Understanding and Measuring Marketing Incrementality

Understanding the true impact of each campaign is crucial, yet increasingly complex.

With multiple channels and touchpoints influencing consumer behavior, traditional measurement methods often fall short in isolating what truly moves the needle.

This is where incremental analysis comes in. By focusing on the actual lift generated by your marketing efforts—beyond what would have happened organically—incrementality provides a more clear and accurate picture of campaign performance.

This guide offers a detailed overview of incrementality in marketing, including its various types, methodologies, and ways to measure incremental lift.

What Is Incrementality?

This approach allows marketers to determine the incremental lift generated by their efforts, rather than relying on overall performance metrics that may be influenced by external factors.

Consider a simple scenario: A brand launches a new ad campaign and notices an increase in sales. While it's tempting to credit the entire boost to the new campaign, other factors might be at play.

Perhaps there was a general increase in market demand or maybe another concurrent campaign also influenced the sales. Incrementality seeks to pinpoint the exact contribution of the campaign in question, providing clarity on its true return on investment.

Incrementality vs. Marketing Attribution vs. Marketing Mix Modeling

Incrementality and marketing attribution are both essential tools for understanding campaign performance, but they serve different purposes.

Incrementality measures the impact of your marketing activities by determining the incremental lift they provide beyond what would happen without any intervention. It’s about understanding the net effect of a campaign.

Attribution models assign credit to various touchpoints in a customer's journey, helping you understand how each channel contributes to conversions. While it’s helpful in tracking paths to purchase, attribution doesn’t always reveal whether a conversion would have occurred without a particular touchpoint.

Marketing mix modeling takes a broader approach by analyzing the impact of all marketing activities, including offline channels, over time. MMM helps in understanding the overall effectiveness of your marketing strategy, but it operates at a higher level and is less granular than incrementality or attribution.

Why Is Incrementality Important?

Understanding the real impact of marketing activities is like having a roadmap for spending a marketing budget. It helps in avoiding the pitfall of throwing ad spend at campaigns that don't genuinely add value.

Incrementality also means:

• Optimized Resource Allocation: By understanding incrementality results, marketers can allocate budgets more effectively. This ensures that marketing dollars are spent on strategies that produce the highest ROAS rather than on activities that merely shift existing demand or cannibalize other channels.
• Avoiding Wasted Spend: Without incrementality analysis, marketers risk over-investing in strategies that don't actually generate the desired outcome. By identifying non-incremental activities, companies can reduce wasted ad spend.
• Customer Behavior Insights: Incrementality studies can uncover insights about customer behavior that are not visible through standard metrics. For example, understanding what truly triggers a purchase can lead to more effective retargeting and personalization strategies, ultimately improving customer engagement and loyalty.

Types of Incrementality

Incrementality can be viewed from various angles, each offering unique insights into marketing performance. Understanding these different methodologies is crucial for anyone looking to make the most of their marketing activities.

Here are some of the most common types of marketing incrementality that are often considered in performance analysis.

1. Channel-Silo Incrementality: A Closer Look at Individual Channels

For example, if a business is investing in paid search advertising, this approach will measure how many conversions are directly attributable to that channel.

But it goes a step further. It also considers how many of those conversions might have happened anyway, perhaps due to organic search results or other marketing activities.

This type of incrementality is particularly useful for businesses that invest in multiple marketing channels. It helps to isolate the effectiveness of each channel, making budget allocation easier. For instance, if paid search is found to be less effective than initially thought, resources might be shifted to more productive channels like retargeting or email marketing.

2. Media Incrementality: Evaluating Media Channels and Campaigns

The goal here is to understand which media activities are contributing the most to desired business outcomes, such as increased sales or customer engagement. By doing so, it becomes possible to allocate the media budget more effectively. For example, if a social media campaign is found to have a high level of incrementality, it might make sense to increase investment in that area.

Media incrementality is almost like a micro-level of media mix modeling (MMM). While MMM considers a broader range of factors, including historical data, external variables (such as seasonality), and interactions between media channels, media incrementality specifically measures the additional outcomes.

3. Campaign-Level Incrementality: The Big Picture

This approach helps identify which elements of a campaign are driving the most value. It can also highlight areas where the campaign might be falling short. For example, if an email marketing component of a broader campaign is found to be particularly effective, future campaigns might include a heavier focus on this channel.

How to Measure Incrementality in Marketing

There are several reliable methods to measure incrementality, each offering its own set of insights. Here's a detailed look at some of the most commonly used techniques.

1. A/B Testing: The Basics and Benefits

A/B testing is one of the most straightforward methods for measuring incrementality.

In this approach, the audience is divided into two groups: a treatment group, or group A, and holdout group, or group B. Group A is exposed to the marketing activity, such as an online ad, while group B is not. By comparing the conversion rates between the two groups, it's possible to determine the incremental lift.

This method is especially useful for online campaigns where tracking is easier. It helps in quickly understanding whether a particular ad or marketing message is effective. If the treatment group, which saw the ad, has a significantly higher conversion rate than the control grop, it's a good indicator that the ad is effective.

2. Conversion Lift Studies: A Deeper Dive into Conversions

Conversion lift studies go beyond basic A/B testing to offer a more nuanced understanding of how marketing activities affect consumer behavior. These studies measure the increase in conversions that can be directly attributed to a specific marketing activity. 

For example, if an online store runs a special promotion, a Conversion Lift Study could measure how much that promotion increased sales compared to a period without the promotion.

This method is particularly useful for more complex marketing activities that might have multiple touchpoints with the consumer. It can help marketers understand not just whether an activity is effective, but also how effective it is in comparison to other activities.

3. Randomized Controlled Experiments: The Scientific Approach

Randomized Controlled Experiments are the most rigorous method for measuring incrementality in marketing.

In these experiments, the audience is randomly divided into different groups, and various factors are controlled to ensure the results are as accurate as possible. One group is exposed to the marketing activity, while the other is not, similar to A/B testing. However, these experiments often involve more complex statistical analysis and longer time frames.

The benefit of this method is that it provides highly reliable data. It's especially useful for large-scale campaigns or when the stakes are high. The insights gained from Randomized Controlled Experiments can be invaluable for making informed decisions about future marketing activities.

Choosing the Right Method

The choice of method depends on various factors, including the scale of the campaign, the available resources, and the specific questions that need answering. Here's a table that can help you settle on the right method.

A Common Pitfall for Marketing Incrementality

One of the most significant challenges in measuring marketing incrementality is the collection and management of data.

Data fragmentation across various platforms, inconsistent formatting, and the overwhelming volume of data points can significantly hinder the accuracy of marketing measurement.

Without a streamlined process, the data required for incrementality analysis may be incomplete, improperly aligned, or delayed—leading to flawed conclusions and misguided decisions.

Improvado addresses these critical challenges head-on by providing a comprehensive solution that automates the collection, transformation, and preparation of marketing data.

Designed for enterprise-level needs, Improvado seamlessly integrates with over 500 data sources, ensuring that all relevant data is aggregated into a single, consistent platform.

The platform not only collects data but also applies the necessary transformations to standardize formats and align metrics, making it perfectly suited for any analytics use case, including marketing incrementality. This automation drastically reduces the risk of errors, enhances data integrity, and frees up your team to focus on deriving actionable insights rather than managing data logistics.

Frequently Asked Questions