How to do (The Right) A/B Testing and Increase Conversions as a Growth Marketer
The big promise of a/b testing experimentation is to put effectiveness in the way marketers provide and manage user experience and usability.
As a marketer, before you develop a new feature, create a new copy or messaging, or launch a new design or make anything on a product. It’s ideal that you know for sure what the impact of the changes or new development will cause your users and the brand.
A simple wrong change in product architectures, messaging, features, or designs can wreck a business within a wink of an eye.
Thus, before making any big change or launching a big developed element on a product, you must be sure that the change isn’t based on mere opinions or ideas. It should instead be based on tested hypotheses, that is, theories that have gone through a series of experimentation and are validated by data.
What is A/B Testing
A/B testing, which is also known as split testing to some industry professionals, is a type of marketing experiment where you split your audience to test a number of variations of a campaign and determine which performs better.
In this experimentation, you can show a version of a piece of marketing content to one half of your audience, and the other version to another audience. Afterwards, you collect, analyze and interpret the data set produced from the experiment and discover which of your marketing content your audience responded best to.
When Should You Use A/B Testing
A/B Testing can be used for many purposes but the first reason, companies should use A/B testing is for deployment.
When you deploy something on your website, which could be a new feature, an update, or even something related to legal reasons or anything. You want to deploy this as an experiment in other to learn whether your deployment will have a negative or positive impact on your KPI.
Another reason to use A/B testing is for research and this research. For example, if you have a specific webpage, let say a product page with a picture, and some lines, and a button, etc.
You can run experiments by leaving out elements. Here, you are not looking out for a winner. What you want to measure here is whether there’s an impact or not. You want to know which elements are making an impact on your website.
When you leave out an element and nothing happens to your KPI then it means its absence doesn’t mean much for your business. An example is contemplating whether to have a header background image on a homepage of a website or to just make the homepage designs texts only.
What KPI should you Pick when Conducting your A/B Testing Experiment
If you run an experiment, an A/B test, what metrics should you track and measure?
Should it be clicks, transactions, lifetime value, etc?
How do you decide what to measure?
Every business has more than one key performance indicator, which is also where you draw out your a/b testing metrics.
KPI in a/b testing is mostly more of goal conversions rather than activity metrics. It’s mostly like transactions, clicks with quantifiable economic benefits, or offers.
The goal of tracking a/b testing metrics is to understand what shifts users’ behavior
Writing Hypothesis for Marketing in A/B Testing Experimentation
Before you run a growth or research experiment, you need to conduct a great hypothesis. The potential for your marketing improvement depends on the strength of your testing hypotheses
Where are you getting your test ideas from? Have you been sourcing competitors’ sites, or pulling from previous designs on your sites?
You can quite easily come up with something to test. But coming up with something you should test (i.e testing the right thing) can be challenging.
This is why taking time to write a proper hypothesis will help you understand ideas, structure them, get better results and avoid wasting time and resources testing the wrong things.
What is a hypothesis?
A hypothesis is a testable statement that a researcher uses to predict the outcome of a study. It’s an assumption made on the basis of limited shreds of evidence and is subjected to further investigation which proves its fallibility or authenticity.
A well-structured hypothesis provides insights into whether or not it is proved or disproved.
When developing a hypothesis, never phrase your hypothesis statement as a question instead it should be written as a statement that can be rejected or confirmed.
For example, your statement should read this way:
- Changing_______into will increase conversion because:
- Changing_______into will decrease conversion because:
- Changing_______into will not affect conversion because:
The because in the statement is to help set the expectation that results are not guaranteed and that there will be an explanation behind the results of whatever
You are testing.
Thus, when writing your hypothesis, begin by collecting data and information via calculated and educated observations, creating a tentative description of what is being observed, forming the hypothesis that predicts the different outcomes based on the observations. Lastly, design the experiment, implement, analyze the results and interpret the results of the experimentation in reference to validate or invalidate the hypothesis earlier developed.
Presenting your Learnings
Now you have completed your test, and want to document your outcome. What learnings should you share and not share.
You have to understand or identify what information is valuable to present to which people, what information is valuable to store in a database, and you have to also be able to create your own presentation template.
In growth marketing, the process of experimenting and improving website performance, usability, and experience is the foundation to achieving customer success
To see your customer counts grow, you need to get a deep understanding of your target audience, serve them what they need, and make their experience on your site better and better.
To make their experience better is where your need for conducting proper a/b testing experiments comes into play.