Unfortunately, these retargeting programs are not particularly productive (about a 0.1% response rate), and let’s face it, even retargeting is fairly impersonal; we don’t know who the person using that device is unless they place an order. Once they do, we use incredibly sophisticated tracking tactics to try to retrace the steps that customer took before they placed their order, in the somewhat misguided belief that we will be able to replicate that string of behavior and get better results next time.
But what about the rest of those website visitors: the ones who did not visit an ad network site, or make a purchase when they did? A very small percentage of them will give us an email or postal address when we provide that opportunity on our home page or via a pop-up box. The rest disappear, leaving behind a trail of data that is aggregated and analyzed in umpteen ways via a variety of analytics tools. But those individual prospects and customers are mostly lost to us. Well, it does not have to be that way.
Every day, large numbers of shoppers are demonstrating their interest in our brands and products by visiting our website, and today, we can capture as much as 50% of that intelligence at the individual consumer level!
Until recently, the cost of capturing, storing and analyzing data made the concept of a comprehensive “digital browsing” database difficult to justify. But today, with those costs literally approaching zero, we can capture an almost unlimited amount of browsing behavior, including number and timing of visits, product pages viewed, device and original source of the browsing session, etc. And we can match about half of this data to buyers and prospects at postal addresses, and of course, an even greater percentage to our email address files.
And there is a mother lode of valuable customer level intelligence available to us in that browsing data. In addition to sharing their general level of interest via the number and duration of their visits, customers are also telling us what product categories they are interested in, and even the channels and devices they prefer.
Think about all the possibilities:
- Identify active browsers who would otherwise “miss the cut” for our catalog mailings,
- Also identify marginal names in the mail plan for suppression who have shown no intent via web browsing,
- Identify active customers who are so engaged online that we might reduce (or even eliminate) catalog mailings to them,
- Find brand new catalog prospects who are “raising their hand” by actively browsing our website,
- Use browsing intelligence to trigger outbound calls to engaged visitors to our website,
- Supercharge personalized triggered emails to include a much greater number of consumers.
Let’s discuss briefly the catalog customer acquisition aspect of this opportunity. Most of our businesses rely heavily on cooperative databases to provide us with catalog prospects. That model identifies prospects who have recently purchased from catalogs or product categories that are similar to ours. Those purchases from competitors may, or may not, be indicative of a desire to purchase from our brand. By using digital browsing behavior to identify potential catalog prospects, we are looking instead at consumers who have demonstrated genuine intent by showing up on our website and engaging extensively with our brand. They are telling us that they are interested in making a purchase from us (or perhaps a competitor), so why don’t we provide those folks with a catalog to help them choose us?
So before you invest another dollar in trying to track the behavior of people who have already purchased from you, think about utilizing the wealth of browsing behavior available on your website to reach out to customers and prospects who are literally “raising their hands” to shop with you.
Allen Abbott is the Senior Vice President of Consulting and Client Services at Cohere One