Tracking the impact of marketing projects is an obvious top priority for most businesses. This process isn’t as straight forward as analyzing some easily-obtained stats and knowing what to do next.
Standard metrics provide a surface-level overview of how your campaigns are performing. Incrementality shows you which touchpoints are driving customers to act. It’s a sophisticated method for maximizing your ad budget, using data wisely, and retargeting potential customers effectively.
Incrementality isn’t something you hear about as often as some other marketing metrics. That is because there’s still plenty of confusion about how it works – or even what it is. Incrementality is a specific version of AB split testing. Split testing that focuses on the increase of key purchase indicators and measures the conversion rate.
Attribution models are a method of determining which touchpoints are engaging your audience. Yet, some marketers are suspicious that such models don’t provide a definite answer. Or worse, mislead brands into thinking certain elements are more critical to sales than they actually are. One of the biggest complicating factors of attribution is online vs. offline measuring. And brands’ inability to find a unifying way to measure attribution across all channels.
Ultimately, poor data can skew the results generated from attribution models. And because of the variety of ways you can weigh them, they’re ultimately a more subjective measure than incrementality.
Scientists consider control variables or factors that may be interfering with the cause and effect relationship between two things. Incrementality allows marketers to do the same thing – single out one specific variable and test its true impact. It’s a way of determining whether specific actions are creating more sales. Or if those sales are organic and would have occurred anyway.
For example, a brand might test how an ad performs on its target audience (the test group) vs non-targeted audience (the control group).
Another brand might want to know how their retargeting is working among different demographic or psychographic groups. Incrementality can also provide insight into the elusive ‘halo effect’ brands experience when a potential customer’s positive interaction leads to a sale, opt-in, or brand loyalty down the line. Thanks to advances in AI, even lower budget campaigns with a smaller reach can be significant and yield a valid measurement. By coordinating and comparing data sources, incrementality provides reliable insights into these and other marketing scenarios.
At its core, what incrementality does is optimize your marketing campaigns and tell you how much revenue your ads are generating. The problem with simpler metrics is that we can’t know for sure that they matter. Is it important that you received 200 likes on a Facebook post? Is it actually having the effect you want on brand awareness? ROI? Even more advanced metrics like click-through rate or cost per acquisition don’t provide the full picture you need to make effective decisions and budget. This is where incrementality shines.
Measuring incrementality is simple, but not always easy considering the many ways audiences can be split. Not to mention all the factors that can be taken into account. Though, it’s a practice worth adopting and tweaking for any business that wants to be data-driven.
Just as one would when conducting an AB split test, marketers must decide how they want to split viewers up into two groups. One of these groups will then be exposed to a specific variable while the other is not. Each group’s performance must be measured before and after the test to determine the impact.
The difference between incrementality and regular AB split testing is that incrementality is measuring deeper business impacts like sales and new customers (the impact of campaign elements on the business itself), while AB is measuring the impact on campaign performance (things like click-through rate, open rate, etc). In other words, we’re measuring impact on business, not the campaign itself.
The formula for calculating incrementality is: Test (conversion rate) – Control (conversion rate) / Test (conversion rate)
A closely related metric will be your ‘lift’ rate, which is the likelihood that a customer will convert. So if your lift rate is 15%, that means that showing an ad makes viewers 15% more likely to convert to paying customers.
The formula for calculating your lift is: Test (conversion rate) – Control (conversion rate) / Control (conversion rate)
Once your incrementality rate is calculated, this is the percentage that sales will increase when a specific action is performed. So if your incrementality rate is 20%, that is the percentage of conversions your ad garnered.
It’s a more precise indicator of ad performance than your average AB split test or an attribution model that weighs the potential importance of specific elements. Incrementality gives a birds-eye view of how to boost your ROI on ad spending.
To make more accurate, data-driven decisions and reach new revenue goals, marketers must become accustomed to using measures like incrementality. With quality data, incrementality can provide a breath of fresh air for marketers who struggle to pinpoint the true ROI. Brands who build a culture of collecting, analyzing, and acting on data insights will be the ones who thrive in the years to come as we move into a digitally-dominated marketing world.
If you’re seeking help in this area, reach out! We’ll help you optimize your marketing campaigns so you can focus on what matters most – running your business.