Anomaly Detection Example: It is No Longer Difficult to Detect Anomalies in PPC Data

Expect the unexpected.

It’s a saying that’s meant to keep us on our toes because, as everyone experiences at some time or another, things rarely go according to plan.

Essentially, it teaches us to expect anomalies.

Of course, if we take this saying literally, it’s an oxymoron. You can’t expect something that’s itself unexpected.

The same is true of anomalies. By nature, these occurrences go against the grain. They are opposite to what we expect or plan for, which makes them impossible to anticipate.

At best, marketers hope to capture an anomaly detection example. In other words, they hope to identify an anomaly in their data, analyze it and still have time to act.

In this discussion, we’ll explore the importance of anomaly detection in PPC marketing. You’ll learn vital anomaly detection techniques that will help you in your own campaigns.

Let’s get started.

How To Understand An Anomaly Detection Example

Before diving into specific anomaly detection examples, let’s go over some of the basics of data and anomalies.

What Is Anomaly Detection?

In any dataset, there are patterns, trends and other events that are considered normal or expected.

For instance, you expect that your sales will increase the more your business grows. Alternatively, you also anticipate that the more clicks your PPC ads receive, the more conversions your site will generate.

Anomalies occur when the reality of events doesn’t match these expectations. Despite increasing your ad clicks, your conversions decline.

These abnormal events are exceedingly crucial to detect and analyze. Sometimes, they represent that one faulty gear in the machine that can slow or derail your entire performance.

Anomaly detection is the process you must take to find these unusual occurrences in your datasets. It’s finding the items that don’t belong or don’t align with usual behaviors.

By utilizing the proper anomaly detection techniques, you can uncover issues, opportunities and other events happening in your data.

Types Of Anomalies

Anomalies come in different shapes and sizes. Understanding these different types will help you navigate each anomaly detection example in later sections of this guide.

1- Point anomalies

This is the most common form of anomalous behavior in your data. Point anomalies are just outliers. They occur when a data point is far removed from the rest.

For instance, if you were looking at daily temperatures for the week, you may spot one day that is significantly hotter or colder than the rest. This is a point anomaly.

Business use case: Detecting sudden conversion on Sunday which normally never received from the past many months.

When you chart data, point anomalies are easy to spot. Any significant spike or dip in the data is a solid clue to detecting an anomaly.

2- Contextual anomalies

Here, there is a contextual background that is needed to detect the abnormality. Without this context, things may even appear normal. You only get the complete picture once you have this essential knowledge.

Let’s say your business runs a special promotion one day to drive more sales. This promotion has been successful in the past, so you expect sales to spike on this date. Unfortunately, it doesn’t work this time.

Business use case: You are offering 50% discount on your campaign before and during Christmas season, so getting lots of clicks on your ads is expected to be received but you didn’t get any unusual clicks.

You know this is an anomaly because you have the context that you held a promotional event on this day. However, the anomaly is lost on paper without this information. It looks like another typical sales day.

3- Collective anomalies

These anomalies are not readily apparent on the surface. You can’t detect them by looking at a single data set. Instead, you have to combine different metrics and dimensions to see the pattern that is outside the norm.

Collective anomalies are relatively standard in PPC marketing analytics because there are so many dimensions and targeting options at play. It’s easy to lose these insights in the aggregate.

For example, your overall clicks seem fine, but as you add other dimensions to the data (device types, locations, etc.), you may see that performance is not as regular as first thought.

4- Inverse correlation anomalies

There are often known or established correlations in your data. For example, a correlation exists that says if data point A increases, then point B also increases.

Many of these correlations exist in PPC marketing. If your ad receives more impressions, your clicks will (most of the time) increase as well. In turn, more clicks will lead to more conversions and so on.

An inverse correlation anomaly occurs when data performs converse to the expected relationship. Your impressions increase, but clicks do not. Your clicks grow, but your conversions decrease.

Anomaly Detection Techniques

We’ve established that anomaly detection is a process to find unusual points in your data. Now, we need to discuss how to go about this process. What are the best anomaly detection techniques?

1- Manual Approach

For years, anomaly detection could only be done by hand, even in businesses. You’d look at your spreadsheets, sales figures, etc., and look for numbers or items that didn’t fit.

Consider the following anomaly detection example:

You can immediately spot the outlier in this group.

At best, manual anomaly detectors can detect outliers. More sophisticated types of anomalies are too tricky to spot with manual analysis alone.

With this anomaly detection example, you can still manually detect the outlier, but it takes much more time than the above example.

For this reason, the manual approach is the worst anomaly detection technique. Modern technology, sophisticated data systems, artificial intelligence and other advances have made manual anomaly detection obsolete.

2- Visualization Approach

The only way that a manual approach to anomaly detection is even slightly feasible in the Digital Age is through data visualizations.

The human brain isn’t designed to number-crunch and analyze massive volumes of data on paper. It is, however, trained to absorb, analyze and understand visual information.

When you chart data, anomalies become much more apparent. Consider the following anomaly detection example of an inverse correlation:

You can immediately tell that something is wrong. Impressions and clicks were steadily rising together, as expected, but suddenly the clicks line diverged.

The visualization approach allows you to detect slightly more advanced anomalies than the manual method. The downside is you may have to compare several charts to detect collective anomalies.

3- Computerized Anomaly Detection

Over the years, businesses grew more and more data that needed to be analyzed and checked for anomalies. Manual anomaly detection just didn’t hack it anymore.

Now, the best anomaly detection techniques are computerized. These are the only tools capable of handling modern big data. These tools use simple to advanced statistical methods to scan your data for outliers.

More sophisticated algorithms are capable of deeper anomaly detection. With advances in machine learning and artificial intelligence, computerized anomaly detection can identify hidden outliers and other unusual behaviors in your marketing data.

Anomaly Detection In PPC

With your PPC campaigns producing mountains of data at all times, it’s imperative that you have proper anomaly detection techniques in place.

After all, when there is more data in play, the number of anomalies increases. This means the need for proper anomaly detection techniques also increases.

The Importance Of Analyzing PPC Performance

PPC performance changes at the drop of a hat. Because PPC metrics share such close correlations, every adjustment you make to your Google Ads account will create a ripple effect that may cause unintended results.

Not to mention, audiences changing their search behaviors, competitors altering their strategies or Google updating the platforms will collectively affect your PPC performance.

Staying on top of these constant changes is crucial to success. If you can’t adapt your campaigns to align with the latest customer and competitive environments, you’ll struggle to reach your ad goals.

The key to optimizing your PPC campaign performance is identifying risks and opportunities. Risks are issues in your campaigns that threaten to decrease performance if you don’t resolve them swiftly and properly.

Opportunities are positive changes to your metrics that you want to capitalize on to maximize their benefits.

Essentially, analyzing paid search performance shows you what’s working, what isn’t and where improvements are needed. Without this crucial analysis, your strategies are flying blind.

Challenges Of Anomaly Detection And Analysis In PPC

While analyzing PPC performance is essential, it’s also incredibly challenging. PPC data produces several cumbersome obstacles.

First, you have to overcome the sheer size of the data. If you operate a large Google Ads account, you’re analyzing data across hundreds of campaigns, thousands of ad groups and tens of thousands of keywords!

That’s a lot of stuff to manage.

The second challenge is the speed of the data. Every ad interaction produces valuable information that helps you gauge your PPC performance.

You need to track these interactions to test the current PPC environment and know how to adjust your strategies best.

Then, there’s the complex nature of PPC data. You have to track several metrics and dimensions at the same time. Plus, you need to take into account the many correlations and relationships that exist in your campaigns.

All of these challenges make anomaly detection difficult. Essentially, you’re trying to detect unexpected patterns in highly complex data that are changing all the time.

It’s not easy.

Viewing Data From Different Angles

Most PPC anomalies are the collective type. To recap, this anomaly detection example is found by analyzing metrics from multiple dimensions.

It’s an excellent anomaly detection technique because each dimension presents a new angle to view your data.

With each fresh angle, you gain more information – more pieces to the puzzle of solving your campaign goals.

Consider the following chart.

This simple PPC visualization shows that conversions are steadily increasing. That’s a pretty good sign, right?

What happens if you add click data alongside conversions?

Now, you see that clicks are increasing at a much more rapid rate compared to conversions. In theory, these two metrics should be growing at similar rates. This is an example of an inverse correlation anomaly.

Measuring the data from just one angle (dimensions) shows only a tiny glimpse of the bigger picture. By entering added metrics and/or dimensions into the equation, you see a more complete view.

Let’s look at another anomaly detection example.

This chart shows conversions by the time of the day. You see that conversions are consistent throughout the day, aside from late night and early morning, which is expected.

You see a slightly different picture if you take the same data but view it by the mobile device type.

It gets even more interesting when you compare desktop to mobile conversions by the hour of the day.

On the surface, your conversions seem consistent and ordinary throughout the day. However, when you look at the data from more angles, you see this interesting anomaly where mobile conversions are low and desktop conversions spike.

Making different combinations doesn’t end here there are more you can add to check

Let’s add ad group on the same analysis, you will see different results on different hour of day.  So every dimension or angle matters in knowing the unexpected behavior in PPC data.

PPC Signal: The Best Anomaly Detection Example In PPC

Is it possible to detect anomalies manually? Yes and no. While detecting some anomalies by hand is possible, the process is incredibly tedious and resource intensive. It is not efficient at all.

Plus, some anomalies require sophisticated analysis to find. Smart tools, like PPC Signal, are vital to detecting these more profound and complex anomalies.

Remember, it’s these deep actionable insights in advertising that offer the most potential value.

What Is PPC Signal?

PPC Signal uses AI technology to discover valuable, actionable insights in your PPC accounts automatically.

Every insight comes as a packaged signal that includes all the relevant information you need to understand what’s happening and how to act on the change.

Basically, PPC Signal tracks every notable change across your Google Ads account. It’s like having an automatic anomaly alert system!

Each signal is organized and presented on the main PPC Signal dashboard.

You can explore each insight further to gain more details.

There are several key features on the Explore screen.

Not only do you get an expanded view of the chart, but you can also add other metrics. This helps you see the data from new angles.

By adding clicks to the chart, you see that the story is more interesting than just conversions decreasing. At the same time, clicks are increasing, which is anomalous behavior.

You can also view the data as a table and export it to use in your spreadsheets and other programs from this menu.

There is also the Take Action button.

Understanding how to act and resolve each anomaly detection example is not always easy. With this feature, PPC Signal’s AI engine recommends a possible action that you can take to capitalize or resolve the signal.

Thus, you’re not only receiving a steady stream of complete, verified insights; you also get a next-step action for each one.

Don’t Be Afraid Of Automation Anxiety

Some PPC marketers are hesitant to implement automated solutions. It seems like a no-brainer, but it’s easy to have apprehensions.

Unfortunately, automation anxiety prevents you from obtaining efficient, proactive PPC campaign management.

This anxiety often originates from the fear of giving control of your PPC optimization over to AI algorithms. It feels like you’re placing the future and success of your ad campaigns in the hands of a machine.

In reality, AI isn’t about losing control; it’s about productive management. Automation alleviates the strain caused by tedious, time-consuming tasks that, while necessary to the process, carry low impact themselves.

Managing a PPC campaign is full of these types of tasks. For instance, monitoring your PPC campaigns for anomalies and other data changes, while a critical step, is a massive undertaking without the help of AI.

Rather than spending resources on this preliminary step in the process, imagine if you could spend all your time, energy and money on making decisions and optimizing your campaigns.

That’s what PPC Signal offers. Don’t let automation anxiety prevent you from becoming a more efficient advertiser.

Detect Anomalies That Align With Your Goals

One of the first challenges of PPC data analysis is selecting what metrics, dimensions and other details you want to investigate.

PPC campaigns produce mountains of data, but not all of it aligns with your goals. You need to separate the relevant details from the rest.

In any Google Ads account, there is a small group of keywords, ad groups, etc., that produces the majority of your PPC results. This is known as the 80-20 rule, or the Pareto Principle.

This standard dictates that roughly 20% of your efforts produce a high percentage of your results. These are the parts of your Google Ads account that require the most meticulous care. They need to be constantly optimized to improve your PPC performance.

To help you find the changes that matter most to your goals and KPIs, PPC Signal offers several filtering options. With these settings, you can hone in on the vital few insights that offer the most potential reward.

You can filter your active signals by:

• Account
• Campaign
• Ad group
• Keyword
• Device type
• Location
• Hour of the day
• Metrics
• Signal type
• Signal quality

When you have a huge Google Ads account, you may have hundreds of active signals. These filters help you remove the noise and focus on the most significant changes and anomalies.

Anomaly Detection Examples With PPC Signal

To showcase the anomaly detection power of PPC Signal, here are some sample alerts regarding anomalous changes in a mock PPC account.

Example #1:

In the first example, you see an inverse correlation anomaly in the conversions and clicks relationship.

The expectation is that clicks and conversions will increase at similar paces. The more clicks your campaigns generate, the more traffic to your landing page and the more potential conversion opportunities.

With this signal, the opposite happens. As clicks increase, conversions are actually decreasing.

When you click Explore, the expanded view of the data gives a clearer picture.

There are a few key takeaways from this view. First, you see that the number of conversions is not very high. It’s the difference between 1 and 3 conversions.

Since this is not a significant figure, this anomaly may be just a random draw. Some days you’ll have more conversions than others.

The other interesting detail is that this inverse correlation occurred before, between July 22 and July 23. It may be worth investigating why this type of pattern is recurring.

Example #2:

Here is another anomaly detection example of an inverse correlation. This time it’s the impressions and clicks relationship that is behaving abnormally.

As you can see from the chart visual, clicks and impressions were following similar paths, which is to be expected, but then they suddenly began to diverge dramatically.

When your impressions increase, it means your PPC ads are displaying more frequently. The expectation is that this spike in potential ad views will increase the number of clicks.

In other words, the clickthrough rate of your ads should, in theory, remain unchanged, no matter how many impressions you receive.

For example, if your CTR is 5% and you receive 100 impressions this week, you should expect around 5 clicks. If your impressions jump to 1,000, you expect your clicks to jump to 50.

If you only receive 10 clicks after this significant spike in impressions, something is happening that requires deeper investigation.

If this was your account, you would want to immediately understand the reason behind this anomaly detection example. It may be an issue with your ad copy or you’re losing clicks to competing ads.

FAQs:

Why do we use anomaly detection?

We use anomaly detection to spot unusual cases within a set of data. Data doesn’t always behave according to plan. So, your expectations for how that data will act may deviate from reality. Anomaly detection spots these deviations and enables you to act accordingly. The goal of anomaly detection is to identify abnormalities and help explain why this unusual behavior occurred.

Are all anomalies bad?

Absolutely not! Anomalies get a bad reputation because, by definition, they are counter to what you expect. That said, anomalies can exceed your expectations. You may anticipate that you’ll have $3,000 in sales, because that’s your typical average. Then, one month your sales triple to $9,000. Does that sound like a bad anomaly to you?

How can anomalies be detected in PPC data?

The best way to detect anomalies in PPC data is through AI-powered tools, like PPC Signal. With this system, you don’t have to waste valuable time scanning your data for anomalies. The algorithms behind this impressive tool do the work for you and present each anomaly as a complete insight, including a data visualization example.

Wrap Up

The random, unexpected nature of anomalies gives them a bad reputation.

However, if you have the proper anomaly detection techniques to capture and understand these events, they become powerful opportunities.

Every anomaly detection example sparks thought-provoking conversions and interesting questions.

If you have the tools to help you analyze these odd events, you can seize hidden opportunities that competitors don’t see.

PPC Signal is the best anomaly detection tool for PPC marketing. It automatically tracks any significant shifts in your data to efficiently detect outliers and other anomalies.

With the tool’s straightforward interface and advanced AI systems, it’s capable of capturing all types of anomalies.

You’re never facing unexpected challenges or obstacles in your PPC management, thanks to PPC Signal’s automated early warning alarm system!