Mike King is a consultant to Fortune 500 brands and the founder of iPullRank, a technical marketing content strategy agency.
He also has a unique understanding of how you can leverage existing data to quickly create personas.
Mike joined us on Wynter Games to tell us how iPullRank makes this possible.
“Most people think about personas, they're like, ‘Oh, that's something that's going to take forever.
We've got to do one of those sessions with the post-it notes and all of that.’ And a lot of marketers just don't feel like it's worth doing,” Mike said.
Data-driven persona research is changing
In the old days, marketers creating & researching personas would get together in a room. They would come up with alliterative names like “Business Bobby” to try to personalize their target customers.
Then came the phase of “guessing and checking.” Marketers would test an idea to see how it performed against the data stream feeding back in.
Mike said that the problem here is that most data tracking is not accurate. And the quality of data is only going to get worse.
The problem with third-party data
Browser tracking and privacy rules are constantly being updated.
“The reality here is that your data was never actually as real as you think it is,” Mike said.
“Your data was never a hundred percent representative of the people coming to your website.”
More recently, we are seeing the slow elimination of cookies and crackdowns on Facebook insights following the Cambridge Analytica scandal.
“Effectively, we don't have that level of audience intelligence with respect to search that we used to,” Mike said.
The result? Third-party data is losing its marketing value.
The rise of first-party data
With the allure of third-party on the decline, Mike says that the focus is now shifting to first-party data: information that companies collect directly from customers.
As media suppliers like Google and Facebook dramatically change their ways, “You're not going to be able to rely on the after-action data like you could before,” Mike said.
The solution is targeting specific customers and looking to them specifically to see how things are performing.
Where personas and data connect
According to Mike, personas aren’t just a marketing strategy. They are a measurement tool.
“We can say okay, the Active Amy persona is the one that's coming to the site far more than the other two. And then the fourth one Energetic Erin is the one that comes the most,” Mike said.
You can track how the various customer personas convert, and experiment with your messaging accordingly.
“We can do a variety of different things to see how can we get these personas that react differently to what we're trying to do,” Mike said.
It’s more than just analytics
The important thing to note here is that you are not just using analytics when mapping your personas. You can also include custom dimensions and custom user properties.
“Whatever first-party data you're collecting, as people are coming to the site, whether that's pages they viewed or other data points that you're collecting, you can use those to inform or tag a given user as a persona,” Mike said.
You can also factor in what you’re hearing from paid media channels. “Use your UTM parameters to indicate what type of persona you targeted. And then that way you have that in your analytics as well,” Mike explained.
Be mindful of the user journey
Part of building personas based on data means paying attention to where customers are in the buying process.
“It's not just like, who are the users or how are we segmenting these users,” said Mike. “It's also how do we measure the stages of that user journey?”
You can do this by using analytics in conjunction with content groupings to map your content to the different stages in that user journey.
“Ultimately what I'm saying here is that measurement is data-driven storytelling. And so our personas are just this people layer of things,” said Mike.
How to build a data-driven persona in one hour
How do you go about building a data-driven persona? You carefully gather first-party data, then use cluster modeling to generate personas.
According to Mike, the six steps to follow are below:
- Take your customer email list
- Upload your list to TowerData
- Choose features
- Compare the difference in each feature
- Utilize the K-modes clustering algorithm to group customers
- Receive a result dataset
Based on the resulting dataset, you can begin to build out your user segments. The story that you generate from those segments will ultimately become your personas.
1. Your customer email list
It should be simple to download your existing customer email list from your CRM, MailChimp, or similar service.
2. Upload your list to TowerData
TowerData has a built-in tool specifically for email intelligence.
While TowerData offers access to US-based data, there are similar services available in other countries.
What TowerData does is give you data based on the email addresses provided. It will tell you where your users live, their age range, household income, and more.
3. Choose features
Now you will have the opportunity to choose the data you want to use based on what TowerData says is available to you.
“Ultimately what you want to do is look at the match rate across the data points,” Mike said.
“For our purposes, we generally don't pull data that has less than a 30% match rate.”
4. Compare the difference in each feature
This step involves some coding. In the first section of the code, you install a library or a series of libraries that will allow you to do the K-mode clustering in the next step.
Then you want to clean your dataset by removing the rows that have missing data points. You can also remove the email addresses at this stage.
5. Utilize the K-modes clustering algorithm to group customers
Now, you’ll do a bit of feature engineering. Depending on the variables that you downloaded, you want to convert whatever those are to ‘yes’ and ‘no.’
“It's as simple as looking at the data that you had and then changing the names as though you were changing the headers in an Excel sheet,” said Mike.
“From there you can effectively just run the code and it's going to tell you how many clusters are, and what the central point of clusters are.”
Once you've determined your inputs, you update a line of code and let it run.
6. Receive a result dataset
TowerData will then provide the resulting clusters and all of the data associated with them.
These data points effectively represent your different first party data user personas. And now you've got to tell them the story on top of it.
Mike said that the six steps above should take you about 10 minutes to complete. The remaining 50 minutes can be spent on writing the stories to layer on top of the data.
Finding new customers
The above process is an excellent hack to determine the type of customers that you are currently capturing.
But Mike also had advice on how you can figure out who you aren’t capturing, but should be. These include tools like:
- SparkToro - for audience intelligence based on affinities from data across social media.
- DemographicsPro - for a complete report based on who follows you or someone else
- Segmentation Portal by Mosaic - for upper-tier market research level
Mapping personas to keywords and needs states
A final practice that Mike and his company have pioneered is using keywords to determine where buyers are in the buyer journey.
They then map those relevant keywords onto the personas they created to make them even more effective.
While you can do this with surveys too, Mike found that it was not as scalable as using keyword tools. Tools you can use include:
In summary, personas are simply data-driven stories.
Today, there are many programs that you can use to leverage first-party data as traditional third-party platforms lose their appeal.
You can watch Mike talk about building personas here