Incrementality in E-commerce, What is it and How Can I Measure it

The world of eCommerce has grown dramatically over the last few years, creating a vast and competitive landscape for online businesses. With the increased competition, businesses are striving to make more informed decisions about their advertising campaigns to ensure they are getting the best return on investment (ROI). In this context, the concept of incrementality has become increasingly important.

But what is incrementality and how can it be measured effectively in eCommerce? This blog post aims to demystify this concept and provide a practical guide on how to measure it in your eCommerce business in a world where priacy restricts tracking capabilities.

Understanding Incrementality

At its core, incrementality measures the impact of a specific marketing campaign or a change in business strategy on overall business performance. It’s the increase in revenue, conversions, or other key performance indicators (KPIs) that can be directly attributed to a specific marketing campaign or action.

In other words, incrementality is about identifying the causal impact of your marketing efforts. It answers the question: “What additional benefit did we get from our marketing efforts that we wouldn’t have gotten without them?”

The Importance of Incrementality in eCommerce

In an eCommerce setting, understanding incrementality can be crucial for several reasons:

Optimizing Marketing Spend: Incrementality helps determine the actual value brought in by different marketing channels, allowing businesses to optimize their marketing spend.

Understanding Customer Behavior: Incrementality can shed light on customer behavior and the factors that motivate customers to make a purchase.

Informing Business Strategy: Data on incrementality can guide business strategies by revealing what works and what doesn’t in driving additional sales or conversions.

How to Measure Incrementality in eCommerce

While the concept of incrementality might seem straightforward, measuring it can be complex, especially in eCommerce where numerous factors can influence a customer’s decision to purchase. Here are some steps you can follow:

1. Set Up a Control Group and a Test Group:

The most effective way to measure incrementality is through an experiment involving a control group and a test group.

The test group is exposed to the marketing campaign or strategy change. The control group is not exposed to this change. The difference in results between the two groups can be attributed to the strategy or campaign under examination.

2. Track Relevant Metrics:

Next, you need to decide on the metrics to track. These could be clicks, conversions, revenue, average order value, or any other KPIs relevant to your business.

Remember, the goal is to measure the incremental difference, i.e., the change that can be directly attributed to the marketing campaign or strategy change.

3. Collect and Analyze Data:

Collect data over the course of your campaign. Once the campaign is over, compare the performance of the test group to that of the control group.

4. Calculate Incrementality:

The basic formula for calculating incrementality is:

Incrementality = (Performance in Test Group - Performance in Control Group) / Number of Individuals in Test Group

5. Make Data-Driven Decisions:

Use the data to inform your marketing strategies. If a campaign shows high incrementality, it’s a good sign that it’s positively impacting your business.

Challenges in Measuring Incrementality

While the above steps provide a broad overview of the process, measuring incrementality isn’t without its challenges.

For instance, online customers typically interact with multiple marketing touchpoints before making a purchase, making it difficult to accurately attribute a sale to a specific campaign. Furthermore, external factors, such as market trends and seasonal variations, can influence customer behavior, complicating the measurement of incrementality.

To overcome these challenges, businesses may need to employ more sophisticated techniques such as causal inferance, multi touch attribition through an web and app tracking tool such as GA4, (previously Google Analytics and Google Firebase), and finally MMM (Marketing Mix Modelling).