How well do you really know your perfect customers?
What are their backgrounds and interests? Do they have families and kids? What goals do they have, and what keeps them up at night?
If these questions sound too detailed, it means you’re on the right path. You will not be able to find your perfect customers without knowing who they are.
The more detailed your buyer personas are, the more effective your marketing becomes.
Why are buyer personas important?
Long gone are the days when you could blanket your entire market with the same message. In 2017, digital tools allow you to target your ideal customers with meaningful personalization across every touch point. This will not only save you money from better conversions, but you will also build your product for the right audience.
Michael the Marketing Manager. Anna the COO. Jenny the restaurant owner. Create detailed profiles for these fictional buyer personas. Then use them in your marketing, sales and service design to reach different groups of people in a personal and meaningful way. Depending on your business, you could have as few as one or two personas, or as many as 10 or 20.
Traditional way of identifying buyer personas
Your existing customer base is your best source of buyer personas. Survey or interview them to understand what makes them want to buy from you.
But what if you’re launching a new product or service, and have no customer base to work with?
The new way of identifying buyer personas is data-driven. Every assumption can now be quickly tested online using digital marketing tools. Most importantly, the product doesn’t even have to exist for you to identify the future buyers.
Our client was developing an innovative new fashion accessory. A pre-order campaign was chosen as the best way to measure the market reaction.
The original assumption was that the perfect buyer persona is a professional male in his mid-twenties. He has recently graduated and started a career in marketing or sales. Here’s a short summary of the persona we made after a strategy session with the customer:
Michael, 28 Chicago
Michael is a sales manager in a software company in Chicago. He meets clients every day, and has to look sharp. After work Michael enjoys sports or going out with friends. He doesn’t have much disposable income yet, but can afford to reward himself with new things every once in a while.
Michael loves technology and follows the latest trends. He bought our product online after reading a glowing review on a gadget website.
You can reach Michael through tech publications, social media, lifestyle bloggers, and ads.
Anna, 24 Stockholm
Anna has just graduated from university and started working at a marketing agency. She’s sharing an apartment with a few roommates, and likes to spend her extra money on trips abroad and fashion items.
She follows the latest fashion trends through bloggers and influencers. Looking good and having matching outfits is important to her. She likes to compliment her outfits with accessories.
You can reach Anna through social media, fashion bloggers, and ads.
While these were great personas to start with, it was too dangerous to bet the whole marketing budget on them.
We knew the personas were likely to change over time, but how? The only way to find out was through rigorous testing.
Using data to identify buyer personas
The main goal of the campaign was to generate thousands of email leads through online advertising in key Western markets. However, at the same time we took it as a chance to drill down on the buyer personas.
We used automatic Facebook ad optimization software called AdEspresso. It allows you to quickly create thousands of ad variations to test any audience assumptions you may have. This includes age, location, education, career, interests, devices, hobbies and many others.
We started with Michael and Anna as the only personas we had. At the end of the experiment we were hoping to learn everything about them. From which smartphone they use, to how much money they make and which magazines they read.
The first ad campaigns we launched would target men and women from 25 – 35 in several Western countries. We wanted to test the most basic assumptions including age, gender, location, device preference, and a few related interests.
We used beautiful product images and different copy variations. The traffic was sent to landing pages to be converted to email leads. Ad click-through rate (CTR) and the cost per conversion (CPA) were used as the key indicators of buyer interest.
These were some of the key buyer persona learnings from the first campaigns:
- Men are most likely more interested in our client’s product than women
- Buyers are more likely to be from Western Europe than the USA
- Facebook newsfeed outperforms Instagram feed by a large margin
We used the learnings to optimize the campaigns and tried again. This time we focused on the Facebook newsfeed and targeted fashion-related interests. We also added new age groups to the test mix, to see if we were right about the age of our personas.
Here’s what we discovered:
- Men from 45- 54 are more interested in our client’s offering than men from 25 – 35, or 35 – 44. However, the lowest CPA sweet-spot is in the 35 – 44 range
- Women care less about the product
- Netherlands, Scandinavia and Germany responded most positively
- Instagram is still the worst channel. Our client’s buyers are more likely to respond from a mobile Facebook newsfeed
- People who used iPhones and iPads were more likely to convert than Android users
Surprisingly, it turned out that the older the audience was, the better the results were. However, on average male-oriented campaigns performed 5x better. This allowed us to cut our audience in half by focusing on men for the next experiments.
At this point we had generated hundreds of leads and were getting closer to the final portrait of our client’s ideal buyer. We used the Facebook pixel to create a custom audience of all the people who converted into leads. We then used the custom audience to create 1% lookalike audiences in our key territories.
AdEspresso was used to add new layers of assumptions on top of the lookalikes. For example, we refined the interests, education and income. After a few days the CPA was much better than our original target of €2, telling us that we were close to our perfect customer.
The final persona
Our client’s original buyer personas were two professionals in their mid-twenties. One of them lived in the US and enjoyed reading tech media. The other was a Swedish girl that loves fashion blogs.
Data-driven testing has completely changed our buyer persona.
After a few weeks of automated testing we learned that our client’s product doesn’t resonate with women at all. However, men are very excited about it, but only after they reach the age of 35 and a certain income level. We also learned that he’s much more likely to live in the Netherlands or Sweden than the USA.
Meet our client’s new and improved buyer persona:
Daniel, 41, Amsterdam
Daniel is a Dutch business owner. He has a wife and a young daughter. Daniel appreciates designer products, and technology that makes his life simpler. With a master’s degree in his pocket, Daniel makes €80,000 – €120,000 a year and can afford nice things for himself and his family. He does most of his web browsing and shopping on his iPhone.
The best way to reach Daniel is through Facebook and occasional “grownup” lifestyle publications, such as Monocle.
Throughout the campaign Daniel helped us generate thousands of warm leads at a low cost. We exceeded all the targets and the CPA was at an all-time low. Most importantly, we now knew exactly who to target in our marketing and product development.
Because accurate buyer personas are so critical, you have to make sure they are based on solid data. The latest digital advertising tools help you test thousands of hypotheses in a matter of days, crystallising the portrait of your perfect customer.
No matter how good your assumptions are in the beginning, data-driven buyer persona testing may completely change your perspective of who actually needs your product. In the case of our client, we were able to quickly pivot our marketing strategy and increased our lead conversion rates by 8x in just a few days.