Behavioral Targeting With Attitude

In “Improve Your Ad Spend With Innovation,” I discussed some simple ways to use landing page optimization with an A/B or multivariable test.

Today we’ll look at behavioral targeting, which simply tells you what visitors do on a site. This step builds on your testing, because a basic A/B or multivariate test doesn’t care how the visitor arrived on the page being tested or who that person is. Testing is about randomly serving different pages so you can measure whether a design improves a KPI (define). By adding behavioral targeting, you can fine-tune which page designs to serve up based on such factors as:

  • Where people go on the site

  • Which page drives them to request more information
  • How they enter a site
  • What the conversion rates are for key behaviors
  • At what point people abandon the purchase funnel
  • Which calls to action are most effective

Behavioral data sources include Web analytics tools, advertising-reporting tools, and other tools or software that report on user behaviors.

All these things do a good job of telling you what’s happening on a site. It allows you to understand and aggregate data about a visitor’s behaviors. It can even tell you how to segment users to maximize opportunities. With the current state of the world, quick answers that improve performance are top-of-mind for all of us.

You can use these behavioral measurements based on your overall KPIs to identify areas of opportunity where your site can be improved. You can hone in on the most promising possibilities; then examine other data types to deepen your understanding of the problem and suggest potential solutions.

Translation: You look like a rock star for saving your company time and money.

Behavioral data shows the impact a problem has on all visitors, not just a core sample. It’s an unvarnished, unedited, macro view of site traffic patterns. On a site built to generate leads, for example, it may show that most visitors find a crucial contact form but quit before completing it. Behavioral data will give you the exact drop-off rate and the defectors’ entry and exit routes. This is useful information.

Unfortunately, behavioral data won’t tell you why so many people aren’t completing the form or why they exit the site in a huff. (This is where the attitude comes in.)

After you use behavioral data to isolate a problem, move on to an attitudinal analysis. Surveys, follow-up e-mail messages, customer sessions, and feedback form submissions can often tell you why a problem is occurring. Perhaps the form is too long or visitors think it’s too invasive. Perhaps the form is fine, but the copy in the link to it is misleading. Maybe the site doesn’t offer enough orientation.

That’s the logical side of things, the tactics and measures for taking action during these trying times. But let’s not forget that the Web’s about persuasion: persuading visitors to make a series of decisions. Persuasion, like advertising, is an art, not a science. Overanalyzing what we do can eliminate the creativity that fosters persuasiveness. It’s imperative to consider attitudinal data, customer feedback, research, and competitive information, but don’t create more anxiety for your team by getting into crazy minutia. Keep it simple.

To sum up our discussion: Behavioral and attitudinal data provide some key information on how your Web site is functioning, but it’s up to you to use that data to create an ongoing optimization cycle. The optimization cycle includes setting goals, benchmarking, measuring progress toward your goals, and measuring the impact of your changes.

Once you’ve identified the potential ROI (define) of proposed changes, you can chart the information against industry standards and overall business goals for both the short and long term.

Translation: you’ve just given your company a short-term plan for the anxiety attack we’re experiencing right now and a long-term goal that’s calculated, direct, and easy to understand.

Next time, we’ll discuss competitive data. And a warning: your competitors aren’t perfect.

Related reading

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