A B testing examples : 5 best steps to implement them
A B testing examples don’t ring a bell to you? Or maybe you don’t know how to get started? Ok, stick with us, we’ll give you our best tips to optimize your campaigns 🔥.
First of all, you should know that you use A B testing by comparing two (or more) procedures to :
- A page,
- An item,
- One email.
This is set up to determine what performs best. When prospecting, we have to test the best approaches so that, in the end, we can convert the best. In A B testing, it’s all about optimization. Here we go, we explain it all to you 🚀
If you double the number of experiments you do per year, you will double your inventiveness. – Jeff Bezos.
What is A B testing?
Before moving on to the fun part 🎉, we will go throught a small definition so that we can start on a good basis.
A B Testing is a technique in digital marketing which consists in comparing the effectiveness of two media, two contents, by testing a variant relating to the content or the form (we can, of course, test more than two variables, we then talk about A B C tests if there are three variables for example).
We test an “A version” from a content, which is the original version, against a “B version“, the version which implies a variation in substance or form, to a representative sample of people.
The advantage of A B Testing is that it obtains quantifiable results at the end of the test.
What can we A B test ?
We will be able to compare two versions (or more if you feel like it) from:
- An app,
- a landing page,
- an email,
- a button…
Variations of your proposal are randomly presented to your user segments. You will have the opportunity to find out if a particular color attracts your audience, if a particular email subject generates a better opening rate, if the location of your CTA (call to action) is in the right place… In fact, you can really test everything🧪!
Once you’ve found the message that shows the most performance, you’re going to be able to use it on the rest of your lead base.
Therefore, A B testing becomes an indispensable step to experiment ⚗️ what works best among your users.
The different tests you will perform are done on samples of your database. Be careful ☠️ to segment them well. The audiences must be similar so that they have the same final intention (like buying your products for example).
Let’s say you run these tests on 200 people, 100 of them will test version A and another 100 people will test on version B. If one of these versions performs better, it is considered a “winner” 🏆 because it will have given better results than the other. Of course, your customers or prospects are not aware that they are “undergoing” this test, so they act as if the presented solution is the right one ✔️.
How to implement A B Testing ?
Okay, in theory, we figured out what it was. So now, how do we do it ?
Step 1 of A B testing : Ask yourself the right questions
Absolutely. The first step is to focus on what you want to test :
- What to test? (A new CTA, a new homepage, a new color?)
- Who? ( What is your segmentation, on which target 🎯 are you going to set up these tests?)
- What is the purpose of doing these tests? (Better response rate? Opening mails? Better acceptance rate?)
- How to do it? (What tools will I use?)
Step 2 of A B testing : Do simple A B tests
Take it easy: before embarking on overly complicated tests, start slow. Limit yourself to testing only one variable at a time in the early stages.
For example, if you want to improve your opening rate on your emails, don’t change the visual, the subject of the email and the color of a CTA at the same time.
Start by changing the subject line first and see what you get. Once you’ve got the hang of these simple tests, you can move on to the next step.
All things come to those who wait
And since we’re super nice ❤️ at Waalaxy, we’ll give you a case study: as a marketer, you want to do some A/B testing on an element of your form to find out what gets the highest click-through rate on it. You start by changing your title.
Here, this simple little change can have a significant impact on your newsletter sign-ups and is a great way to start small with A/B testing if you are not familiar with the technique.
Step 3: Continuous testing
As we know, it is time consuming ⏲️ to perform continuous testing. Yet it is recommended to keep setting up A/B tests and thus, optimize them.
If the data you collect becomes more refined as you go along, it will become easier and easier to optimize 💸 your marketing campaigns.
Step 4: Analyze the results of the A/B tests
When we talk about analyzing your results, we mean, on the one hand, to see what the statistics of your campaigns are, but above all, to understand why you had these results. The heart of the matter is your target.
It is recommended to leave at least 10 days in order to analyze your results, because you have to allow your users to connect, to answer your messages or to open your emails. You will only have to consult them afterwards.
Of course, some results may disappoint you. If you don’t have convincing results, it doesn’t mean that you did it in vain. It may simply mean that what you wanted to implement only brings a small change and that your test has no effect on the open rate or the click rate.
Step 5: Implement your results and iterate
As said before, once you have a “winning” variable 🏆 you need to implement it (or at least we advise you to do so).
Don’t forget that these tests are going to allow you to optimize your marketing strategy, so it would be a shame to go without. If your results are not convincing, try again and test new variables!
If you’ve passed the simple tests, you can also do tougher A/B tests. In fact, there is nothing to stop you from running tests with more than two variables.
A more “difficult” test to set up since you have 3 variables to take into account. In this example, the form with 3 boxes to fill in could be our version A (so the original version which we call the control version). The next two are our variables B and finally C (the modified versions are called processing versions).
Why do A B testing examples?
If everything we’ve told you before still hasn’t convinced you, we’ll give you some more reasons to do A B testing.
Knowing what works best for your business is a priority? A B testing is part of this process.
When you go into acquiring a market, new customers, you’re basically making assumptions. Yet, what works for one company, won’t necessarily work for another, and that’s even if they both operate in the same industry.
And even when you are well established🌱 acquiring quality traffic to a site can be extremely difficult. Thanks to this web marketing method, one manages to get new customers but also to retain the older ones. The interest of doing these different tests is that you will be able to collect data 📊.
And data is very valuable, especially when you want your solution to work. All your decision making is going to be able to be thoughtful because based on the results you get.
Little bonus 🍀: you’re going to have a better view on the tastes of your audience. You’re going to know them, and therefore, be better able to propose topics, products or even content that interests your visitors.
This will leave little room for doubt since you will have done tests and you will know what converts best among the different variations you will have set up.
Cherry🍒 on the cake, you will be able to increase the performance of your campaigns, capitalize on what engages the most. And we don’t teach you, gaining performance means saving time 🕛, money 💳and efficiency.
Yes you read that right 🤯, you can totally do A B testing marketing on LinkedIn.
For your information, LinkedIn has a database of approximately 774 million users. Therefore, you are taking advantage of a GI-NOR -MOUS- 🦛 prospect base. Under these circumstances, a variation as small as 5-10% in response or acceptance rate can prove to be huge upon arrival.
But how do you do that? Well, you can use:
- To find out if invitations on this professional social network work better with or without a note,
- What type of message gets the highest response rate,
- To know which title works best on your profile…
By the way, we recommend you to go and read the article on how to boost your LinkedIn title. In case you don’t know how it works on this professional social network. Here is a little tuto 😉 (no, it’s for us, no worries) to set up an A B test.
Ok, let’s say you’re an“SEO specialist” and you want to connect with other professionals 👓 in the industry. Start by typing in the job you’re looking for, find people and connect with them.
Take the connection test with rating and without rating and see which invitations were most accepted.
Then you’ll just have to wait and see which invitations performed the best 💪.
When doing A B testing, the most important thing is to keep testing. The market is changing, so are your targets. It’s important to do different tests on the best approaches and over time ⌛.
This is because you will be able to base yourself on qualitative and meaningful data. We do A B testing, yes, but we don’t make decisions hastily.
And speaking of testing over time, we suggest you associate LinkedIn with our tool.
So, how do you experiment with A B testing on LinkedIn with Waalaxy? Well, it’s a good thing we’re giving you some tips 💡 right now.
Start your prospecting: exporting your targets
In order for your approach to make sense, you’re going to need to target your prospects. Indeed, in order to do A B testing, you need to have a large enough sample of users so that you can base your approach on data that will serve you well. At the risk of repeating ourselves, quantitative but especially qualitative data 👌.
In order to guide you through our Waalaxy tool, I decided to show you how to run an A/B test with it.
On today’s menu, I’m going to find out what works best in terms of invitations on LinkedIn. Indeed, I’ve heard that invitations work best when there is no note associated with it. I invite you to read this here.
Let’s test this now.
Log in to Waalaxy, go to LinkedIn and start targeting and then exporting your various prospects. Choose a fairly large sample 🧪. Here, we decided to choose 100 prospects for campaign A and 100 for campaign B.
Let the festivities begin.
Defining your A B testing examples sequence
Maybe you are not familiar with the term “sequence”, so let’s introduce it to you very quickly. In Waalaxy, what we call a sequence or template is the combination of actions that you will implement and in which your prospects will advance.
These are the steps your targets 🎯 will go through to get to a final action quite simply.
If you need more information about sequences in Waalaxy, and how to find the right ✔️ sequence, the one that fits you, we suggest you go read this great article.
Define your sequence according to your goals and needs.
A B tests for LinkedIn invitations
Once you’ve chosen your sequence, all that’s left is to define your message ✉️.
Setting up A/B testing for LinkedIn invitations with Waalaxy
As a reminder, we decided to send connection invitations with and without a message but you can send two very distinct messages.
🔥 In campaign A, I send connection requests without a note to 100 prospects.
🔥 In campaign B, I send connection requests without a note to 100 other prospects.
What’s next? Now we’ll wait a little bit.
The actions will be sent to the queue and will be processed according to the daily quotas:
- connection requests: between 80 and 100 per day
- messages : between 100 and 120 per day
Ok, so what’s next? Well, I want to see where I can find my campaigns anyway 🧐.
When I’m on my dashboard, in the campaigns tab, I can have a global view on what I’ve already set up.
I even have information on the number of leads I have in progress. And if I click on a campaign I’ve set up, I can get information on the progress of my prospecting.
Analysis of A B testing on Waalaxy
Well after a few days (28 to be exact), I can analyze my results. When I click on “campaigns” on the left side of the Waalaxy home screen, I have access, as you know, to a view on my two current campaigns.
I click on one of the two campaigns and I will be able to access my different results. Let’s see what happens on both sides.
- Results of campaign A without message.
- Results of campaign B with a note.
I can see that campaign B, the one with a grade performs worse 📉 than campaign A, the one without a grade. The difference is not glaring. We are at 56% performance versus 48%. So it turns out that the difference is minimal. However, on a larger scale, the gap may well widen ⛏️.
In any case, I can pretty much say that my campaign without a score performed better than my campaign with a message (which was additionally personalized).
In the future, I will therefore prefer to send invitations without a message.
Of course, this is not set in stone. If I try it again months later, these results might change and I might have to change my tune 🔫 and send invitations again, this time with a message.
If I hadn’t done A B testing examples on Waalaxy, I wouldn’t have been able to make this decision.
A B testing for sending messages
Another sequence that you can couple with A B testing is of course the sending of messages. In particular, sending messages to people who are in the same group as you on LinkedIn. Once you’ve imported your leads, you can send two versions of messages and see which version gets you the most responses 👊.
Doing A B testing in Waalaxy for mailing
We already talked about it earlier ⬆️ during this article. A B testing examples work perfectly with mailing to reach your targets.
It would be really silly to do it without Waalaxy. You have the choice of a sequence that will allow you to visit a profile and then send an e-mail to that person.
Again, you can test on a sample, a message, a title and see which version gives you the most results. Frankly, nothing rocket science 🧙 , Waalaxy ‘s interface is easy to use and extremely intuitive. But we’re thinking of you (don’t thank us), in case you forgot how to set up a campaign, we’ll see you here.
Analyze your results
Once you are done targeting 🎯, exporting, choosing your sequence and message, you are going to have to wait. Wait a bit so you can read your results.
You need to give your various targets time to open the emails, or simply give them time to log into their LinkedIn account. Thanks to our tool, you have access to a very nice💎(we remain very objective) dashboard. On your screen, on the left click on “Campaigns”.
You will then have an overview of the campaigns you have been able to set up. Click on one of them and compare your best open rate.
This way you can follow in real time the results of the versions you have set up and keep optimizing them if needed.
In what other cases should we use A B testing ?
You are probably wondering in what cases we can use A B testing examples. The answer is that we can use it in almost any situation, whether it is for :
- 🟢 A physical product (color choice),
- 🟢 The size or location of a button on your site,
- 🟢 the design of your solution, the subject of your mails,
- 🟢 the efficiency of a feature…
As you can see… The possibilities are endless. If you want to maximize conversion, you will have to conduct tests.
Use it for your landing pages
You have in mind several solutions to propose to your users in the case of a landing page… But here’s the thing: everything is getting in the way of what you want to offer your future customers and several questions are “chafing” you:
- What visual will I put forward on my page?
- Is my call to action visible enough or well placed?
- What color is best suited to my site?
- Is my form easily answered?
- Is my title catchy enough?
You are right to ask yourself all these questions 🤔 because you want to convert. The fact that you can test several visuals, change the location of your CTAs (or change their size or color), gauge the graphic charter of your site are important elements to take into account. When we arrive on a landing page, it must be sexy.
Yes, yes, yes, the first impression counts when you arrive fresh on a site! What we want is to convert 💰. So it’s crucial to convince your users to stay on your page from the first look 👀.
Thanks to A B testing examples you will be able to set up several scenarios for your landing page. All you will have to do is choose the one that performs the best. With these tests, you don’t have to rethink your entire page, you can make subtle changes and, thus, reduce the risk of compromising your current conversion rate 💸.
Use A B testing examples to measure the effectiveness of a feature
In case you don’t know whether a feature will please or not, you can also implement A B testing. Whether it is a feature on Desktop 💻 or even mobile.
More than a formality, if you want to optimize your conversions and app downloads, you need to conduct tests. Setting up A B testing examples on a feature will help you understand if the change you want to implement will win the hearts 💖 of your audience.
Maybe you want to implement the “share on social networks” feature with your app. You don’t know if this feature will have a significant click-through rate 🖱️…
Simple: do some A B tests. Implement your idea and have your users test the version with and without the said feature. All you have to do is analyze what performs better and make your decision afterwards.
A B testing in emailing
Email is still an excellent solution when it comes to prospecting. The years go by, yet it is a solution that is still used as much as ever.
It is very simple to do A B testing in marketing emails 📧. You send an A version to a part of your subscribers, and the B version to the other part. Again, you can totally change the subject line, the visual, the buttons embedded in your email. Once you have decided, test it.
Once again, the solution that gets the best result is to be retained and established on your entire list of customers and future customers. It’s always a great idea 💡 to use A B testing examples for your prospects but also for those who are already customers with you. When running a web campaign, you have plenty of options to choose from when it comes to what will drive the best open rate. Once you have done your tests, you will have all the data you need to know what works best. But don’t stop experimenting. The mailing is a special case because we often keep in touch with this mode of communication. It is therefore necessary to be consistent in its use.
Implement it in your sponsored ads
A B test Facebook Ads
Did you know that you can conduct A B testing examples with different social media 📱? Let’s say you are an advertiser and want to get started on A B testing with Facebook(Facebook ads). It’s really easy!
🌟 Step 1: Go to your ads manager. Then click on “Create.”
🌠 Step 2: Choose your campaign goal based on your objectives ️.
🌟 Step 3: Once you have chosen your campaign goal, give your campaign a name and activate the “create A B test” button.
🌠 Step 4: Choose the page you want to promote, set up your budget, start and end date, and define your audience.
🌟 Step 5: Set up your ad, choose its placement and click “Publish”. You’re probably thinking,“Nonsense, there’s only one version here darn it” 🤪. And we’ll tell you that you’re right but it’s not over yet! Once you’ve published your ad, you have version A.
You will then be asked to duplicate the published campaign and make the necessary changes for version B. The platform actually allows you to test your variables such as audience selection, placement of your various ads or even test content. This allows you to have total control over your different campaigns. All you have to do is to bet on what is the most relevant.
A B testing examples on Google Ads
You can also do A B testing with Google Adwords (unfortunately not yet with your video and shopping ads). You can test several elements available in your ads:
- The title,
- The landing page,
- The text of the ad,
- Your keywords,
- The bidding strategy.
It is then Google that will be in charge of displaying your A and B ads. It will alternate your ads in the results of your potential customers. Once you’ve analyzed which ad performs better, you just have to put the other one on hold.
What to remember about A B testing examples?
As you can see, the possibilities are endless in terms of A B testing. But the key🗝️, above all, is consistency in your testing. Don’t rest on your laurels and look for continuous improvement.
Remember that your audiences behave differently and it will take time in order to understand 🧠 them. And it’s a battle over time; because once you understand them, you’ll definitely have to start over, because your audiences evolve too.
Continue to conduct A B tests before, during and after you collect your data based of course on the Kpi you set up and measure your ROI.
Track campaigns and keep a constant eye 👁️ on your dashboard to adjust your strategy. Because yes, that’s how you’ll be a leader in your industry.
Now you know how and why you should do A B testing, this technique that has and will continue to prove itself in the future. Whether it’s to increase your awareness, set up a new newsletter, A B testing is a real lever 🎰 marketing and should be used in order to continuously improve its strategy.
And as a bonus, we gave you all the keys to use it with our great solution . The possibilities are numerous, go for it.
Faqs of the article A B testing examples: 5 steps to implement them
A B testing examples are mainly used to test different solutions on your customers and leads. If you’re a marketer, A B tests should be part of your different tasks. The more relevant your content is and the more your users like it, the more likely you are to convert but also to build loyalty 💑. All your marketing actions will be able to be based on the results obtained.
How do A B Testing examples work?
This method will allow you to establish an experimentation process in which two versions (in the case of A B testing) or more (there is A/B/C testing in case you want to present 3 different solutions) are presented.
These solutions are then going to be presented randomly to your targeting 🎯 audience. This will allow you to determine which version leads to the maximum impact 💥 and generates the best ROI.
A B testing examples on LinkedIn?
On LinkedIn, you can do LinkedIn marketing A B testing on :
- Sending messages,
- Connection requests with or without a note,
- Know which title works best…
Of course you can do it from the LinkedIn application, but we still encourage you to use Waalaxy for its convenience and ease of use.
A B Testing examples : what can you optimize for incredible campaigns?
You can optimize anything with A B testing. For you, marketers or not, it allows you to continuously improve your users’ experience while minimizing your costs 💲. Let us put together a short list 📃 of what you can optimize:
- Landing pages,
- Product sheets,
Why do we do A B testing?
More and more companies are questioning 🤔 what direction they should take in order to have optimized content for their website visitors. To do this, they need to base their decisions on A/B testing. These various variants of solutions will allow:
- Achieve statistically significant results,
- Reduce bounce rate,
- Solve your prospects’ pain points,
- Benefit from a better return on investment,
- Keep control of costs.
You know everything now 😀 on your marks, get set, fire, test!