Performance marketing (which is a funny term—shouldn’t all marketing aim to perform?) usually refers to channels like Pay-Per-Click (PPC), Search Engine Advertising (SEA), Search Engine Optimization (SEO), Social Media Advertising, Affiliates, Email Marketing, Influencer Marketing, and Display Advertising.
Black Friday isn’t just a day anymore—it’s a full-blown phenomenon. What started as a 24-hour shopping frenzy has morphed into “Black Week,” where retailers offer deep discounts and irresistible promotions for days on end. We all love some discounts right? However, for retailers it’s the ultimate sales event, where a significant chunk of annual revenue can be made in a matter of hours. But behind the excitement, there’s a tough question marketers are grappling with: How much of these booming sales are driven by savvy marketing campaigns, and how much is simply the result of slashing prices?
Marketing Mix Modeling (MMM) is a method used to measure the effect of marketing efforts on sales and performance metrics. It's gaining popularity among marketers as they aim to maximize their marketing budgets and comprehend the ROI of various channels and strategies. Plus, with the decline of third-party cookies, MMM has emerged as an ideal tool for marketers. However, it can be tough for beginners to grasp as it involves several technical terms and definitions. This blog post aims to shed light on the most common terms used in MMM and what they mean.
Weather doesn’t just decide whether we grab an umbrella or throw on a pair of sunglasses—it shapes how we shop. From surprise cold snaps to scorching summer heatwaves, weather has the power to instantly sway consumer behavior, either fueling demand or putting it on ice across industries like fashion and grocery retail. Traditionally, Marketing Mix Modeling (MMM) zeroes in on the message (what you're advertising) and the channels (where you're advertising). But often overlooked are the external forces, like weather, that can make or break marketing performance.
Are you prioritizing growth or efficiency when making decisions about the upcoming campaigns and budgets? What if you didn’t have to choose between the two?
Before adopting Marketing Mix Modeling (MMM), many of our customers have been using Google Analytics 4 (GA4) or multi-touch attribution (MTA) for cross-channel digital media measurement and optimization. When our customers see MMM results for the first time, the performance marketing teams are often surprised to see a significant increase in the effectiveness of paid social advertising, compared to their previous measurement methods.
Before adopting Marketing Mix Modeling (MMM), many of our customers have been using Google Analytics 4 (GA4) or multi-touch attribution (MTA) for cross-channel digital media measurement and optimization. When our customers see MMM results for the first time, the performance marketing teams are often surprised to see a significant increase in the effectiveness of paid social advertising, compared to their previous measurement methods.
Digital marketing professionals spend a significant amount of time every week optimizing bid levels for the tens or hundreds of campaigns they are running. One major challenge for them is optimizing Target ROAS (Return on Ad Spend) for the campaigns. The problem is that ad platform-reported ROAS does not account for the true incrementality of the campaigns, and thus the ROAS metrics are not comparable across campaigns and ad platforms.
We understand that businesses are always looking for ways to improve their marketing strategies and measure their results. Marketing mix modeling is an incredibly useful tool that can help marketers achieve their goals, however, it's important to understand how reliable and accurate the results that MMM provides can be. One common question that pops up is the level of accuracy of the results obtained from such models. The accuracy of a model depends on several factors such as: the quality of data used to build it, as well as the assumptions and parameters utilized. In this blog post, we will dive into the accuracy of marketing mix modeling results, examining the different factors that can influence the model's accuracy and the level of confidence we can have in its predictions. Whether you are considering investing in marketing mix modeling for your business or simply seeking to understand the accuracy of its results, we have you covered in this blog post!
As the marketing budgeting season begins, marketers and media agencies face the critical task of allocating resources in a way that maximizes returns and ensures the company can reach its growth objectives. This process is more complex than simply repeating last year’s budget with a slight increase; it requires an approach that considers factors such as the true performance of each media, saturation levels for each media channel, and the sales targets of the company.