Leverage Customer Data To Turn Browsers Into Buyers

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Online shoppers are more distracted than ever. As the count of e-commerce and comparison shopping sites on the Internet expands daily, an e-retailer’s job of attracting and retaining customers becomes even more difficult. This fierce competition online has left online retailers grappling with how to convince shoppers to buy at their site over competitors’. Fortunately, there are strategies online retailers can use to reach their most valuable customers — and it all starts with using a resource all retailers have, but few understand: their own customer data.

For most retailers, the prospect of figuring out how to tap the stream of anonymous but valuable data shoppers leave behind is overwhelming. Most have difficulty making sense of the data, and most analytics teams are already overwhelmed with staying on top of site analytics alone.

Customer data, rich with the information retailers need to determine the “value” of each visitor -- which products they looked at, how much they’ve spent in the past, how interested they are in purchasing now -- simply lies buried somewhere in a data warehouse. But not investing in its use is a big mistake because the opportunity cost is high.  For example, making sense of customer data is crucial to extracting more revenue & profit from retargeting campaigns.

If the data shows that a shopper is highly likely to purchase, the retailer can react by showing that shopper relevant, personalized display ads, at the right time and on the right sites. If the data shows the shopper frequently browses but can’t be persuaded to purchase, there’s little sense wasting time and marketing resources to appeal to them. New advertising technologies, namely real-time bidding, and the emergence of data-driven advertising technology vendors, can assist retailers in analyzing & acting upon this valuable, but often unused, data.

Performance retargeting, driven by retailers who make use of the anonymous trail of data their customers leave behind, has changed how everyone (publishers, advertisers and even consumers themselves) connects online. From the consumer’s perspective, these data-driven ads make for a smarter, better shopping experience. No more generic ads for products they’re not interested in. No more ad spam.

With minimal effort, and with the help of advertising technology partners, advertisers can put their own customer data to work as one of their biggest online marketing assets.

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2 comments on "Leverage Customer Data To Turn Browsers Into Buyers".

1. Peter Rosenwald from Consult Partners
   

   commented on: December 9, 2011 at 3:52 p.m.
   Very good clear exposition of the fact that all but the most sophisticated just are not taking full advantage of their marketing databases, enormous assets that are most frequently under used and under valued. Part of the problem is that they don't understand the basic economics of using the marketing database. Readers of my book, Accountable Marketing (Cenage) and users of the accompanying economic templates tell me that had they known how to do the numbers, they would have mined their customer data long ago. The bottom line (and that's what we are principally interested in; aren't we?) is that if we can step back from the data mountain, model and choose those data characteristics that will provide the highest return on the marketing investment (ROMI), we'll exert the greatest leverage on increasing profitable sales. Peter Rosenwald (rosenwal@uol.com.br)
2. Cameron Davidson-Pilon from 70percentfatfree.com
   

   commented on: December 9, 2011 at 10:24 p.m.
   I am often curious, and very interested, as to how much data these e-commerce websites have. I would imagine the bare minimum is clicks, time on pages, past purchases, where they linked from and if they are a frequent user, some sort of classification/clustering assignment. Assigning a value to each online user - and then turning around and selling that value - is a great idea, and probably uses some great machine learning algorithms to crawl through the data and determine such a value. I have seem some seminar where constant, positive feedback can really influence an individual's short term and long term behaviour, perhaps ideas like this can be applied. Another interesting idea is given a high-value user and his or her history, we can probably price discriminate more effectively than in the bricks-and-mortar world. I am very interested to see where this technology can go. Cam Davidson Pilon, UWaterloo

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