How to use data analysis methods and techniques to create customer-centric marketing campaigns

Putting on your creativity cap to strategize a new marketing campaign is exciting—but what makes your hard work really feel worth it is when it resonates with your audience and performs well. 

While some marketers might be lucky enough to find a winning formula on the first try, you need to use data analysis methods to guide your campaign strategy.

Putting customers at the center of your decision-making through data analysis helps you create relevant campaigns that connect with your audience and hit your KPI goals. This guide reviews five data analysis methods marketers need to make informed decisions.

5 popular data analysis methods for marketers

Marketers use data analytics to review performance, prioritize campaign updates, and understand customers. As you’ll see, the method you choose depends on what you learn and the data you collect. 

The types of data marketers most commonly use are:

• Quantitative data. Numeric insights from website analytics, multiple choice surveys, polls, and heatmaps give you an objective summary of marketing performance and customer responses. 👉Check out our guide on quantitative data analysis

• Qualitative data. Text-based and observable insights from customer interviews, open-ended survey questions, and session replays let you learn what customers do in your product and how they feel about it. 👉Check out our guide on qualitative data analysis

• Combination of data types. Multiple data points from quantitative and qualitative methods give you a well-rounded look into customer motivations, goals, and challenges. 👉 Read how Sephora uses qualitative and quantitative data to drive customer satisfaction. 

Now that you know the different types of information marketers collect, use the methods below in your data analysis process to get the best results, prioritize product updates, and inform your business decisions. 

1. Descriptive analysis

When you want to measure what happened in the past, use descriptive analytics. This data analysis method summarizes quantitative data results, like how many likes a social media post got or your newsletter sign-up rate. Popular descriptive analysis methods include average, median, mode, and simply comparing survey response rates of a multiple-choice question.

How marketers use descriptive analysis:

• Benchmark organic traffic each month to understand the impact of content marketing 

• Compare campaign engagement and conversion results to quarterly goals

• Use surveys to measure how prevalent a goal is in your customer base 

Use Contentsquare to launch customer surveys faster. We offer 40+ customizable templates to choose from, or you can use AI to generate your questions.  

Benefits and challenges of descriptive analytics:

• Many people have some experience with straightforward analysis methods like calculating an average or ranking percentage response, which makes this method quick to implement

• Data collection becomes easy through existing experience analytics or short surveys

• Quantitative data is objective, which means there’s no room for differing interpretations

There are limitations to descriptive analytics:

• The process measures an outcome but doesn’t describe why customers chose the response they did or behaved a certain way

• You may lack context about a problem if you only look at one particular data point

• You need enough data points to have statistical significance if you want to apply decisions to your entire audience confidently

💡 Pro tip: use Contentsquare AI to create surveys in seconds and collect customer feedback easily.

Asking your audience questions with multiple-choice surveys is a great way to collect quantitative data—assuming you ask the right questions. Your research questions need to be specific enough to get relevant data but not so detailed that you accidentally lead customers to the answer you want to hear. 

Use Contentsquare to create a survey with AI. Simply describe your goals, and it’ll generate clear, unbiased questions to help you collect the data you need. You’ll get a ready-to-launch survey in seconds. 

Contentsquare’s AI will drastically reduce the time it takes you to create a survey—and mitigate against the kind of leading questions that produce skewed results 

2. Inferential analysis

Sometimes, you have a hunch but want to back up your ideas with data. Inferential analysis lets you hypothesize about your customers’ preferences and motivations by using a mix of multiple quantitative or qualitative data points. You create an inference by stacking insights observed at the same time. 

For example, Contentsquare’s Heatmaps tool gives you a visualization of how users interact with different zones, or areas, of your website. Let’s say a heatmap reveals that a zone that contains an important CTA has a low exposure rate (that is, the percentage of pageviews where at least half the zone was visible on the screen) but still a relatively high click rate from those users that do see it. In that case, you might infer that the CTA’s messaging and styling are appealing to users, but that it would get more clicks if moved to a more prominent position.

Contentsquare offers you five types of heatmaps: move, click, scroll, engagement, and rage click

How marketers use inferential analysis:

• Ask multiple questions in a customer interview and connect common themes to create a product narrative

• Research customer preferences or priorities between item categories with surveys

• Compare on-page customer feedback from different referral sources to learn about your audience coming from each traffic source

Benefits and challenges of inferential analysis:

• Comparing customer responses across multiple questions or touchpoints gives you a fuller understanding of user behavior

• Weighing customer responses based on specific conditions—like customer segments with the highest average order value—helps you prioritize which feedback and suggestions to implement

• Creating hypotheses from actual customer interactions provides campaign ideas you may not have thought about before

There are drawbacks to inferential analysis:

• Your hypothesis-building can be subjective, so look for multiple customer responses or data points that validate an assumption instead of relying on a single insight

• You need to collect and manage multiple data sets, which can be time-consuming

• An inference is an informed guess, so you still need to test your hypothesis with A/B testing

📹 Pro tip: If Contentsquare is your go-to behavior analytics platform, use the Journey Analysis tool to spot where users are dropping out of your conversion funnel—and infer why. 

Journey Analysis pulls together data from all your user journeys, and gives you a colorful visualization of which pages they visited and which pages saw the most drop-off. 

If you use the visualization to hypothesize why a particular page seems to encourage drop-offs, you can easily get more qualitative insights to test it. Simply click through on the chart to see session replays for the relevant page, and you’ll be able to see what your users saw the moment they decided not to convert. This way, you’ll be confident that your A/B test hypotheses are based on more than a good hunch! 

Click on the blue ‘See Replays’ button to watch recordings of your users’ cursors at exactly the moment you need to understand their thinking process

3. Regression analysis

Regression analysis is a powerful statistical method that measures the relationship between data points, such as whether increased marketing spending is related to more revenue. The basic process of regression analysis involves plotting your two variables on a chart and then seeing how far those points stray from the regression line. If the data sits close to the line, there's a correlation. 

Since regression analysis includes multiple variables and some equations, it’s common to use a spreadsheet add-in or a tool like Tableau or The R Project.

Linear regression analysis can help answer the question, ‘Does more SEO investment lead to more sales?’ Image via PracticalEcommerce

How marketers use regression analysis:

• Discover which blogs shared on what social media channels resulted in the highest website traffic to update your social sharing strategy

• Compare email engagement metrics to website sales to measure the potential impact of the channel

• Learn which customer segment is happiest with your company and product through a survey to refine your targeting and messaging

Regression analysis benefits and challenges:

• Measure how variables relate to one another to prove marketing impact

• Evaluate what to do more of—for example, if you find a correlation between an investment or campaign and increased sales

• Analyze large data sets using regression analysis tools and spreadsheets

There are downsides to regression analysis:

• The process is a bit more complicated than simply checking your Google Analytics dashboard, so you’ll need a specialized tool or spreadsheet

• Correlation isn’t causation, and you might not account for all potential variables that affect an outcome

• A few outliers can easily skew results

Pro tip: Contentsquare’s AI Copilot offers a shortcut to creating a regression analytics data visualization. Prompt the AI using a chat-style command to plot the two variables you think might be correlated against each other. It’ll do it for you, and even explain where it drew the data from. 

For example, you could prompt the tool with the query: “Create a chart that shows the relationship between average cart size and customer purchase frequency. I want to see if customers who buy more frequently also tend to spend more per transaction.” 

If you have a gut feeling that two of your metrics may affect each other, you’ll get a data visualization to prove or disprove it in just a few seconds. 

Contentsquare’s AI Copilot can help you verify whether there are trends in your data without you needing to spend lots of time number-crunching 

4. Content analysis

Content analysis turns qualitative insights into quantifiable results to help you make conclusions about customer perspectives, perceptions, and motivations. For example, you can count how many open-ended survey question responses mentioned particular themes to rank their importance to your audience—or, if you’re using Contentsquare, you could allow the platform to do this for you.  

Pull content analysis data from open-ended surveys, session replays of real website interactions, interviews, reviews, testimonials, social comments, and brand mentions. You could even run a content analysis on competitor reviews to find what their customers dislike to position your brand against it.

How marketers use content analysis:

• Compare repeating themes across customer interviews

• Map the most common customer journey steps by watching session replays or reviewing customer journey analysis visualizations 

• Review testimonials to discover what stands out to customers to use in future campaigns

Benefits and challenges of content analysis:

• You can pull from a wide range of data sources depending on what you already have access to and the time you have to research

• Quantifying responses turns subjective responses into objective numbers

• It’s easier to share customer response summaries with stakeholders than sharing multiple clips or large qualitative data sets

There are obstacles with content analysis:

• Manual text analysis is slow, but there are tools like Lexalytics that help

• There’s still some subjectivity involved since you decide how to group responses (or, if an algorithm is grouping them for you, algorithmic biases may influence what gets tagged) 

• Reducing long responses to simple ideas can leave valuable insights behind

Pro tip: use Contentsquare to skip the manual work of processing open-ended survey results into quantitative data.  

If you’ve got dozens, or hundreds, or thousands of open-ended survey responses, the content analysis process quickly gets unmanageable—and your outputs can become riddled with human error. 

With Contentsquare, you can automate the process. You can run a sentiment analysis on open-ended survey responses in just a couple of clicks, sorting them into “positive”, “negative” and “neutral”. 

The platform can also automatically tag responses with labels that sort them into themes, and even use AI to write a short report of the overall findings—content analysis at turbo speed! 

With Contentsquare, AI will synthesize the main findings of your survey into a report—and save you hours of pouring over spreadsheets  

5. Predictive analysis

Predictive analytics anticipates future trends or analyzes customer behaviors with big data sets, predictive models, artificial intelligence (AI), and machine learning tools. In other words, it’s a bit advanced. However, marketers can unlock powerful insights, like when L’Oréal and Synthesio used AI to forecast beauty trends.

If you don’t want to work with a specialized agency or consultant, there are predictive analytics tools that help marketers without advanced data analysis skills extract insights from customer data.

How marketers use predictive analytics:

• Uncover new customer segments based on small differentiating behaviors and psychographics

• Find related products to recommend to customers based on past purchases for personalized experiences 

• Anticipate trends in your industry to create innovative campaigns

Benefits and challenges of predictive analytics:

• You can review vast amounts of quantitative data faster than previously possible with technology like machine learning and AI

• Nuanced customer insights and trend data give you a competitive advantage 

• Easily analyze customer behavior at scale, as opposed to manually reviewing a few interview transcripts

There are drawbacks to predictive analytics:

• The output is only as good as the raw data input, so incomplete or inaccurate data within a large dataset can skew results

• Collecting the volume and variety of data you need for predictive analytics can be time consuming

• You’ll likely need to use a specialized tool or work with a data analyst

🚦 Pro tip: monitor customer behavior with custom dashboards. 

With Contentsquare, you can create an unlimited number of dashboards that report on metrics relevant to a particular project. You can share these dashboards with your team, and even subscribe to them to receive notifications on key data points every day, week or month. 

This way, you’ll have enough visibility to remember where your KPI data has been, and predict where it might be going. 

With Contentsquare, you can create as many dashboards as you need to, either based on customizable templates or from scratch

Combine data analysis with empathy to create effective campaigns

When you’re knee-deep in spreadsheets and up to your eyes in statistics, it’s easy to view customers as just numbers on the screen. Leading with empathy and curiosity will give you a new perspective on data analysis methods. 

If you have a question, ask your customers in a survey. If you want to understand their motivations, chat with them in an interview. If you want to see how they move through your website’s marketing funnel, watch a session replay of their behavior. 

Your best strategies and campaigns come from a blend of data and humanity. Simply begin with a question or hypothesis and start investigating and analyzing. 

FAQs about data analysis methods for marketers