The First Step for Data-Driven Buyer Personas [How-To Guide]

Since the dawn of marketing, buyer personas were the targeting & messaging core for every brand. Now that the era of mass advertising is dying, and digital marketing providing us with powerful capabilities (from user targeting standpoint), identifying the right buyer personas is more important than ever. I want to share our methodology (step-by-step) of how to create actionable data-driven buyer personas that drive our actions and decisions in marketing. This first blog post is dedicated to the essential step of data gathering that provides us with valuable information about our users. The more relevant information we have about our users, the better user personas we will create.

For data gathering process, we use information that is based on Google Analytics & Facebook audience insights data.

Google Analytics*

Google Analytics is the “homepage” for many marketers when they search for quantitative data but it can be used as a rich source of qualitative data as well. Enabling demographics and interests reports will allow Analytics to collect additional information from the DoubleClick cookie (web activity) and from Device Advertising IDs (app activity). Demographics, Interests and Geographical data will give us most of the qualitative information that we need. 

*All data are from Google Demo Account

Age / Gender
Google Analytics - Age / Gender
Please note that this report is based on 58-65% of sessions, because Google can provide us data only for users that opted in.

Age & gender information is the first piece of buyer personas data gathering. In order to have that data, Google Analytics gets data from DoubleClick -Google’s programmatic advertising platform- and creates automated groups for the key metric that we’ll set (I suggest keeping sessions as the key metric). From data above, we understand that the group of users that are between 18 – 44 years old is driving more than 80% of traffic overall. Moreover, it is clear that male audience prefers our products more than female audience.

TIP: Due to best key metric in Overview tab being”sessions”, it is recommended to dig deeper into Age & Gender data in order to focus on transactional/performance data rather than sessions.

Here is a good example. Although most of users that come to our website are between 18-24 years old, they have relatively low conversion rate. Actually, their performance is directly compared with an age group that isn’t within our primary focus (45-54 years old). This can help us adjust our selected age groups and identify potential challenges & hidden opportunities.

Transactions of our “main” age group 18-24 are fewer than age group 45-54 that isn’t within our focus


Advanced Segment Creation

Advanced Segments

Now that we know the age groups and the gender of users that we want to focus, we can implement advanced segments and separate our selected audience from all users. Summary tab will indicate us if segments that we create are big enough and are worthwhile analyze them. We can build as many advanced segments as we want but we can compare up to 4 at one time.



Google Analytics - Device

Device overview helps us understand specific devices in selected segments that drive traffic and performance.  Desktop is clearly the best device, although mobile has good potential as well due to decent amount of traffic.


Analyzing traffic & performance metrics is a good way to understand where visits come from and if they convert or not.


Geographical Data

Google Analytics - Geographical data

Geographical data indicate where our users located. United States are dominant both for performance & sessions. Although it is just one country, additional analysis can help us dig deeper and narrow our focus in specific cities.

“TIP: Check cities tab as well. This can help you laser target your audience both in offline (billboards) & digital (Facebook/Google) marketing. Also, add any secondary dimension where applicable. In our example, I’ll check cities along with devices so I can understand how people in specific cities are reaching my website.”



Google Analytics - Buyer personas

Users that are located in Mountain View and use desktop can help us shape an efficient & actionable data-driven buyer personas that consist of users that have more than double conversion rate from website traffic overall. There we go! 🙂



Google Analytics - Language

No questions asked English (US) is the only language that we will use for our messaging.


Now, I want to check users’ interests. In interests tab, there are 3 sub-categories: Overview, Affinity Categories, In-Market Segments and Other Categories. All of them provide data for your users. Google’s interest categorization is kind of messy but in general, it works like this: Affinity Category (broad interest) /Other Category (specific sub-category of the broad interest) / In-Market Segment (users buying behavior).

Interests Overview 

Google Analytics - Interests overview

I prefer to explore 1-2 affinity categories for each buyer persona (since they are too broad) and 4-5 Other Categories & In-Market segments that are more specific. From data above, seems that our users are technophiles (they love technology), like watch TV/movies (kind of vague interest) and have specific preferences in Mobile Phones (that are highly correlated with technology), employment and travel. Finally, they like online videos, arts & entertainment and they are active in social media.


TIP: Replicate your top buyer personas and do some “reverse” research. In this example Males, Technophiles 25-44 are the most valuable users. So, I can use advanced segments in order to create buyer personas and compare it against all users. Let’s do this for channel performance evaluation.

Channel performance

Google Analytics - Channel Performance

From the analysis above I understand that our most valuable users are:

  • Between 18-44 years old
  • Males
  • Prefer desktop
  • Live in US (Mountain View specifically)
  • Speak English
  • Love technology


Facebook Audience Insights

Audience Insights is a tool that is provided by Facebook and gives us qualitative data about Facebook users. This data comes from two main sources:

  1. Self-reported Facebook data: This information is given by users when they fill in their personal information, including age, gender, relationship status etc.
  2. Third-party data partners: Household income, home value & purchasing behavior information is available through external companies that match these data with Facebook IDs. Please note that this information is available only for U.S audiences.

There are 3 ways to categorize users in Facebook:

  • Generic category that consists of data from generic user demographic & interest groups
  • Connection category that includes data from people that are connected to our Facebook fan page, place or app.
  • Custom audiences that consist of data from users that engage with our website (from visitors to most frequent users)

I mostly use custom audiences that include visitors & buyers from my website, but for our example, I will use demographic data from users that are located in California and are interested in Google services (as we seen in GA data before).


Age & Gender

Facebook Audience Insights - Age/Gender

This report gives us information about gender & age for our audience -along with general FB benchmarks-. Note that although the percentages are not the same, the dominant audience sector is almost the same with GA data.


Job Title

Facebook Audience Insights - Job Title

Job title provides data for industries where people work, based on self-reported data on Facebook. IT, Technical, Computer & Mathematics have correlation with Google Analytics “Technophiles” audience, so the correlation between GA & FB data is still on.


Relationship Status – Educational Level

Relationship status-Education level

Basic relationship & education level data inform us that users are mostly married and college graduates while singles & grad school have potential as well.


Top Categories

Top Categories inform us about top pages for every category that can help us understand our personas and optimize our targeting & messaging. For example, if you wonder which TV channel should you use for your TV commercial here you can find some good insights. Moreover, this information can help us define ideal placements for our online (display) or offline (TV, Partnerships etc.) marketing.


Page Likes

Page likes indicate Facebook pages that are likely to be relevant to your audience based on Facebook page likes. Relevance provides us pages that are most likely to be relevant to our audience based on affinity, page size and the number of people in our audience who already like that page. Affinity score gives us benchmark score about how likely our audience is to like a given page compared to everyone on Facebook.


Frequency of Activities

This report gives us behavioral data based on frequency of selected activities. Based on this we see here our targeted audience doesn’t consist heavy social media users.


Device Users

Desktop (computer) has the biggest percentage of usage, while the combination of desktop & mobile has the biggest share of traffic. Targeting desktop & mobile devices is mandatory and we should consider a tracking solution for cross-device conversion (but that is another topic).


Below there is data that is available on for US audience and give us more depth and details about our targeted audience, but I won’t dig deeper in this post.

Household Income/House Ownership


Household Size/Household Value


Spending Methods


Retail Spending/Online Purchases


Purchase Behavior


In Market for a Vehicle



Facebook Audience Insights - Lifestyle


Now, combining Google Analytics & Facebook Insights data, we know that our primary user is:

  • between 25-34 years old,
  • male
  • married (at least the biggest percentage),
  • prefers desktop & mobile devices,
  • lives in US, California,
  • speaks English,
  • works & likes technology
  • likes specific pages
  • has a lot of specific aspects that can be applied in specific concepts.


As you can see Google analytics & Facebook audience insights can provide us with a big amount of data. What we have to do now, is to use them wisely in order to build actionable data-driven buyer personas that can help us effectively connect with our users and amplify our performance.