How to use A/B Testing to Create Buyer Persona
Have you ever wondered why do companies ask you to fill out forms whenever you ask them for a service? Or talking about modern day world where everything happens through an app, have you ever wondered why do more and more companies give you an option to login through Facebook or any other social media platform? In a way, it increases their cost by providing more options for login and capturing the data; still, companies want you to login through Facebook, Google or Twitter or any other platform. But why??
The simple reason behind this is that companies want to gather more and more of your personal information. And in today’s world, even your family members can’t provide as accurate information and as much details about you as your Facebook account. Right?
What do companies do by capturing this information? What is the significance or use of this information? Companies use this personal information to create your customized profile. For example, by accessing Facebook profile of a user, a company may be able to profile the user as someone who lives in metropolitan city, who is a fan of Manchester United, who likes to party with friends, who often seeks information about gadgets on Facebook, among many other attributes. This could be a sample profile of a user, or ‘persona’ as we call in technical terms. This is basically a buyer persona, someone who wants to buy my products and I have created a profile of the user to understand more about her. This is more relevant in terms of high value products or services such as banking or investing services. Whenever you sign up for any investment management service, the first task that your relationship manager does is to assess your profile based on which he will suggest products. Having said that, with the advancement of technology and availability of big data tools to crunch huge volumes of data with ease, it has become possible to create personas at individual level as well and for other industries as well. In fact, if I were to look from a different dimension, Tinder — one of the most popular dating apps, also does the same thing. It asks you to login through Facebook account and then create a customized profile for you and then suggest you matches based on your interests and likes.
Now, why do so much of hard work? Is it really that important to go to this level? I believe the success of Tinder app answers this question itself. Creating buyer personas help me understand my customers in a better way and identify their needs and wants. If I know my customer is a male between the age group of 20–25 years and loves sports, then I would suggest him movies (assuming I sell movies) which are related to sports or based on sports persons rather any random movie. This increases the chances of customer buying the product, and reduces the time, effort and money spent by me on advertising. Having knowledge of your customers or creating buyer personas of customers helps in channelizing marketing efforts and improving sales, while reducing marketing spend. According to Kissmetrics,
- “71% of companies that exceed their revenue and lead goals use personas
- 56% of businesses have created higher quality leads, 24% have generated more leads, and 39% experienced higher conversion rates by using personas
- 55% increase in organic search traffic
- Email campaigns using buyer personas experienced twice the open rate and five times the click-through rate as those without them”
To summarize the above points, persona can be defined as “a composite sketch of a person or a group of persons which can be represented by a bunch of attributes and helps a company to better strategize to improve sales.”
We have now realized the importance of creating buyer personas but where should I get data from to create persona?
There are multiple of sources of data which can help us gather data to create buyer personas. Let’s first understand the data points which can help us create a buyer persona:
- Demographic details such as
- Gender
- Age
- Profession
- Location, etc.
2. Likes, dislikes
3. Characteristics, personality
4. Interests
5. Goals and aspirations
Now, what are the various data sources that can help us capture above data points? There could be ample data sources which can provide us details about customers — in-house teams, external agencies, social media, among others. Some of the sources have been listed below:
- Customer application forms
- Social media
- Customer surveys
- Market research reports
- Contact databases
- Internal departments such as call center or marketing/sales team
- Organic searches on internet that drove customers to the website
- Search terms on the websites
- Search terms on the competitor websites
- Purchase history
- Location from where customer is accessing the site
- Device through which customer is accessing the site
There could be a lot of other data points and data sources which can help us further develop buyer personas. In summary, a buyer persona will include all that information that can help us understand the customer better and improve chances of customer buying the product.
Let’s have a look at a sample buyer persona below.
Persona:
Having a look at the above buyer persona, we can figure out that the person is interested in technical books/magazines, latest Apple gadgets and Manchester United merchandise. We should sell these things to this customer rather than putting efforts on selling other products which may be difficult to sell to this customer. This is how creating buyer personas can help a company to understand and analyze their customers better.
Buyer personas can be extremely important for e-commerce companies where understanding customer profile and needs become very important because there is no in-person contact with the customer. Now, the question arises how to effectively build buyer personas and gather information about customers in the case of online companies where there is no in-person contact with the customer? There’s a very simple and powerful concept called A/B testing in the world of internet. I am sure you must have heard of this concept and a lot of you must have used this in your work environment.
According to Wikipedia, “In web analytics, A/B testing (bucket tests or split-run testing) is a controlled experiment with two variants, A and B. In online settings, such as web design (especially user experience design), the goal of A/B testing is to identify changes to web pages that increase or maximize an outcome of interest (e.g., click-through rate for a banner advertisement).” To explain this definition, A/B testing basically helps us understand which of the two variants on our website is liked by visitors more. For example, if I were to check whether color of toolbar on my website should be blue or green, I will use A/B testing to answer my question. I will randomly divide the population (website visitors in this case) in two categories — A and B. To one category, let’s say A, I will show the website with blue toolbar; while to the category B, I will show green toolbar. All the other things remain same on the website, there is just one change to study its impact on visitors. I will then study the behavior of visitors in the two groups — which category of visitors spent more time on website? Which category bought more on the website? This will help us in identifying which color is more liked by customers or to go a level further, which tab color is liked by what kind of customer? This is how A/B testing can help us gather more information about customers and improve our buyer personas. The advantage of A/B testing is that we can test our buyer personas through this method and validate or refute depending on the outcomes in a data driven manner.
Let’s take an example of a bike manufacturing company selling sports bike. The company wants to understand if customers buy sports bike due to the coolness factor it brings or the thrill of riding at high speeds. This becomes a perfect application of A/B testing scenario. The company will create two randomly chosen categories of customers. To one category it will show advertisements and features which promote the coolness factor; while to the second segment it will show the thrill and high-speed features of the bike. The responses of two categories of customers will help us identify the true reason for customers buying the sports bike. After analyzing the results and understanding the customers’ interests, the company can position its entire marketing campaign around either of the themes.
To carry out A/B testing, there are tons of tools available in the market which can help you carry out A/B testing. These tools range from free to much more expensive tools. Each of the tools offer different features and has its pros and cons. Paid tools certainly offer more functionalities and features to carry out analysis. Some of the popular tools have been listed below. The list includes both free and paid tools.
- Google Analytics Suite
- Optimizely
- Visual Website Optimizer
- Unbounce
- KISSmetrics
- Adobe Target
- AB Tasty
- A/Bingo
- MailChimp
- AWeber
- Sentient Ascend
- Qubit
- Convert Experiences
All the above tools provide different services and come at different price points. Before finalizing on any of the tools, you should first understand your objective and then evaluate the various tools so that your investments don’t go in vain.
Now, let’s take a couple of examples where companies have used A/B testing to improve their customer conversion ratios. Lucidchart, a website which helps users create sketches and professional diagrams, used A/B testing to understand what resonate well with their customers on website. The company used A/B testing to track and analyze how visitors are interacting with every page on their website. With A/B testing, the team came up multiple pages and analyzed the results before zeroing on the final page. Using this methodology, the company achieved 30% lift in conversions across Home and Product Tour pages. While in the second example, company used A/B testing to understand more about their customers. Manillo, an e-commerce company in Denmark, had an assumption that their valuable buyers were young moms in their mid-thirties with at least two kids. But after analyzing their data and conducting multiple A/B tests, they were startled to know that their highest value customers were women over 60 years old. They were the ones places high value orders and ordering frequently. Having knowledge of their customers, they were able to achieve 50% increase in ROI from their Facebook ad campaigns. The tool, it may sound very simple, can improve your business a lot if used in the right way.
A company needs to be aware of the best practices for carrying out A/B testing for creating buyer personas. Some of them have been listed below.
- Creating a clear hypothesis: A lot of times marketers are not clear about hypothesis; they don’t have clarity as to what they want to achieve. Unless you have your objective, you may not be able to design your test in the best possible manner leading to ambiguous results
- Sufficient population: Population or visitors on which you are going to carry out the test should be sufficient in number, in each of the two categories, so as not to provide skewed results
- When carrying out A/B testing, you can play only with one test change at a time; you should keep the entire website consistent. If you make multiple changes for control and treatment group, you won’t be able to identify which change is providing the impact
- Keep your assumptions and biases aside. Though it may be hard to keep your existing knowledge at a distance, but basis your results on existing results may influence your decisions. Alfonso Prim from Innokabi.com advises: “The main thing in A/B testing for me is not a technical issue. It is a concept issue: Forget the image of the customer that you have in your brain before the experiment, and don’t try to discern the results… because this can condition the experiment.”
I am sure after going through this article you will be better able to relate to the point I discussed at the beginning of the article. You will now be able to relate what is the rationale behind whenever a company asks you to login through Facebook or asks you to fill out a form.
Author Bio:
This article was contributed by Perceptive Analytics. Chaitanya Sagar and Saneesh Veetil contributed to this article.
Perceptive Analytics provides data analytics, data visualization, business intelligence and reporting services to e-commerce, retail, healthcare and pharmaceutical industries. Our client roster includes Fortune 500 and NYSE listed companies in the USA and India.
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