Mastering the data analysis process: a 5-step guide for marketers

Your data analysis process gives you a snapshot of who your users are, what they expect from you, and how successful your marketing campaigns are at converting them. It also provides you with the evidence you need to demonstrate your successes and helps you understand if and when your marketing strategy needs to change.

With so many metrics and priorities competing for your attention, it can be hard to settle on a data analysis process that’s time-efficient but still gives you meaningful insights to base decisions on.

This article helps you cut through the noise with a five-step user-centric data analysis process, so you can dig deep into your customers’ needs and desires, and plan and execute campaigns that really resonate.

Use Contentsquare to analyze your data

Contentsquare is the data analysis platform for understanding your key metrics and the customer experience that feeds into them.

A 5-step data analysis process for marketers

Digging into quantitative and qualitative data helps you understand your customers on a deeper level, so you can plan campaigns that speak to them directly.

Follow these five steps to come up with a user-centric data analysis process. Regularly check in with your data, and you’ll know when to pivot your marketing tactics, and when to celebrate your wins.

1. Pick your metrics

As a marketer, there are dozens of metrics you could be tracking to get insights into who your users are, what drives them, and whether they’re responding to your campaigns.

Many marketers center their data analysis around one North Star metric—the number that’s most connected to your revenue and long-term success—such as conversions, sales, or active users. When planning which metrics to track, prioritize those connected to your North Star, and which you’ll be able to infer patterns from over time.

It’s important to track quantitative data—information that’s expressed in numbers—but don’t overlook qualitative data—non-numeric information about how your users feel and behave. It’s easy to over-prioritize hard numbers, but the two data types complement each other; if you want to understand why key figures look the way they do, subjective user insights are gold.

Quantitative data to track

• Conversion data: revenue, customer acquisition costs (CAC), average order value (AOV), customer lifetime value (CLV), customer retention rates
• Ad data: ad spend, return on ad spend (ROAS), engagement rate, click-through rate (CTR), cost per click (CPC), cost per lead (CPL), return on investment (ROI)
• Social media data: reach, impressions, CTR, engagement rate, follower growth rate, number of likes and comments
• Email marketing data: open rate, CTR, reply rate, unsubscribe rate
• Website data: traffic, bounce rate, exit rate, CTR, drop-off rate

Pro tip: tracking your quantitative metrics can be a scavenger hunt that involves flicking between tools. If you’re using Contentsquare, that’s no longer the case—it delivers updates on all your most important quantitative metrics, side-by-side with reporting on your qualitative metrics.

Qualitative data to track

• Customer demographics: the age, gender, geographical location, hobbies, career sector, income, likes and dislikes of your target user base
• Brand awareness: the extent to which people in your target market have heard of your business, and the sentiment they feel toward it
• User behavior: time spent on page, signs of customer satisfaction or frustration when experiencing your site or product, and how their customer journey looks, bearing in mind that it may be across multiple sessions and devices

💡 Pro tip: use qualitative data insights to build a clear picture of your ideal customer persona (ICP)—an imaginary best customer ever, who would adore your product or service.

2. Select your tools and start collecting data

Whichever metrics you’re tracking, you’ll need to collate data from a number of sources. Here’s the software you’ll need to track your data points:

• Experience analytics: use a platform like Contentsquare to collect and analyze user behavior data on your site
• Web analytics: use tools like Google Analytics and Contentsquare to help you log essential metrics like bounce rate, time on page, and number of sessions
• Social media analytics: use social media platforms’ own native analytics tools or third-party ones like Hootsuite, Sprout Social, or Buffer to understand your performance on social media
• Ad analytics: use the native analytics tools of advertising platforms such as Google Ads, Meta Ads, or Amazon Ads
• Email marketing software: use platforms like MailChimp, Constant Contact, or Klaviyo to track email campaign metrics, like open rates and click-through rates (CTR)
• Voice-of-customer tools: use feedback collection tools to ask your customers directly about their demographic info and brand sentiment.

3. Analyze your data

Now that you’ve gathered all the data you need, it’s time to look for patterns with data analysis techniques.

To analyze your quantitative data sets, examine how a metric has increased or decreased over time, and see if there’s any qualitative data that may account for it. For example, you might discover:

• Emails with an emoji in the subject line received a 20% higher open rate on average than ones without
• Social media posts about the company’s mission gathered 50% more engagement on average than posts about the product
• Users on mobile dropped out of the sales funnel 45% more frequently than users on desktop did

Next up, look for patterns in your qualitative data by checking whether results have elements in common and if so, grouping them together. This helps you identify trends that tell you more about your users, how to market to them, and what to fix in the user experience.

4. Share your findings

Review your key marketing metrics at least once a month to assess how effective your strategies have been. Many marketing teams produce a simple, one-page performance report to share with leadership and a more in-depth analysis for their own reference.

To put your figures in context, your marketing data analysis report should include historical data going back at least three months. Leave space beside each result for a comment so that you can add your analysis.

5. Plan your next steps

After interpreting your data, the only thing left to do is act upon what you’ve learned. Optimize your marketing strategy for your North Star metric by judging how effective your past efforts have been, and, if necessary, pivot your tactics. When changing tack, the insights you’ve gathered about your customers’ demographics and preferences should be your guide.

Here are three great ways to use your data analysis.

• 🤝 Get buy-in for your marketing strategies: when your reports demonstrate that your ads are returning more leads every month, you can confidently invest more budget in them
• 🛬 Craft content that lands: once you’ve used tools like surveys and user interviews to understand your users, you can plan blogs, social media posts, and ads that speak to them directly
• 🔮 Predict the future: an understanding of your data trends helps you estimate what your business will look like in the coming months, so you can plan marketing efforts strategically

FAQs about the data analysis process

• Why is data analysis important in marketing?

  • Data analysis is crucial in marketing because, without it, you’d never understand who you are marketing to, whether your efforts are paying off, or what you could be doing to achieve better results.

• What data do marketing managers need to track?

  • The data marketing managers need to track depends on the nature of your product and business. However, common metrics marketers measure include conversion metrics, ad data, social media data, email marketing data, experience analytics data, customer demographic data, and user behavior data.

• How do I know if my data analysis process is successful?

  • You’ll know your data analysis process is successful if it helps you understand how to increase your key metrics, understand more about your users, and is easy for your team to understand.

• How can I improve my data analysis skills as a marketer?

  • The analysis skills you’ll need to master depend on the tools you’re using to collect data. Look for online courses and industry resources for further education.