The Distinct Roles of Incrementality Testing and A/B Testing

Among the myriad of testing methodologies, Incrementality Testing and A/B Testing stand out as powerful tools, each offering unique insights into the impact of marketing efforts. Yet, there often needs to be more clarity about what sets these two methods apart and when to use each.

Incrementality Testing and A/B Testing are both pivotal in assessing marketing strategies, but they serve different purposes and provide different kinds of insights. Incrementality Testing is focused on understanding the additional value or 'lift' that marketing efforts bring over and above what would have happened naturally without those efforts. It answers the critical question: "Is this marketing activity adding any extra value?"

On the other hand, A/B Testing, also known as split testing, is about comparing two versions of a webpage or app against each other to determine which one performs better. It's often used for optimizing website design, ad copy, or other elements to improve user engagement or conversion rates.

This blog will explore both methodologies' differences, benefits, and best use cases. Understanding the distinction between incremental testing and A/B Testing is not just an academic exercise; it's a practical necessity for marketers aiming to make data-driven decisions in a competitive digital marketplace.

Decoding A/B Testing

A/B testing originated in the 1920s with statistician and biologist Ronald Fisher defining its basic principles and doing agricultural experiments using the method. Marketers adopted A/B testing in the 1960s and 1970s to evaluate responses to different marketing campaigns. Would sending a discount voucher in magazines drive more engagement and brand visibility, or would sending a postcard about the latest product do the same?

A/B testing in marketing as we know it today began in the 1990s with the advent of the internet. Today, marketers use A/B testing to compare different email campaign variations (different subject lines, CTAs), app UIs, and more. A/B tests help marketers determine which campaign version generates more responses and drives more conversions. Today, everything from the size and color of a subscribe button to an app or website homepage design, promotional emails, and more is tested using A/B testing. It is one of the most basic forms of a randomized controlled experiment.

A/B Testing – Methodology

A fashion website may create two versions of a buy now button. To run the test, they showed two sets of users randomly picking the different button versions (the only difference being color) and determining which version got more clicks and conversions.

A/B testing focuses on conversion metrics such as bounce rates, click-through rates, and other engagement metrics to evaluate what works better. The end goal is to optimize a design, campaign, or strategy. It enables marketers to discover the most effective strategies and designs to drive desired customer behaviors and enhance customer engagement.

In a LinkedIn post, Abhishek Patra, a DTC email and SMS marketing expert, stated that these 6 A/B tests achieved 47% to 60% lifts.

However, more than comparing one aspect of the overall campaign is needed in the privacy-first internet era and an over-crowded marketplace. What marketers need today is a solution that can help them assess the impact of a marketing campaign on their key performance indicators, such as how many people installed their app or the uplift in profitability or conversions before and after running a campaign, etc. This is why incrementality testing has become a hot favorite of modern-age growth marketers. Let's explore more about incrementality testing.

De-mystifying Incrementality Testing

Incrementality enables brands to analyze the performance of their campaigns and methods by measuring the lift in desired outcomes like profitability, revenue, conversions, and brand awareness.

Incrementality testing is about quantifying the actual incremental effect of a particular advertising strategy. It goes beyond just tracking metrics and comparing different creative versions, unlike A/B testing. It helps brands determine whether or not a particular campaign positively affected a specific outcome, such as increased sales. Incrementality testing also helps assess whether the campaign was necessary and if it resulted in any net gain.

Let's understand the incrementality test with the help of an example.

An online garment company launched a promotional campaign offering customers who downloaded the app a shopping voucher worth $100. One thousand people installed the app after the campaign was launched. But, say, 600 users (60%) may have installed it just because they wanted to and not because of this campaign. Incrementality testing helps marketers get an answer to this.

Types of Incrementality Testing

Geo-testing in Incrementality: Geo-testing is a popular incrementality method that assesses a tactic's impact by altering media delivery in specific locations and then evaluates the resulting changes in advertising sales to determine the media's true value.

Known Audience Testing: Offering a granular approach, known audience testing facilitates experiments at a detailed level, leveraging specific audience segments to understand how distinct media treatments impact conversions.

Traditionally, marketers performed incrementality testing using the A/B testing methodology where one group of customers was exposed to a campaign and one group was not. The results of both were compared to see how much was the difference. From the above example, say the group exposed to the campaign resulted in 120 app downloads, whereas the group not exposed led to 100 downloads. Which means the uplift was 20%. However, the traditional method of incremental testing is plagued with innumerable challenges.

1. Implementing A/B tests for large user groups is time-consuming and costly.
2. Given the new rules and regulations surrounding customer privacy, the detailing required for the old methodologies is impossible.

This is where more modern solutions like Lifesight can be instrumental, easy, and effective ways to implement incrementality testing and gauge the effectiveness of online and offline campaigns by a brand.

Marketing expert Michael Kaminsky explains how brands can know if their brand search spend is incremental or not using incrementality testing.

A/B Testing vs Incrementality Testing

While both Incrementality and A/B Testing share similarities, like segmenting audiences, their overarching objectives set them apart, with A/B testing serving broader purposes and Incrementality Testing honing in on specific media tactic impacts.

Why Understanding These Differences Matter

Despite a few similarities, such as audience segmentation, it's important to note that not all incremental tests are A/B tests and vice-versa. As a growth marketer, understanding the distinction between the two is essential, especially in this multi-channel and omnichannel marketing era. With online and offline marketplaces co-existing, understanding what truly works is important to retain customers across channels.

Marketing campaigns cost a lot of money, and identifying the right channels that generate returns is imperative. Grasping the differences between A/B testing and Incremental testing is more than academic; it's pivotal for marketers aiming to pinpoint their campaigns' genuine value and refine their strategies for optimum results.

Final Word

As marketers, it is essential to understand that both A/B testing and incremental testing have different purposes. Which is an ideal fit depends on the specific outcomes one tries to address. However, with an average person being exposed to a whopping 4,000 to 6,000 advertisements in a day, marketers, more than ever, must determine which marketing initiatives are truly generating results and which of them are just noise. This is where incrementality testing helps. A/B testing helps fine-tune the little details. Take your pick basis what you wish to achieve.

Lifesight's Incrementality testing is an easy and effective way to measure the incremental impact of online and offline marketing initiatives. The meticulously designed automated incremental lift tests are easy to implement and measure the impact of campaigns across multiple channels. Scale your marketing spending with confidence in a privacy-first era with Lifesight.