Less data from PPC campaigns does not always have to mean less action! The biggest digital advertising platforms such as Google, Facebook, Amazon, and Apple are taking huge steps to accommodate the present and upcoming consumer data regulation. As a PPC marketer, you might think that having access to less data might make it harder to manage client campaigns. However, the available data can be used in a valuable manner and can even make your decision-making simple and easier. It’s the same with conversion tracking; which has been used for quite some time now and it’s either been a hit or a miss.
Just because some data and control levers have been taken away, it does not mean that we are at a dead-end. It simply means that we have to learn to adapt to the changes brought about by the regulation. There are still plenty of actionable insights that can be gleaned from the available data at hand. Here are three ways you can succeed with PPC ads despite having lost data.
Use Automated Marketing Mix Models to Improve Data Measurements
PPC campaigns come under the large umbrella of marketing. For many years now, PPC’s have been focusing on conversions and have been using last-click attribution models. However, automated marketing mix models (MMMs) is the most happening statistical analysis methods in the world of advertising. It uses machine learning, automated data ingestion, and cloud computing and storage to evaluate the campaign performance. This is done by measuring the impact of marketing strategies parallel to customer retention efforts and sales.
Earlier, the MMMs were surprisingly costly and incredibly complicated such that it would take weeks, months or at times years to put together. But now, it can be open-sourced. Anyone who knows Python can make one in a couple of days.
Marketers can now experiment and calibrate with the automated marketing mix models to gather maximum customer insights.
Use More Accurate Client Data to Form Better Models
Having to work with less data isn’t the only issue faced by white label PPC services today; many marketers strategize based on data that is not clean and accurate. Most of the agencies do not know their clients. They are not aware of how much money they make, the cost of capital, the cost of revenue, the target rate of their returns and the time horizon for the same. Agencies are not able to deliver realistic forecasts or models for their clients. By not giving importance to the accuracy of data, marketers are going to be lost when the amount of data they have is negligible.
You can solve this issue by combining both client business metrics and campaign metrics into one spreadsheet. By having both data side by side, you can compare and avoid mistakes and save yourself from developing unrealistic models.
Use Lower Intent Goals to Obtain Actionable Data
The digital advertising landscape is facing data scarcity at an increasing rate. At the same time, the machines that run PPC ads continue to demand more data. Even though they require lesser data compared to the past years, they still require a lot to make campaigns run effectively.
To make it worse, the major digital advertising platforms are going to deprecate third-party cookies and other identifiers. This means the data will have more gaps, making it more difficult for white label PPC services to obtain seamless, actionable customer data.
Address this issue by forming lower intent goals to forecast consumer behavior despite the lack of insights from higher-value actions. Instead of aiming for conversion, push the goal up slightly and go for a white paper download. Likewise, you will be able to measure incrementality more accurately by capturing first-party data early in the journey and also feeding the data into the machines. This will pave your path to success and avoid unforeseen events from derailing campaigns. This happens way too often when marketers only focus on bottom-of-funnel goals.
Lower intent goals also serve as useful forecasting barometers to monitor PPC campaigns and check any derailments. By tracking early, you can prevent all the plausible mess that can potentially ruin the integrity of the data.