Marketing analytics: A guide to improve your skills

Despite all the buzz about data-driven marketing, marketing analytics remains a challenge for many marketers who still struggle to track and optimize performance. 

Only a fraction of marketers use quantitative tools to measure the impact of marketing spend. In fact, 80% of companies collect more data than they know what to do with, while 38% report a lack of data skills, according to a Braze survey of marketing executives. 

As marketing becomes more technical, you’re expected to know how to interpret and use data to do your job. It’s not always easy, but this article will help you manage the growing pains. 

Skip ahead:

• What is marketing analytics?
• The importance of marketing analytics  
• PPC analytics
• SEO analytics
• Social media analytics
• Email analytics 
• Examples of marketing analytics use cases  
• Marketing analytics tools 
• Marketing analytics techniques 
• Level up your marketing analytics skills 

What is marketing analytics?

Marketing analytics is the process of using data to guide and refine your marketing decisions. Analytics is closely connected to strategy: having a solid marketing strategy makes it a lot easier to create an effective analytics strategy. 

While some may think marketing analytics is all about building complex models, a big portion of the work actually involves maintaining clean and trustworthy data—even small errors or inconsistencies can undermine trust in your data, which can lead to C-level stakeholders dismissing the insights altogether. Building trust in data takes time, but losing it is easy. That’s why clean, accurate, and standardized data is the foundation of effective marketing analytics. 

The importance of marketing analytics  

Plain and simple: the purpose of marketing analytics is to help you make better decisions. Instead of relying on guesswork, you use data to guide your marketing choices. It’ll help you:

• Evaluate your business’s marketing performance and spending
• Continuously refine your strategies through testing and marketing measurement   
• Leave behind strategies that don’t work
• Prove the value of your work to your company or clients 
• Align your campaigns with business goals for better results

While not every campaign will be a success, the only real failure is not learning from your efforts. 

Different types of marketing analytics

PPC or Performance analytics

Pay-per-click (PPC) or performance marketing is a type of advertising that targets specific audiences with tailored messages. As the name suggests, advertisers pay each time a user interacts with the ad. 

The performance analytics side involves tracking ad performance across platforms, including Google, Facebook, and Instagram, by measuring metrics like click-through rates and conversion rates. 

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SEO analytics

SEO analytics involves collecting and analyzing data to grow engaged organic traffic. Unlike keyword or market research, which relies on external data, SEO analytics focuses on your own marketing data. It’s not just about where your website ranks in SERPs,  but about understanding what drives meaningful traffic that leads to conversions.

By using your own data, social media analytics uncovers trends, gaps, and opportunities, allowing you to make data-driven marketing decisions and take your SEO work from good to great. Semrush and Ahrefs are our two favorite tools here, and you can use Supermetrics to pour Semrush and Ahrefs data into your reports.

Social media analytics

Social media analytics focuses on collecting and analyzing data from social media platforms to gauge the impact of your marketing activities. 

You can quickly iterate and refine your approach by understanding your target audience’s online habits, identifying platforms with the most traffic and engagement, and discovering what style and tone works best in your posts. Beyond vanity metrics, focus on improving your engagement and conversion rates here. 

You can even use marketing analytics for brand testing before making any changes to your social media channels or website.

Email analytics 

Email analytics examines the effectiveness of your email marketing campaigns. The key metrics to keep an eye on include open rate, spam rate, and conversion rate. 

By measuring your email marketing success, you can see how well your emails resonate with your target audience and make adjustments to improve your brand reach—even the smallest tweaks can make a difference. 

Examples of marketing analytics use cases  

Marketing reports often suffer from a lack of clarity. To overcome this, focus your reports around specific use cases. For example:

Keep track of your ad spend and budget

Let’s say you’re running ads across multiple platforms, including Facebook, Google, and Microsoft, and you want to understand how the money you’re spending translates into returns.

To achieve this, you’d create an ad spend tracking report. This report shows how much you spent on a particular campaign, its goal—whether conversions, leads, or awareness—and the exact results.

By comparing results across channels, you’ll quickly see if your spending is delivering the results you want.

Creative testing

Manscaped, known for its premium men’s grooming products and bold, humor-driven marketing campaigns, relies on A/B testing to refine its creatives. The marketing team frequently tests different ads and messages across different demographics and platforms. The results help them tailor their ads, improve spending efficiency, and maximize reach.

Budget stimulation and forecasting

Swappie, a leading platform for buying and selling refurbished phones, uses marketing mix modeling (we’ll talk more about this) to evaluate various investment scenarios:

• Channel efficiency forecast: This shows how a channel’s performance changes over time, identifying when it’s most and least efficient and ensuring the predictions are accurate.
• Budget impact on sales: projects sales outcomes at different budget levels, helping them determine the optimal spend for each channel.
• Optimal investment point: identifies the spending amount needed in each channel to maintain profitability.

With these stimulations and insights, the marketing team increased their ROAS by 15%. 

Marketing analytics tools 

To keep your marketing reports accurate, ensure your reporting tool stack is relevant, and automate data extraction and transformation wherever possible. Before choosing a tool, define your budget and the specific use case for your reporting needs. 

Spreadsheets like Google Sheets and Excel

Spreadsheets are often your best bet for quick and effective marketing reporting. Their versatility and ease of use make them a staple in nearly every company.  

If you’re handling campaign performance alone, a well-organized spreadsheet can be a lifesaver—as long as you understand the formulas and structure.

But once you start working with larger volumes of data and more people start creating and modifying reports, using spreadsheets as your only marketing analytics tool can quickly become chaotic. When this happens, you’ll want to explore data visualization tools. 

Data visualization tools like Looker Studio and Power BI

For small to mid-sized enterprises with multiple teams aggregating and sharing insights with each other, marketing data visualization tools are a must. Without visualization, all your data is just numbers, lacking the context and clarity needed for actionable insights.

If you’re already using Google’s ecosystem, Looker Studio is a great option, while Power BI is ideal for Microsoft users. Tableau is another solid choice. 

Training tip: If team members struggle with understanding or modifying reports, consider organizing a series of workshops dedicated to upskilling everyone on basic marketing analytics concepts. Everyone should understand how your spreadsheets are structured, where the data comes from, and what the formulas represent. You can also address how to adjust formulas in Google Sheets or Looker Studio. When it comes to data warehouses, the team should understand how different tables can be joined to generate the insights needed. Also, clarify the distinction between managing data in a warehouse vs. directly reporting through visualization tools.

Marketing data warehouses 

Marketing data warehouses are ideal for mature organizations and agencies managing marketing analytics across multiple data sources or clients. They centralize data from different channels into one system, providing complex and scalable reporting.

Setting up and maintaining a data warehouse is quite a complex and time-consuming process, often requiring a data engineer. Plus, you’ll need at least two to three months of data for accurate trend analysis and comparisons. But the investment pays off through improved reliability, scalability, and the ability to perform more advanced analytics techniques like marketing mix modeling (which we’ll get to soon).  For teams who want to reap the benefits of having a marketing data warehouse but don’t yet have the technical resources to get started, you can try Supermetrics Storage.

In large organizations, a hybrid approach works well—the data warehouse handles extensive reporting, while spreadsheets and visualization tools handle quick, ad-hoc analyses, like pulling the performance of a specific blog article using Google Analytics 4.

You can use Supermetrics to automate the transfer of marketing data into your preferred reporting tool (goodbye, copy-pasting). By centralizing data access and transformation, you can reduce errors, improve team collaboration, and get reliable insights much faster. For companies with a 1 million ad budget, even a 10% boost in efficiency can translate to savings of 100K.

Marketing analytics techniques 

The best marketing analytics approach depends on what you’re trying to achieve. Let’s dive into some common techniques to guide your strategy. 

Test, measure, iterate  

When working with marketing data, aiming for a perfect outcome from the start can waste time and lead to frustration. Instead, adopt an iterative approach to marketing—start with a simple hypothesis, test it, gather feedback, and refine it as you go. 

Marketing experiments and testing help you continuously improve and respond to new insights. You may not end up exactly where you expected, but you’ll get closer to solving real business challenges.

Data storytelling 

Making sense of your data goes beyond dropping numbers into a report. Even the clearest table or graph can fall flat without context. To make your data meaningful, tell a bigger story.

Start with the big picture in your report by first highlighting the general KPIs without diving into details. As you progress through the report, use granular graphs to explore specifics. For example, if conversions increased by 8% over the month, break it down: which campaign drove this spike, what were its goals, and how did the target audience respond?

Data storytelling keeps stakeholders engaged and provides clear direction for future actions.

A/B testing

A/B testing can be as simple or sophisticated as you need it to be, but the essence remains the same: compare two versions to see which performs better. Whether you’re testing ads, website copy, or email subject lines, you can start by analyzing results in a spreadsheet. For example, for a brand test we did in 2023, we used A/B testing to test which creative direction resonates well with our audience.

While basic testing involves comparing performance metrics or conversion rates, scaling up or increasing complexity may require more advanced tools. Start with the fundamentals and expand your toolkit as your needs evolve.

Advanced marketing analytics techniques  

We define advanced techniques by two key factors:

1. The volume of data needed 
2. The complexity of the calculations 

To decide if you need an advanced approach, start by asking: Can this be handled with a simple spreadsheet formula, or do we need a complex SQL query to integrate data from multiple tables? This will help you choose the right method for your analysis.

And before diving into advanced methods like linear regression or AI-driven prediction analytics, make sure you’ve got the basics covered: clear campaign naming conventions and plenty of clean data. 

Messy campaign names or inconsistent formatting can make data integration and analysis nearly impossible. As the saying goes, “garbage in, garbage out.” Without clean, structured data, even the shiniest new tools and techniques won’t give you the insights you need. 

With that in mind, let’s explore some advanced marketing analytics techniques you can try once you have a solid foundation in place. 

Incrementality testing

Incrementality testing helps you determine the true impact of your marketing efforts. For example, if you’re testing an ad campaign, you want to find out if the ads genuinely drove revenue or if the same results would have happened without the ads.

Here’s how it works: Divide your target audience into two groups. Run ads in one group and withhold ads from the other. By comparing the results—such as sales or conversions—between these groups, you can isolate the effect of the ads.

By measuring and comparing performance metrics like conversion rates between the two groups, you can pinpoint the incremental impact of your advertising efforts.

Multi-touch attribution 

With multi-touch attribution, you analyze which interactions—such as clicks from different ads or campaigns—played a role in a conversion. This approach relies heavily on UTM tags and clean campaign naming conventions.

Unlike last-click attribution, which only credits the final touchpoint, multi-touch attribution acknowledges the contribution of multiple touchpoints. To get accurate results, you need plenty of data from different campaigns and channels.

Marketing mixed modeling (MMM)

Marketing mix modeling is used to evaluate the impact of various marketing channels and tactics on sales or other KPIs. It uses regression analysis, such as multiple linear regression, to isolate the effects of different marketing activities and external factors while accounting for potential interactions. 

MMM requires a lot of data gathered over months or years, integrated with external factors like global financial trends or weather conditions. The humble spreadsheet can’t handle this volume of data—you’ll need to use a data warehouse.

Level up your marketing analytics skills 

Hungry for more? To keep learning about marketing analytics, focus on two key areas: 

1. Domain knowledge

Better understand the data you’re tracking by following industry leaders and resources like Martech, Search Engine Journal, HubSpot, and Google.

Never underestimate the power of asking the right questions. Consulting with different marketing professionals can save you time and provide essential context, making your analysis and reporting much more efficient.

2. Technical Implementation

For the technical side, focus on how to track metrics and build reports. Take Google Suite online courses, and join the dbt Community to deepen your technical skills and stay updated on new methods. Learning SQL is also valuable, as it’s a user-friendly language for powerful data manipulation.

Invest in enhancing your first-party data strategies to gain detailed customer insights, which will improve targeting and campaign effectiveness. Familiarize yourself with Conversions APIs to integrate and leverage client data more effectively.

And, of course, here on the Supermetrics blog, we share practical tips on staying up to date with industry changes and tools. 

Over to you 

Marketing analytics might seem daunting at first, but starting with clean data and basic tools will put you on the right path. As you gain experience, experiment with different techniques and refine your methods, always keeping your use cases in mind. 

Stay curious and keep learning: By building both your industry knowledge and technical skills, you’ll be better equipped to uncover actionable insights from your marketing reports.