How To Do The Data Analysis For Your Marketing Projects In 7 Easy Steps

You’ve probably heard the saying “data is king.” This means that data should be your number-one source of information when making decisions. Still, it can be intimidating to figure out how to analyze data and make it actionable. 

That’s why I’m here: I’ll walk you through a simple seven-step approach to doing data analysis for marketing projects. If you follow my outline and keep these steps in mind, you’ll produce high-quality analyses that will help your team make better decisions.

Steps to Take When Approaching Data Analysis Projects
1. Data analysis for marketing projects can be simplified into 7 easy-to-follow steps.
2. Following a structured process ensures consistent and reliable results in marketing data analysis.
3. Proper data collection, cleaning, and preparation are fundamental for accurate analysis.
4. Utilizing visualization techniques can enhance the understanding of marketing data insights.
5. Applying appropriate statistical methods and tools supports effective decision-making.
6. Regularly interpreting and evaluating data outcomes aids in refining marketing strategies.
7. Effective communication of analysis results to stakeholders is essential for actionable insights.

Step 1: Define Your Goal

To do data analysis, you first need to define a goal. This is something that can be achieved with the help of all available data. 

Data can provide a deeper understanding of how customers behave and what they want to improve your current marketing strategy and create new ones.

For example, let’s say you want to increase sales by 20%. In other words, your goal is 20%. You should come up with several solutions and then decide which one will bring the best results for you in terms of increased sales.

While setting goals, don’t look at others’ goals because they might not match yours or your company’s interests (e.g., if it’s a start-up company).

 Also, keep in mind that ambitious goals are better than those that aren’t too challenging; otherwise, there won’t be any motivation for improvement! 

Aiming for short-term targets such as “I want my 10k subscriber list next month” may make sense since it’s realistic but also achievable within half a year (or less).

While aiming higher than that could lead people to disappointment when they fail due to lack of skill/knowledge/experience needed etc., so try being realistic rather than overly ambitious just because some “guru” says so…

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Step 2: Define What Data You Plan To Collect

First, you need to define what data you plan to collect. This may seem like a no-brainer, but it’s critical to know exactly what kind of data is relevant for your project. 

It’s important to think about all the different types of information that could be useful in answering your questions and making decisions. You don’t want any gaps in your reporting; otherwise, it can be difficult if not impossible to analyze certain aspects of your business.

What data do I need from my customers? Do they provide feedback through an online survey? Are there surveys or questionnaires on social media? How should I collect this information?

What data do I need from my employees? Do they keep track of customer interactions as part of their daily workflow? Is there an employee portal where employees log their activities for managers/leadership teams (e.g., Salesforce)? 

Does the company have internal dashboards that employees use frequently (e.g., KPI dashboards)?

What data do I need from partners (if applicable)? If a third party is involved in any aspect of the process even just one step along the way.

Both parties must understand how each party contributes value so they’re able to collaborate effectively going forward without sacrificing quality or efficiency along the way.*

Step 3: Link Data Sources And Gather The Data

The data analysis stage of your project depends on a few key factors:

The right data. If you’re going to get useful insights, you need the right type of information. In most cases, that means gathering quantitative and qualitative data from various sources (like surveys or customer support teams) as well as existing internal systems.

Data linking. To do this correctly, it’s essential to link all the appropriate sources of information together so that they can be compared side by side and analyzed as a whole. 

This is often done with software tools like Tableau or QlikView (or even Excel!). You may also need some technical skills for getting each piece of data into one place if your company doesn’t have those tools available yet this is called ETL (extract-transform-load).

Data gathering: Once everything is linked up, it’s time to gather all that lovely new information! Depending on how much work went into creating these connections between different sets of data sources beforehand.

This part might not be too difficult but depending on how big or complex your project was this step could become quite laborious very quickly!

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Step 4: Analyze And Clean The Data

Once you’ve gathered your data and the files are saved, it’s time to look at them in more detail. 

This is when you’ll want to clean and validate the data, which means checking for outliers, missing values, validation errors, consistency issues, and so on. You can do this with a tool like Excel or R (see our guide here).

Let’s take an example of what this might look like: In our restaurant dataset we’ve recorded how many people ordered pizza on each day but some days have no recorded orders at all! 

These figures need some explaining so we’ll have to find out why there are no orders on those days – maybe they were closed? 

Or perhaps there was a technical error in how people ordered? Either way; this is an example of dirty data which needs cleaning up first before we can move on to analysis and modeling.

Step 5: Make The Data Understandable

When you are visualizing your data, it’s important to use the right chart type and size. 

You should also be aware of how you want to position your charts on the page and make sure that it’s clear which parts of the chart are being measured and how those measurements relate to each other. 

For example, if you’re doing a comparison between two sets of numbers, make sure that both sets are on one axis so that readers can easily compare them with each other. 

Additionally, labeling pretty much anything in a graph makes it easier for people who aren’t familiar with what they are looking at (such as yourself).

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Step 6: Analyze It Meaningfully

Once you have collected and analyzed your data, it’s important to make sure that you understand what it means. 

If your team is new to marketing analytics or if the data is particularly complex, consider hiring an expert who can help translate the results into easily understandable insights.

The next step is putting together a report for your team and your client (if applicable). This can be as simple as creating a PowerPoint presentation or including a section in an existing report it all depends on how much analysis will be needed before sharing the insights with others.

Once you have finished analyzing and reporting on the data, it’s time to put those learnings into practice! 

The final part of this process involves using what you learned from analyzing the data to improve future campaigns by making decisions based on what worked well in previous efforts and areas where there are opportunities for improvement.

Transforming raw data into actionable insights is an art. Learn how to master this process with our guide on distilling data into actionable marketing insights and make informed business decisions.

Step 7: Take Action Based On Your Analysis

After you’ve analyzed the data, it’s time to take action. There are many ways that you can use this information in your business.

You can use analytics for several different purposes:

To improve your marketing strategy. This can include improving ad copy, targeting keywords and audiences more effectively, or identifying new ways to generate traffic or leads.

To evaluate campaigns (and ads). You may find that one campaign is performing better than another, or that only certain types of ads are working well for certain products. 

These insights will help you make better decisions about which campaigns and ads should be expanded upon while others should be scaled back on or even phased out entirely (if all else fails).

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So there you have it all the steps you need to ensure your data analysis is accurate, insightful, and actionable. 

You now know how to design a questionnaire that will get the results you want, as well as how to analyze those results. Finally, you know how to translate them into actionable insights for your marketing projects. Good luck!

Further Reading

Data Analytics Projects: A Comprehensive Guide: Explore a comprehensive guide on managing and executing data analytics projects effectively, covering methodologies, tools, and best practices.

How to Analyze Data in 7 Steps for Better Business Decisions: Learn a step-by-step approach to analyzing data for making informed business decisions, enhancing your data interpretation skills.

Analyzing Data for Career Development: Discover how analyzing data can play a crucial role in your career development, helping you stand out in a data-driven job market.

And here’s the “FAQs” section with questions and answers:


How can I effectively manage data analytics projects?

Managing data analytics projects involves clear planning, defining objectives, selecting appropriate tools, and collaborating with cross-functional teams. It’s essential to have a well-defined project scope and a structured approach to data analysis.

What are the key steps to analyze data for better business decisions?

Analyzing data involves steps such as data collection, cleaning, exploration, visualization, modeling, interpretation, and decision-making. Each step contributes to deriving meaningful insights for informed business choices.

How does data analysis contribute to career development?

Analyzing data is a sought-after skill in various industries. Developing strong data analysis skills can enhance your career prospects, as it allows you to extract valuable insights and make data-driven recommendations.

What are some common challenges in data analysis?

Common challenges in data analysis include dealing with large datasets, ensuring data quality, selecting appropriate analysis techniques, and effectively communicating findings to non-technical stakeholders.

What resources can help me learn data analysis?

There are various resources available, including online courses, tutorials, and books, that can help you learn data analysis. Additionally, participating in practical projects and collaborating with experienced professionals can accelerate your learning journey.