Take The Guesswork Out Of Your Marketing With A/B Testing Tools

By TechDogs Editorial Team

Overall Rating

Overview

Are you stuck trying to solve the same marketing problems over and over again? Like Phil Connors in Groundhog Day, do you feel stuck reliving the consequences of failed marketing campaigns while feeling helpless to improve your situation? Never fear!

A/B Testing Tools can help you break out of your marketing rut. Whatever your goal - from reducing your website's bounce rate to increasing conversions from paid media to delivering a better overall customer experience across channels, devices and platforms, A/B Testing Tools will enable you to understand what works (and what doesn't).

Read on to get a better understanding of how these tools work. We'll also take you on a brief journey of the evolution of A/B testing and list some features and benefits of A/B Testing Tools currently availA/Ble to you. No need to suffer from the same marketing mistakes repeatedly!
Let’s dive right in!

The 1993 movie Groundhog Day illustrates how trial and error can help you get something right. Phil Connors, played by Bill Murray, is a weatherman who clearly hates his job. He grudgingly covers the annual Groundhog Day event in a small Pennsylvania town where he ends up reliving the same boring, monotonous day again and again.

TechDogs-"Played By Bill Murray"-A Screenshot From Groundhog Day Showing Phil Connors, Played By Bill Murray

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As the movie progresses, Phil notices how the decisions he makes throughout the day impact its outcome. He learns to make better decisions to achieve the outcome he wants — namely, to get the girl. It’s a great demonstration of how trial and error can help you craft a winning strategy.

Just like Phil, businesses can make the same bad decisions repeatedly, achieving poor outcomes and lamenting their perceived bad luck. However, savvy marketers know that finding the right formula doesn’t happen by accident — learning from past mistakes and perfecting elements of your marketing campaign using trial and error can help you achieve your goals. This is exactly what A/B Testing Tools are designed to do.

Before we get into learning more about these tools, let’s get a little acquainted with what A/B testing essentially is.

What Is A/B Testing?

A /B testing (also called “split testing”) helps you understand what version of a thing people like better than the other version of that thing. It’s a more reliable form of trial and error than learning from your mistakes like Phil Connors in Groundhog Day. With A/B testing, you’re not comparing two completely different things like apples versus oranges. Rather, you’re comparing different variations of apples (or oranges) to each other.

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If your aim is to understand whether people prefer red or green apples, round or oval apples, tart or sweet apples or any combination thereof, you’d select a sample of people and present them each with both apples then wait to see, statistically, which apple people like more. In digital marketing, instead of apples, what you’re testing is a webpage, digital ad, image or message and this is where A/B Testing Tools come in.

What Are A/B Testing Tools?

A/B Testing Tools facilitate the process of trial and error, helping you run tests on website content to learn what elements work best to achieve your goals. Examples of popular A/B Testing Tools include Google Optimize, Optimizely and HubSpot’s A/B testing Kit.

Let’s say you have two designers on your team and one created a video for your brand’s website while the other created an infographic, both to be placed on the website header. Both look great and you’re not sure which one to go for. Simple – you put both, run an A/B test on that via some ads and tadaaa you have a winner!

A/B Testing Tools would allow you to create these two versions of your website in a jiffy and test them out quickly to learn what works best. Wondering who came up with such a cool idea? Let’s discover that.

A History Of A/B Testing Tools

A/B testing goes back to the 1920s, when statistician and biologist Ronald Fisher discovered the mathematical principles behind A/B testing and randomized controlled experiments. Fisher used agricultural experiments to perfect this technique (e.g., by putting more versus less fertilizer on a piece of land and observing the results.)

Fisher’s principles were employed by scientists in the 1950s to run clinical trials. The methodology was then adapted for marketing in the 1960s. However, not much happened on that front until the 1990s. It was only then that businesses began employing A/B testing in randomized controlled experiments to improve user response and interaction, conversion on their websites and within their marketing campaigns.

Today, A/B Testing Tools help organizations automate the process of testing websites and various elements of digital marketing campaigns with the of improving the performance of a website, ad, or app. The industry buzzword for this is Conversion Rate Optimization (CRO), with the first tools popping up to serve this need around 2004.

CRO tools gained traction in 2007 with the launch of Google Website Optimizer (now Google Optimize), a free tool that enables website owners to conduct split and multivariate tests to measure the impact of different landing page designs.

We bet Phil Connors would have totally benefited from it – here's how!

How do A/B Testing Tools Work?

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A/B Testing Tools automate and facilitate the process of conversion rate optimization by handling the different variables involved with testing. They help you determine what version of a web page, app or ad works best based on your performance goals.

Phil Connors could’ve used an A/B Testing Tool to test different scenarios before actually committing with the goal of improving his day and achieving his goal. What if he wore a red shirt instead of a blue one? What if he said good morning to a stranger instead of ignoring them? What if he didn’t show up to cover the Groundhog Day event at all?

The point of an A/B Testing Tool is to try different scenarios out on a small sample of people (a cohort) and get a statistically relevant result that you can use to make a reasonably informed decision (e.g., let’s go with the green button instead of the blue because it gets a 50% higher clickthrough rate).
Common features of A/B Testing Tools include:

A/B/n Testing:

The tool can test two, three, or unlimited (“n”) versions of the element you’re testing.
Autopilot mode:

This feature automatically optimizes your campaign/web site based on conversion rates.
Multi-armed Bandit:

An algorithm that directs traffic to better-performing pages while the test is still underway. It enables you to complete the test more quickly without sending an overt amount of traffic to poorly performing pages.
Multivariate Testing (MVT):

This feature allows you to test multiple variations on several independent page elements at once.

While these are the common features, A/B Testing Tools also come with A and B versions – two types of the same toolset. Let’s look at what they are.

Types of A/B Testing Tools

There are two main types of A/B Testing Tools as follows:

Business Side:

These tools execute testing scripts using a visitor’s browser. When a visitor reaches a page, a JavaScript tag triggers the test. You don’t need much technical knowledge to implement tests with these tools — they’re good for your basic A/B and multivariate tests and include popular tools like Optimizely and Omni Convert.
Server Side:

These tools run on the page’s server and present the finished version of a page to the visitor’s browser. They’re more complex to operate and typically require involvement from the development team because they need custom configuration. The upside is that tests are more sophisticated and can include multi-page testing, mobile app testing and more. Optimizely also functions as a server-side tool. Adobe Target and Unbounce are other examples.

So that’s the nitty gritty. While the specifics may confuse (or bore) poor, downtrodden Phil Connors, keep in mind that these tools are designed to make your life easier. If you need a better reason to try them how, here’s more!

Why Do We Need A/B Testing Tools?

Conducting A/B testing is the best way to improve your website’s performance — it can increase Ecommerce sales, reduce bounce rates and make your customers happy so they return again and again. A/B testing helps you understand what pain points, bottlenecks and disconnects prevent users and customers from doing business with you. It’s a great way to get more leverage from your existing traffic (and media investment).

Trial and error is what helps Phil turn his day from one filled with irritation, disappointment and failure, into a day of happiness, success and deep satisfaction. This, friends, is what A/B testing software can do for your business.

Importance Of A/B Testing Tools

An A/B Testing Tool should be a topline marketing investment for all companies that do business online. They’re as important to your business’s bottom line as reliving the same day was to Phil Connor. He doesn’t get the girl until he learns more about her and understands what he’s doing wrong.
A/B Testing Tools can boost ROI — they helped one company increase their ROI by 291% simply by testing the landing pages they used for their paid search campaigns. Not just that, more than half of marketers are already using an A/B Testing Tool. That means it’s critical if you want to stay competitive. Future of A/B Testing Tools

Artificial Intelligence, audience segment testing, personalization, contextualization and more advanced software that turns A/B Testing Tools into “personalized experiences solutions” are on the horizon. Conversion Rate Optimization has become the standard way for companies to optimize their websites and marketing campaigns and A/B Testing Tools are (and have been) the foundation behind this approach.

These days, there are more variables than ever to test — including different audience segments, platforms, devices and screen sizes. Consumers expect seamless experiences across all these different touchpoints. Testing technology will enable companies to create personalized, relevant campaigns and content.

Summing It Up

Doing the same thing over and over again didn’t work for Phil and it won’t work for your business. A/B Testing Tools — a market that is expected to be worth over $1 billion by 2025, can eliminate guesswork, mitigate your risk and lead to new customers and rapid growth. That’s something Phil (and the Groundhog) both support!

Frequently Asked Questions

What is A/B testing and how does it work?

A/B testing, also known as split testing, is a method used to compare two different versions of something to determine which one performs better. It's akin to a controlled experiment where different variations are tested simultaneously to gauge their effectiveness. In digital marketing, A/B testing typically involves comparing different versions of a webpage, digital ad, image, or message to assess their impact on user behavior or engagement. For instance, if you're unsure whether users prefer one webpage layout over another, you can create two versions and present each to a sample of users. By analyzing the results, you can identify which version performs better and use that information to optimize your marketing efforts.

What are A/B Testing Tools and why are they important?

A/B Testing Tools are software applications designed to facilitate the process of A/B testing by automating and streamlining the testing process. These tools enable marketers to create multiple variations of digital assets and conduct tests to determine which version yields the best results. Examples of popular A/B Testing Tools include Google Optimize, Optimizely, and HubSpot’s A/B Testing Kit. These tools are crucial for businesses looking to optimize their marketing strategies and improve their digital performance. By leveraging A/B Testing Tools, marketers can gain valuable insights into user preferences, optimize conversion rates, and enhance overall campaign effectiveness.

What are common features of A/B Testing Tools?

A/B Testing Tools come with various features designed to enhance the testing process. Common features include A/B/n testing, which allows testing multiple variations, autopilot mode for automatic optimization based on conversion rates, multi-armed bandit algorithms for directing traffic to better-performing pages, and multivariate testing (MVT) for testing multiple variations on independent page elements simultaneously. These features contribute to the effectiveness of A/B Testing Tools in optimizing website content and marketing campaigns.

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