How to create personas using Google Analytics

We reveal how you can create multiple buyer personas using all the aggregated data found in Google Analytics.

First thing’s first, a little refresher for the newbie…

What is a persona?

Personas are a way to help organisations understand their potential and existing audience in a more personal way.

Personas are detailed profiles of a particular audience member, who represents a distinct group of people – in that they share similar behaviour, attitudes, personalities and preferences of your product, but are the ‘figurehead’ for a larger demographic.

Typically, personas are constructed by researching and interviewing real people to gain qualitative data, and this information often shapes how a design team develops a product and how a marketing team builds their messaging around it.

However, in this ‘how-to guide’, we’re going to show you a way to build personas through quantitative data – of the kind you can discover in your Google Analytics package.

Where do you start when creating personas?

First of all, what’s the problem you want to solve?

It’s important to first figure out what you want to achieve. Do you want to sell something? Do you want to improve user engagement? Do you want to create brand awareness? Generate content?

Once you’ve figured out the problem(s) you need solving, you then need to understand who you are talking to. You need to identify your audience.

You’ll hear this called many different terms: user, audience or customer segmentation – but whatever you call the human being who is actually using your product, the process behind identifying them is very simple and straightforward. You just need to break down your aggregated data into various dimensions (that are already available in Google Analytics) to build a full-picture of your customer. These include age, location, gender, language, device preference.

Everything you need to get started with UX research

How do you collect data for creating personas?

You need to identify the key patterns that are driving the majority of conversions in your business – and identifying these patterns will help you to reveal your core audience.

You may find out that 90% of your website sales are driven by one type of customer – perhaps a young, woodland-dwelling, non-professional woman, aged 25-35. This would be an actionable and valuable insight for your business because you know exactly who you’re targeting.

There are so many methods of data collection, but how do we know which one to use?

Here’s where it’s important to understand the difference between quantitative and qualitative methods of research.

Qualitative research:

This type of research will help you understand the underlying reasons, opinions and motivations behind customer behaviour.

Qualitative research can take the form of:

  • Focus group discussions
  • Individual interviews
  • Observations (the ‘think aloud’ protocol used in Remote UX testing)

All provide actionable, real-world insights without you having to become a specialist in data-science.

However the limitations of using qualitative studies for discovering patterns are that it’s difficult to derive statistical analysis, it’s time consuming and it can also be costly.

Quantitative research:

This is any kind of investigation where the results can be presented with numerical values. The data you’ll uncover in quantitative research is all to do with ‘how many, how often and/or how much, etc.’

Types of quantitative research include:

  • Online surveys
  • Paper surveys
  • Mobile surveys
  • Telephone interviews
  • Online polls
  • Longitudinal studies
  • Anything that can generate data.

The drawbacks with quantitive data are that it can tell you what is happening and how many times, but it can’t tell you WHY it’s happening. If you want to use quantitive data to make changes or improve your product or website, you’ll only be making a guess as to what those changes need to be.

It’s important to have a holistic approach to understanding your audience. A blend of quantitative and qualitative research will cover all bases.

If you’re using quantitative research, then is a good chance that the huge volumes of aggregated data can result in this unwieldy type of spreadsheet…

It can be tremendously helpful to to create this kind of data overview, but how can you take this massive document and use it to understand your audience personally and convert the information into something that’s easy to understand for your stakeholders, colleagues and project managers? You need to give life to your data.

You can do this through persona research.

This allows us to visualise quantitative data so anyone can understand it. Persona research can help achieve many goals…

  • Reveal buyer’s concerns
  • Evaluate buyer’s behaviour so you can see their journey
  • Create content strategy with topics you need to target
  • What types of buyers are interested in your services (age, location, medium they use, social network preferences)

What data do you need to create a persona?

Here’s how to generate a fully-rounded persona by using the data in Google Analytics.

The first step is…

Age and Gender

Here is an example of the analytics from our own platform. You can see the breakdown of ages by clicking Audience>Demographics>Overview

As you can see, the largest demographic we have is ’25-34 year-old male’. Here’s where you can start creating your first persona. You can build the group with the highest representation first, but don’t forget that this isn’t your only audience. You’ll also need to build personas for all your high performing demographics.

To add more detail to your persona, you’ll need to click Age, then the ‘secondary dimension’ section where you can search for ‘Affinity’. Then click on the category to add the dimension.


Affinity categories helps you identify your ideal online customers at scale. Google Analytics uses different types of factors such as browsing history, time on page, and then associates this with a ready-made user profile (i.e. ‘shoppers’, ‘technophiles’, ‘foodies’, ‘music lovers’).

Below, we’ve added the ‘Affinity’ secondary dimension to the Age demographic to reveal the main interests of our 25-34 year-old audience.

This gives us more insight into the background of our audience in order to give more detail to our persona.


Within the ‘Secondary dimension’ drop-down menu we can then search for ‘In-Market Segment’

The In-market segment is a way to connect with customers who are actively researching and comparing products or services across the Google Display Network (YouTube, paid search results via AdWords, display ads via AdSense, etc).

Below you can see what products/services our audiences are interested in…


We can also filter by language and location in order to see where in the would our audience is visiting us from. To find this filter, click Audience>Geo>Language and Audience>Geo>Location.

Bear in mind that language is very important. If your site has a mainly English-US speaking audience, it means that perhaps you should start removing ‘u’ when you write ‘colour’ – something we may need to consider, although it chills me to the very fiber.

Now we need to find out what devices our audience is using to access our site. By clickingAudience>Mobile>Devices, you can see exactly which brand of mobile they’re using and even what service provider or operating system they prefer.

Building your persona

The above data can be accrued using any analytics package, not just Google Analytics. Just remember that however you obtain it, when you’re putting it together in some form other than a giant spreadsheet, you need to make it look super clear and give it personality.

Try to avoid putting these together in a clinical, sterile way (by naming them Persona 1, Persona 2, etc) – we need to give the persona a name, a personality, a soul.

You don’t have to be a graphic designer to do this. You can put all the information into a simple table, grouping personality traits under your chosen names.

Or if you have some skills in design (or the resources in your organisation), you’ll be able to make something a little more appealing. Like this example…

There are a lot of specific details here about Johana, a 23 year-old student who wants to ‘grow a strong marketing reputation’, that hasn’t been gathered from the above GA data we’ve covered, as this will potentially come from qualitative research. However, this will give you an idea of how to present the data you have acquired in a more accessible manner.

Main image by Raw Pixel, Frank by Foto Sushi, Cezar by Filipe Almeida, Ariel by Eli DeFaria