One of the biggest line items for any company is marketing and so naturally an equally big question is “How do I know if my Marketing is working?” Unfortunately, the answer is not a simple one to answer because of how complex Marketing actually is.

Here’s what Marketing actually looks like:

The complexity comes from the fact that here are 4 layers of Marketing efficiency that you have to unpack. In today’s article, we’ll go deeper on how to measure the efficiency of all of these layers and the hierarchy between them.

A campaign is the building block of any Marketing function. It represents a singular entity in a channel with an objective. Example. A Facebook campaign that drives conversions in New York City. The question we need to answer is “Is this individual campaign performing?”

Surprisingly, it’s the one layer where I believe it’s okay to look at platform metrics. In platform metrics are full of biases / inaccuracies, BUT they do show trends and patterns you can use to your advantage. Depending on the platform, there are a host of metrics you can use to compare:

All of these platform metrics tell you how your campaign is doing but the beauty is that they are ALL beholden to the same attribution and tracking challenges you’d normally have because they’re in the same channel. Attribution and tracking issues on Meta are different than Google, but within Meta, it’s all the same. This makes campaign optimization less data science related and more of an optimization based on comparing campaigns.

Example

You have 2 Meta campaigns running. One in the US and one in another market. You can then compare everything from CPM, CTR, ROAS / CAC etc to understand which campaign is performing better. Now, “better” is a bit subjective but you can try to understand structural differences. Why is CTR lower? Is the ad different or the messaging ? Are keywords different? Is the intent different?

Channel efficiency looks at how an entire marketing channel performs when you aggregate all campaigns within it for a specific objective. It’s important that you narrow it down to an objective as each channel has its own goal but you can also run multiple goals within a channel.

This is where you move beyond individual campaign metrics to understand the overall effectiveness of the channel itself for that objective. Here, you’ll take the same approach to looking at campaign metrics but aggregate them up at a higher level. In addition, we’ll also need to introduce higher level metrics that are better at assessing performance.

I’d take a look at metrics like:

Now, attribution is still going to be a challenge here but if your attributed campaigns are performing worse than you’d like them, it’s a high chance that you’ll need to think about how to better improve this channel.

You can take this one step further with incrementality tests. This’ll help you understand the impact of that channel on that objective in isolation and it’s one of the best ways to identify over or under invested marketing spend.

At the objective level, you're measuring how well a specific marketing objective performs when you combine all channels working toward that goal. This is where you evaluate whether your awareness, consideration, or conversion goals are being met holistically. Now, we move away from platform metrics and into broader objective level goals.

Key metrics to track:

I’m a huge fan of Blended #s because you can’t hide from them. You can’t hide under attribution or any other excuse you might want to find. The blended #s are the blended #s.

Is your acquisition funnel efficient? Well it’s easy to calculate. How much did you spend on acquisition vs how many you brought in across all channels. Done.

To optimize Objective level efficiency you have to move into the world of data science. This is where tools like Multi-touch attribution (MTA) and Media Mixed Modeling (MMM) come in. Marketers have to now think about Marketing as a portfolio approach. What % of my budget do I invest in Meta vs Google vs Snapchat because that’s how you optimize across channels for an objective.

In addition, to measure channel effectiveness, compare channels against each other using incrementality testing rather than relying solely on last-click attribution. For example, you might discover that while Google Ads shows the lowest blended CAC across all your campaigns, when you run incrementality tests, Meta actually drives more net-new customers. Or you might find that your TikTok campaigns drive low-cost acquisitions but customers from that channel have 50% lower retention rates compared to customers from your email campaigns.

Incrementality tests work hand in hand with any attribution model + MMM. To understand Objective performance, you need all 3 in what I call the Measurement Hallows (hopefully you’re a Harry Potter fan…)

Combining these 3 will give you a clear sense of how you’re performing when it comes to your target objective.

The last level is Business-level efficiency measures which shows how your entire marketing function drives company performance and growth. This is the highest level of measurement focused on sustainable, profitable growth across everything you do.

Key metrics to track:

Of course, you can also use these metrics at the objective level but they really matter at the business level because these are the numbers you’ll likely need to steer the business with investors, executives, the public, or even just your own internal team.

This is one of the trickiest layers and so you again must use more data science than normal to ensure that your objectives are working together. Is your performance Marketing better with or without Brand Marketing? How much is Brand Marketing driving long term revenue? All of these questions require some complex modeling + conviction to run incrementality testing.

In addition, the definition of many metrics change at the business level. For example, CAC when you’re at an early stage startup might be just based on Performance marketing but then it’ll evolve to include Brand and then even further to include Marketing Salaries (if you’re reading this you’re likely years away from needing to do this) but it’s important to point out that business level Marketing efficiency looks different than at the campaign level. If you tried to apply the same rigor at the campaign level you’d never get marketing off the ground because it always looks inefficient.

The last thing I want to point out is that I’ve ordered Campaign → Business from bottom to top but the strategy should come from top to bottom. How much are we willing to invest in a tradeoff for growth vs profitability is a business question. Then you use data science, intuition, and experimentation to identify what objectives to invest more in. Then the channels to let you hit that objective. Then you look at campaigns.

Top down. Bottoms up. That’s how you measure Marketing efficiency.

If you’re wondering how your Marketing is doing remember there are 4 layers that you have to think through:

It’s never so simple as just answering one of the layers and each layer has a different set of metrics you should use to analyze performance. When in doubt, just run an incrementality test. It’s by far the best way to immediately answer “How is my marketing doing?”

And remember: Tops down and bottoms up.

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