{"id":17074,"date":"2023-12-13T15:50:32","date_gmt":"2023-12-13T10:50:32","guid":{"rendered":"https:\/\/ppcexpo.com\/blog\/?p=17074"},"modified":"2023-12-22T12:52:51","modified_gmt":"2023-12-22T07:52:51","slug":"anomaly-detection-example","status":"publish","type":"post","link":"https:\/\/ppcexpo.com\/blog\/anomaly-detection-example","title":{"rendered":"Anomaly Detection Example: It is No Longer Difficult to Detect Anomalies in PPC Data"},"content":{"rendered":"

Expect the unexpected.<\/p>\n

It\u2019s a saying that\u2019s meant to keep us on our toes because, as everyone experiences at some time or another, things rarely go according to plan.<\/p>\n

\"anomaly<\/div>\n

Essentially, it teaches us to expect anomalies.<\/p>\n

Of course, if we take this saying literally, it\u2019s an oxymoron. You can\u2019t expect something that\u2019s itself unexpected.<\/p>\n

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.<\/p>\n

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.<\/p>\n

In this discussion, we\u2019ll explore the importance of anomaly detection in PPC marketing<\/a>. You\u2019ll learn vital anomaly detection techniques that will help you in your own campaigns.<\/p>\n

Let\u2019s get started.<\/p>\n

How To Understand An Anomaly Detection Example<\/h2>\n

Before diving into specific anomaly detection examples, let\u2019s go over some of the basics of data and anomalies.<\/p>\n

What Is Anomaly Detection?<\/h2>\n

In any dataset, there are patterns, trends and other events that are considered normal or expected.<\/p>\n

For instance, you expect that your sales will increase<\/a> the more your business grows. Alternatively, you also anticipate that the more clicks your PPC ads receive, the more conversions your site will generate.<\/p>\n

Anomalies occur when the reality of events doesn\u2019t match these expectations. Despite increasing your ad clicks, your conversions decline<\/em>.<\/p>\n

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.<\/p>\n

\"ppc-signal-optimization-tool\"<\/a><\/div>\n

Anomaly detection is the process you must take to find these unusual occurrences in your datasets. It\u2019s finding the items that don\u2019t belong or don\u2019t align with usual behaviors.<\/p>\n

By utilizing the proper anomaly detection techniques, you can uncover issues, opportunities and other events happening in your data.<\/p>\n

Types Of Anomalies<\/h2>\n

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.<\/p>\n

1- Point anomalies<\/h3>\n

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.<\/p>\n

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.<\/p>\n

Business use case:<\/strong> Detecting sudden conversion on Sunday which normally never received from the past many months.<\/p>\n

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.<\/p>\n

2- Contextual anomalies<\/h3>\n

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.<\/p>\n

Let\u2019s 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\u2019t work this time.<\/p>\n

Business use case:<\/strong> 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\u2019t get any unusual clicks.<\/p>\n

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.<\/p>\n

3- Collective anomalies<\/h3>\n

These anomalies are not readily apparent on the surface. You can\u2019t 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.<\/p>\n

Collective anomalies are relatively standard in PPC marketing analytics<\/a> because there are so many dimensions and targeting options at play. It\u2019s easy to lose these insights in the aggregate.<\/p>\n

For example,<\/strong><\/em> 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.<\/p>\n

4- Inverse correlation anomalies<\/h3>\n

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.<\/p>\n

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<\/a> and so on.<\/p>\n

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<\/a>.<\/p>\n

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Anomaly Detection Techniques<\/h2>\n

We\u2019ve 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?<\/p>\n

1- Manual Approach<\/h3>\n

For years, anomaly detection could only be done by hand, even in businesses. You\u2019d look at your spreadsheets, sales figures, etc., and look for numbers or items that didn\u2019t fit.<\/p>\n

Consider the following anomaly detection example:<\/p>\n

\"anomaly<\/div>\n

You can immediately spot the outlier in this group.<\/p>\n

At best, manual anomaly detectors can detect outliers. More sophisticated types of anomalies are too tricky to spot with manual analysis alone.<\/p>\n

\"anomaly<\/div>\n

With this anomaly detection example, you can still manually detect the outlier, but it takes much more time than the above example.<\/p>\n

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For this reason, the manual approach is the worst anomaly detection technique. Modern technology, sophisticated data systems, artificial intelligence<\/a> and other advances have made manual anomaly detection obsolete.<\/p>\n

2- Visualization Approach<\/h3>\n

The only way that a manual approach to anomaly detection is even slightly <\/em>feasible in the Digital Age is through data visualizations.<\/p>\n

The human brain isn\u2019t designed to number-crunch and analyze massive volumes of data<\/a> on paper. It is, however, trained to absorb, analyze and understand visual <\/em>information.<\/p>\n

When you chart data, anomalies become much more apparent. Consider the following anomaly detection example of an inverse correlation:<\/p>\n

\"anomaly<\/div>\n

You can immediately tell that something is wrong. Impressions and clicks<\/a> were steadily rising together, as expected, but suddenly the clicks line diverged.<\/p>\n

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.<\/p>\n

3- Computerized Anomaly Detection<\/h3>\n

Over the years, businesses grew more and more data that needed to be analyzed and checked for anomalies. Manual anomaly detection just didn\u2019t hack it anymore.<\/p>\n

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

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.<\/p>\n

\"Tips\"<\/div>\n

Anomaly Detection In PPC<\/h2>\n

With your PPC campaigns producing mountains of data at all times, it\u2019s imperative that you have proper anomaly detection techniques in place.<\/p>\n

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.<\/p>\n

The Importance Of Analyzing PPC Performance<\/h3>\n

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

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

Staying on top of these constant changes is crucial to success. If you can\u2019t adapt your campaigns to align with the latest customer and competitive environments, you\u2019ll struggle to reach your ad goals.<\/p>\n

The key to optimizing your PPC campaign performance<\/a> is identifying risks and opportunities. Risks are issues in your campaigns that threaten to decrease performance if you don\u2019t resolve them swiftly and properly.<\/p>\n

Opportunities are positive changes to your metrics that you want to capitalize on to maximize their benefits.<\/p>\n

Essentially, analyzing paid search performance<\/a> shows you what\u2019s working, what isn\u2019t and where improvements are needed. Without this crucial analysis, your strategies are flying blind.<\/p>\n

Challenges Of Anomaly Detection And Analysis In PPC<\/h3>\n

While analyzing PPC performance is essential, it\u2019s also incredibly challenging. PPC data produces several cumbersome obstacles.<\/p>\n

First, you have to overcome the sheer size of the data. If you operate a large Google Ads account, you\u2019re analyzing data across hundreds of campaigns, thousands of ad groups<\/a> and tens of thousands <\/em>of keywords!<\/p>\n

That\u2019s a lot of stuff <\/em>to manage.<\/p>\n

The second challenge is the speed of the data. Every ad interaction produces valuable information that helps you gauge your PPC performance.<\/p>\n

You need to track these interactions to test the current PPC environment and know how to adjust your strategies best.<\/p>\n

Then, there\u2019s the complex nature of PPC data. You have to track several metrics and dimensions<\/a> at the same time. Plus, you need to take into account the many correlations and relationships that exist in your campaigns.<\/p>\n

All of these challenges make anomaly detection difficult. Essentially, you\u2019re trying to detect unexpected patterns in highly complex data that are changing all the time.<\/p>\n

It\u2019s not easy.<\/p>\n

\"Tips\"<\/div>\n

Viewing Data From Different Angles<\/h3>\n

Most PPC anomalies are the collective type. To recap, this anomaly detection example is found by analyzing metrics from multiple dimensions.<\/p>\n

It\u2019s an excellent anomaly detection technique because each dimension presents a new angle to view your data.<\/p>\n

With each fresh angle, you gain more information \u2013 more pieces to the puzzle of solving your campaign goals<\/a>.<\/p>\n

Consider the following chart.<\/p>\n

\"anomaly<\/div>\n

This simple PPC visualization shows that conversions are steadily increasing. That\u2019s a pretty good sign, right?<\/p>\n

What happens if you add click data alongside conversions?<\/p>\n

\"anomaly<\/div>\n

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.<\/p>\n

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.<\/p>\n

Let\u2019s look at another anomaly detection example.<\/p>\n

\"anomaly<\/div>\n

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.<\/p>\n

You see a slightly different picture if you take the same data but view it by the mobile device type.<\/p>\n

\"anomaly<\/div>\n

It gets even more interesting when you compare desktop to mobile conversions by the hour of the day.<\/p>\n

\"anomaly<\/div>\n

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.<\/p>\n

Making different combinations doesn\u2019t end here there are more you can add to check<\/p>\n

Let\u2019s add ad group on the same analysis, you will see different results on different hour of day. \u00c2\u00a0So every dimension or angle matters in knowing the unexpected behavior in PPC data.<\/p>\n

\"anomaly<\/div>\n

PPC Signal: The Best Anomaly Detection Example In PPC<\/h2>\n

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.<\/p>\n

Plus, some anomalies require sophisticated analysis to find. Smart tools, like PPC Signal, are vital to detecting these more profound and complex anomalies.<\/p>\n

Remember, it\u2019s these deep actionable insights<\/a> in advertising that offer the most potential value.<\/p>\n

What Is PPC Signal?<\/h3>\n

PPC Signal uses AI technology to discover valuable, actionable insights in your PPC accounts automatically.<\/p>\n

Every insight comes as a packaged signal that includes all the relevant information you need to understand what\u2019s happening and how to act on the change.<\/p>\n

Basically, PPC Signal tracks every notable change across your Google Ads account. It\u2019s like having an automatic anomaly alert system!<\/p>\n

Each signal is organized and presented on the main PPC Signal dashboard.<\/p>\n

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You can explore each insight further to gain more details.<\/p>\n

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There are several key features on the Explore <\/strong>screen.<\/p>\n

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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.<\/p>\n

\"anomaly<\/div>\n

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.<\/p>\n

You can also view the data as a table and export it to use in your spreadsheets and other programs from this menu.<\/p>\n

There is also the Take Action <\/strong>button.<\/p>\n

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Understanding how to act and resolve each anomaly detection example is not always easy. With this feature, PPC Signal\u2019s AI engine recommends a possible action that you can take to capitalize or resolve the signal.<\/p>\n

Thus, you\u2019re not only receiving a steady stream of complete, verified insights; you also get a next-step action for each one.<\/p>\n

Don\u2019t Be Afraid Of Automation Anxiety<\/h3>\n

Some PPC marketers are hesitant to implement automated solutions. It seems like a no-brainer, but it\u2019s easy to have apprehensions.<\/p>\n

Unfortunately, automation anxiety prevents you from obtaining efficient, proactive PPC campaign management.<\/p>\n

This anxiety often originates from the fear of giving control of your PPC optimization over to AI algorithms. It feels like you\u2019re placing the future and success of your ad campaigns<\/a> in the hands of a machine.<\/p>\n

In reality, AI isn\u2019t about losing control; it\u2019s about productive management. Automation alleviates the strain caused by tedious, time-consuming tasks that, while necessary to the process, carry low impact themselves.<\/p>\n

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.<\/p>\n

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.<\/p>\n

That\u2019s what PPC Signal offers. Don\u2019t let automation anxiety prevent you from becoming a more efficient advertiser.<\/p>\n

Detect Anomalies That Align With Your Goals<\/h3>\n

One of the first challenges of PPC data analysis is selecting what metrics, dimensions and other details you want to investigate.<\/p>\n

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.<\/p>\n

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<\/a>, or the Pareto Principle<\/a>.<\/p>\n

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.<\/p>\n

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

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You can filter your active signals by:<\/p>\n