What is Marketing Analytics? A Complete Guide (With Examples)

Marketing analytics is how your company digitally tracks and analyzes the impact of your marketing efforts.

It uses software to gather data about user behavior and extract patterns, giving your marketing teams insight into your customer base. This data enables you to optimize your marketing approach to increase conversion and revenue to achieve business goals.

Marketing analytics can help you understand how well your current marketing efforts are working and decide how to improve your approach.

Marketing analytics is just one part of your business’s digital optimization efforts. It gives your teams access to high-quality digital data in real-time, enabling them to make informed decisions quickly and confidently.

Marketing analytics has many uses outside of marketing. In fact, every team in your business can benefit from these insights, which can help them make more decisive choices.

• Marketers: To understand customer behavior, spot new market opportunities, optimize campaigns, measure performance, and allocate marketing budget.

• Sales teams: To identify high-value customer segments to target, better understand customer needs and pain points, and measure the impact of marketing on the sales pipeline and revenue.

• Executives and leadership: To find growth opportunities, monitor the impact of marketing investments and campaigns, and make better decisions about product and service development, pricing, and market selection.

• Finance department: To measure the ROI and effectiveness of marketing spending by tracking monetary metrics and non-monetary performance indicators.

• Product teams: To understand customer preferences, desired features, problem areas, and demographics to help inform new product roadmaps.

• Ecommerce teams: To optimize site performance, improve conversion rates, personalize customer experiences, and test pricing options.

Marketing analytics gives you insight into how your marketing efforts perform based on real data.

With this information, you can effectively tailor your approach to support your business goals.

Ensures your decisions are backed by data

Marketing analytics isn’t just about gathering data—it’s about analyzing data to identify trends and gain deeper insights into customer behavior. Those insights help your teams make better strategic decisions.

If your team can comfortably work with data, your marketing efforts will be more informed and effective. Democratizing your data promotes a culture of data fluency across your team.

Gives you real-time feedback on marketing campaigns

Marketing analytics helps your team tweak your marketing approach to increase effectiveness based on customer behaviors.

If you’re running an email campaign with low conversion, analytics can help you understand why, identify the problem, and improve it.

Imagine you have an email campaign with a high through rate but an equally high bounce rate. These results might indicate your email copy was compelling, but the CTA on your landing page wasn’t. A low click-through rate suggests your email copy could use a second look.

Demonstrates the value of your marketing efforts

Marketing analytics also helps your marketing teams communicate the impact of your efforts and prove your market strategy’s return on investment (ROI). It gives leadership solid numbers to back up your team’s approach.

In a recent McKinsey study, 83% of CEOs globally saw marketing as a major growth driver. Backing up your successes with data shows leadership and how your marketing team contributes to that growth.

You can apply marketing analytics across every channel you use to communicate with customers.

Your analytics approach will vary depending on what you’re analyzing, such as customer engagement with your website, email campaigns, third-party advertising, or social media. However, analytics software helps you aggregate and draw conclusions from that information.

Web marketing analytics

Marketing analytics can help you understand visitor behavior on your website and evaluate how well your messaging and design capture and convert customers.

Your website is one of your main marketing assets and a primary interaction channel between your company and your customers, so understanding its performance is critical.

Some of the main metrics web analytics looks at are:

• Pageviews
• Page load times
• Effectiveness of calls to action
• Time spent on site
• Downloads
• Visitors’ geographic location
• Returning versus new visitors

Digital marketing analytics

Digital marketing analytics collates your broader marketing efforts beyond your website.

It’s vital to get the big picture of where you reach your customers and how your overall marketing strategy works.

However, it can be harder to capture good data here as it comes from multiple sources that likely collect data differently.

Digital marketing analytics tools might look at:

• Paid advertising
• Email engagement
• Content marketing
• Search engine marketing (SEM) and search engine optimization (SEO)
• Lead generation
• Online sales
• Offline sales
• Specific campaign performance
• Social media

Social media analytics

Though digital marketing analytics includes social media, it’s worth discussing this channel in more depth.

Social media platforms often have built-in analytics, which you must combine with your data. This may include user interactions with your promotional pages, profile, and paid advertising on social channels.

Examples of social media analytics are:

• Follower demographics
• Likes, shares, and comments
• Video or story views
• Post impressions
• Profile reach or visits
• Post saves
• Watch time
• Subscriber or follower growth
• Link clicks

Marketing analytics enables you to gain valuable customer and business insights.

Here’s how you might use it.

Customer segmentation

• Group customers into segments based on common characteristics to target marketing campaigns.
• Identify the most valuable customer groups to focus on.
• Personalize messages and offers for each segment.

Product insights

• Understand which features customers use the most or least.
• Highlight desired new features and functionality.
• Calculate user preferences for pricing options or packaging.
• Feed insights to product teams to help guide roadmaps.

Customer service improvements

• Spot common complaints and pain points.
• Connect service interaction data with other customer data to categorize issues.
• Use text analytics on comments to identify recurring themes.
• Inform customer service training and improving plans.

Forecasting demand

• Predict future sales based on past performance, seasonality, and external drivers.
• Improve inventory and production planning.
• Set realistic sales targets and revenue goals.

Feedback analysis

• Analyze survey results, online reviews, and feedback forms.
• Monitor customer satisfaction and Net Promoter Scores (NPS) over time.
• Spotlight improvement areas across the customer journey.

UX performance

• Identify website pages with high bounce rates.
• Test new page layouts and flows to enhance conversions.
• Measure the impact of site changes on KPIs, such as time on site.

Analyzing marketing data can be challenging. Customer behavior can be unpredictable, data volumes can be overwhelming, and not every tool offers the same results.

Here are the greatest challenges marketers face when analyzing data.

Getting high-quality data

Not all data is good data. If you gather data incorrectly or incompletely, you’ll likely draw wrong conclusions and make incorrect business decisions.

Ensuring high-quality data can be tricky when pulling it from multiple channels. Consider implementing systems to ensure the data you collect is consistent, complete, and segmented in useful ways.

Dealing with large quantities of data

Depending on your business's size and number of channels, you may need to manage large quantities of data.

It’s essential to use quality software to process and collate your data quickly and effectively.

Though an individual might be able to make conclusions from small data sets, more significant volumes of data require analytic software that can categorize and visualize findings so they’re easier to understand.

Integrating data from multiple sources

When collecting data from many channels, integrating it into one place is key. This enables you to compare channels and draw overarching conclusions about your audience and campaigns.

Integrating data can be difficult if you collect it inconsistently or lack software to integrate it effectively from all your tools and sources.

Knowing what to do with your data

Another challenge of marketing analytics is drawing valuable conclusions and determining what actions to take. But without action, you’re just measuring for measuring’s sake.

You might have the best data, but you likely won’t experience your desired results if you can’t use it to improve your marketing approach. Analytics software can help your team pull useful insights from your data, and training your team will enable them to make strategic decisions based on your findings.

Let’s examine how specific sectors use marketing data and effective analytics to guide their organizations.

Ecommerce

Ecommerce companies analyze customer behavior to:

• Optimize conversion rates across the purchase funnel.
• Personalize product recommendations to improve cross-selling and upselling.
• Analyze pricing and promotional strategies.
• Inform decisions about inventory levels and product assortment.

Financial services

Banks and other financial services can use their marketing data to:

• Determine customer lifetime value (LTV) to guide retention programs.
• Identify usage patterns of their digital banking solutions.
• Analyze the results of A/B tests for digital marketing campaigns.
• Measure the ROI of marketing spending.

B2B

B2B businesses may use marketing insights to:

• Score leads to focus sales efforts on high-value accounts.
• Attribute pipeline contribution to various campaign channels.
• Analyze buyer journeys to optimize touchpoints.
• Customize messaging for individual accounts.

Media

Marketing analytics are critical to media organizations to:

• Evaluate content consumption across their platforms.
• Support decisions about content development and syndication.
• Inform digital ad targeting and personalization.
• Optimize monetization by analyzing subscriptions and ad revenues.

Healthcare

Healthcare companies can benefit from digging into marketing data to:

• Improve patient engagement and experience.
• Enhance population health management programs with better data.
• Accurately forecast patient volume and bed needs.
• Optimize clinical supply chain and inventory management.

Having clear goals and well-organized data will enable you to get useful results from your marketing analytics process.

Though software can do a lot of the legwork, how your team approaches and draws conclusions from your data will determine the success of your marketing analytics.

Set goals for your marketing analytics

Establish clear goals when you begin your marketing analytics process.

What information do you need about your customers or marketing campaigns? What insight do you need to guide future efforts?

Your goals might start with a problem that needs to be solved or a question about your audience or marketing campaign.

The specific data you gather should enable you to address these questions. To track success, ensure goals are measurable and focus your objectives on the campaign elements that your team can control and influence.

Choose which metrics to track

While your data may be extensive, it’s best to focus your analytics on specific metrics to draw helpful conclusions.

Select metrics that tie to a specific business goal and track them over the lifetime of your campaign.

Some important metrics might be:

• Events: Actions taken by users that are important to your business. This could be anything—such as clicks, adding items to a cart, viewing time on certain assets, and more.

• Lead generation: A lead is generated when a customer enters their contact or personal information, demonstrating interest that could turn into a sale.

• Conversion: This is when a customer completes your desired goal, such as a product purchase, account sign-up, or subscription.

Set up audience segmentation

You want to know what your customers are doing—and who is doing what. Create segments that make sense for your business and empower you with information about the customers you want to target. You can segment your audience by demographics or behavioral cohorts.

Demographics might include:

• Age group
• Gender
• Geographical location
• Education level
• Income

Behavioral segments might include:

• Time: Time of day or day of the week a user is active. Users engaging during working hours versus weekend evenings likely have very different needs or behaviors.

• Activity: The specific activity the user is participating in. Is the visitor reading your blog or shopping online?

• Customer loyalty: Is a visitor a long-time loyal customer or a curious newcomer to the brand? A campaign targeting frequent customers looks different from one targeting brand-new ones, so it helps to have specific data on each group.

Establish a baseline and target numbers

Determine your current numbers to establish a baseline against which to compare campaign performance. Set targets to measure the success of your efforts. Segment your goals, establishing minimum, target, and stretch goal numbers. That way, you can work toward continuous improvement.

Collect data and run tests

Once you’ve defined your process, goals, and instrumentation, you can gather data. This might involve downloading data from a third party, passively tracking data on your website, or running specific tests to generate information.

Useful tests include:

• A/B testing: Track multiple variations of a page, feature, or message. A/B tests tell you more about your user preferences and what approaches are more effective. Are newsletter sign-ups stronger at the top or bottom of your page? Which email type gets more recipients to click through to a page? Which call to action message results in more conversions?

• Split testing: Less granular than A/B testing, split testing splits your audience into cohorts to test entirely distinct designs.

• Visual behavior data: Track users’ mouse movements and create a heatmap for their interests and site behavior. It shows where users click and whether they scroll down a page, etc., making it good for improving interfaces and conversion. Knowing what areas users pay more attention to enables you to put important features and messages there.

Decide what model to use to analyze your data

Choose how you will model your marketing data analytics to draw valuable conclusions from your data. Different models use different data to give you insights into customer behavior.

There are three main types of modeling.

Attribution modeling

This approach helps you understand which online touchpoints you can attribute your marketing traffic to, helping your marketing team determine which channels are most successful in driving traffic and sales. Given the volume of digital touchpoints customers have with companies today, this approach is increasingly common.

In attribution modeling, you set up rules within your marketing analytics software, such as last-, first-, or multi-touch rules, determining which channel gets credit for a conversion.

It uses various techniques, such as cookie data and statistical modeling, to determine where traffic is coming from.

Reach, cost, quality (RCQ) modeling

RCQ modeling breaks touchpoints into engagement types rather than assigning full attribution to one channel. It’s useful when you have limited or incomplete data, giving all touchpoints similar measurement units to compare them easily.

Marketing-mix modeling (MMM)

The most advanced approach, marketing-mix modeling links analytics data directly to spending by channel. This is a great way to see if your marketing investments are paying off in sales, but can be more complicated because it requires large quantities of high-quality data to get effective results.

Implement strategies based on your findings

The final step is to take action based on the insights you gain from your marketing analytics.

This could be tweaking an existing campaign to improve a specific metric or big-picture, long-term marketing campaign changes. Your actions could be as small as targeting a specific keyword or as broad as replicating the structure of a previously successful campaign.

Marketing analytics is still evolving. As we capture more data and implement innovative technologies, we’ll likely see a shift to deeper, more impactful analysis.

This might look like:

• More sophisticated analytics: AI and machine learning (ML) will enable more advanced customer data analysis, which will power more robust prescriptive and predictive analytics in marketing.

• Real-time engagement: Marketing analytics will support rapid, personalized cross-channel customer engagement by combining big data, automated ML, and real-time processing.

• Increased business integration: Tighter links between marketing analytics and sales, product, and finance via unified revenue or value-focused analytics will break down silos.

Amplitude Analytics can help you be at the forefront of this exciting future.

With real-time data pipelines, cross-functional integration, and many other features, Amplitude empowers businesses to achieve more from their marketing analytics.

Use your data to make a difference. Get started with Amplitude today.