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.

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. 

• Headlines gives you an overview of all the most important changes to your website’s quantitative KPIs. It automatically updates every week, and every metric includes a ‘learn more’ button, in case you want to dig deeper. 

• Contentsquare’s dashboards allow you to build a custom dashboard to track your website acquisition data. If you want to keep an eye on how your conversion rate or bounce rate differs by channel, for example, you can create a dashboard to do this. 

Contentsquare gives you a high-level overview of your most pressing quantitative 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. 

Use Contentsquare to easily launch a user persona survey on your website for free. You can target it to appear only to users with qualities you want your ICP to have—repeat customers, for example.

Once you’ve compiled your survey findings to create an ICP profile, you can tailor your marketing to appeal to exactly this demographic of users—and attract superfans.

Gather essential demographic information about your customers with Contentsquare’s user persona survey template

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 a tool like Contentsquare Interviews to meet your customers and ask them directly about their demographic info and brand sentiment. You can also use surveys to gather demographic data and user opinions.

💡 Pro tip: use Contentsquare to collect the data you need to make informed marketing decisions.

• The Heatmaps tool visualizes user behavior trends on your web pages, using colors to reveal what users click on, engage with, or ignore. They also show whether the elements and information on a page are in the best order to help users achieve their conversion goals.

• Session Replay captures anonymized videos of users’ cursors as they navigate your site. Watch session replays to understand what your users’ paths to conversion look like, and spot ways to smooth the path to conversion.  

• Funnels visualizes your user journeys and shows how many users drop off at each stage. Click on any stage to watch a session replay of users’ cursors at each step in your funnel, in the moments before they drop off. This helps you understand why users decide to exit your conversion funnel at the point they do.

• The Journey Analysis tool creates a sunburst-shaped visualization of how users move through your site, and how many drop off at each step. It helps you understand at a glance which pages need optimization, and which are driving revenue. 

Combine Funnels and Session Replay to quickly watch playbacks of users exiting your funnel and see what went wrong

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

🔥 Pro tip: If you’re using Contentsquare and want a cheat code for understanding your product analytics, check out AI CoPilot. 

If you have a question about your product analytics data, type it into the search bar, chat style, and CoPilot will run the analysis for you. It’ll even show you which events and queries were used to get the results. 

AI CoPilot will answer your product analytics questions with a diagram and an explanation of which data it crunched to create it  

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. 

For example, you might discover:

• Several interview participants said they bought your product as a present

• The majority of survey respondents mentioned health-related factors when asked why they chose your service 

• Dozens of users struggle to find the ‘Pay now’ button, as evidenced by session replays 

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. 

You can also include a data visualization of an important metric—think a graph or bar chart—to make your results even more digestible.

💡Pro tip: with Contentsquare, you can create unlimited, customized dashboards for yourself and your team. This ensures everyone has an updated view of the metrics that matter to supplement your reports.

In fact, you can even autopilot your reporting thanks to dashboard subscriptions. These allow users to subscribe to a dashboard, and receive a summary of its most important updates on a daily, weekly or monthly basis. This way, everyone gets the information they need to stay aligned, with no extra work. 

Contentsquare allows you to create unlimited dashboards to slice and dice your analytics data in exactly the way you need to 

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

Data-informed marketing is effective marketing 

Once you’ve established a data analysis process that helps you take the temperature of your progress and understand your users, the only thing left to do is keep it up. 

Maintain a regular cadence of measurements and more and more patterns will start to emerge in your data. With regular check-ins and analysis, you’ll become an expert at delighting your users and meeting your marketing goals. 

FAQs about the data analysis process