You've likely heard that data is your organization's lifeblood — yet many businesses aren't harnessing its power. As a result, there is a disconnect and a skills gap, which can be costly. While 59 percent of marketing teams claim to be highly data-driven, even performance marketers and analysts within these teams struggle to make sense of their piling data. Roughly 41 percent say they're not very comfortable "collecting and analyzing" or "reporting and presenting" data, while 35 percent aren't comfortable "reading and understanding" it.
In the golden era of data, you have access to more information than ever, but are you analyzing it to maximize its potential? If not, you're leaving invaluable knowledge on the table.
So whether you want to find solutions to internal issues, make more informed decisions, or find benchmarks and set performance goals, it's time to take marketing data analysis seriously. Mastering marketing data analysis could make all the difference — this year and beyond! As marketing platforms become increasingly automated, how you share data insights and provide signals to these third-party channels or algorithms is the best way to beat the competition. Data is simply your competitive advantage here.
From data to insights: 6 tips to consider
Along with solutions for greater control over your marketing performance, consider the following six tips on how to analyze marketing data. These tips will help fuel your company's growth and kickstart your data-driven, winning digital marketing campaigns.
1. Compare data to the same period last year
The first tip is simple: compare website and marketing campaign data to the same period a year ago.
The reason is that plainly stating the numbers (for instance the number of sales or leads, the number of website visits, and the average cost per lead) does not tell a story. It doesn't tell you if your campaign was successful or not.
But if you compare these numbers to the same period a year ago, you might see a steady improvement. Take these fictional monthly results for example:
Was this a good month? And why?
You can not even answer the first question.
The easiest way to begin answering it, is by adding some context:
Now you can spot trends:
- With less budget, your campaign brought in more leads
- Part of the reason why is because the conversion rate from session to lead was higher
- The cost per lead almost halved
This is looking good!
When comparing data from the year before, always consider macro trends and seasonality. For example, 2022 was a challenging year for many businesses concerning inflation, rising interest rates, COVID lockdowns, supply chain problems, and geopolitical instability. You need to basically understand how these variables are influencing your marketing campaigns and keep track of your most valuable marketing metrics at all times.
2. Compare data to the previous period
Comparing data with the same period a year earlier can offer insights. But to gain a deeper understanding of your campaign performance, it is smart to also look at recent results and compare them to a previous period..
Now, it may not be very helpful to compare one day to the other. Daily fluctuations are to be expected (just like in the stock market). However, weekly and monthly reports are valuable to understand patterns.
Monthly reports are vital to gain insights from your marketing performance data. They provide insight into your strategies and marketing progress, showcasing their effectiveness and overall patterns — especially during seasonal periods. In addition, a year-on-year report will help showcase growth and the realization of new, unexpected trends.
When comparing monthly, quarterly, or yearly customer data, include these key metrics:
- Digital marketing return on investment (ROI)
- Sales and leads
- Cost per acquisition (CPA)
You can compare figures to analyze the impact on your overall digital marketing strategy before analyzing individual channels.
3. Compare actual numbers to your goals or projections
Comparing actual figures against your goals or projections will help you gain the insight needed to make more confident decisions. This is one of the most common practices in data analysis and one of the most vital.
For example, consider using marketing performance data collection to create revenue projections. During the forecasting process, you can uncover figures and trends that help you address your budget, better predict inventory needs, optimize strategies, determine staffing needs, and present data-driven insights for potential investors.
After thorough data analysis, you might compare actual figures against your most recent sales forecast. If sales are lower than anticipated, it's time to dive deeper. Was there customer interest but not enough conversions?
If yes, explore why. Did you recently change your pricing model? Did you alter your marketing campaign, focusing more on specific marketing channels than others? If unsure, now is the perfect time to conduct A/B testing to gain actionable insights.
4. Look for outliers in the data
Since outliers are values on the extreme ends of a dataset, they may relate to one of two scenarios:
- Sampling, measurement, or human errors. In these cases, they don't represent true values, so the sooner you identify them, the better. For example, Google Analytics wasn't working properly for a few days, resulting in missing data, or conversions in GA4 weren't set up correctly, leading to double counting.
- Something caused human behavior to change for a short period. Outliers aren't always an indication of incorrect data and can be legitimate observations. For example, an online store may see a spike in sales around Black Friday. Marketers couldn't go ahead and compare November data to October; it would be much higher for obvious reasons. In this case, you would compare November 2023 to November 2022.
Pinpointing the cause of outliers can help ensure more accurate and clean data.
Speaking of clean data, if you're a marketer combining performance data from multiple sources, remember the importance of data cleaning. A vital component of any data management process, data cleaning involves several steps, including removing outliers and joining data sets together. For example, when you bring cost data and campaign conversions together to get a clear idea of your cost per conversion.
Related: What are misleading visualizations, and how do you avoid them?
5. Understand what's happening
Data tells a story. If you know how to interpret that story, you help your company make the actionable, informed decisions needed to adapt and evolve. For example, when conversion numbers drop, you're given an opportunity to learn what's happening with your marketing efforts.
Consider the following:
- Which pages are getting less traffic?
- Which campaigns are generating less traffic?
- Are you paying more per click, resulting in less traffic for the same budget?
Answering these questions based on real-time data lets you pivot to achieve higher conversion rates and a supercharged ROI. Therefore, getting into the habit of using raw data to gain insights will help ensure successful data transformation.
6. Calculate metrics for more insights
When using web analytics tools such as Google Analytics or the tools available in your advertising platform, you'll find some data readily available and accessible. However, it often pays to dig deeper, which is why you'll want to calculate the metrics that make the most sense for your business.
For example, consider customer lifetime value (CLV). As its name suggests, this metric is the value a customer provides across their lifetime. It is any accumulated revenue they contribute from their first purchase until they stop buying your goods/services. Once you determine this metric, you can make more informed decisions based on other robust marketing metrics.
Another example would be a reasonable customer acquisition cost, which will help determine whether you're spending your budget appropriately. You can also better decide where to invest your messaging and marketing dollars. If one customer costs $600 to acquire but returns a CLV of $9,000, they love whatever you offer enough to keep making purchases. These values show you should invest more of your marketing budget in broad campaigns to get customers through the door and less in retention campaigns.
How do you analyze marketing data?
There's no clear-cut method for analyzing marketing data because what matters most to your company may be less critical to others. For example, as a retailer, you may be most interested in your conversion and consumer retention rates. If you're in the SaaS industry, you may be more concerned with customer churn and acquisition costs.
However, marketing data analysis is ongoing and fluid. When you have one metric down, industry marketing trends may force you to move the goalpost. You can also focus on specific metrics within an upcoming campaign and then pivot once you have the data insights you need. The above tips are an ideal starting point, regardless of your business model, goals, or industry.
The more you practice these suggestions, the more they'll become second nature. Of course, knowing how to approach the marketing data analysis process is half the battle — but relevant marketing data analysis tools will help take your marketing initiatives to the next level. The key is to invest in data automation to eliminate silos so that you can scale accordingly.
Why is it essential to analyze marketing data?
Without marketing data analytics, you're essentially swinging in the dark. Data is your guiding light for near- and long-term goals. As you gather reports on the past and analyze data from the present, you can make actionable decisions to influence your company's future. These data-driven predictions can help you be more strategic to boost your ROI.
Think of the data you collect as a pool of knowledge. From data on your target audience and customer preferences to trends in lead generation and customer interactions, you're opening the door to actionable information when you analyze marketing data. As insights flood in, you can make more concrete connections and conclusions about what's driving value. As you unleash the power of performance marketing, you'll maximize your marketing budget via quick, confident decisions.
What is the role of marketing analytics in a marketing strategy?
Your business collects valuable data every day. As a marketing team, you can break this data into customer, operational, and financial categories to drive marketing campaigns. For example, the data you collect this month can help you focus on the highest-priority initiatives to reach your goals and business objectives. Over time, you can leverage your data to make educated predictions.
As you collect more data, you can determine marketing program performance by measuring the effectiveness of your marketing-related activities. Then, based on that data, you can choose what you should do differently to optimize results across all channels. First, you must invest in data analysis to reach this point and transform your data into useful information.
You may have hunches, but those hunches are just guesses without data analytics. When you learn how to analyze marketing data, you can find the answers to your questions and base your marketing decisions on facts. This process can help you remain more goal-oriented while improving business growth.
The options are nearly endless, and the possibilities are powerful!