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The path to success and growing your business is always present, yet also always obscured.
It’s hidden by your data.
Data analysis and market research can teach you everything you need to know about your business, customers, competitors, strategies and more.
By detecting patterns in your data, you discover exciting correlations and relationships between your metrics and dimensions.
These insights hold many benefits that we’ll cover in this article. You’ll also learn how to overcome the challenges of data analysis in marketing research.
After all, data is incredibly complex and creates many obstacles. If you want to conduct successful market research, you need the roadmap to navigate these hurdles.
Let’s dive in.
Data analysis existed long before computers, spreadsheets, databases, etc. Even ancient merchants performed basic data analysis in their heads.
For example, a restaurant remembering your favorite order or that you’re a Tuesday regular is a case of data analysis.
It’s a pattern.
Patterns come in different shapes and sizes. They fall into a couple of categories.
A trend occurs when there is a steady increase or decrease in your data. Each data point is slightly more or less than the last. When you plot the data on a line chart, the steady increase/decrease shows the line climbing up or down.
Here’s an example:
In this case, the trend line is climbing up. However, this doesn’t necessarily mean it is a positive pattern. If the data shown is your wasted spending, you certainly wouldn’t want it to be increasing.
The same is valid with descending trend lines.
If this chart shows your cost per acquisition, it’s a good thing that it’s trending downwards.
This is why context is so vital to measure the success of an advertising campaign!
When there’s a shift in your data, several data points appear above or below what’s normal. This is different from a trend because it is not a steady climb, but a sudden change that continues for multiple data points.
Here’s an example:
You’ll notice there’s a substantial jump from the last blue data point to the first red one, which signifies the start of the shift.
Daily temperatures are a good example to showcase the difference between a trend and a shift. If temperatures are steadily climbing (50 degrees, 52 degrees, 55 degrees, 59 degrees, etc.), it’s an example of a trend.
If the daily temperatures started in the fifties, but there was a heatwave that caused temperatures to spike and stay into the seventies, this is a case of a shift.
Sometimes, there is a spike in data that is not followed by similar data points, as is the case in a shift. When a solitary point falls well below or above the normal, it’s an outlier.
In other words, outliers are one-off occurrences. As quickly as they appear, they are over.
Here’s an example of what an outlier looks like:
You can see that this spike in the data is a significant deviation from what’s normal. This is a common cause of outliers.
Anomalies in data may also appear because defined patterns are acting counter to what’s expected. This is known as an inverse correlation.
This happens commonly in PPC campaigns. For instance, if your clicks increase, the expectation is that your conversions will also start to climb. If the opposite happens (clicks increase, but conversions decrease), it’s a form of an anomaly.
How do you detect these patterns in your own data? The process of data analysis and market research can be a tricky one. It’s a multi-step activity full of obstacles and potential roadblocks that will sideline your investigation or produce lackluster results.
By understanding the process, you’re better equipped to detect these potential roadblocks and overcome them without interruption to your data analysis or market research.
It starts with a question. You conduct data analysis to answer a specific question. For example, you may ask, “What are my most lucrative PPC keywords?”
This question will guide your entire data analysis process, including what data you gather and analyze.
Remember, your time is crucial and market research and data analysis are time-consuming.
If you spend too much time asking the wrong questions, you won’t receive actionable results from your data analysis.
So, what constitutes a wrong or right question in data analysis? There are a few things you need to consider:
Once you have your analysis question in mind, it’s time to gather your data.
Again, things like value and relevance come to mind. Your marketing campaigns produce heaps of data, which means you have to strategically decide what data you select for your analysis.
If you collect too much data, it will muddle your analysis and cause problems down the road. However, if you compile too little information, you will struggle to achieve actionable results from your research.
Marketing campaigns involve many different metrics and dimensions. You have to think critically about which ones are most relevant to answering your analysis question.
Typically, these are your key performance indicators (KPIs), or the metrics that closely align to your goals.
You also have to consider the source of your data. You don’t want to utilize data from untrusted or unverified sources. This will hurt the accuracy of your analysis results.
Chances are, you’re gathering data from multiple sources. Before you begin the analysis phase of the process, you have to prepare your data.
Cleaning the data is itself a multi-step process. You have to filter, normalize and verify the information you’re using.
Cleaning your data is a time-consuming process with few available shortcuts, but it must be done if you want to have the best results by the end of your analysis and market research.
Finally, your data is ready for analysis. You’d expect most of the work to be in your rearview, but sadly this is not the case.
Data analysis is the process of transforming your now-cleaned and collected data into valuable and actionable insights.
To do this, you need to view the data from many different angles and dimensions. Each new way that you view or manipulate your data adds context.
What is context in data? Context gives you the essential background and knowledge to understand what’s going on behind all the numbers, metrics and figures.
The analysis doesn’t just answer what’s happening, but why it is happening and how this affects your marketing and business.
By the end of your analysis, you should have some form of resolution to your initial question,
You may have to analyze the data using several different methods and approaches before fully extracting all of the potential insight.
Sometimes, your questions spur on new queries that need to be analyzed for a complete resolution to be reached.
That said, when you reach a conclusion or result, you need to think about how to apply that information or discovery.
Your insight should lead you to some form of action or decision that improves your performance or business growth.
However, connecting your insight to a defined action may not always be immediately apparent. There are cases where you may be uncertain how to act on an insight and need to consult your team.
As you can see, data analysis in marketing is a rigorous process. Market research is very rewarding, but you have to navigate these multiple steps to reach these benefits.
So, what exactly are the rewards of data analysis in market research? What are the ways that data analysis supercharges your business?
Data analysis shows you what’s happening across your campaigns and how to improve performance by either preventing loss or improving your returns.
As mentioned, data holds tons of valuable information about your strategies, competitors, customers and more. These are all vital cornerstones of successful marketing.
By identifying patterns in your marketing analytics, you detect emerging opportunities and potential risks to your campaigns.
Data analysis in marketing enables you to swiftly capture these negative or positive events, which maximizes the value of every decision you make.
Mitigating a risk sooner reduces the potential damage and capturing an opportunity early on means you can capitalize faster than competitors.
So, the speed of your analysis is everything!
Successful marketing is largely founded on your ability to detect and meet customers’ needs. The problem is that these interests change frequently.
Through data analysis and market research, customer interests rise to the surface. This helps you align your strategies, messages and products to the latest audience trends.
Essentially, data analysis and market research allow you to actively “listen” to your customers and respond to what they want from your brand and products.
These audience insights will not only empower your marketing, but can also inform your product development.
By understanding your audience, you can produce more relevant and valuable products and services.
Many of the opportunities and performance changes that you find during data analysis represent gaps in the market.
A gap occurs when there is something that audiences want that is under delivered in the market.
In other words, a gap is something your customers want, but they can’t easily find from you or your competitors.
It may be a product feature, service, location, offer or otherwise. With market research and data analysis, you can discover these gaps and capitalize accordingly.
For example, as you’re conducting keyword research for your PPC campaigns, you may notice certain product-centric keywords that audiences are searching.
A keyword like “data analysis software with accurate reporting” suggests a product (data analysis software) and a feature (accurate reporting) that users are looking to find.
If no competitors actively offer or market accurate reporting capabilities, it’s a gap in the market.
The culminating effect of these benefits — better marketing, customer-centric offerings, competitive agility, accelerated growth — is increased ROI.
Since ROI is a ratio of your costs versus your returns, there are two ways to improve it: lower your costs or improve your results.
Data analysis and market research can handle both.
As you capitalize on opportunities and gaps in the market and offer more relevant products to customers, you improve your performance.
Meanwhile, data analysis will also return problematic areas of your campaigns. These are tactics that are under-delivering results and likely wasting your ad budget.
Once you resolve these sources of wasted spending, you have more budget to put towards the strategies that actually produce solid returns.
This style of budget optimization is essential in improving your PPC ROI.
In the pursuit to optimize your marketing strategies, you should also think about the efficiency of your approach to data analysis and marketing research.
The time you commit to data analysis is just as valuable as the analysis results themselves. If you can remove the obstacles and focus on the rewards, it drastically enhances your results.
That’s exactly what PPC Signal aims to do for marketers. It automatically produces complete insights ready for your action.
Through artificial intelligence technology, PPC Signal acts like a 24/7 watchdog for your ad campaigns. This allows you to stay on top of every notable change to your ad performance, with minimal effort.
Let’s take a look.
Rather than explaining how PPC Signal works, it’s easiest to see it in action.
In this sample Google Ads account, there are 21 active signals present. You can see the complete list of these signals from the PPC Signal dashboard. You’ll also see sections for bookmarked and actioned signals.
Bookmarked signals are ones that you’ve set aside for future review. Actioned signals are insights that you’ve resolved in the past.
Your actioned signals act as a historical account of the past changes you’ve made to your account, thanks to PPC Signal’s system.
You can filter signals with the options along the left side.
This allows you to remove any signals that don’t fit your criteria, leaving only the vital few that matter most behind.
You can filter by specific marketing metrics, a campaign, ad group or even a specific keyword. You can also select to filter by dimensions, including geo-location, hour of day, device type and more.
When you’ve found an active signal that piques your interest, you can elect to Explore further.
After choosing to Explore a signal further, you’re shown a deeper view of the data and insight.
In particular, you’ll see a much more detailed chart of the data. You can even add other metrics to the chart, which is an excellent way to see the context of how this performance change is impacting other areas of your account.
For instance, with the sample signal, we can compare how clicks are moving alongside conversions.
You can also zoom into the data and see how your metrics have changed day by day.
The other notable feature from this page is the Take Action option.
By tapping this button, PPC Signal suggests a follow-up action that will help prevent a negative performance change or maximize the value of a positive one.
With recommended actions, you always know the next step and how to make direct changes to your Google Ads account to improve performance.
PPC Signal is not the only marketing data tool available, so what makes it the best PPC optimization tool?
It really comes down to the tool’s commitment to complete, verified insights.
Arguably, insights are the entire purpose behind data analysis in market research. Complete insights give you the full picture behind your data, making it much easier to take action and make smarter decisions.
Many tools promise automated data insights, but they only half-deliver. They let you know when a performance change has occurred, but provide little background that helps you understand the shift, trend or other patterns.
PPC Signal only presents complete insights. All the data you need to understand and act on the change is packaged and presented in a straightforward, no-nonsense manner.
You never have to go digging for extra details or have to analyze the data yourself; PPC Signal takes care of all of it.
Your only responsibility is choosing which signals to pursue first and what to do with the insights you explore.
Data is one of the most crucial resources of modern businesses. Every interaction between your business and its audiences creates data. Every success, failure or in-between is cataloged and stored as data. While one single interaction doesn’t tell you much about your business, stringing all of this data together paints a complete picture of how your business is winning or losing in its ever-changing marketplace.
There are many different types of data analysis, including sentiment, text, diagnostic, predictive, prescriptive and many others. Despite these various methods, the process remains the same: ask a question, collect data, clean your data, analyze it and extract insight.
That said, the two primary methods of data analysis are manual and automated.
In manual analysis, every step is completed by a human (or team of humans). This is an incredibly time-consuming method that may not produce the best results.
In automation, artificial intelligence and machine learning algorithms do the bulk of the heavy-lifting, while human intervention is called upon to put the analysis results into action.
The best way to find patterns in a data set is to chart or visualize the information. The human brain is much more receptive (thousands of more times, in fact) to visual signals than raw numbers or text.
When you see a chart, the patterns and correlations almost jump off the page. You can immediately see the visual depiction of the data and have a pretty accurate sense of what’s going on behind all those numbers. The same is not true when you have just a tabular view of the data.
Data analysis in marketing research is vital in today’s hyper-competitive business world.
Many companies deliver finely tuned, personalized experiences to each customer thanks to their ability to collect, process, analyze and act on data.
This means that today’s consumers have the increasing expectation of campaigns, products, offers and brands that directly match their interests, preferences, beliefs, etc.
To remain competitive, you need the ability to manage and interact with your data efficiently and effectively.
In short, you need to transform your real-time data into actionable insights that help you improve your business on a daily basis.
PPC Signal is the #1 solution for Google Ads management because it provides action-ready insights based on your current and historical performance.
If you find yourself struggling to keep your head above nauseating spreadsheets and constant fluctuations in your campaign metrics, PPC Signal is the solution you’ve been waiting for.
Simplify the data analysis process and amplify your results!
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