Performance Improvement and The Cost of Doing Nothing

In every marketing campaign, there’s room for improvement. A marketing campaign is never perfect, but the more you can improve upon it, the better you’ll be able to reach your goals. You need to 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 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.

Data Quality x Optimization Frequency = Performance Improvement

Data Quality

Data quality is the first piece teams need to put in place. Let’s imagine two scenarios: one where you have no marketing analytics and one where you have an omniscient understanding of attribution.

With zero marketing data, you would have no idea what was working and what wasn’t in your campaigns. If forced to make optimizations, you’d be just about the same as my drunk uncle trying to hit a piñata blindfolded. Any changes you made to the campaign would have a 50/50 chance of making things better or worse.

With an omniscient view of attribution, you would be able to understand how every customer journey goes and how valuable each touchpoint on that journey is. When optimizing, you would know exactly what needed to be done and you would be perfectly confident making changes to the campaign.

All this is to say that data quality matters. With zero marketing data or poor-quality data, you can’t optimize. With perfect data, you would know exactly what optimizations to make to maximize response.

Frequency

Once data quality is solved for, the next piece to figure out is optimization frequency. If your data quality allows you to make an average improvement of 2% with every optimization, the frequency at which you optimize now becomes the driving force behind how much you can improve performance. Advertising optimizations don’t add together, they compound.

Remember: Performance Improvement = Data Quality x Optimization Frequency.

Now we’ll dive into how you can improve data quality and optimization frequency to improve performance as quickly as possible.

Measurement

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.

Tactical Examples

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:

As you can see, over the course of a year, having higher quality data for optimization makes a big difference.

Optimization Frequency

The quality of data informing optimizations combines with optimization frequency to determine your overall performance improvement. Even if you’re stuck with low-quality data, you can make significant improvements by making positive changes every 2-3 weeks.

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 key is to be consistent with your optimizations so that you don’t waste time waiting for months before trying something new

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.

Examples 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
Monthly	5%	80%
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 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.

Bimodal Attribution

Bi-Modal Attribution is the choice data source for media buyers because it gives the highest quality data and you get fresh campaign insights every 2 weeks to optimize performance as frequently as possible.

Wrapping Up

If you’re looking to get the most out of your campaigns, the first step is to get the right measurement technology in place to improve your data. Then, work to optimize frequently using the Marginal Gains Approach (don’t try to be a hero).

Don’t forget:

Performance improvement = Data Quality x Optimization Frequency