One of marketers’ challenges is understanding the tangible impact of the media they run. With many data and analytics platforms promising the silver bullet solution, it has never been more critical for marketers to have the right tools to understand marketing performance.
In the last year, Artificial Intelligence (AI) has become a hot topic for marketers. With capabilities including generative art creations, machine learning, and voice recognition, AI’s uses seem to be limitless. These developments have not gone unnoticed by social media, as AI-generated art and AI chatbots (e.g., ChatGPT) have been trending on Twitter, TikTok, Instagram, and Reddit. While AI is being used to write ad copy and enhance digital customer experiences, it can also be an effective tool for marketing experts to measure marketing’s true impact on business.
AI Marketing Applications:
By itself, AI lacks the business context required to make accurate marketing predictions and draw insights from marketing results. However, with proper direction, AI-powered tools can be ideal for measuring the incremental value of marketing. Here are a few examples of how AI in the right hands can drive maximum value from media spend:
Self-Organizing Map:
Self-organizing maps (SOM) are one example of AI’s application in marketing measurement. A SOM is an unsupervised neural network that organizes data points based on similar properties. One direct use for SOMs is in cluster analysis. Researchers can use a SOM to organize customers into groups based on demographic and behavioral data. These segments can then be used by marketers to target specific consumers based on their shared characteristics.
Causal Machine Learning:
One of the best ways to understand marketing incrementality is through testing. In an ideal world free from business constraints and real-world factors, conducting a marketing test is as simple as creating test and control groups and measuring the effects once the test is completed. This scenario rarely plays out as expected and often is complex and expensive for the company to conduct. This is where Causal Machine Learning (ML) could assist. By training the ML model on covariate, treatment, and outcome data collected over a set period, the causal ML model can begin to understand the relationship between customer data (e.g., purchase recency and demographic data), media exposure, and purchase decisions. Once trained, the model can predict outcomes of decreasing, increasing, or changing marketing actions based on outcome variables, leading to a better understanding of incrementality.
Leverage AI in Your Measurement: Meet Our Modern MMM+
Ovative’s AI-driven Modern MMM+ helps marketers drive a 15% increase in their marketing return. Powered by Ovative’s holistic marketing metric, our Modern MMM+ combines MMM and MTA measurement to help marketers understand the true value of their marketing investments across consumer touchpoints.
Data-driven decision-making is part of our DNA at Ovative, but it is only as valuable as the ability to activate learnings. That’s why our Modern MMM+ is always supported by a team of Measurement Experts that help our clients take meaningful action on the tool’s insights. Connect with our measurement experts to talk to you about your testing and measurement goals.
