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Google Analytics Personas

Persona Creation Using Google Analytics: Summary of Methods

This post is co-authored with Noor-ul-Anam Ruqayya, a Software engineering graduate from the University of Karachi. Noor is currently working as a content writer and digital marketer.

In this post, we summarise seven articles that explain the process of creating buyer or user personas with Google Analytics. The purpose of this post is to discuss various methods bloggers and marketers use to create buyer personas. In brief, Google Analytics can be used to discover clients and gather data about them through reports.

5 Google Analytics Reports to Help Build Your Buyer Persona 

In the article, the author Clayton Coomer explains why the buyer personas are important. He further reasons that Google Analytics makes the process of creating personas simple and presents five analytics reports to help build buyer personas. The reports are as follows.

Report 1: Demographic Overview 

The demographic overview report is quite generic and presents the basic age and gender information. A quick analysis of data shows:

  • The age range is between 25-34-year-olds
  • Majority of the audience is male

To pull the age demographic review report, navigate to the Audience tab -> demographics -> overview. 

Report 2: Demographics: Age

This report presents the age breakdown and details about the targeted age segments. This report is useful as it tells us the specific age group we should be targeting. 

A quick analysis of the data shows:

  • Ages 25-34 range:
    • Generates the most revenue
    • Are responsible for the most transitions and sessions 
    • Have the best conversion rate

To pull the age report, go to the Audience tab -> demographics dropdown -> age. 

Report 3: Demographics: Gender

Similar to the age report, the gender report gives insights into the gender of our client. The usefulness of this report depends on the product/service, as some products or services are gender-neutral. 

A quick analysis of data shows:

  • Males have:
    • A higher conversion rate (over 1% better)
    • Generated over 1M more in revenue compared to women
  • A majority (56%) of the traffic is generated by the male 

To pull this report, find the Audience tab -> demographics -> gender. 

Report 4: Demographics: Age (+Gender)

This report is not a generic report, so it’s not available in the left sidebar. 

To access this report, pull the age demographics report, and click on the age range to display gender breakdown. A quick analysis of data shows:

  • Males between the ages of 25-34,
    • Generate the most revenue (nearly ¼ of the total)
    • Account for 22% of all transactions 
    • Account for 25% of all website sessions.

This report gives more details but we still lack personal information, which the author targets in the next report.

Report 5: Demographics: Age/Gender + Interests 

Staying in the current report, click male. Your Google Analytics should take you to the “other categories”, change to the “Affinity categories” and click the green correspondence box. Once the process is complete, the report should be ready. 

The quick analysis of the report indicates that the segment is: 

    • Tech-savvy
    • Loves watching TV (Netflix mostly) 
    • Loves to travel and watch the news (on mobile)

Example of the Persona

The next step is to go back and see who the next profile should be and create at least three. Use the data collected from the Google Analytics report to create the buyer persona.

Takeaways

In this post, the author has discussed five audience analytics reports: demographics, age, gender, age+gender, and age+gender+interests. Each report is extended from the previous report and adds some new information which is then used to create a buyer persona. For example, the first report (demographic overview) shows the gender and age of the audience, and the next report (age demographics) presents the age breakdown and targeted age segments, and so on. 

Segment Analytics Data Using Personas 

A persona is usually created at the beginning of the design and strategy phase of a project. In this article, the author has introduced a method of creating persona-inspired segments using the analytics tool. Although the author does not specify the tool being used, the reports listed by him indicate the tool is Google Analytics. 

By creating the user segments based on personas, one can analyze how real users interact with your website. This information can be used to validate and refine the assumptions made during the research phase and uncover the information you may have missed before. 

The key step in this method is to include segment filters that separate the prime characteristics of a specific group of users by analyzing user activity. 

According to the author, a few ways to segment users derived from the persona include: 

  • Demographics
  • Geographic locations
  • Device and/or browser
  • New vs. regular visitor
  • Source (how they found the website)
  • Keywords and pages targeted by the users 

As personas are categorized into segments, the volume of data is reduced. That makes it easier to analyze and draw very specific conclusions. Segments do not just enable us to analyze metrics but help uncover behavioral patterns in specific segments.

Consider an example of analyzing the bounce rate of a page. You have the bounce rates of two users, Mary (83%) is a subscriber, and Mark (37.5%) is not. The difference between the bounce rates of Mary and Mark is 65%. This information gives you a better understanding of how the page performs given the specific goals of each user. 

In conclusion, the quantitative data from the analysis tools (like Google Analytics) tells you how the user behaves. To understand why they behaved in a certain way, you can help with user-research activities like qualitative usability testing. The triangulated data from both sources can help optimize your website to address the needs of each user.

Takeaways

The author discussed the creation of user segments to analyze buyer persona in order to improve website performance over time. This process can be completed using any analytics tool and users can be segmented in a number of ways like demographics, interests, etc. 

How to Create Personas using Google Analytics

User personas are usually created using qualitative data. In this how-to guide, the author shows how to build personas using quantitative data collected from Google Analytics.

In the post, the author explains that the best way to achieve business goals is by figuring out who the audience is. Then identify the key patterns that are driving the most conversions to the business. Identifying these patterns reveals the core audience.

Before going forward, one must understand the difference between qualitative and quantitative research. 

Qualitative research helps one understand the underlying reasons, opinions, and motivations behind customer behavior. Qualitative research is conducted through methods such as:

  • Observation
  • Focus group sessions
  • Interviews  

In quantitative research, the results can be presented in numerical values. It answers the ‘how many’, ‘how-often’, and ‘how much’ kind of questions. Types of quantitative research include:

  • Surveys
  • Online polls

Both research types have their advantages and drawbacks. To understand the audience accurately, The holistic approach is taken to combine the quantitative and qualitative research results. 

To generate a complete persona, the author pulled four reports in different steps. 

Step 1: Age and Gender

To pull this report, go to the Audience tab -> Demographics -> overview. 

The largest demographic is, ‘25-34-year-old male’. It’s not much but you have the basis to create your persona. 

To add more detail, click Age and then the secondary dimension section to find and add ‘Affinity’.

Step 2: Affinity 

The ‘Affinity’ category helps identify the online prospective customers at scale. Google Analytics uses factors like browsing history, time-on-page, and associates them with the user-profile (foodie, shopper, etc).

In the image, the affinity category has been added to the age demographic that shows the main interest of the client.

Step 3: In-market

In the Secondary Dimension category, find the in-market segment. This gives you customers that are actively searching for products/service across GDN (Google Display Network) i.e. YouTube, AdWords, AdSense, etc.

See the image below:

Step 4: Geo

The language and locations reports tell you where your ideal client segment lives and their language. For instance, if your major clients are UK-based, then you may benefit from adding ‘u’ to color. 

To see the devices you audience use, go to the Audience tab -> Mobile -> Devices. 

Build the persona

Use the collected data set to build a buyer persona. It is crucial to name the person and give it a face. That makes it easier for marketers to empathize with their customers. Present your user attributes and character in a table under a chosen name.

You can also present the persona in a more appealing way. 

Takeaways

The author discussed how important it is to conduct both qualitative and quantitative research to collect user data. He further used four Google Analytics reports, age and gender, affinity, in-market, and geo to analyze user data to build the buyer persona.

How to Create Buyer Personas With Google Analytics

The author, Ben Jacobson discusses why it is important to create a social media buyer persona. He also discusses the step-by-step method of creating personas with Google Analytics. 

Jacobson argues that the definition of buyer personas is not just about the target audience but about finding similarities among the target audience. 

With a perfect social media buyer persona, one can create content that targets user concerns and interests making for a more engaged audience. 

The data about the user persona can be found in the website analytics. The data can be used to create a buyer persona with Google Analytics in four steps.

Step 1: Research your Website Traffic by Keyword

To begin, open Google Analytics and go to Acquisition -> All Traffic -> Google/Organic, and set the ‘Secondary Dimension’ to Keyword. 

This will pull the keyword searches that bring visitors organically to your site. You will be able to see all the keywords, but there’ll be enough to get started. Copy and store the list to a spreadsheet.

Step 2: Find Similar Users in Search Traffic

The author suggested going through the spreadsheet and grouping the data into themes and categories. 

For instance, if you market sports goods, then place them into separate categories like, clothing, footwear, or location-specific queries, etc. These categories can determine who is searching and find the right questions to ask yourself.

You can make a list of personas such as, ‘serious runner looking for sneakers locally under $100’. This is a rough version of a buyer persona.  

Step 3: Refine Buyer Personas by Social Channels

One a rough persona has completed. Referral traffic data from Analytics can be used to create prototypes of the audience for each of your social media channels.  

To create the prototypes, go to, Acquisition -> All Referrals. Choose your secondary dimension and click on the landing page.

The preference and patterns will help create specific content for each social network. Through this data, a restaurant may find that Facebook refers to people looking for reservations and Twitter refers mostly to people looking for daily specials.

Step 4: Fill Out Social Details for Your Personas 

You can also look for patterns among your fans and followers on social media. Twitter analytics can give relevant insights from ‘your followers also follow’ and ‘interests’ sections.

To pull common keywords, the author used FollowerWonk for Twitter and Graph Search (original graph search was discontinued by Facebook, Graph API can be used as a replacement) for Facebook. Examining other social media platforms like Behance, Empire, Quora, and OpenForum can further narrow down your search. For instance, people who spend time on Q&A sites enjoy content pieces written by experts while those who like picture-sharing based social media networks look for content that is rich in images.

These analytics help you connect with your understanding of the community and connect with them on a more personal level. This is vital for effective social media marketing (SMM).

Takeaways

The author discussed the importance of user persona in creating a social media marketing strategy. He explained Google Analytics and other analytics tools like Twitter Analytics, and tools like followerWonk and Graph search to help understand one’s social media following.

Your Website Personas – How to Quickly Find out with Google Analytics 

In this post, the author explains that while creating personas can be difficult, there is a way to create personas in 10 minutes.

As Google Analytics data usually narrows it down to one persona, this persona can be used as a guide for content and marketing development. One can start by asking if the user was a person, who would he/she be? 

Create a user statement, who the person is, and match it to the ideal user. This step helps make sure that one is targeting the right users and if not, then how to find and target the ideal user.

The author uses six types of data to create a user statement with Google Analytics:

  1. Country 
  2. Gender
  3. Age 
  4. Device
  5. Operating system 
  6. Coming from

The author pulls data from Google Analytics and puts each one in a generic statement: 

“Our typical user is [age] years old [gender] from [country] on a [device type], who has found the site by [where they come from].

With the data in the image below, we use the most popular brackets to create our user persona. For example, the most popular bracket is 25-34, but the 18-24 group is close behind. So, we will skew the age towards the younger end of the former bracket. Our user statements should look like:

Our typical user is a 26 years old woman from the USA on a windows desktop, who has found the site by originally searching Google.

The author urges people to ask themselves if the user statements match the ideal customer persona. If not then how can we improve the marketing strategy? The insights are credible and can lead to multiple features, and ideas on how to position a brand. 

Takeaways

In the blog post, the author discussed six types of data to create a personalized user statement. He argues that although creating full user personas can be difficult, a user statement can help guide the design process. 

He pulls analytics reports from different websites and uses them to create user statements and urges people to see if their user statements match their ideal users, and if not, then how to handle the situation.

How to Design your Buyer Personas with Google Analytics

In this article, the author discussed why understanding the prospective client is important and how we can use Google Analytics to collect data and create a prospective buyer persona using that data. In the end, he discusses how to create a strategy considering your prospective buyer persona.

Recognizing your client, understanding what they need, and creating useful content at every stage can generate dialogues. The author suggests collecting the following data for persona creation with Google Analytics:

  • Demographics 
  • Interests 
  • Sources of traffic 
  • Visitor’s Geolocation 
  • Use of website 

Google Analytics gives you three areas to understand your clients and how they interact with your site: audience, acquisition, acquisition, and conversion. These are explained in the following.

Audience

In this area, you can analyze four reports: demographics, interests, geography data, and mobile devices.

  1. Demographics: this report show, age, and gender of the client. It tells which age bracket and man/woman is creating the highest rate of conversion.
  2. Interests: This report tells us three things: affinity (interests like tech, sports, etc), in-market segments (purchase interests of the users), others (custom interests added in the affinity category).
  3. Geographics data: gives you the locality and language spoken by the user.
  4. Mobile device: provides the data related to how the user is connected to your website. For instance, if the device is mobile, tablet, or desktop.

Acquisition

In this area, the channels used by the client to connect to your site.

  • Sources
  • AdWords
  • Search Console
  • Social 

The report compares the traffic to the conversion rate to evaluate which platform generates the most clients.

Behavior

Behaviors give us reports about:

  • Behavior flow of the users
  • Contents of the site (Session duration and rebound frequency)
  • Website speed
  • Research related to the site

This report tells us about the user interaction with the website.

Conversion

In the conversion state, we measure:

  • If the objective was achieved: for e.g. if the user got until the thank you page after filling the form
  • User’s behavior on your site: sales yield, purchasing behavior
  • How users made the purchase: time from form initial interest to conversion, attributing a particular conversion to a specific channel, etc.

By analyzing the audience, acquisition, and conversion reports, the user persona looked like (see image):

The prospective buyer gives us useful insights to create a strategy to share effective content. It involves social media campaigns, improving SEO, identifying pages that are abandoned the most, implementing content through videos.

This strategy is carried out in two steps. In the awareness stage, the company needs to be found by the prospective buyer. In the consideration stage, Anna must know that your cake designs and courses are beneficial to her. 

In the end, it is important to consider as much as you can before moving onto the next stage.

Takeaways

The author explores three areas to collect data about the user, audience, acquisition, and conversion. In the audience report, the demographic data about the user is shown. Acquisition tells you how the visitor found your site, and conversion reports tell you how many visitors you converted into clients.

How to Create User Personas with Google Analytics

In this post, the author describes how his company (Seer) created a user persona for a credit counseling client using dimensions in the Audience section of Google Analytics. 

To begin, your website should meet the following criteria:

  • It has a sub-folder or keyword(s) in a group of URLs that can be identified with an audience
  • It has sufficient data on Google Analytics to analyze the subfolders or URLs

According to the author, the most relevant dimensions in Google Analytics for persona creation are:

  • Age 
  • Gender
  • Affinity categories
  • In-market segments
  • Location 
  • Other categories

These dimensions can be found in the Google Analytics menu. 

Dimensions in the interest section can be explained as:

  • Affinity category: to make the buyer aware of your brand.
  • In-market segments: which user segments are ready to purchase the product.
  • Other categories: more granular categories, and identifies the users that are not in either one of the above categories i.e. affinity and in-market segment.

We start by setting the time range from about six months to a year. Set the primary dimension to ‘Affinity Categories’ and secondary dimensions to ‘Landing page’. 

The advanced filter will be set to show landing pages with the keyword ‘student’ in them. 

Export the data to excel and create a pivot table using Sessions and Affinity Category. Eliminate the other fields and the table looks similar to the image below:

After hitting enter, the pivot table looks like:

Sort the ‘sum of sessions’ column from largest to smallest. The table gives you the most common affinities of users who visited pages related to students and student loans. 

Once we have the data, the last step is to turn it into a persona that looks like this:

Also, as the two key interests of the student loan audience are cooking and deals, so cooking with coupons can be a great idea for a blog. 

In this post, the session was used as the metric but the process can go further. For instance,  you can use purchases or specific landing page views as segmentation criteria.

Takeaways

The author uses dimensions like age, location, and affinity to create buyer personas for a credit counseling client. He creates a pivot table with the data pulled from the Google Analytics report and creates a persona with it.

Conclusion

We discussed seven posts from different authors and websites where they explain the process of using Google Analytics to create personas (and in one case, user statements). 

In the blog posts, the author used primary dimensions like demographics and secondary dimensions like age and gender to pull custom landing pages or session reports about the buyer or users. The information from the reports is combined to create personas that represent the dominant users’ demographics, location, and interests.

Some authors also noted weaknesses when using Google Analytics for persona creation. Particularly, creating personas based solely on website analytics can take us further away from the users rather than bringing them closer. It gives us the where and who but not the “Why” that we are after. On the other hand, Google Analytics is important too as it presents solid quantitative data about the users of the website. 

The best way to create personas is to use not just Google Analytics or any other analytics tool but explore as much data as possible, and taking time to understand the users as real people. For this, combining quantitative data from Google Analytics and qualitative data from user interviews (5-10 is enough to get a basic idea of user needs) is advisable. 

By Joni Salminen

Dr. Joni Salminen works as a Scientist at Qatar Computing Research Institute, Hamad Bin Khalifa University, and as a Postdoctoral Researcher at Turku School of Economics, University of Turku. His research interests are heavily focused on personas, including topics such as automatic persona generation from social media data (YouTube, Facebook, Google Analytics), persona perceptions, biases in data-driven personas, optimal number of personas, etc.

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