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How to Enhance the Quality of Your Media Signals


The signals you need to get in your ad platforms are: when I spend, what's the impact of the price of the media, and what is the saturation, plateau, or fatigue of that media?

At LiftLab, we’ve developed two methods to address this: 1️⃣ Agile Mix Model We’ve pioneered this model. It involves examining all your historical data simultaneously. Some also call it the next-generation or the high-frequency marketing mix model. This approach is now becoming a best practice in the industry. This two-stage model focuses on understanding the impact of spending on prices.


-- The first signal is CPC changes. For instance, consider the case of search advertising: as you invest more in an auction, the CPC increases. We need to know to what degree it increases – you can't be directional anymore. -- The second is saturation. We also need to know how saturated these ad platforms become as you keep spending, as they will ultimately run out of their best prospects to show your ads to. They don't have an infinite list of people who will buy from you. Our Agile Mix Models are designed to tease those two signals from your data. 2️⃣ Match Market tests or Experimentation The challenge with the Agile Mix Model is that the historical data can sometimes be low signal data. Consider a recent example: a customer who allocated $300 daily on Snapchat. After a few months, they upped it to $375 daily, maintaining this for the next two months. That's a very flat line. There is no signal in that data for anybody's model to work with. I challenge anyone building a model to say they can get an answer from that. So, what's the move? You could shrug your shoulders and move on or choose to refine your data strategies. Our strategy? Enter experiments. These are often known as randomized controlled trials. In our case, they are geo-based tests or, more specifically, match market tests. It doesn't imply picking one market versus another. You have to choose a basket of markets in one and a basket of markets in another. We have algorithms to do that. The result? You make data. We control the environment, fluctuating the spend among different markets while accumulating a mountain of evidence to determine how spending impacts pricing and saturation across different media. What we learn from that experiment is routed back into our model. I recently did a video on this, which you can check out, or if you want to learn more, you can schedule a demo here, and we'll walk you through it!


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