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

Putting customers at the center of your decision-making through data analysis helps you create relevant campaigns that connect with your audience and hit 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 in the data analysis techniques below, the method you choose depends on what you learn and the data you collect. 

The types of data marketers most commonly use are:

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. 

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 web 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 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

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, an Engagement Zone heatmap combines interaction data on a website page—like clicks and scrolling—to highlight the elements visitors engage with the most. If your heatmap reveals that a particular image or headline draws visitors in, you can use it in a future campaign to test if it drives engagement across other channels.

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

3. Regression analysis

Regression analysis is a powerful statistical analysis method that measures the relationship between data points, like comparing 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. There's a correlation if the data sits close to the line. 

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.

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

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. 

Pull content analysis data from open-ended surveys, recordings 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.

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

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

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 pull insights from customer data.

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

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 recording 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.