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How to find anomalies in data is like asking how to find a needle in a haystack.
By definition, anomalies are unexpected occurrences that deviate from the norm. This makes them tricky to spot. After all, how do you detect something that you don’t even expect?
Yet, detecting these odd data occurrences is crucial to your PPC success. While anomalies often have a negative connotation, positive ones lead to highly rewarding opportunities.
It’s all a matter of how adept you are at finding and understanding examples of anomaly detection.
This discussion will help you understand how to apply anomaly detection to your PPC campaigns and transform them into actionable insights to improve performance through a tool PPC Signal.
Let’s get started.
PPC data changes faster than the weather in a rainforest. Marketers are tasked with the supreme challenge of tracking these performance changes and proactively adapting to the current environment.
Many shifts and trends are easy to detect, track and act upon, but anomalies are not. These unexpected occurrences can lead to significant opportunities or dangerous risks.
Thus, it is crucial that you know how to find anomalies in data. Otherwise, you could be missing out on critical insights.
Here are several reasons why anomaly detection is essential to successful PPC marketing.
Issues always start small, but they can quickly grow into a substantial crisis if they aren’t dealt with promptly.
For instance, let’s say one of your landing pages goes down. Anyone that clicks an ad linking to this page will not be able to access your site. They’ll press the back button and return to the SERPs, while you’ll be left paying for a click that never even reached your website.
If you catch this problem early, you may only miss one or two clicks – no big deal, right? What happens when you don’t notice the issue for weeks and you’ve paid for hundreds of clicks that never reached your site?
Now you’re in crisis mode. The sooner you mitigate minor problems, the less damage they cause.
These types of errors are always unexpected and abnormal by nature. If you have proper anomaly detection techniques, you can prevent molehill problems from becoming mountains.
You have goals that guide your PPC campaigns and allow you to judge whether or not they are successful.
For instance, if your goal is to drive conversions, every campaign component needs to create the best converting ads possible.
Anomalies represent significant shifts away from what’s expected. Thus, you can use it as a means to locate your highest and lowest performers in each campaign.
Detecting your worst-performing ads is your top priority. These are areas of your Google Ads account that are wasting your budget and hurting your results.
By pausing or removing poor-performing ads and keywords, it preserves more of your budget to allocate to the strategies that are providing high results.
Plus, these poor performers can bring down your Quality Scores and hurt the overall health of your Google Ads account.
Once you’ve filtered out poor-performing ads and keywords, you can begin focusing on the strategies with the highest performance.
This is the core principle behind PPC campaign optimization: focus on what’s working, deviate from what’s not.
Again, you need to consider the goals of your PPC campaign to select which strategies are the best performers.
Your best performers should receive most of your time and resources because they produce the majority of your results. This is known as the Pareto Principle, or 80-20 rule. It estimates that roughly 80% of your results come from around 20% of your efforts.
Find the ~20% of keywords, audiences, ad groups, etc., that produce the majority of your results!
Cost should play a substantial role in how you decide which strategies are low and high-performing. You don’t want to overpay for performance.
Even though a particular keyword may help you towards your goals, it may not be worth the investment if the costs are too high. It will rapidly deplete your budget and make it difficult to bid on other keywords.
You have to measure the performance of each ad component against its costs. This method will enable you to secure the best PPC results and at the appropriate costs.
Essentially, you need to think about your ROI. How much are you going to spend and what will be the returns?
This is why it is vital that you stop underperforming strategies and only focus on the ones that produce results!
While knowing how to find anomalies in data is important, it’s not easy. Many challenges prevent marketers from accurately detecting anomalies in their PPC campaigns.
This section will outline these challenges to set the foundation for learning how to overcome them.
PPC campaign data is already challenging to work with, even when you’re not looking for tricky anomalies.
Several challenges make this data a significant obstacle.
All of these challenges are present in anomaly detection. In some cases, they are even amplified.
As mentioned above, PPC campaign data is always complex. Anomalies are even more so.
Trends, shifts and other changes in your data happen over time, making them easier to detect. Acting on an emerging trend or data shift is also simpler. There is usually a clear-cut response.
Anomalies, by nature, are harder to detect and even harder to act on because of their unexpected nature. It’s nearly impossible to predict a sudden spike or dip in performance.
Other PPC anomalies exist because the data is behaving counter to what makes sense. This occurs when studying the correlations between different metrics.
For example, if your clicks are increasing, you expect your conversions to also improve, right? More clicks mean more site visitors and more site visitors mean more opportunities for conversions.
What happens when conversions are decreasing steadily, even though clicks are increasing? That sort of anomaly can be harder to detect and even harder to understand.
Part of the challenge of detecting and analyzing anomalies has to do with their randomness. Some abnormalities occur out of random chance.
For instance, if you suddenly see a spike in conversions one day, there may not be a reason behind the sudden swing. More people just happened to make a purchase that day.
Other times, real factors are contributing to the anomaly.
It’s a real struggle to know when an anomaly is random and when there’s a reason behind the sudden change.
This is especially true when data relationships are behaving abnormally because these types of occurrences are complicated to explain and require significant analysis.
By the time you’re done getting to the bottom of an anomaly, it may already have passed!
Alternatively, if you assume that an anomaly is random when it isn’t, the outcomes can be highly damaging. You may accidentally ignore a substantial risk or opportunity in your data.
At the account or campaign level, anomalies are much harder to spot. When you have several ad groups and hundreds of keywords under a single campaign, anomalies will blend in with the rest of the data.
Anomaly detection has to be performed at the lower levels of your campaigns. The more granular you are with your analysis, the easier it is to spot anomalies.
For example, let’s say that one of your keywords is suddenly seeing 300% more clicks than usual. That’s a significant anomaly that is worth exploring further.
However, once you combine this data with all of the clicks across the campaign (which could include hundreds of other keywords), the overall impact may be so slight that it goes unnoticed.
That 300% increase on one keyword is not significant enough to be detected at the campaign level. It gets lost in translation.
The best way to spot anomalies is to visualize the data. You’ll be able to detect any unexpected spikes in performance immediately.
The challenge is manually creating charts for each keyword. That’s a significant and time-consuming undertaking.
The above challenges make it exceedingly hard for marketers to consistently know how to find anomalies in data and what to do with them.
PPC Signal is a PPC management tool designed to make your campaign data more accessible than ever.
Using artificial intelligence and machine learning algorithms, PPC Signal automates the detection and analysis of PPC data changes, including anomalies.
This section will explore the PPC Signal system. It is the best PPC optimization tool for detecting anomaly examples in your campaigns.
Without getting into the specifics behind PPC Signal’s AI technology and algorithms, the system is straightforward to use. After all, the system automatically does most of the work for you!
Each time you access PPC Signal, you’ll see the main dashboard interface.
This page includes all of your current, active signals. Every signal represents a noteworthy performance change that the system detected, analyzed and packaged for your viewing.
In other words, every signal on this dashboard is a complete insight that is ready for action.
PPC Signal saves you significant time and alleviates headaches that are common with manual analysis. You no longer have to dig through data to find PPC insights; PPC Signal does it for you!
Along the left side of the dashboard, you’ll notice a set of icons. These are filtering options that allow you to pick and choose which active signals are displayed.
For instance, you can select a specific campaign to investigate first. Or, you can filter by a certain metric, like your key performance indicators. This tells the system to only show you the insights that closely align with your goals, you can check the results based on account or campaign level. There are other filters too which can help you to sort out the results based on your requirements.
Other filters include:
The signal type will allow you to look at risk or opportunity-based anomalies.
These filters make your data-related processes more efficient, including anomaly detection. You can get right to the changes that matter most to you and your objectives, while removing any distracting noise from irrelevant data.
With anomalies being some of the most demanding data events to detect, this easy system is a welcomed change.
As we covered, deciding how to resolve anomalies is a difficult task. Due to these occurrences’ strange, abnormal nature, it can be challenging to know what action to take to resolve anomalies.
You don’t want to waste too much time on these decisions, especially when you have an account full of other campaigns and data patterns that also require attention.
PPC Signal has a suggested action feature that you can use to resolve each signal quickly. This is an optional feature, but it can be the time-saving tool you need to remove alerts from your dashboard swiftly, especially when you’re unsure how to act.
To find this feature, click Explore on any individual signal.
The Explore option opens a more detailed view of the insight and includes additional features not seen on the main dashboard.
For instance, you can zoom into the chart or add additional metrics. This enables you to see how the anomaly may be affecting other parts of the campaign.
You can also choose to view the data as a table and export it via download or copy-paste.
You want to select the Take Action button just above the chart. With this tool, PPC Signal will suggest a follow-up action to resolve the anomaly.
This suggestion is based on the system’s own understanding of your campaigns and PPC optimization.
It’s important to note that PPC Signal is not just an anomaly detection solution. It is a complete PPC optimization tool.
It will detect any significant positive or negative change in your Google Ads account data. This permits you to stay in complete control of your campaigns.
No risks or opportunities will go unnoticed!
By automating the detection and analysis of insights, you can consistently improve your campaigns. You’ll respond faster to market changes than competitors and always maintain positive growth.
It’s an unbeatable advantage that you shouldn’t ignore. If you’re struggling to keep up with the demanding workload of PPC campaign management, PPC Signal will be a much-needed relief.
Now that we’ve covered how to find anomalies in data using PPC Signal, let’s look at some sample anomalies.
This is an anomaly in the relationship between conversions and clicks.
The expectation is that an increase in clicks will also generate a slight increase in conversions (more clicks equals more conversion opportunities). With this particular campaign/ad group, conversions dropped, even though clicks were increasing.
With this particular anomaly, the sparkline chart is your friend. You can see the significant dip in the middle, but now the data is beginning to return to normal. This could be a sign that this was a random occurrence.
This sample anomaly is similar to Example #1, but now we’re looking at the correlation between Impression share and impressions.
This relationship typically follows the same pattern. When you have an increase in impression share, it’s common for impressions to also increase slightly. If you look at the trend line of impression share, it is going upward. While impressions are going downward.
If this was your anomaly insight, you may want to check your keywords and analyze competitor strategies to see if there are any reasons why you’d be losing impressions.
Anomaly detection is the process of identifying abnormalities and unexpected patterns in data. This includes outliers, deviations from regular correlations and other events. The method of anomaly detection involves analyzing data and looking for points that stand out. Charting often plays a sizable role in anomaly detection examples because it makes it easier to see oddities in your data.
Anomalies can represent critical incidents in your data, such as technical glitches, new emerging trends, spikes (or dips) in performance and more. In other words, anomalies can be a detrimental risk or a prosperous opportunity waiting to be seized. If you aren’t actively detecting these unexpected occurrences, you’ll miss the chance to fix errors or resolve potential risks before they graduate to more significant crises. Similarly, you’ll fail to capitalize on fresh opportunities that could be rewarding to your business.
With manual anomaly detection, you’ll be able to find surface-level spikes in performance, whether negative or positive. If you want to seize deeper anomalies, you’ll need a tool like PPC Signal. With this automated system, you’ll highlight any unusual behaviors and bring them to the attention of your team. You’ll detect risks and opportunities swiftly and maximize the value of these events.
Anomaly detection is used to detect the early signs of risks and opportunities in your data, as well as catch other odd behaviors in your data. The sooner you find significant spikes or dips in performance, the faster you can act.
PPC Signal is one of the best answers for how to find anomalies in data.
While it’s possible to detect some anomalies without a data tool, the value of these findings won’t be very high.
The sophisticated AI system of PPC Signal will help you find and understand the more challenging anomalies in your data.
These are the risks, opportunities and other events that you definitely want to detect and capitalize on!
The design of PPC Signal makes it easy to filter your insights for anomalies and get right to exploring these events.
It’s a great example of automated anomaly detection for your PPC campaigns!
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