“It’s great to hear this campaign has been driving consumers to stores, but can you tell me what the real incremental effect is? I know some of those consumers would have visited my stores anyway… did my advertising campaign actually change their behavior?”
This note has been on my desk for over a year now, and the concept of “incrementality” has come up over and over again as one of the biggest problems marketers face. They are looking to measurement platforms for a solution — in Cuebiq’s case, an offline measurement platform. As a measurement company, we need to meet the increasingly sophisticated demands of an industry that is embracing data-driven solutions.
Introducing Campaign Incrementality for Marketers
The concept of incrementality has been analyzed by economists and researchers in multiple academic papers for different use cases. When it comes to advertising, it is crucial for marketers who aim to change consumer behavior to understand the real impact of their campaigns. In order to distinguish between those exposed to ads who were already going to visit the store (the natural effect, driven by intent and brand identity) vs those who visited because of that exposure (the incremental effect, driven by ad sensitivity).
Marketers haven’t fully embraced incrementality because attribution companies have not taken selection bias seriously. For those unfamiliar, selection bias refers to the bias introduced when researchers select a sample for analysis in such a way that the conclusions are driven by the selection process itself. In advertising terms, marketers can be subject to selection bias when they only analyze data on consumers who were already planning to visit their stores — therefore, they are not seeing the true or real effect of their advertising on influencing new visitors and changing consumer behavior.
At Cuebiq, we take this issue seriously and have developed new and exciting tools to help marketers understand campaign incrementality at the consumer level, so their ad dollars can be spent more efficiently. A few weeks ago I read an article on the Correspondent in which the authors reported, “The brightest minds of this generation are creating algorithms which only increase the effects of selection.” We are essentially trying to debunk this myth that marketers are promoting selection bias. Our mission is to provide data-driven tools that minimize bias and help marketers understand incrementality and make decisions upon it so they can increase advertising performance with maximum efficiency.
Cuebiq’s Enhanced Platform: Answering What Marketers Need to Know
Cuebiq has invested heavily in state-of-the-art methodologies and algorithms that enhance our existing solutions to provide new capabilities that help our customers keep their competitive edge in the market. This innovative mindset is at the core of our company and reflects our commitment to shaping the industry by using the latest advancements in causal machine learning that help bring revolutionary tools to our clients.
At Cuebiq, we focus on supporting marketers who wish to drive consumers to stores, providing them with the tools they need to understand how their advertising activations are changing consumer behavior. This understanding will ultimately enable them to be more efficient by decreasing their cost per incremental visit.
With the new enhancements to our platform Clara, which features a real-time dashboard, where marketers can decouple organic store visits from incremental store visits. They can now calculate the cost per incremental visit much more easily, giving them the ability to optimize their strategies to lower this metric.
To achieve these results, we teamed up with some of the brightest researchers in this field, implementing state-of-the-art developments in causal machine learning. The result? We deployed a scalable environment to bring our enhanced offline measurement solution to life.
Consumer-Level Metrics to Measure Advertising Impact
Our methodology allows us to understand whether a consumer changed their behavior after ad exposure, or whether they visited a store because they would have anyways. This is a key aspect of our solution that sets Cuebiq apart from its competitors. For the first time in our industry, advertising impact is calculated at the consumer level and not as an aggregate measure for the entire campaign (as has been the standard until now).
This means marketers and analysts can now integrate data-driven activations into their strategies to increase their return on advertising spend (ROAS) by lowering their cost per acquisition (CPA). Post-campaign analytics can determine which subgroups in the exposed group were more sensitive (the audience that is generating the most incremental visits) to the campaign message in terms of visitation patterns, competitive brand analysis, mobility patterns, hyper-detailed (but privacy-compliant) demographic breakdowns, and so forth. These insights, accumulated campaign after campaign, provide an evidence-based framework for audience building, campaign targeting, and brand insights that focuses on increasing the incremental advertising effect by lowering the cost per incremental visit.
Establishing New Industry Standards
Speaking personally and for the entire team at Cuebiq, we’re not only excited to help marketers but also proud to be at the forefront of deploying incrementality solutions the industry needs. There were many challenges we had to overcome as our product evolved; not only did we have to turn complex theories into practice, but we also had to address practical challenges such as the fact that random ad exposure is currently not done at scale, in a cross-channel setting.
Ultimately, we want to help marketers better understand how their campaigns are driving visits. The gold standard to understanding a causal relationship of this sort would be to create a control group by randomizing exposed and control conditions in the target audiences. The lack of random assignment to A or B groups presents a downstream challenge to companies like us because it introduces bias in the causal inferences we make about campaign effect. Minimizing this bias was the biggest problem we had to address in creating this solution, which we did using the latest developments in causal machine learning.
Get a Sneak Peak and Schedule a Demo
In my next article, I will be sharing details about our upcoming white paper that provides a deep dive into our methodology, also known as C.A.T.E. (Conditional Average Treatment Effect) or Individual Treatment Effect (ITE). This paper will include the results of the experiments we have run so far and more details around how they can help marketers make the most out of their cross-channel advertising investments.
We can help you answer those tough questions with incrementality, so why wait? Interested in learning more about how Cuebiq is using incrementality to revolutionize campaign measurement? We’d love to chat with you about how you can activate incrementality — reach out to schedule a demo to see how your brand can be at the forefront of measurement too.