Daddy or chips? A/B Testing to Make the Right Ad Copy Decision

Remember the sweet little girl contemplating one of life’s toughest decisions in the iconic 90s television ad for McCain Oven Chips? Here’s the ad if you’re wondering what I’m going on about.

Daddy or Chips? A classic piece of advertising from the 90s

Daddy or chips?

As the advert shows, making decisions can be difficult; and the more people and opinions added to the process, the harder the decision becomes. You believe that putting 10% discount messaging in your ad headline will produce a higher CTR than in description line 1, but your client thinks otherwise. You feel that a short and punchy description line 1 and 2 will be more engaging to potential customers than utilising the entire character length, but your colleague disagrees.

More often than not, we choose actions and form opinions which are heavily influenced by emotions, memories and bias, but what if there were a superior way to better inform our decisions?

Fortunately, a more scientific method known as A/B Testing (occasionally referred to as split testing) takes the guesswork out of making these crucial decisions, by allowing you to put multiple ad choices in front of your audience to determine which ad customers find the most engaging, and thus which boost CTR.

So let’s get into the nitty gritty of A/B testing.

What Is A/B testing and why is it SO important?

A/B ad testing is the process of conducting an experiment where you test two different versions of an ad simultaneously. In PPC marketing, when you only have a limited amount of time to separate yourself from the competition in SERPs, it is vital to constantly be testing ad copy.

By using systematic testing to collect real-world data, you can shed some light on which messaging works best for your brand and the products you sell. When you discover that the difference between putting your percentage discount in the headline and description line two is a CTR increase of 20 percent or more, while your quality score increases and your cost per click decreases, the thought of not using A/B testing becomes scary.

What are your goals in A/B testing?

Before an A/B test can be put into motion, knowing your desired outcome is essential. Arrange a meeting with your client to discuss the goals of your PPC campaign. This discussion should cover everything from USPs, competitor copy and special offers, to calls to action and message tone in compliance with brand guidelines.

Once your goals have been agreed, you can tailor ad copy tests towards the goal and have something concrete to measure success. At this point I recommend creating a Google doc with the ad copy you are looking to run, with columns that include the test stage, expected start date, messaging and test type, which you can share with your client for approval. This is beneficial for adding a level of transparency, reassuring clients that you have a solid action plan and for keeping yourself on track.

What can you actually A/B Test?

In PPC marketing, there are four basic components that you can change to create new ad variations: the headline, description lines 1 and 2 (also known as body text) and the display URL.

Once you have decided which variables you want to test, the next step is to list potential product features, CTAs and test ideas. When you have completed your brainstorm, weigh up your suggestions against your goals and delete where necessary. Once approved by your client, you will then have a solid list of CTAs and product features that you can combine to begin ad copy testing. I recommend creating a testing schedule and giving each ad a specific label. Here’s one I made earlier.

Bonus: Apart from changing the headline, body text and display URL to create new ad variations, there are a few additional things that can be tested. Don’t be afraid to be creative, extend your testing to include ad extensions, english vs. native language, location in headline vs no location, dayparting and more.

The 7 golden A/B test rules

Although there are endless variables that you can A/B test for PPC ads, there are a few vital rules to remember to keep you on track:

1. Don’t forget your goals - Despite outlining the importance of goals earlier in the post, they are so important I must stress it again! In order to be successful, knowing what success looks like is paramount. Select your quantifiable metric(s) and stay true to them throughout the test.

2. Test one thing at a time - When A/B testing ad copy it is very easy to become overzealous and throw multiple variations into your test at once, which can kill a test before it’s even begun. Fight the urge to do so and methodically work through testing one variable at a time. By doing so, you will know that when one ad outperforms another it is near certain that is is due to the variable that you have changed.

3. Run your test for an appropriate amount of time - It is important not to run your A/B test for too short a period of time, but it is just as important not to let the test run too long. If you do the former your results may produce inconclusive results, while doing the latter might mean that you miss the window for implementing real change.

The best way to ensure that your test comes to a natural end where a data-driven decision can be made is through statistical significance. Statistical significance is achieved when your ad copy test has matured and is accurately informing you which ad is better. This confidence level is usually set at 95%, signifying that there is a 5% chance that your test results are coincidence. For ease, some bid management platforms provide built in statistical significance calculators, however these calculators are readily available online -Cardinal Path has a great statistical significance tool, which gives you the option to change the confidence level and has the added benefit of showing the absolute difference between two ads’ CTR and CR, and points out if further testing is needed for clear cut results.

4. Use the correct ad settings - To set up a fair A/B test, ad impressions need to be divided evenly between your ads. Failing to do so could mean that your new ad may not even show.

To help ensure that the new ad variation gets a fair chance of showing as frequently as existing ads with history, (in Google) click the campaign settings tab then go to the adgroup which you are going to be testing is located and update the setting to ‘Rotate Indefinitely’. Bing also provides a similar setting to ‘Rotate Ads More Evenly’.

5. Do not make a golden ad for a bronze landing page - So you have found out which variables improve your CTR and have now created ‘the golden ad’. Why follow this up with a less than golden landing page? Make sure that your potential customers’ expectations are met by doing/offering/presenting what you have outlined in your ad, on the landing page. Don’t make them work hard for it, or you could lose your visitor just as quickly as you gained them. Where your ad sends visitors can have just as much impact on conversion as the ad itself.

6. Eat, sleep, rave, repeat -  Once you have completed your first round of testing and paused your underperforming ads in favour of the winner, your job is not done. The next step is to mark down the results of your test in your Google doc and move on to testing the next variable. It is essential to continuously test new ads in order to stay ahead of your competition.

7. Be patient -  It is unlikely that you will improve in leaps and bounds each time you run a test. Success can also be found in knowing what does not work for your business, as the point of testing is to learn and improve. As Benjamin Franklin said:

“I didn’t fail the test, I just found 100 ways to do it wrong.”

Final thoughts

To wrap up for those of you who looked at the post and thought TLDR (too long didn’t read), A/B testing is an integral part of PPC success.

Test results can be seen quickly at a minimal cost, providing invaluable real-world data about how potential visitors respond to different messaging. Creating one great ad is a fluke, but consistently creating engaging ads is an art which takes careful planning, vigilance for competitor strong points and a methodical testing schedule, which eliminates the feeling to ‘go with your gut’ or stalling on the testing process.

When you know which copy provides quantifiable results, it becomes easier to make better decisions, better advise clients who have a ‘gut feeling’ about copy and create an efficient strategy for future campaigns. Take full advantage of the options available to you when A/B testing, and remember to keep testing as what works today may not work in a few month’s time.

And by the way, it’s definitely chips...

If you have any anything to add or you think it's daddy please say so in the comments below. 

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Your Step-by-Step Guide to A/B Testing with Google Analytics

A/B testing can be as simple as reciting the alphabet…

You design two versions of a web page (A & B), divide the traffic between the two, and choose the one that gives you the maximum conversions.

Simple, right? Wrong.

Most newbies to A/B testing struggle with which tools to use, how to set up their test, and how to know when it’s done. In this article, I’ll show you a free tool, readily available to every website owner. I’ll also give you some guidance on setting up your test and knowing when to call it done.

Read the Step-by-Step Guide to A/B Testing with Google Analytics

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Before you start

There are many things you can learn from an A/B test. It can be used to determine whether you should focus on single conversion goal or strive for multiple conversion goals. It can help you know which design elements and messaging are most persuasive for your audience.

No matter what you’re testing, keep your priorities straight. The end goal of any CRO (conversion rate optimization) process should be to increase your total revenue.

Imagine you’ve set up an A/B test to choose the best page design for increasing your subscriber rate. It works. Your subscription rate goes through the roof—but the design somehow hurts your sales rate and results in lower revenue.

This might make you insane. Do you keep your winning design? No. Always choose the page that will increase your bottom line, not just your conversions. Remember, companies run on revenue, not on conversion rates.

A conversion for a business could be anything.

• For bloggers, a single subscription could be considered a conversion.
• For eCommerce, a conversion might be a sale, subscription, newsletter sign-up, product carting, or even an event click.

Before you can start conversion testing, you’ll first need to define what your goal is for each test, so you can accurately identify the winning page design.

There are many conversion testing tools on the market, but the best are usually paid and add to your marketing expenses. The one exception to this rule is Google Analytics.

It’s very simple to use Google Analytics for A/B testing or split testing with two or multiple variations in the website design. Now, I’m going to provide you with a step-by-step guide for easily testing your web page through a Google Analytics content experiment.

• Choose an Experimental Objective

Google has combined A/B testing and split testing into one term—content experiment.

You’ll find it in Google Analytics under “Behavior” and “Experiments.” On that page, simply click the button for “Create Experiment.”

You’ll be take to a screen (pictured below) where you can set up your experiment.

First, add in the experiment name. For instance, if you want to perform A/B testing for a sign-up form or product selling button, then create a descriptive name that makes it easy to identify the experiment.

Under “Objective for this experiment,” you’ll define the metric you’ll use to evaluate the results from your test. Metrics can be chosen from Adsense, Ecommerce, Goals, Site Usage, etc.

• If you’re looking to improve ad clicks or impressions, then choose the Adsense option.
• If you want to boost revenue or the number of transactions, select eCommerce.
• If you have predefined goals like session duration, event attendances, or destination page clicks, then opt for the goal metric.
• Lastly, if you’re looking to better user experience through average page views or time on site, go for site usage.

The best part is you can set multiple metrics at one time.

• Divide Your Web Traffic

Once you’ve set the objective, you can divide the percentage of web traffic for the content experiment. This will control how many people visiting your website will see one of your test pages as opposed to your original page.

For quick results, you may want to include a high percentage of visitors in the experiment. However, if your experiment is rather drastic or risky, include only a small percentage of your website’s traffic. It’s also smart to turn on the email notification to stay updated on any changes occurring in the experiment.

In the “Advanced Options” tool, you can control how to divide the traffic by turning on the “Distribute traffic” toggle button. Enable this option to assign an equal amount of traffic to each variation for the life of the experiment.

If this button is left disabled, content experiments will follow the default behavior by adjusting traffic dynamically based on variation performance.

From there, you should set the minimum experiment time at three weeks for the best results.

Google Analytics also allows you to fix the confidence threshold for your content experiment to determine the minimum confidence level that must be achieved before a winner can be declared.

The higher the threshold, the more confident you can be that the winning web page has competed well against the other design. Keep in mind that higher thresholds can make your content experiment considerably longer as Analytics waits to crown a champion.

Use this confidence calculator to determine your confidence threshold.

• Configure Your Experiment and Code

The next step is to configure the experiment by adding in your original web page and your test pages.

As you can see in the image below, you simply need to enter the URL of your current page and all variation web pages.

Once you add the original and test pages, look over the preview image to be sure you’ve entered the right URL. Hit the “Save Changes” button after you check for completeness to head to the next experiment section.

Now, you’ll need the experiment code for your testing project.

If Google Analytics tracking codes are properly installed on your original and variation pages, an experiment code will be immediately visible in the box.

Place this code immediately after the opening head tag at the top of your original web page. Once it’s added, hit the “Save Changes” button again to progress to the final step.

You can use the Google Content Experiments plugin to enter the code on your page.

• Review and Get Started

When you’ve added the code, Google Analytics will validate it and show any errors that have been encountered if applicable.

Sometimes Analytics isn’t able to find the code. In this case, you can skip the validation phase as long as you’re sure the code was properly added. Google recommends skipped validation as only a last resort move. Instead, check your page for any errors that may have been introduced.

Otherwise, you’ll be given the green flag to start your content experiment. Your experiment will launch and you’ll start seeing reported data within one to two days.

• Check Your Results

After your experiment has run its course, Google Analytics will declare the winner based on your previously defined metrics and confidence threshold. It will take at least three weeks to reach this step.

By reviewing your results, you’ll identify the page that performs the best. You can then publish this as the page you want viewed by all website visitors.

Easy. But does it work?

Kapitall increased conversions by 44 percent through A/B testing from a Google Analytics content experiment. So clearly it does.

Overall, Google Analytics is a free tool that’s very easy to configure for running testing experiments because the search engine handles all of the dirty work.

There’s one downside though. Analytics doesn’t support multivariate testing, which is a well-known technique for testing multiple variables like color, text size, and buttons all at once. Google Analytics can’t be used for an email campaign either.

That said, it’s still the best option for running A/B testing on your landing page and conducting a content experiment at the very low price of free.

Have you used Google Content Experiments? What was your biggest win?

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7 Ways to A/B (Split) Test Low Traffic Sites

Remember that time when you paid for a brand new Optimizely account?

Overwhelmed with excitement, you set up a couple of split tests in between sips of coffee, ready to prove to the world that changing the color of your beautiful call to action would dramatically increase your growth rate?

It never did, huh?

Because your website just had really lousy traffic.

source: my side project 

Unless your website drives enough traffic, brilliant split testing will never give you meaningful results. Never.

In my early days as a startup founder, I quickly learned one hard-ass lesson in statistical significance.

When testing changes to a website, you must test for a period long enough for the results to become statistically significant. This means that the result is not likely to occur randomly but rather is likely to be attributed to aspecific cause.

The most important first step in this process is determining your target confidence level. The higher your percentage of statistical significance, the more confidently you can claim that changes you made on your website lead to specific outcomes.

A good rule of thumb is to go with at least 80% confidence.

A 95% confidence rate is what testers aim for but if you’re feeling like Kanye West, just remember, with numbers, you can’t fake confidence.

The reason we aim for statistical significance is if your website doesn’t have enough traffic, you won’t be able to distinguish between users actions that are random and actions that are happening because of a change made to the site.

Yet, there’s hope for low-traffic sites.

How To Make Statistical Significance Work For Your Puny Numbers

If you’re feeling down about the lack of interest in your site, just remember,according to Hubspot the average B2B company with 6-10 employees gets 124 unique visitors a week. That’s paltry 17 visitors a day! (I bet your numbers are looking better now)

1. Test big changes. Avoid the local maximum (hat tip to Andrew Chen)

Unless your website generates tens of thousands of hits per day, testing small changes will take too long to obtain meaningful results (Check with a sample size calculator). Test big changes that will give you new insight. For example, pit two completely different landing pages against each other or change entire layouts.

2. Record your visitors (with their permission)

The best way to determine what your visitors need is to watch them, LIVE! Thankfully, through tools like Crazy Egg, you can record live user sessions and heatmaps to create testable hypothesis.

3. Talk to your visitors

Don’t be creepy about it. Engage your visitors transparently by asking questions. Use a tool like Qualaroo.

As visitors use your product, target questions to them in order to obtain as much insight as possible before running any type of A/B testing.

4. Just Say No to multivariate testing

In theory, multivariate testing sounds amazing. Why wouldn’t you want random changes to multiple sections of your website shown to visitors automatically?

Sounds like a great idea right? Wrong! The more variables you test, the longer testing will take, chief.

Stick to one change at a time and be methodical about what you choose to test. Big decisions yield big outcomes so when faced with testing a button color vs. a redesign, go with the bigger choice.

5. Some A/B tests just never work

The more insight you have before you run your tests, the more chances you will have at a successful outcome. Remember, not all A/B tests succeed. Sometimes, when you don’t have enough information to build a proper hypothesis, you will fail because you tested the wrong thing.

Driving additional traffic to your website

On occasion, you just need an extra push to run your tests within a reasonable amount of time. There are several things you can do to drive additional traffic to your pages, lowering your test time.

1. Guest blog and contribute to sites within your target market

In the early days of search engine marketing, you could drive a ton of traffic to your page simply by paying someone to create thousands of backlinks to your website. Ah, those were the days.

Comment spam, keyword stuffing, and other black-hat SEO methods no longer generate traffic effectively. I sure don’t miss them; I hope you don’t either—they were skeezy.

Today, you have to put in some real effort in order to generate meaningful traffic.

Using BuzzSumo, search for keywords relevant to the content you’re going to be testing and collect a list of 10 or more websites that accept guest postings. Browse through the site to determine what the readership enjoys reading and write your own content for the site. Then, pitch it to the contact email for each website, light a candle, and spend a few minutes praying to your favorite deity.

Getting even one guest post could drive enough traffic both short term for the benefit of your test, and long term for your site overall.

2. Giveaways. Free makes everyone happy

Everyone loves free stuff. If you’re website offers a product or service, you’ve already done half the work!

Using BuzzSumo again, discover sites that your users read. Contact the site owners and offer special promotions to their users.

If you’re willing to pay a small fee, purchase a WordPress plugin like KingSumo Giveaways to create your campaign. On occasion, site owners might decline your giveaway and instead steer you towards sponsorships but, depending on your goal, that may be an excellent option. Which leads us to our final suggestion…

3. Pay up (or as marketers call it, “PPC”)

PPC (pay per click) can be used in conjunction with any other traffic generating efforts. Purchase clicks on on Google Adwords for intent driven traffic.

Because your ads display when relevant search queries are made in Google, the users that click your ads are looking for what you’re advertising. They will be more engaging and, more importantly, more eager to provide feedback. One thing to keep in mind with PPC is that it should never be used as a sole traffic generation method—use it sparingly.

If you’re ever in doubt, just remember, you ain’t Aziz Ansari. You don’t have the budget to make it rain.

What Do You Think?

I’d like to hear about recent experiments that you’ve run.What are some low traffic experiments that have yielded great results for you?

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