Misinterpreting data is like getting into a small fender bender in your car. You try to avoid it at all costs, but sometimes it just happens –it’s an accident, a minor lapse in judgment, a moment of being distracted or in a hurry, and then bang.
These small accidents, whether with data or a vehicle, can be costly. When you damage your car, repair costs can be staggering. Misinterpreting your data can carry similar costs, especially when you make strategic decisions based on trends that are not what they seem.
Accidents with your data can easily happen, especially when there are a lot of metrics and dimensions at play. This is very true of PPC campaigns, where there may be thousands of different data combinations possible. Forgetting to include a variable or oversimplifying what a metric means can lead your campaigns down the wrong path. Course correction is critical while analyzing a lot of PPC data. You can use the tools like PPC Signal to get actionable insights from your PPC data and keep your campaign on track. Doing small changes on regular basis can boost the ROI over time.
In this guide, you will learn about the negative impact that these data misinterpretations can have on your PPC decisions and what types of marketing analytics you can use to avoid these mistakes.
Before exploring the types of marketing analytics that can help you avoid the misinterpretation of PPC data, you first need to understand the basics of data analytics.
By definition, analytics is the scientific process of discovering useful patterns within a set of data or numbers and communicating those results to others. Essentially, the goal of data analytics is to turn that giant spreadsheet of raw numbers into actionable insights that can help a marketer make better and more accurate decisions.
It’s a process that involves the use of statistics, computer programming and operations research. These studies work together to unravel complex data and find the meanings behind the numbers. It is especially useful in areas that create lots of data, which makes it a meaningful practice for your PPC campaigns.
In a world that is increasingly data-driven, analytics is becoming a must. Every company, sports team and other organization is producing unfathomable volumes of data and all of this information is useful, at least to some degree. But, making sense of it all is impossible without analytics.
As a result, analytics is being used in a number of different ways, including:
As mentioned, PPC is one of the areas where analytics shines. The vast amount of data produced by your campaigns is impossible to navigate without some form of analysis. If you want to improve your PPC marketing, you need to analyze this data and draw accurate conclusions.
There are several advantages of data analytics in PPC:
Your PPC campaigns are in constant motion because there are a number of factors that are always influencing performance. The actions of competitors and the behaviors of users/customers are dynamic factors that cause the valleys and peaks of your PPC data.
With analytics, you can cut through all the noise and get to the root of what the latest changes mean for your campaigns and how to maximize results, while simultaneously mitigating risks.
Data, analytics, and actionable-insights have become popular buzzwords and there’s an almost gold rush type effect to acquire and utilize data. When it comes to PPC campaign management, marketers that neglect to utilize their data are destined to fall behind the competition.
This raises an interesting debate over whether it’s better to trust data over experience and opinions. For older and traditional companies, the thought of ignoring years of experience and expertise is severely flawed. They are right to question the significance of data because over-reliance is a common pitfall. Data can be wrong. When you allow it to fully drive your campaigns, then these data inaccuracies and missteps can have disastrous results.
Data and analytics are not designed to replace or substitute your expertise and experiences. Instead, they should be used to provide evidence to back your opinions and gut-feelings. Your PPC strategies and decisions should be data-informed
In this debate between experience and data, many executives begin to question the quality and reliability of their data. How do you know when to trust data? How can you test its reliability? If your data is not reliable, then the accuracy and validity of your analysis and insights are also faulty.
To make sure that your data is reliable and high-quality, there are strategies that you can implement. These steps will better ensure that your data is used correctly and accurately. For instance, conducting controlling measures is crucial if you want to protect against the misuse of statistics.
To ensure the credibility of insights and accuracy of data, you need to think about how you collect and utilize information within your organization.
There are two main approaches: top-down and bottom-up.
These are common terms that appear in economics, finance and other areas. For data collection, a top-down approach starts general and then becomes more specific, while the bottom-up strategy begins specific before growing broader.
To explain further:
Top-Down Approach: In top-down data collection, you create an overarching system of data collection and analysis. You gather everything and anything that may be useful. Then, you develop smaller systems within this broader framework. These subsystems are created as needed by your analysis projects.
The top-down approach also dictates how data is controlled and moved through the organization. In this strategy, data is handled by upper management. Field workers submit requests for data, and management decides what information to send and where.
This method is often safest and better ensures that data is being handled appropriately across the organization. Management has a better overall idea of how data is being utilized.
Bottom-Up Approach: With this technique, data travels in the opposite direction. Field workers choose what to gather bases on their analysis needs. The information is then passed on to management to decide how to capitalize on the data and insights.
This approach is common in PPC management because paid ads are often part of a larger, overarching marketing strategy. It’s up to the PPC manager to conduct pertinent analyses and then pass the insights on to the rest of the marketing department.
The bottom-up approach also helps you avoid data fatigue and overload because only the data that’s needed is collected. Thus, there’s less superfluous information to distract you from your analytics objectives.
When it comes to analyzing PPC and other marketing data, there are three primary types of analytics involved.
They are:
There is some overlap between these three approaches. Marketers should implement all three to get a full view of their environment and to extract the most value out of their big data.
Descriptive analytics is focused on answering, “What happened?” In this analysis, real-time and historical data are used to understand where you were, where you are now, and how the difference between these two points can help you thrive in the future.
Ultimately, it is used to figure out why failure or success happened. For example, if a particular keyword in your PPC campaigns used to generate thousands of impressions a day, but now only sees a hundred, what happened? Are people no longer interested in this topic? Has something changed that the query is no longer relevant? Descriptive analytics aims to find out.
Any time you look at how your metrics have changed in the last quarter, month, and week or even the last day, you’re conducting a descriptive analysis.
Instead of figuring out what happened, predictive analytics looks to the future and tries to answer: “What’s maybe going to happen?” As hinted above, predictive analytics often uses the insights found during a descriptive analysis. After all, you don’t know where you’re going unless you know where you’ve been, right?
Predictive analytics has limitations, hence the “maybe.” It is not a crystal ball and there is no perfect guarantee that the results of this analysis will be 100% accurate. Instead, predictive analytics makes data-informed guesses about the probability of a certain event occurring.
It’s the same model used to forecast the weather or create a credit score. Meteorologists look at weather data to predict temperatures, rainfall, and other natural events. It’s not uncommon for these forecasts to be off or entirely incorrect – the results of data misinterpretation! Similarly, credit scores are basically the finance industry’s way of predicting how likely you are to pay back a loan on time.
For PPC, predictive analytics can be used to make proactive campaigns that don’t just react to changes, but plan ahead for them. It can be used to identify the early stages of a growing trend and predict when it will hit a point of criticality.
First, you find insights based on your historical performance (descriptive analytics). Then, you use those insights to forecast what the future may look like (predictive analytics). What’s the next step? Prescriptive analytics is about choosing what to do about it all. How do you change the course of your analytics for the better?
Compared to the other types of marketing analytics, the prescriptive variety is notably more complex. You’re not just looking at your internal data, but also external factors that may be influencing the numbers.
The goal is to optimize the data to reach the best possible outcome. To do that, you need to first understand which decisions will lead to that best-case outcome. This is known as stochastic optimization. In this respect, prescriptive analytics answers two questions, instead of one:
The complexity of prescriptive analytics leads to the highest number of data misinterpretations. However, when conducted properly and accurately, this type of analysis leads to the most significant impacts on the success of a PPC campaign and the overall growth of a business.
In PPC, for example, prescriptive analytics can be used to answer questions like, “What will happen if I increase my budget by $500 a month?” Or, “What new keywords do I need to target if I want to increase revenue by 12% this month?”
Descriptive, predictive, and prescriptive analytics are all necessary for exploring the data generated by your PPC campaigns. However, for a more comprehensive data analysis for marketing, you also need to incorporate external data from other sources. This may require you to use additional types of marketing analytics beyond the three just described.
Sentiment Analysis: Customer sentiments control how your target audience perceives your brand and products. In short, it’s how the audience feels about you. These sentiments can impact your PPC performance. If sentiments are overwhelmingly positive, people will be more likely to convert. If those sentiments are negative, they won’t even bother clicking.
Sentiment analysis looks for positive, neutral, and negative responses and feelings from reviews, social media responses, and other sources. This data can be an influential variable for your PPC metrics.
Competitive Analysis: Your competitors have a very significant impact on your PPC campaigns. There is a high chance that you are competing directly with these neighbors for ad ranks and clicks. The moves that these other entities make are important variables to consider when analyzing your PPC performance.
Given their impact, you need to have a keen eye on the competition at all times. A competitive analysis will compare and contrast the strengths, weaknesses, and strategies of each competitor, which may offer you valuable insight into how to approach your PPC marketplace more successfully. Plus, you won’t be caught off guard by shifts in the competition’s strategies.
Data misinterpretations can happen during any type of analysis, even a simple descriptive one. It’s important to identify the common reasons behind these data mishaps because it will help you avoid these mistakes in your own analyses.
Before it even leaves the gate, you can shoot your analysis project in the leg if your data collection process is erred. It’s especially easy for mistakes in collection to occur when you are gathering data from multiple sources. Often, these foreign data sets need to be cleaned in order for them to “talk” with one another. Otherwise, it can create wild inaccuracies and other errors.
Example: If you’re pulling data from Google Ads and Google Analytics, you can run into problems with conversions. Not only do both of these platforms define and attribute conversions differently, but it may cause you to double-count conversions, which would lead to inaccuracies.
PPC managers and business professionals are not data scientists and data scientists are not marketing or business experts. But, you need both business and data expertise to successfully navigate the complexities of your PPC analytics. An individual that thinks strictly as a data scientist may fail to understand the metrics vital to achieving business goals. Conversely, a business professional without much data expertise may reach the wrong conclusions or miss key insights that could be used to scale up PPC campaigns.
Example: A data scientist may not understand what metrics are pertinent to the business’ marketing goals. They may think that PPC ad impressions are important because it creates brand awareness. The business, however, has conversion-related goals that are not supported by impressions.
PPC campaigns create a lot of data. Your Google Ads dashboard is a library of metrics that can easily become overwhelming. One of the chief difficulties is knowing which metrics and variables are important for your given analysis. When you omit key dimensions or information, it’s practically a guarantee that you’ll reach the wrong conclusion. It’s like reading a mystery novel with entire chapters missing. You need that vital information to solve the case!
Example: This is a common reason for misinterpretation in PPC campaigns and there are many ways that it can manifest itself. A good quality score can reduce your costs, improve ad ranks and provide several other benefits to your campaigns and their performance. If you neglect to measure quality scores, it is easy to misinterpret what these lower costs and improved ad ranks are the result of.
You can examine your data from many different lenses and magnifications. Each one is like looking at the same problem from a new angle. In other words, different levels of aggregation can tell a different story. If you spot a trend at one level, you may have to adjust your magnification to see at what point the data reflects a different result. Or, look at different variations of the same data to find discrepancies or similarities.
Example: If you discover an exciting insight at the campaign-level, don’t forget to investigate each ad group and keyword to see if the same trend exists. If it doesn’t, then you may find a clue as to why this trend only affects certain ad groups or keywords. But, if you don’t look at these different levels, then you’ll never know!
As you’re investigating different levels of your data and examining these different “lenses,” you can run into another risk, which is forgetting to look at all of the variations possible. You need to turn over every rock and check every angle to test your data conclusions for the same reasons that it’s crucial to look at all the different variables. Sometimes variations are obvious, but other times they are not.
Example: Broader keyword match types create a lot of variations. A keyword term may perform very well, but this performance may be the result of only one or two match types within the wider range of match variations.
While the majority of your PPC data and variations are important, they don’t all matter. You should test the impact of certain variations and aggregations, but including these different dimensions into your analysis project is extremely messy. Whenever you conduct an analysis, there should be a goal or objective. You’re trying to answer a specific question. Thus, you only want to include the data that supports finding the answer or disproving your conclusion.
Example: PPC marketers use key performance indicators to measure the success and performance of their campaigns. These indicators are selected because they directly impact the marketing goals of the business. Your data analyses should add to your understanding of these KPIs, not distract from them.
Every data insight you extract from your various analysis projects are conditional, which means you can’t apply them account-wide. Typically, the insights only reflect a small population of users. When you make inferences about PPC audiences based on what you’ve learned from other analyses, it’s common for these inferences to be wrong. These wrongly assumed properties will impact your judgment and severely skew your understanding.
Example: If you have ad copy that works exceptionally well in one campaign, it isn’t a guarantee that it will work in another. The campaigns target different keywords with different intent behind the words. Thus, users aren’t going to react to the same types of headlines, offers, etc.
A correlation between two numbers can often be confused as causation. This is especially common in PPC because so many metrics share close relationships to one another. A small change in one metric will have a ripple effect that impacts several others. More clicks, for example, will lead to more conversions. But, that doesn’t mean that clicks are causing the conversions.
Example: Every PPC marketer has, at one point or another, made an adjustment to their campaigns and then seen a number of positive changes in all different types of metrics. It’s easy to think that this is all the direct cause of the change you made, but it’s not that simple. There are any number of reasons why a spike in performance may have occurred: seasonality, changes to the competition, a higher quality score rating, etc.
PPC data takes time to mature. Many marketers make the mistake of jumping to conclusions and making changes to their campaigns the second they recognize a potential trend or insight. They want to be the first to capitalize on the opportunity. In a month, that rock-solid insight may start to look a lot less concrete.
Example: When you look at PPC data at a day-to-day scale, you’ll see some big spikes every once in a while. Often, these are simple outliers that marketers know to ignore. Outliers can also happen across multiple days. You need to give data and insights time to mature. If it is a solid insight, it’s not going anywhere.
Seeing is often believing. It’s hard to recognize some trends when you’re only looking at numbers. In other cases, the significance of a trend or outlier may be hard to visualize with only numerical data. By converting numbers into a data visualization, you can often see what your brain doesn’t recognize by just looking at the raw figures.
Example:
Having only the data in the data tables to understand and infer the results is not only the solution. Having detail level charts and different data visualization analysis can give you bigger idea about what is happing in your PPC campaigns.
It’s important to realize that data misinterpretations are not a singular problem but an organizational one. You may understand your PPC data perfectly, but your stakeholders or colleagues may not. And, misinformation has a knack of spreading like wildfire. Thus, when you are presenting your findings and creating reports for others, it is important to use simple jargon that everyone understands. Clarity is of the highest priority!
You want your data to work for you, not against you, which is why it is important to understand the reasons why these issues occur. Once you understand the reasons behind data misinterpretations, you can begin discovering ways to tighten your analysis processes to better guarantee that the insights you do unlock are stable, accurate and trustworthy.
With all of the potential reasons behind a data misinterpretation, it’s not uncommon for it to occur. Naturally, you may be wondering, “Should I even be worried? What’s the worst that can happen as a result of misinterpreted data?”
The answer depends on your reaction to the inaccurate data or faulty insight. Some marketers will put all of their trust in a data-born insight. They’ll shift entire mountains in their campaigns as a result of discovering what they feel is an infallible truth. If they are wrong, this high-trust decision can cause devastating fallout.
Your first step is to have a clear objective for your analysis. PPC analytics is not one giant undertaking, but rather the culmination of many smaller and ongoing analysis projects. If you try and answer everything at once, you’re sure to run into problems with misinterpreted data.
Start by asking a single, specific question. For example: “What will happen if I increase bids on competitor keywords by $0.50?” Or, “How can I improve my conversion rate by 2 points?” Your question will guide your analysis and ensure that you aren’t sidetracked by unnecessary information or insights.
Your goal is to answer this question as completely and accurately as you can. As you’re analyzing, other questions may pop into your mind. Don’t get distracted by trying to answer these questions now. Instead, you should make a note of them and chase down those leads afterward. It’s common for one analysis question to lead to others.
It’s common for your analysis to lead to follow up questions. It’s also common that you’ll need to utilize different forms of analytics. As mentioned, descriptive, predictive, and prescriptive analytics share close connections to one another. There may be times when you need to utilize all three types of marketing analytics to chase down the answer to your question.
For example, you may first have to look at historical data to measure why you’re in the current situation. Then, using the insights gained in this analysis, predict what your campaigns will look like in the future. Finally, look at insights towards changing the trajectory of that forecast.
You may also need to look at external data to find the correct solution.
For example, a decline in campaign performance may have nothing to do with your PPC adjustments, but rather a decline in customer sentiments or a change in a competitor’s pricing. You need to also conduct a competitive analysis or sentiment analysis to capture these insights.
Before you begin conducting each analysis, think about what data you need to gather. Don’t worry about small variables or variations yet. Instead, focus on the broad strokes. What’s the obvious data that you need to gather and analyze to reach your answer?
It’s best to gather data step-by-step, rather than all at once. The more dimensions you add, the more complex the analysis, and the harder it is to obtain insights. Adding one data set at a time is much easier to navigate then dropping them into the pot all at once.
As you’re adding each data piece to the puzzle, measure its impact on the objective. Is it putting you further or closer to your answer? This process may be slower, but it will help you avoid data misinterpretations and ensure that all of the data you incorporate into your analysis is useful and relevant.
Once you’ve established the primary data that you need to reach a possible resolution to your analysis objective, it’s time to begin looking at the variables and variations that impact those figures. This is where your time and patience in the previous step will pay off. Instead of investigating every possible angle, you will have a much shorter list of variables and variations that you need to explore.
When testing variables and variations, your goal is to figure out what pieces fit together that support your hypothesis. Or, what pieces are necessary to lead you to the answer you’re looking for. Sometimes, this requires you to manipulate the data to see what the impact of a major change would be.
If you can’t find the data to support your theory or conclusion, then you need to find evidence as to why this is the case. This will help you avoid making the same wrong conclusion in the future. It will also deepen your overall understanding of your data and numerical relationships.
At this point in your analysis, it is wise to consult your team. Remember, there is a line from data scientists to business professionals. Every stakeholder and colleague lies somewhere along that line. This means that they each lend a unique vision to your analysis project.
Think of it as a second, third, or even fourth (depending on the size of your team) opinion on your analysis. These fresh eyes may introduce a variable you hadn’t thought of or a variation that had gone unexplored. It may also introduce new data that you weren’t aware of before.
It’s better to check in with your team early in the process, rather than in the late stages. If they do have a vital angle or set of data that impacts your analysis, you don’t want to wait until the end to find out. In some cases, they may have already answered your objective question in an analysis project of their own!
Your PPC analytics process can quickly become a complex morass that simple paper-pencil and spreadsheet statistical analysis can no longer handle. Your campaigns produce oceans of data that can be impossible to traverse without the help of algorithms and artificial intelligence tools.
For instance, verifying a correlation between a dependent variable and multiple independent ones may require you to plug your data points into a regression formula to find a “line of best fit.” This allows you to see a more accurate view of the relationship between the variables to then draw better conclusions.
The market is rich with AI tools that can make your data analysis easier. If you want to invest in such a solution, aim to find one that offers analysis support specific to PPC. It’s better to pay for a specific tool with the solutions you need than a robust one with lots of features that will overwhelm you.
There are two substantial reasons why you need to visualize your analysis findings. The first is because, as mentioned earlier, it helps you see what the raw numbers obscure. Outliers and anomalies, for example, can easily go undetected when they are just another entry in a spreadsheet. Once these numbers are graphed, it’s much clearer to see.
Visualization will also help you convey your most complex data insights to stakeholders that don’t have a very stats-forward mindset. Your hard work and careful analysis can easily go awry if these individuals don’t understand the insights, or, even worse, misinterpret them. In both scenarios, they will misuse the valuable information you’ve handed them!
Data can be visualized in many ways. For all of these different types of marketing analytics, there are even more ways that these analyses can be aesthetically represented. Bar, line, and pie charts are just the start. Dual-axis, radar, dayparting, stacked grid, Sankey – there is a long list of other visualizations that may include the perfect way for your data reports to tell a story and help stakeholders see the details that matter.
You’re now at the end of your analysis and your results have been organized and visualized in an easy-to-digest report. Ideally, your report should outline some next-steps that various departments and team members need to carry out, especially if the insights gained are large in scope.
It’s also a good time to request additional feedback. Are stakeholders happy with your findings? Are there additional questions that they have? If stakeholders are unhappy with the results, what can be done to improve them? This is why it is good to not only present the data findings but also a plan of action to optimize based on the insights acquired.
Again, the different viewpoints of your staff are valuable assets. It’s important that they have the opportunity to offer their own thoughts and expertise regarding the data. They may see an advantage or opportunity in the data that you hadn’t considered in your report. This will help stretch the value of your insights! Best tool to create PPC Reports and share with your team.
Each time you complete analysis, you should review the process. What aspects of the project were conducted well? And, where –if any– were the roadblocks that slowed down the process or caused you to discover misleading insights?
By taking the time to review your process, you’ll not only gain insights that help your PPC campaigns, but also find ways that help you perform higher quality analyses in the future, and in less time!
This is also the time to think of follow-up questions that need to be solved with additional analysis projects. If you’ve written some down during the process, look at your list and determine which questions are most pressing. Then, you can start the analysis journey over with that question as your objective!
Data is a valuable and necessary resource for your business to utilize. If you want to improve your PPC campaigns, then you need to be consistently exploring and analyzing your metrics to find opportunities to increase performance.
That said, there are plenty of dangers regarding data and analytics. Misinterpreting your insights can hurt your campaigns, instead of helping them. To avoid this, you need to ensure that your insights are accurate and interpreted correctly.
By following the advice of this guide, you’ll better protect yourself against the common pitfalls that can lead to incorrect decisions based on data. Thus, you’ll be more successful in your implementation of the different types of marketing analytics!
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