Advertising Campaign Metrics – We have so many marketing metrics at our disposal that we measure what is important and what is not. Google’s Avinash Kaushik shares three tips to cut through all the noise and understand the true business impact of a marketing campaign.
In digital, every view, click, and scroll generates a wealth of data that we can use to better define our marketing campaigns and inform future decisions. While this helps us understand our digital marketing efforts to a degree that wasn’t possible even a decade ago, it can be difficult to isolate the signal from the noise. Often, we measure what is important, what is not important, and what should not be important.
Advertising Campaign Metrics
This is a problem that almost every marketer can relate to. To overcome this, my colleagues and I in the advanced multichannel analytics team at Google have identified three steps that allow us to understand the noise and be smarter in measuring the bottom line impact of our advertising. I think that, by following these steps, other marketers can do the same.
Marketing Dashboard Examples Based On Real Companies
As a marketing analyst, I love data. But data is only a means to an end. The end goal is not to create a beautiful chart showing how many impressions different campaigns got. This is to have an impact on the bottom line of the company. To separate the wheat from the chaff, at Google, we classify tsunamis by the metrics we use using the impact matrix.
Axis indicates whether a metric can be useful. For example, impressions are available and useful in real time. Other elements of customer lifetime value are also available in real time, but it takes months for them to become useful. Organizing advertising metrics in this way helps classify who to focus on and when.
Axis above — on a logarithmic scale to sharpen its value — indicates whether a metric is tactical or strategic. Following the same examples, impressions are more tactical and add, for the most part, pennies of value to any decision made. Customer lifetime value, on the other hand, is super strategic. The insights gained from this metric can add hundreds of thousands of dollars of value to any decision made.
Once you’ve categorized your marketing metrics this way, your reports and dashboards will be much cleaner, your marketers will have 50% less data to go through, and your analysts will have 50% which is more time to spend on conducting analyzes (as opposed to just data puking).
Social Media Metrics That Matter Throughout The Customer Journey
Marketers often use the terms “metrics” and “key performance indicators” interchangeably, but they are very different things. As I explained before, a KPI is a special type of metric that helps you understand how you are doing against a specific business goal.
The reason it’s important to remember this distinction is that, if we don’t, we can set KPIs that don’t really tell us if a marketing campaign is helping us reach our business goals. Why? Sometimes we choose a metric because it’s easy to measure, other times we don’t spend enough time thinking about what exactly we’re trying to achieve.
So my team took all of the marketing metrics we track on Google and analyzed which ones made the most sense as KPIs for different types of campaigns. We then ranked them in order of effectiveness, with gold being the best of the bunch.
So if a Google marketer is running a brand campaign, and their goal is to create awareness, we’d rather they monitor how often an ad is heard and seen in full (AVOC) than how many impressions it got, because we know from internal testing that it is a more accurate predictor of brand awareness increase.
Campaign Reporting On The Advertising Cloud Dashboard
Of course, creating KPIs that actually measure whether you’re achieving your business goals is difficult. This simple five-step digital marketing and measurement model I created should make that process easier. The end result will be a one-page document that can act as the north star for your entire company’s marketing analytics efforts.
Last year, my team audited marketing metrics that were reported back to our CMO. The number we recovered – which is a conservative estimate – is 70. Marketing leaders need to make strategic decisions about where to invest and where to scale back. A data dump of every metric available won’t help them do that.
So, here at Google, we take all the data points from our impact matrix and divide them based on who needs to act on them. The metrics in the lower-left part of the impact matrix — click-through rates, unique page views — help make small decisions in real time. To the extent possible, we have automated all decision-making related to these criteria.
We then select some relevant business metrics from the top left and right side of the impact matrix for our managers and directors to focus on. These are high-value metrics that may take some time to become useful and require interpretation in a human context. The upper right section includes metrics that may have a significant strategic impact on our business. This is what we share with our VPs and CMOs. Each individual receives only the data they need for the decisions they can make to make the most impact.
Marketing Campaign Planing
It’s not how much data you have that matters, it’s what you do with it. I hope these three steps help you do something better with all the data you have at your disposal.
Cookies help us deliver our services. By using our services, you consent to our use of cookies. Learn More We have many marketing metrics at our disposal that we measure what matters and what doesn’t. Google’s Avinash Kaushik shares three tips to cut through all the noise and understand the true business impact of a marketing campaign.
In digital, every view, click, and scroll generates a wealth of data that we can use to better define our marketing campaigns and inform future decisions. While this helps us understand our digital marketing efforts to a degree that wasn’t possible even a decade ago, it can be difficult to isolate the signal from the noise. Often, we measure what is important, what is not important, and what should not be important.
This is a problem that almost every marketer can relate to. To overcome this, my colleagues and I in the advanced multichannel analytics team at Google have identified three steps that allow us to understand the noise and be smarter in measuring the bottom line impact of our advertising. I think that, by following these steps, other marketers can do the same.
The Art Of Choosing Right Metrics For Your Advertising Goal
As a marketing analyst, I love data. But data is only a means to an end. The end goal is not to create a beautiful chart showing how many impressions different campaigns got. This is to have an impact on the bottom line of the company. To separate the wheat from the chaff, at Google, we classify tsunamis by the metrics we use using the impact matrix.
Here, the x-axis shows whether a metric would be useful. For example, impressions are available and useful in real time. Other elements of customer lifetime value are also available in real time, but it takes months for them to become useful. Organizing advertising metrics in this way helps classify who to focus on and when.
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The y-axis above — on a logarithmic scale to sharpen its value — indicates whether a metric is tactical or strategic. Following the same examples, impressions are more tactical and add, for the most part, pennies of value to any decision made. Customer lifetime value, on the other hand, is super strategic. The insights gained from this metric can add hundreds of thousands of dollars of value to any decision made.
Once you can classify your marketing metrics this way, your reports and dashboards will be much cleaner, your marketers will have 50% less data to go through, and your analysts will have 50% which is more time to spend doing the analyzes (as opposed to just data puking).
Marketers often use the terms “metrics” and “key performance indicators” interchangeably, but they are very different things. As I explained before, KPI is a special type of metric that helps you understand how
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