Drive Incrementality With Predictive Analytics
In mid-March, we ran a webinar on how to modelIn the context of machine learning, a model is a specific instance or example of an algorithm that has been created based on a particular… More your marketing mix with machine learning. During the webinar, we polled the audience to find out their most significant challenge when measuring their ad campaigns. The poll results, as shown below, indicate that 44% of the audience felt that properly attributing success was the biggest challenge, followed by 26% who said that proving incremental lift was the biggest issue.
That means 70% of the audience felt that properly attributing success and proving incremental lift were their biggest challenges. So, we wanted to provide an in-depth view of these topics and show how AIArtificial intelligence (AI) refers to the development of computerized systems that can carry out tasks and perform actions that augment or take the place of… More and predictive analyticsPredictive analytics uses data, statistics, and machine learning techniques to build mathematical models that can generate predictions about things likely to happen in the future…. More can help overcome these challenges.
In a previous blog post, we covered the attributionThe process of identifying and assigning credit to the various marketing touchpoints that contributed to a conversion or another business outcome, such as a sale… More challenge; here, we’ll cover how AI and machine learning can help prove incremental lift for an ad campaign, channel, or media partner
What do we mean when we say “incremental results”?
When we talk about incremental results in the context of marketing performance, we’re referring to the change in an outcomeA prediction is the ultimate goal of a predictive model. In Pecan, a prediction is often tied to a specific customer. After learning from data… More attributable to a specific intervention or action. In other words, incremental results are the additional gains or benefits we achieve by taking a particular action compared to what we would have achieved if we had done nothing.
This concept is particularly relevant in performance marketing campaigns, where marketers are constantly looking for ways to drive more results, whether conversions, sales, or revenue. Measuring incremental results is essential for determining the effectiveness of different marketing strategies and tactics.
By measuring the incremental impact of a particular campaign or action, we can assess its true value and make informed decisions about allocating resources. This approach allows us to optimize our marketing efforts and maximize our return on investment (ROI). Focusing on incremental results is critical to continuous improvement and growth in any marketing organization.
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Why is it difficult to measure the incremental lift of an ad campaign?
IncrementalityThe ability to measure the additional impact or value of a specific marketing campaign or strategy. It is used to determine the incremental impact of… More goes beyond measuring conversion rates, ROI, ROASReturn on ad spend (ROAS) is a metric used to assess the performance of marketing efforts. It is equal to the amount of revenue generated… More (return on ad spend), or other metrics. It captures lift that is uniquely attributable to the campaign under evaluation. Because incrementality is such a complex metric, it can be hard to measure due to several factors.
One of the main challenges is the presence of confounding variables that can influence the campaign’s outcome. These variables could be external factors, such as changes in the economy, seasonalityRefers to patterns that repeat over a specific period of time. These patterns can be observed in various types of data, such as customer transaction… More, or the competitive landscape, or internal factors, such as changes in pricing or product features. If these variables are not properly controlled for, it can be challenging to attribute any changes in the outcome to the specific intervention being evaluated.
The most common challenge in measuring incremental results is the difficulty of isolating the effect of the intervention from other factors that may be driving the outcome. For example, marketers may launch a campaign simultaneously with other initiatives, such as changes in the sales team or new product launches. Or, a marketing campaign could be launched right when the website gets a refresh. If the impact of the marketing campaign is not measured separately from these other initiatives, it can be difficult to determine the true incremental impact of the campaign.
By carefully controlling for variables, companies can obtain a more accurate assessment of the incremental impact of their marketing campaigns.
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How can you measure incrementality in marketing?
There are several methods that can be used to measure incrementality in marketing. One of the most rigorous and widely accepted methods is the randomized controlled experiment, also known as an A/B testA technique to compare two versions of a product, feature, or webpage to assess which one performs better. A/B tests are a type of experiment… More. In an A/B test, a randomly selected group of individuals is exposed to a marketing campaign, ad treatment, web page, etc, while another randomly selected group is not. By comparing the outcomes of the two groups, we can estimate the incremental impact of the campaign, treatment or page.
Another approach is the quasi-experiment, which involves comparing the outcomes of an exposed group to a control group that is similar in all respects except for the fact that they did not receive the intervention. This method can be useful when it is not feasible or ethical to randomly assign individuals to a treatment group.
Observational studies can also be used to measure incrementality, although they are generally considered less rigorous than randomized controlled experiments or quasi-experiments. Observational studies involve comparing the outcomes of individuals who were exposed to the intervention to those who were not, but without the use of a control group. This approach can be useful when it is difficult to establish a control group, but it’s important to carefully control for confounding variables.
It’s also important to consider the time lag between the intervention and the outcome, as the full impact of a marketing campaign may not be realized until several months after the campaign has ended.
In addition, it is important to ensure that the sample size is large enough to provide a statistically significant result, and to test the statistical significance of the results to ensure that they are not due to chance and can be generalizable to a larger audience. By carefully measuring incrementality in marketing, companies can optimize their marketing efforts, scale their findings, and maximize their ROI.
How is incrementality testing relevant to predictive analytics?
Incrementality testing is relevant to predictive analyticsAnalytics is a business practice that uses descriptive and visualization techniques to gain insight into data; those insights can then be used to guide business… More because predictive models are often used to optimize marketing campaigns by predicting which customers are most likely to respond positively to a campaign. However, predictive models can only make predictions based on past data, and they cannot predict the effect of a specific campaign on a specific customer.
Incrementality testing provides a way to measure the actual impact of a campaign on a specific group of customers, which can be used to validate and improve the predictive models used to optimize the campaign.
By comparing the behavior of the test group to the control group, incrementality testing can help determine whether a particular campaign is actually driving incremental sales or whether the sales would have occurred anyway. This information can be used to adjust the predictive models and improve the accuracyIn predictive analytics, accuracy is a measure of a predictive model’s performance. It’s usually expressed as a percentage, calculated by dividing the number of correct… More of the predictions.
Machine learning algorithms can also identify the factors that are most strongly associated with incremental impact, such as demographic characteristics, purchase history, or online behavior. For example, Pecan’s platform is designed to identify these factors down to the user level.
Overall, predictive analytics can play a valuable role in supporting incrementality testing in marketing by helping to identify promising campaigns, estimate incremental impact, and provide insights into the drivers of success. By leveraging these techniques, companies can optimize their marketing strategies and achieve better results with less waste.
Find out more about how Pecan can help you boost and prove real marketing impact. Contact us to set up a time to chat about Pecan and your marketing team’s needs.