Smith may be delighted that your website is recommending silk ties as she shops for her dad, but when she shops for herself, she’s going to want to see blouses and shoes.

A follow up email offering a discount on her next purchase of men’s silk ties will fall flat, and she may not shop on your site again because she feels like your brand doesn’t understand her needs.

This outcome is especially likely if Mary is a millennial, as her generation has a significantly higher set of expectations for personalization than other demographic groups.

So, what’s the remedy?

If you’re like most retailers and etailers, you’re collecting consumer data. Lots of it. And it may be stored in different databases.

The key to accurately forecasting your shoppers’ needs for better personalization is to be able to use ALL the data in an easy and actionable way. 

Your company may have a super CMS, great analytical software and a website that optimizes the customer experience, but if these systems don’t interface together, your view of the customer will be limited at best.

You could end up trying to sell that young boy a basketball when all he wants a guitar case. Multiply this error by the tens of thousands (or millions) of shoppers who visit your website, and the lost revenue quickly escalates.


Want to learn about how leading retailers overcome this problem of data integration as well as the broader trends in customer experience management?

Join SDL and Econsultancy for our webinar on Wednesday, January 21st, The Retail Imperative: A Strategic Approach to Customer Experience.