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With seemingly limitless options for interacting with visitors, Web sites present marketers with a conundrum on where to start when they want to improve the outcomes of such interactions. “About five or six years ago, there was so much promise in technology itself that everyone tried to predict behavior and create better online targeting based on online behavior,” says Will Hakes, senior director, analytics at Aspen Marketing, an integrated marketing services firm with headquarters in Chicago. “There is still this notion that looking at a customer’s Web traffic is sufficient to predict what he’s likely to do, and in most cases, it’s not.”
For marketers interested in doing a better job of matching offers and messaging to online visitors, Hakes recommends they first take into consideration the following factors that will affect their ability to achieve success in this type of endeavor.
Factor #1: Does your company have sufficient technology to implement an online targeting program? You don’t have to be Amazon.com or eBay to perform effective online targeting, but you do need the ability to track, analyze and deliver customized offers and messages.
Factor #2: Are you in an environment where you know who the person is that you’re talking to online, or is he anonymous? To target, you will need some sort of customer or member log-in—whether that’s asking for a phone number, ZIP code or some other piece of data that will be meaningful to what type of offer or messaging you could select for the visitor. “The first step is getting online people to self-identify and making sure that step passes legal restrictions, so that everything they tell you afterwards can be linked back to a customer record,” Hakes explains. Once someone raises his hand, he adds, you certainly then could ascertain more about him, but the balance in the online world—and not just in terms of targeting—is that the more you try to ask people and the more intrusive the questions are, the more likely people are to back out of the site. And ultimately, he explains, your goal is to match your online data to offline data, so you have to carefully walk a line on what information you need to target offers and what is too intrusive.
Factor #3: Do you have robust offline models? Where Hakes and Aspen’s clients in the telco, financial services and travel industry have found success online is in leveraging behavior-based models from the offline channels to predict what a customer surfing the Web site is likely to buy and the types of messaging that would be most appropriate on an individual level. “Those same types of offline models can be leveraged online so that once you know who a customer is,” Hakes explains, “you can make a decision with some pretty complex background logic to say, ‘I know who this person is, I know what they already have because I have access to some inventory about the types of products and services they already have with me … and here’s what I should offer this person, if and when they come online.’” Then, based on the demographics of the third-party data a marketer might collect, it also can get an idea of how it wants to make an offer to this person. Hakes states, “I think that a lot of folks have it backwards that try to use the Web data first, and then match that with other offline knowledge. I think the foundation is leveraging what you already know about your customers.”
For marketers interested in doing a better job of matching offers and messaging to online visitors, Hakes recommends they first take into consideration the following factors that will affect their ability to achieve success in this type of endeavor.
Factor #1: Does your company have sufficient technology to implement an online targeting program? You don’t have to be Amazon.com or eBay to perform effective online targeting, but you do need the ability to track, analyze and deliver customized offers and messages.
Factor #2: Are you in an environment where you know who the person is that you’re talking to online, or is he anonymous? To target, you will need some sort of customer or member log-in—whether that’s asking for a phone number, ZIP code or some other piece of data that will be meaningful to what type of offer or messaging you could select for the visitor. “The first step is getting online people to self-identify and making sure that step passes legal restrictions, so that everything they tell you afterwards can be linked back to a customer record,” Hakes explains. Once someone raises his hand, he adds, you certainly then could ascertain more about him, but the balance in the online world—and not just in terms of targeting—is that the more you try to ask people and the more intrusive the questions are, the more likely people are to back out of the site. And ultimately, he explains, your goal is to match your online data to offline data, so you have to carefully walk a line on what information you need to target offers and what is too intrusive.
Factor #3: Do you have robust offline models? Where Hakes and Aspen’s clients in the telco, financial services and travel industry have found success online is in leveraging behavior-based models from the offline channels to predict what a customer surfing the Web site is likely to buy and the types of messaging that would be most appropriate on an individual level. “Those same types of offline models can be leveraged online so that once you know who a customer is,” Hakes explains, “you can make a decision with some pretty complex background logic to say, ‘I know who this person is, I know what they already have because I have access to some inventory about the types of products and services they already have with me … and here’s what I should offer this person, if and when they come online.’” Then, based on the demographics of the third-party data a marketer might collect, it also can get an idea of how it wants to make an offer to this person. Hakes states, “I think that a lot of folks have it backwards that try to use the Web data first, and then match that with other offline knowledge. I think the foundation is leveraging what you already know about your customers.”
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