In every marketing campaign, there’s room for improvement. A marketing campaign is never perfect, but the more you can improve, the faster you’ll be able to reach your goals. You should consistently optimize your marketing efforts for maximum impact.
Unfortunately, too many marketing teams do nothing. This inaction normally falls into 2 main categories: doing nothing to improve data quality and not optimizing frequently. These are also the two variables are the heart of performance improvement.
The Performance Improvement Formula
In the long run, performance improvement comes down to a simple formula. The level of improvement you’re able to achieve is a simple function of your data quality that informs optimizations, and the frequency at which optimizations are shipped.
The quality of data informing optimizations combines with optimization frequency to determine your overall performance improvement. There are too many teams that have a ‘set it and forget it’ mindset when running campaigns. Trying to optimize quarterly or not at all is a great way to miss out on a lot of potential performance. Even if your data is low-quality, you can still make small changes to experiment with different ideas and find the ones that work best for your business.
The keys to success here are to:
- Spend time optimizing at all 3 levels and
- Focus on marginal gains
The 3 Levels of Optimization
You won’t be optimizing everything on a campaign all the time. There are actually three levels of optimizations you can make to a campaign and each level differs based on how frequently you’ll make each type of optimization.
Make Sure You Spend Time Working At Each Level
Each of the three levels of data analysis are important for consistent performance improvement. Every level is a different way of looking at your data and a way to find opportunities for optimization.
- Level 1 is where you’re working in platform every week to make improvements.
- Level 2 is where you adjust delivery parameters each month to unlock higher conversion rates
- Level 3 makes larger optimizations to your overall media mix and budget allocations.
Spending time operating at each level is crucial if you want to get the most out of your campaigns.
Marginal Gains → Exponential Improvements
The key to consistently improving performance over time is focusing on marginal improvements.
You’re probably not going to look at your campaign and optimize 25% of it all at once. It wouldn’t be realistic to reallocate 25% of your spend and then immediately get a 25% performance improvement.
The marginal gains approach is to optimize one part of your campaign at a time by just a small amount. Start with 1% here, and 2-3% improvement there.
If you do that consistently, that 1%-3% gain every 2-4 weeks compounds over time. You improve your results by 2%, and now you’re growing that expanded result by an additional 2% every time you optimize.
The Impact of Frequency
If you’re deciding between making an optimization to your campaign every 2 weeks or every month, here’s how performance improvement would differ:
|Optimization Frequency||Optimization Amount||1 Year Total Improvement|
|Every 2 Weeks||3%||103%|
In the long run, optimizing more frequently will make a greater impact even if you’re making smaller optimizations more often.
The way you measure ad performance will determine the quality of your analytics data for improvement. Without proper measurement, you’ll find yourself with very little data making low-confidence optimizations to campaigns. While perfect measurement isn’t possible, getting the best possible data is worthwhile.
Prioritizing proper measurement is not as common as you might think. Roughly two thirds of marketers don’t have an attribution or measurement tool in place to get quality data. Historically, this has been because measurement tech was so expensive. Lucky for us, there are now a number of more affordable options. Other barriers to the adoption of measurement tech are low-trust in attribution methods, low technical sophistication to fully use these tools, and reliance on free tools like Google Analytics.
Levels of Measurement
Here are 5 different levels of measurement that you should be familiar with in your pursuit of quality marketing analytics:
Free Reporting is the lowest-quality level of campaign data. Free reporting will come from sources like ad platforms of Google Analytics. These tools are normally designed to earn more of your ad budget, not tell the absolute truth. If the reporting is free, your ad budget is the product.
Adservers are the next level up of measurement technology and give more cross-channel information than free reporting. With an adserver, you should expect to make better optimizations than with free reporting.
Media Mix Models do more heavy lifting than adservers to give cross-channel insights. While these reports can be very general, there are plenty of startups working to deliver the statistical estimations of MMM’s faster and using less data. With MMM, you get more insight into how channels work together that unlocks new potential optimizations.
Multi-Touch Attribution is akin to media mix models in that MTA estimates how channels work together to produce results. Similarly, MTA will let you make better optimizations than free reporting or an ad server because you’re getting more granular performance data.
Incrementality testing will uncover a different set of insights that MMM/MTA miss out on: how channels work alone to produce results. While Incrementality can’t give terribly specific data, it allows you to see the value of ad channels in a way that free reporting, ad servers, or attribution tech simply can’t provide.
Bi-Modal Attribution is the newest way to get high-quality analytics. BMA lets you combine the unique insights from MMM/MTA and Incrementality to fully understand how ad spend turns into results. With BMA, you can make the best optimizations because it provides the most granular and highest-quality data.
Let’s assume that you are going to make an optimization to your campaign every month. Here’s what performance improvement would look like with 3 different levels of measurement:
|Measurement Level||Monthly Improvement||1 Year Total Improvement|
As you can see, over the course of a year, having higher quality data for optimization makes a big difference.
The Cost of Doing Nothing
Performance improvement is a function of how good your data is and how often you’re shipping optimizations.
If you were to truly do nothing over the course of a year, performance would remain at benchmark.
If you use the highest quality data available (BMA) and optimize every 2 weeks, you would more than double your results in a year with 203% of your initial performance.