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The Bat-Signal always let Batman know when and where Gotham needed him most. He had to ignore the city noise vs. signal to stop the worst bad guys.
Wouldn’t it be great to have this type of system for your PPC accounts? If you understand to differentiate noise vs. signal in PPC data you can be on road to success.
The ability to know exactly where your attention is needed most is every marketer’s fantasy. Unfortunately, there are several reasons why this is a dream for most PPC managers, instead of a reality.
Managing a Google Ads account involves many moving parts that are constantly changing. Keeping track of these fluctuations is a must to improve your campaigns.
It’s also a source of much stress.
You can’t possibly tend to every single change in your account, nor should you because they are not all equal in value.
There are some signals related to your campaigns, metrics, keywords, etc., that are vital to your success. They align directly with your goals. These are the signals you need to focus on.
The rest are noise that will distract you from the most valuable, relevant and actionable changes.
This discussion will look at how to tell the difference between noise and actionable signals in your account. Understanding this distinction of noise versus signal will help you optimize your campaigns and be a more productive PPC manager.
What qualifies as a signal and what is merely noise? All data sets are noisy to some degree and valuable to another.
Marketing data is particularly noisy because there is so much of it, with more collecting every minute of the day.
Right now, in fact, your PPC campaigns are generating data, but not all of it is essential. After all, some metrics are more relevant to your goals than others.
Any data or elements that do not convey helpful knowledge is noise. That’s the simplest definition of the term.
Noise can be data that doesn’t pertain to your needs. For instance, if you are trying to understand why your click costs are rising, there are specific metrics that you need to investigate.
Noise can also be information that you already know. This is not valuable knowledge because it’s already known or been tested.
It can also be non-data elements. If you’re looking at visual chart data, unnecessary attributes, confusing colors, poor titles and other aspects can distract you from the information that actually matters.
To find actionable insights and detect useful data signals, noise needs to be removed from the equation. Otherwise, it distracts and clutters your analysis environment.
For data to be valuable enough to be a signal, it needs to do the following:
It’s worth noting that noise and signals are not always in this state. Noise can become useful knowledge when your question or goal changes and vice versa. It all depends on your current needs.
Detecting signals in raw data and transforming that data into actionable insight is what many marketers strive for in their campaigns. This is how you learn to make the right decisions to optimize your strategies.
This detection process has multiple stages.
These are the raw facts, metrics and figures. On their own, data isn’t very valuable because there is no context.
You need to incorporate multiple pieces of data into a single set for it to be helpful. This is why data is typically collected and organized into databases, spreadsheets and other tools.
For instance, your Google Ads dashboard displays the performance of each metric across your account and various campaigns.
Once your data is properly collected, cleaned and organized in a format that is accessible and provides more context, you begin to acquire information.
Information is clues and added context that leads you to insight. It’s the small breadcrumbs that you see in charts and PPC reports.
The more angles and dimensions you use when exploring the data, the more informational breadcrumbs you pick up along the way.
With the information acquired, you can begin analyzing it to reach a signal. These final conclusions will inform you about what is happening, whether positive or negative, and why.
Signals can directly be applied to action. With the information collected and analyzed, you can use it to make impactful performance changes to your campaigns.
These decisions or changes are not based on gut feelings or hunches. Instead, they are data-driven and backed by plenty of evidence. Thus, they are far more accurate and valuable.
In the Digital Age, data is produced at an alarming rate. The size and speed of today’s data means you’re going to collect a great deal of data, but use only a tiny fraction of it.
Most businesses don’t discriminate about what data they gather. Data that is seemingly useless today may have a purpose down the road. So, companies gobble up as much of it as they can.
After all, data collection is a cheap process. It could even cost you more to be restrictive about the types of data you gather.
This is why noise is such a problem. You have mountains of data to dig through. You’ll have far more noise than signals in this pile. As the pile grows larger, the insights are buried further by the immense noise.
It becomes a needle in a haystack, with more hay piling on each day.
You don’t want to waste time analyzing data that is not useful or doesn’t provide knowledge. Identifying what data is helpful to investigate can be tricky without signal detection.
For this reason, data analysis and signal detection share a close relationship. You need signal detection for data analysis to be effective and efficient.
Let’s recap two critical points covered in the previous sections.
When bad data (data that is irrelevant, low quality, not valuable, etc.) is used to derive signals, these end conclusions aren’t as impactful. The actions you take as a result of the insight won’t produce great results.
There are a few attributes that actionable signals share. Use these characteristics to determine good from bad signals in your campaigns.
As mentioned, signals help you answer questions and provide a deeper understanding of your strategies. Datasets that do not help in this endeavor are noise. The information gained will be useless in solving your current problem.
You need to select metrics that are relevant to your situation.
Whenever you’re asking a question or solving a problem, start by acquiring the data you think is more relevant and valuable. As you move through the signal detection stages, you can always expand your datasets.
Raw data by itself isn’t very valuable. There isn’t enough baseline information to understand if the numbers are above, below or at average.
This baseline information is known as context. Adding other elements to the data set helps you establish context. These elements could be time, dimensions (geography, device type, etc.) and even other metrics.
For instance, if you see a campaign that has 100 clicks, you can’t tell if that’s high or low without context. If you compare these 100 clicks to previous months and see that you typically only have 50-75 clicks, you know performance is up.
Having the right amount of context is crucial for detecting signals and making sense of your data.
Broad insights are less actionable than specific ones. These complete insights always lead to powerful signals that help grow your campaigns.
Specific insights hold more weight and make it much easier to take direct action. A signal needs to be specific enough that you know what has occurred and why it happened. Ideally, you’ll also be able to tell what to do to resolve the issue or capitalize on the opportunity.
The specificity of signals can make excellent fodder for managers, stakeholders and clients who may want to know what steps can be taken to improve ad performance. The more specific the signal is, the clearer and more detailed the information is going to be.
The more specific the insight, the easier it is to understand and use. It is easy to communicate the findings and justify the action you want to take as a result. You’ll be able to clear up any skepticism and get your team on board!
The alignment test is the final hurdle of quality PPC signals. This is similar to relevance. You need to check that the signal aligns with your marketing goals.
Ideally, the metrics involved will be your key performance indicators. These are the metrics you use to measure the overall success of your strategies. They inherently match what you are hoping to achieve with your ads.
Whenever you can detect signals that align with these goals and your KPIs, they must take priority over the rest.
In earlier sections, we looked at the process that data must undergo before being considered a signal. Now, we’ll look at the steps you need to take to detect signals in your PPC data.
Following these steps will help you understand the analysis process and how to go from raw, unusable metrics to actionable insights that help you consistently improve your campaigns.
Data comes in many different formats and from several sources. It can be structured or unstructured, qualitative or quantitative, complex or simple.
Your first step is to compile all of this raw data. You may want to look at the following types:
Anything that you think may be useful in your analysis should be collected.
All of the data you collect isn’t useful yet. It isn’t ready to be processed or analyzed for further discoveries.
The reformatting process involves decompressing, filtering and normalizing your data. This allows your various data sets to communicate with one another without issue. You can incorporate them into a single database or spreadsheet, without errors or other problems arising.
In other words, reformatting your data allows each set to “talk” to one another.
After the preparation phase, there will still be some discrepancies and other imperfections in the data.
Unclean data occurs for several reasons, such as:
Cleaning your data is tedious because you’ll have to search and fix these inconsistencies manually. It can take a lot of time to completely clean all of your data before analysis.
Once your data is carefully organized, cleaned and reviewed, it is time to analyze it to find more information. This is the second stage of the signal detection process, where you’re looking to add details and context to your data.
At this stage, using data visualizations, PPC charts, statistical methods and other tactics are crucial. They will help you uncover patterns, correlations and valuable intel about your raw metrics.
Take your time at this stage. The more information you can discover, the better your results.
By integrating your data sources and systems, you are now ready to draw complete, actionable insights.
However, even with cleaned, processed and charted data, the insights may still be obscured. Data is naturally complex, even when processed in this way.
Marketing data is also fast moving. The longer it takes you to detect signals and thoroughly analyze your metrics, the less value your insights will hold.
You’ll be making decisions based on data that may no longer be valid because performance has shifted in new ways since the start of your analysis.
To overcome this, many marketers turn to AI and automation. Advanced machine learning systems can vastly improve your analysis workflow and uncover more profound insights in less time than the manual approach.
These AI systems are designed explicitly to manage and make sense of your marketing data. They work faster and more efficiently than even a team of human marketers.
Again, the more complete your signals are, the more actionable and valuable they are towards improving your strategies and reaching your business goals.
Ultimately, the purpose of signal detection is to make better choices regarding your marketing efforts. Data-driven decisions are backed by evidence and facts, instead of unsubstantiated opinions, feelings or guesswork.
These are the decisions that directly impact your performance, help grow your campaigns and allow you to reach your business goals.
If you can continuously make data-driven decisions, you accelerate your performance and growth. The challenge is repeating this process again and again.
Given the many stages and steps of signal detection, along with the speed and complexity of PPC data, it’s a painstaking process to handle alone. It’s the perfect type of marketing task for artificial intelligence.
AI helps you make sense of the mountains of data that you’ve been collecting and hoarding.
PPC Signal, an AI-powered PPC tool, automates the signal detection process so you can spend more time making data-driven decisions and less time fussing with raw data.
It works faster and more accurately at finding signals in your marketing data. The AI-generated signals that it provides allow you to make a steady stream of data-driven decisions to improve your campaign performance.
When you’re juggling several PPC campaigns at once, it can be challenging to know which one needs the most immediate attention. Which PPC tasks are most critical at the moment? What data takes priority over the rest? Where do you begin your analysis?
All of these questions plague PPC marketers and slow down the analysis process. By never knowing where to start or what to analyze first, managers live in a constant state of uncertainty.
That’s no longer the case, thanks to PPC Signal. With automatic signal detection, you can drop the uncertainty and stress and have a streamlined workflow. You’ll never have to wonder about noise vs. signal because the tool does it for you!
That said, PPC Signal isn’t just about making your life as a manager easier. It also makes analysis possible again. PPC accounts produce tons of data.
If you’re optimizing several campaigns at once, you’re essentially trying to navigate an ocean of numbers with no compass, maps or other tools to help you reach your destination.
PPC Signal is all of these things in one. The sophisticated AI algorithms analyze the data for you and transform your raw data into positive and negative signals that you can use to make wiser decisions about your strategies.
Any change in performance across your accounts is tracked, measured, analyzed and presented as a signal. Each signal is packed with all of the data and information you need to understand it and make the appropriate change.
Since seeing is believing, let’s take a look at an example of PPC Signal in action.
John is a PPC manager for a fitness smart watch retailer. Naturally, he wants to stay on top of every change impacting his campaigns. He’s heard about the advantage of AI when analyzing complex data.
So, he decides to use PPC Signal.
When John opens the PPC Signal tool, he’s shown all of the active signals currently affecting his accounts.
At the top, John can see the total number of active signals, alongside the number that he’s bookmarked or actioned.
Along the left side are some filtering options that he can use. John can filter his active signals by campaign, device type, metric, account and many more options.
This is an excellent feature because it allows you to focus on the parts of your accounts that matter most to you.
John’s current objective is to improve his Google Ads Quality Scores. He knows that higher Quality Scores can lead to better ad ranks and lower costs.
Since clickthrough rate (CTR) is a factor in determining Quality Score, John filters his active signals to look for ones involving this metric.
Now, John has 6 active signals that involve CTR. It’s an excellent technique for filtering out noise vs signal insights.
John can explore each signal to see more details and complete the insight.
This brings John to a new screen that expands the view of the signal. John sees the entire trend line for the data, along with some other options.
John can add other metrics to the chart to see how the signal is affecting other areas of his strategy. This helps him establish that crucial context that he needs to understand why the change occurred.
Here are some of the other notable features on this page:
As John takes action on each CTR-related signal, he directly improves his Quality Scores and increases overall account performance.
That’s how easy PPC Signal makes it to grow your Google Ads account!
The unwanted data is a noise which doesn’t help in finding any relationship or any context in the data.
There are lot of data available which Google Ads can help you to get for your further analysis. You can get data about performance of your campaigns, ad groups, keywords, quality score, devices and much more.
The noise vs. signal confusion is understandable. You’re trying to find a handful of signals in a sea of data. It’s easy to be distracted by the abundance of noise and miss the data that actually matters.
When you can ignore the noise and focus on the signal, your data becomes one of your greatest assets. It helps you anticipate the behaviors of the market and your customers, which creates the opportunity to always stay steps ahead of the competition.
If that sounds appealing, and it should, then you need PPC Signal on your side.
PPC Signal solves the common headaches associated with signal detection. You no longer have to dig through mountains of data and perform tedious manual analysis to find complete signals.
With AI-driven signals, you always have insights ready to help you make intelligent marketing decisions.
These decisions will swiftly build an unbeatable foundation for the growth of your business and its campaigns.
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