Behavioral Targeting & Google Analytics: How To Create Personas

Following my last post on behavioral targeting, which was an overview of the field, I will provide a more hands-on approach in this post. To briefly recap, behavioral targeting involves creating multiple “personas” that represent multiple users of your site, and using analytics to create a unique experience for each persona group based on observed and predicted behavior.

The first step to a successful behavioral targeting process is finding the right targets. It is not always obvious which users should be “bucketed” together. Creating effective buckets requires knowledge about the site and a careful analysis of the data. It is important to have clear objectives; this way we can pinpoint the type of person we are targeting, measure our success and optimize for maximum effect.

This can be done using any web analytics tool that provides advanced segmentation. But you also need to think hard about the marketing implications of segmentation and persona creation. I chose to use Google Analytics in this example since it is free and it provides a very powerful segmentation feature.

What exactly is a persona?

According to Wikipedia, “A user persona is a representation of the goals and behavior of a real group of users.” This definition meets the objective of behavioral targeting as provided in my last article: create a unique experience for each visitor. If users have different goals and behaviors when they come to a website, why should they have the same experience?

Web marketing guru Bryan Eisenberg is among the pioneers that introduced personas into the online world as a way to segment users on websites and provide personalized experiences. This technique requires understanding the website objectives and users very deeply. As Bryan notes in some of his writings, personas can be used to optimize websites and target users in pretty much every aspect of online marketing: campaign creation and expansion, competitive analysis, offline advertisement and onsite content targeting.

It is important to differentiate between pure segmentation and persona creation. While segmentation is critical to behavior analysis (or, as Avinash Kaushik says, analyzing data in aggregate is a crime), personas cannot be achieved by clickstream data only. This requires a deep marketing analysis: understanding who is your market and what they want from you. Behavioral targeting takes advantage of both techniques. We can target different segments, such as new visitors, returning buyers, Canadian returning visitors, or any segment that shows a special behavior. And we can target different personas, trying to show different content to people coming with different goals to the website.

Creating personas

Building personas is a deep marketing exercise: you must understand your audience and the product you are offering. Although data should be used (as seen below), the structure should come from the company’s understanding of the market and the customers. Bryan Eisenberg provides a series of helpful questions on measuring personas for success, which can be very useful to get started and build your personas. Below are a few of them:

• What does this persona do on a daily basis?
• What’s the persona’s life mantra?
• What’s this persona’s unspoken question regarding this product?
• What does she expect from this product?
• What information will this persona need to be persuaded to take action?
• Why is she motivated to take this action?
• What actions do you want this persona to take, and how will you persuade her to take them?

You can also take a look at this nicely formatted persona sample (PDF) to get an idea on how to create a document to convince management of the value of this exercise.

Creating your persona using Google Analytics

First of all, it is important to note that it is very difficult to translate a persona into a measurable segment based on clickstream data, but it is possible to reach an approximation. Let’s suppose we want to develop a persona called Danny Sullivan which I believe represents the kind of people that visit my site. I would start by trying to answer the questions above about him, and I might as well try to find a person that has the same lifestyle as Danny to help me (Jim Sterne?). Then I would go about trying to find some metrics and dimensions on Google Analytics that help me pinpoint the persona and turn them into an advanced segment. Here’s an example:

Click for a high resolution image.

As you can see above, I believe that Danny (the persona) has the following behavior patterns:

• Since he is always on the road, he probably visits the site using his smart phone (iPhone, Blackberry or Android)
• Usually in the United States
• He has been to the website before, so he is a returning visitor
• He reads many blogs using feeds, and probably gets to one of my posts through Feedburner (see new integration between Feedburner and Google Analytics)
• Since he comes to my site every day, ‘Days since last visit’ equals 1 or 2
• And since visiting my site is probably the first thing he does in his daily routine, his visit probably happens before breakfast, or at least earlier than 10am.

This is a somewhat simplistic example, but it shows that it is possible to create personas using Google Analytics to understand how each targeted audience is behaving on your site. This shows us what we are succeeding or failing to provide to each kind of person on the website.

I’ll illustrate how we will use the information above to feed the behavioral targeting cycle in my next post.

Opinions expressed in this article are those of the guest author and not necessarily Search Engine Land. Staff authors are listed here.

About The Author

Daniel Waisberg

Daniel Waisberg is the Principal og Conversion Journeyand the founder of Online Behavior, a Marketing Measurement & Optimization website. He holds a M.Sc. in Operations Research and Decisions from Tel Aviv University, where he developed a statistical model that helps to optimize websites using Markov Chains. Daniel is a frequent speaker & member of the Advisory Council of the eMetrics Marketing Optimization Summit. You can follow him on Twitter or Google+.