Have you ever stopped to think about why companies ask you to fill out forms when you request a service? Or in today’s app-driven world, why so many platforms encourage you to log in using Facebook, Google, or Twitter rather than just a simple email? At first glance, this seems like an extra cost for businesses—after all, they have to support multiple login methods. Yet, companies actively push for these options. Why?
The answer is simple: data. Logging in through social media provides businesses with rich personal information that even your family might not know about you. Your Facebook profile, for example, can reveal your city of residence, hobbies, favorite sports teams, social habits, and purchase interests. All of this becomes the foundation for building what marketers call a buyer persona—a semi-fictional profile of an ideal customer that helps businesses tailor their products, services, and marketing strategies.
Take an investment firm as an example. When you sign up, your relationship manager evaluates your profile—age, income, risk appetite, and goals—before recommending products. Similarly, apps like Tinder rely on Facebook login data to craft personalized profiles and suggest matches that align with your interests and behaviors. This level of customization improves user experience and, ultimately, customer satisfaction.
The bigger question is: why go to all this effort? The success of platforms like Tinder answers it for us. Buyer personas allow companies to better understand their audience, predict their needs, and deliver more relevant offerings. If you know your target customer is a 22-year-old sports enthusiast, recommending sports-themed movies instead of random titles dramatically increases the likelihood of purchase. Personas don’t just boost sales; they also reduce wasted time, effort, and ad spend.
According to Kissmetrics:
71% of companies that surpass revenue and lead goals use personas.
Businesses using personas report 56% higher quality leads, 24% more total leads, and 39% better conversion rates.
Persona-driven campaigns deliver a 55% boost in organic search traffic.
Email campaigns tailored to personas see twice the open rate and five times the click-through rate compared to untargeted ones.
In short, a persona is a composite sketch of your target customer, built using attributes that help companies strategize more effectively.
To create meaningful buyer personas, you need data. Lots of it. The following attributes usually form the backbone of a persona:
Demographics: age, gender, profession, location.
Personality traits: extroverted, detail-oriented, risk-taker.
Interests: hobbies, sports, entertainment choices.
Goals and aspirations: career ambitions, lifestyle needs.
Preferences: brands, products, or services they interact with.
So, where do companies collect this data? Sources vary widely and may include:
Customer application forms and onboarding documents.
Social media profiles and engagement.
Surveys and feedback forms.
Market research reports.
Contact databases.
Internal teams such as sales, marketing, or customer support.
Website analytics: search terms, navigation patterns, and purchase history.
Geolocation and device usage data.
By aggregating these inputs, businesses can craft personas that accurately reflect their customers.
Let’s look at a quick example:
Persona Snapshot
Demographics: Male, 25 years old, Computer Science Engineer, Software Developer, Mumbai.
Devices: iPhone and MacBook.
Personality: Outgoing, extroverted, intelligent, football fan.
Interests: Coding, Manchester United, parties, sports merchandise.
Needs: Tech magazines, Apple gadgets, sports gear.
From this persona, it’s clear the customer would be more receptive to ads for Apple products, coding resources, and football merchandise than for unrelated items like cooking utensils. This illustrates the power of buyer personas in guiding marketing strategies.
Personas are especially critical for e-commerce businesses, where there is no face-to-face interaction. Online, your understanding of the customer is limited to the data you can collect and analyze. This is where A/B testing comes in.
A/B testing, also known as split testing, is a method of comparing two versions of a webpage, email, or ad to see which performs better. As defined by Wikipedia:
“A/B testing is a controlled experiment with two variants, A and B. In online settings, the goal is to identify changes that increase or maximize an outcome of interest, such as click-through rate or conversion.”
Here’s how it works: suppose you want to know whether your website toolbar should be blue or green. You randomly divide visitors into two groups:
Group A sees the blue toolbar.
Group B sees the green toolbar.
All other elements remain identical. By tracking metrics such as time spent on the site, clicks, or purchases, you can determine which version resonates better. Beyond design tweaks, A/B testing can validate assumptions about your customers—helping you refine and validate buyer personas with real-world data.
Let’s consider a sports bike manufacturer. The company wants to know if customers are drawn to their bikes for the “coolness factor” or the thrill of speed.
Group A sees ads highlighting the bike’s sleek design and lifestyle appeal.
Group B sees ads focusing on speed and performance.
The group with higher engagement or purchase intent reveals the true motivator. Armed with this insight, the company can position its future campaigns accordingly.
There are many free and paid tools available to run A/B tests, including:
Google Analytics Suite
Optimizely
Visual Website Optimizer
Unbounce
KISSmetrics
Adobe Target
AB Tasty
MailChimp
AWeber
Convert Experiences
Each platform offers unique features—some focus on design testing, others on email campaigns, and some on advanced personalization. Choosing the right one depends on your business objectives and budget.
Lucidchart – The online diagramming platform ran A/B tests to see how users interacted with different versions of their homepage and product tour pages. By refining their layouts based on test results, they saw a 30% lift in conversions.
Manillo – This Danish e-commerce company assumed their top customers were young moms in their 30s. A/B testing proved otherwise: their most valuable buyers were women over 60, who placed frequent high-value orders. This insight led to a 50% increase in ROI on Facebook ads, simply by targeting the right audience.
These examples show how A/B testing not only validates assumptions but can also uncover unexpected truths about your customer base.
To make A/B testing effective for persona building, keep these guidelines in mind:
Start with a clear hypothesis – Define exactly what you want to learn (e.g., “Do younger customers respond better to speed-focused ads than design-focused ones?”).
Ensure adequate sample size – Small test groups can lead to misleading or skewed results.
Change only one variable at a time – Keep everything else constant to isolate the impact of the tested variable.
Set biases aside – Don’t let preconceived notions influence how you interpret results. As Alfonso Prim from Innokabi advises: “Forget the image of the customer you have in your brain before the experiment.”
At the beginning of this article, we asked why companies push for social media logins or customer forms. The answer now becomes clear: they’re building buyer personas. And with tools like A/B testing, these personas can be refined, validated, and enhanced with precision.
By combining demographic data, customer behaviors, and controlled experiments, companies can craft personas that not only improve targeting but also save time, reduce ad spend, and increase conversions.
In an era where customer-centricity drives success, A/B testing isn’t just a tool for better web design—it’s a powerful engine for building accurate, data-driven buyer personas.
This article was originally published on Perceptive Analytics.
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