The process for creating marketing personas has evolved—the days of personas based solely on hunches, anecdotes, and guesswork are gone.
Today’s personas should be data-driven and clearly identify the characteristics of your best customers—which is especially important in the competitive B2B marketing landscape. Data-driven personas ensure marketers can build strategies and campaigns to reach the most valuable leads, without wasting unnecessary budget or time.
By leveraging your own database of first-party data and using third-party enrichment tools (such as Clearbit), data-driven and scalable personas are attainable with a few simple steps.
In this post, we’ll review:
How data-driven personas can help growth marketers
The methodology we use to create—or improve—personas using SQL queries
Examples of visualizations we created using a BI tool to derive crucial insights
Creating Data-Driven B2B Personas Using Clearbit
Why personas are so useful
Personas help marketers create customer-centric strategies and content. When created based on data-driven insights, they’ll also ensure you’re targeting the customers most likely to bring in the most revenue—the holy grail of every marketing strategy.
Data-driven, scaleable personas can help lead to revenue growth in many other ways, too:
· Improved product design & marketing
· More precise targeting capabilities
· Budget optimization between marketing initiatives
· More, higher quality leads
Data-driven, scaleable personas can help lead to revenue growth in many other ways, too:
· Improved product design & marketing
· More precise targeting capabilities
· Budget optimization between marketing initiatives
· More, higher quality leads
What we mean when we say “persona”
Let’s start out with a simple definition: a marketing persona is a character created to describe the general attributes of the ideal client for your business. Commonly, each persona is given a name, such as Ellie Enterprise, or Sam Start-Up, that helps describe the characteristics within that persona. Some of the key attributes that can be used to define a persona include company size, industry, age, gender, company growth, and location.
Depending on your business and customer base, the number of personas you need differs. As a rule of thumb, between three and five is a great place to start.
Depending on your business and customer base, the number of personas you need differs. As a rule of thumb, between three and five is a great place to start.
Making Your Personas
So let’s get to the fun part.
To create our marketing personas, we leveraged data from three different sources: customer enrichment, marketing acquisition data, and a CRM.
Before you begin, it’s helpful to recognize that creating personas is an iterative process—meaning there isn’t one “correct” persona for your business. The goal is to establish useful criteria that can guide your company to your most important customers using data. And, as your business evolves, it’s likely your personas will too.
In this example, to identify our personas we analyzed data, focusing on answering questions like:
· Who is the person responsible for making purchase decisions (indicated by job title)? Who makes the largest purchases? Does this differ by location?
· How do various company sectors and industries perform?
· Do any sectors have an upward trend when looking at revenue for the last six months?
Note that in this example, we assume that all marketing data is stored in a single database. (Book some time with one of our data engineers to learn how a single marketing data warehouse can help you build robust personas and do a lot more too.)
To create our marketing personas, we leveraged data from three different sources: customer enrichment, marketing acquisition data, and a CRM.
Before you begin, it’s helpful to recognize that creating personas is an iterative process—meaning there isn’t one “correct” persona for your business. The goal is to establish useful criteria that can guide your company to your most important customers using data. And, as your business evolves, it’s likely your personas will too.
In this example, to identify our personas we analyzed data, focusing on answering questions like:
· Who is the person responsible for making purchase decisions (indicated by job title)? Who makes the largest purchases? Does this differ by location?
· How do various company sectors and industries perform?
· Do any sectors have an upward trend when looking at revenue for the last six months?
Note that in this example, we assume that all marketing data is stored in a single database. (Book some time with one of our data engineers to learn how a single marketing data warehouse can help you build robust personas and do a lot more too.)
Customer Enrichment Data
Third-party enrichment data helps us understand who customers are, making it a great place to start with a persona analysis. This is how we determine important details that define the personas, such as job title and industry.
At Outshine, we rely on Clearbit to provide robust customer information from a single email domain address. (It’s important to note that during any analysis, companies must protect Personally Identifiable Information (PII) by aggregating results to a level that’s anonymized, while still building useful criteria.)
We recommend using the following fields in Clearbit for creating personas:
Email Domain
Person Job Title
Company Sector
Company Industry
Company Location
Company Employee Range
Company Estimated Revenue
The entire list of Clearbit attributes is available here.
At Outshine, we rely on Clearbit to provide robust customer information from a single email domain address. (It’s important to note that during any analysis, companies must protect Personally Identifiable Information (PII) by aggregating results to a level that’s anonymized, while still building useful criteria.)
We recommend using the following fields in Clearbit for creating personas:
Email Domain
Person Job Title
Company Sector
Company Industry
Company Location
Company Employee Range
Company Estimated Revenue
The entire list of Clearbit attributes is available here.
Marketing Acquisition
To better understand marketing performance, we have to join all relevant marketing fields to determine which marketing initiative is reaching each of the personas.
The following fields are useful to include in the analysis:
· Account Email
· Acquisition Channel
· Acquisition Source
· Acquisition Campaign
The following fields are useful to include in the analysis:
· Account Email
· Acquisition Channel
· Acquisition Source
· Acquisition Campaign
CRM
The final source of data comes from your CRM (such as Salesforce or Hubspot). This enables insights to be based on the quality of leads—telling us which personas bring in the most revenue or which become qualified leads most frequently. Start with a list of your current customers from your CRM with the following columns:
· Account Email
· Acquisition Date
· Any relevant questions asked before signing up, such as company size, how they heard about the company, and the use-case of the product.
· Revenue (from SaaS subscriptions, or Bookings)
Once you have all three datasets, use SQL to join the tables by using the email address as a primary key and create one final table to complete the exploratory analysis. We used the following query to create one consolidated table:
· Account Email
· Acquisition Date
· Any relevant questions asked before signing up, such as company size, how they heard about the company, and the use-case of the product.
· Revenue (from SaaS subscriptions, or Bookings)
Once you have all three datasets, use SQL to join the tables by using the email address as a primary key and create one final table to complete the exploratory analysis. We used the following query to create one consolidated table:
select
...
from crm_data
left join marketing_data
on crm_data.account_email = marketing_data.account_email
left join clearbit_data
on crm_data.account_email = clearbit_data.email
...
from crm_data
left join marketing_data
on crm_data.account_email = marketing_data.account_email
left join clearbit_data
on crm_data.account_email = clearbit_data.email
Visualizing Your Analysis
We used Mode—a BI tool—to perform our analysis and create visualizations to derive insights. (Disclaimer: Mode is a client of Outshine). Since the final table was created in BigQuery, to access the data we connected Mode to BigQuery and imported the data using the following query:
select
account_email_domain
, marketing_acquisition_date
, clearbit_person_job_title
, clearbit_person_location_city
, clearbit_company_industry
, clearbit_company_industry_sector
, clearbit_company_employee_range
, crm_revenue
from clearbit_data.persona_analysis
account_email_domain
, marketing_acquisition_date
, clearbit_person_job_title
, clearbit_person_location_city
, clearbit_company_industry
, clearbit_company_industry_sector
, clearbit_company_employee_range
, crm_revenue
from clearbit_data.persona_analysis
Most of the questions listed above can be answered by creating simple bar charts. Let’s start with the first question:
Which job titles have brought in the most revenue overall?
Which job titles have brought in the most revenue overall?
Marketing Associates are clearly making the largest and/or most frequent purchases, resulting in the most overall revenue. In contrast, Sales Directors’ purchases create less than a quarter of the revenue of the Marketing Associates, so this company’s content, targeting strategies, and product design should consider their needs first.
Next up, let's investigate the breakdown of job titles by average revenue.
Although Marketing Associates bring in the overall highest annual revenue, on average, Marketing Managers, Sales Directors, and CEOs make larger purchases, indicating that Marketing Associates make a higher volume of purchases. Executive Assistants (EAs), on the other hand, bring in the smallest amount of revenue for each purchase, and overall the second-lowest annual revenue. They are not the ideal persona for the company in this example.
Following up, let’s determine if the job title that brings in the most revenue differs by location.
Across most locations, CEOs and Sales Directors bring in the most revenue, while EAs are making the biggest purchases in Canada, closely followed by California and England. In NYC, the most prominent job title based on the size-of-purchase is CEO. This fact alone helps inform marketing decisions. (Note: Only the top five locations with the most revenue were included in this visualization.)
Now, how do various company sectors and industries perform?
The largest proportion of 2019 revenue comes from the Internet Software & Services industry, something that this company can use to guide advertising budgets and optimization.
Information Technology and Consumer Discretionary make up the bulk of revenue by sector, again something that this company can use to guide marketing decisions.
Do any sectors have an upward trend when looking at revenue for the last year?
There are no strong trends by sector over the course of 2019. Consumer Discretionary has the highest revenue for 10 out of 12 months. Both Financials and Health Care have a slight upward trend in the second half of the year.
Finally, does company size impact how much revenue a particular role accounts for?
Overall, average revenue spent varies among top job titles when comparing against different company sizes. For instance, in companies with less than ten employees, the CEO is most likely to make the largest transaction. For slightly bigger companies, the Marketing Manager takes on the role of making the larger purchases. When the company grows to more than a thousand employees, the Marketing Associate tends to make the largest purchases.
Now, using the insights gleaned, we can piece together the personas.
To enhance campaign targeting and identify relevant content there are three personas this company should focus on:
CEOs of IT firms in New York City
On average bringing in the most revenue compared to any other job title and location, this persona is an ideal target.
Start-Up Marketing Managers
Marketing Managers of companies with 11-50 employees are likely in the weeds of everyday tasks—including large purchase decision-making, clearly illustrated throughout this analysis.
Mid-Market Marketing Associates
As companies grow and hire more employees (growing to between 1k-5k), the Marketing Managers are no longer making large purchases—handing it off to the next in line: Marketing Associates.
To enhance campaign targeting and identify relevant content there are three personas this company should focus on:
CEOs of IT firms in New York City
On average bringing in the most revenue compared to any other job title and location, this persona is an ideal target.
Start-Up Marketing Managers
Marketing Managers of companies with 11-50 employees are likely in the weeds of everyday tasks—including large purchase decision-making, clearly illustrated throughout this analysis.
Mid-Market Marketing Associates
As companies grow and hire more employees (growing to between 1k-5k), the Marketing Managers are no longer making large purchases—handing it off to the next in line: Marketing Associates.
Final Thoughts
Personas aren’t new to the marketing world, but the methodology around creating personas is continuing to evolve as we learn how to better leverage customer data.
We’re not suggesting to throw away personas previously generated using other methods—like through qualitative interviews—but companies can verify, supplement, and enhance their customer understanding and advertising campaigns using insights generated by aggregating and analyzing data.
It’s one of the ways we know growth marketers can succeed in today’s competitive market, and is only possible by digging into the data.
We’re not suggesting to throw away personas previously generated using other methods—like through qualitative interviews—but companies can verify, supplement, and enhance their customer understanding and advertising campaigns using insights generated by aggregating and analyzing data.
It’s one of the ways we know growth marketers can succeed in today’s competitive market, and is only possible by digging into the data.
