How to Analyze Data as a Marketer: Rising Above the 99%

In today’s fast-moving digital marketing world, the ability to analyze data is more than just an advantage – it’s the foundation of success. Data analysis guides strategies, informs decisions, and, most importantly, offers a clear path through the chaos of information overload. But let’s face it – the sheer volume of data available to marketers today can be overwhelming. From website analytics to social media insights, the flood of numbers and trends can leave even the most experienced marketers adrift in a sea of metrics.

That’s where the art and science of data analysis come in. It’s not just about having access to data; it’s about knowing how to interrogate it, understand it, and use it to your advantage. Think of data as a puzzle. Each piece is crucial, but it’s how you fit these pieces together that reveals the bigger picture and the path to marketing success.

This blog post is your guide to navigating the complex world of marketing data. Whether you’re looking to refine your approach, streamline your analysis, or simply get a better grasp on what your data is telling you, you’re in the right place. We’ll walk through a structured, step-by-step process to elevate your data analysis skills. By the end of this journey, you’ll not only rise above the 99% of marketers who are lost in the data flood, but you’ll also discover your unique superpower in the realm of marketing data.

Step 1: Start with a Burning Question

Every good analysis begins with curiosity and a burning question that seeks an answer. But not just any question; in the realm of marketing, precision is key. A well-defined, specific question acts as a beacon, guiding you through the vast sea of data to the insights that are most relevant and actionable.

For instance, rather than asking a broad question like, “How do we increase conversions?”, a more targeted question would be, “Which demographic is most responsive to our latest campaign?” or “What time of day do our users most frequently make purchases?” These specific questions not only narrow your focus but also set the stage for more meaningful analysis.

This approach transforms data analysis from a daunting task into a purpose-driven exploration. By starting with a precise question, you eliminate irrelevant data, hone in on what’s important, and set a clear direction for your analysis. Remember, the quality of your question shapes the quality of your insights. So take the time to refine your question, make it as specific as possible, and let it light the way to valuable discoveries in your data.

Step 2: Locating the Question in the Marketing Funnel

Identifying where your question fits in the marketing funnel is a crucial step in data analysis. The marketing funnel — awareness, consideration, conversion, and retention — serves as a map, helping you understand the journey your customers take and where your data fits in this journey.

For example, if your question is related to customer awareness, you might focus on data from social media engagement or website traffic. If it’s about the consideration phase, you might look more closely at content metrics or reviews. Understanding this context is vital because it determines the type of data you’ll analyze and the metrics that are most relevant.

• Awareness: Metrics like reach, impressions, and unique visitors are key.
• Consideration: Look at time spent on site, content engagement, or lead generation metrics.
• Conversion: Focus on conversion rates, days to conversion, and quantity sold per product.
• Retention: Pay attention to repeat purchase rates, refund rates, and customer lifetime value.

For a comprehensive look at 44 key metrics for each stage of the marketing funnel, check out our other blog post.

By placing your question within the funnel, you ensure that your analysis is not just accurate but also aligned with your marketing objectives. It’s about analyzing the right data at the right stage to extract the most meaningful insights. So, take a moment to pinpoint where your question sits in the funnel, and let that guide your data journey.

Step 3: Gathering and Grooming the Data

Once you have your targeted question and understand where it fits in the marketing funnel, the next crucial step is gathering and grooming the data. This phase is about collecting the right data from the right sources and ensuring its quality and reliability.

Gathering the Data: Start by identifying the sources that are most likely to provide the data you need. This might include:

• Web analytics tools like Google Analytics for website performance data.
• Social media insights for engagement and demographic data.
• CRM databases for customer behavior and purchase history.
• Email marketing platforms for open rates, click-through rates, and conversion data.
• Ecommerce platform for product and purchase data. 

Remember, the key is not just to accumulate data but to target the data that directly relates to your specific question.

Grooming the Data: Data grooming, or cleaning, is critical. It involves:

• Removing inconsistencies: Look for and rectify any discrepancies in your data.
• Eliminating irrelevant data: Filter out data that doesn’t pertain to your specific question.
• Ensuring accuracy: Double-check that the data is correct and up-to-date.

Clean data is essential for accurate analysis. Errors or irrelevant information can lead to misguided conclusions, so take the time to groom your data thoroughly.

This stage sets the foundation for insightful analysis. By meticulously gathering and grooming your data, you ensure that your subsequent steps are built on a reliable and relevant base.

Step 4: Dive into Exploratory Data Analysis

After gathering and grooming your data, it’s time to dive into the heart of data analysis: exploratory data analysis (EDA). This is where you start to unlock the potential within your data, transforming numbers and stats into actionable insights.

What is Exploratory Data Analysis? EDA is an approach to analyzing data sets to summarize their main characteristics, often using visual methods. It’s about exploring your data, finding patterns, spotting anomalies, identifying trends, and getting a sense of the story it tells.

Techniques for Exploratory Data Analysis:

1. Visualization: Use graphs, charts, and plots to see patterns and relationships in your data. Tools like heat maps, scatter plots, and line graphs can be incredibly illuminating.
2. Descriptive Statistics: Get a basic understanding of your data’s distribution and tendencies. Look at measures like mean, median, mode, range, and standard deviation. Want to learn more about statistics in marketing – check out our blog post.
3. Segmentation: Break your data into smaller segments for more specific analysis. This could be based on demographics, behavior, time periods, or any other relevant criteria.
4. Trend Analysis: Identify any trends over time in your data. Are there consistent increases or decreases? Seasonal patterns?

Why EDA Matters: EDA helps you familiarize yourself with the intricacies of your data. It provides a deeper understanding and a solid foundation for more advanced analysis. By thoroughly exploring your data, you can make more informed hypotheses and strategic decisions.

Remember, the goal of EDA is not just to see what your data looks like but to start understanding why it looks that way and what it means for your marketing efforts.

Looking to learn how to easily analyze customer behavior using Google Analytics 4? Check out this video from our founding partner, Nick: LINK

Step 5: Contextualize Your Analysis

Understanding the data in front of you involves more than just numbers and charts; it requires placing these elements within the right context. Contextualizing your analysis means examining your data from multiple angles to gain a comprehensive understanding.

1. Historical Context: Compare your current data with past trends. Are there any noticeable patterns or significant deviations? This comparison can reveal whether certain changes are part of a long-term trend or an anomaly.
2. External Context: Consider external factors that could influence your data. This includes market shifts, competitor actions, industry trends, and even global events. Understanding the external environment helps you differentiate between internal performance and market-wide changes.
3. Internal Context: Compare your data against internal benchmarks and goals. Has there been a change in strategy, product offerings, or target demographics? Internal shifts often have a direct impact on your marketing data.
4. Contextual Nuances: Delve deeper into specific incidents or anomalies in your data. Was there a marketing campaign that coincided with a spike in traffic? Did external events (like a holiday or major news event) impact consumer behavior? Understanding these nuances is key to a holistic view of your data.

By contextualizing your analysis, you ensure that your interpretations and conclusions are well-rounded and grounded in reality. This approach prevents misinterpretation and enables you to make more informed strategic decisions.

Step 6: Crafting a Hypothesis

The next critical step in data analysis for marketing is forming a hypothesis. A hypothesis is an informed assumption made based on your exploratory data analysis. It’s a statement that you can test and validate, guiding your future marketing strategies.

Developing a Hypothesis:

1. Base it on Observations: Your hypothesis should stem from patterns, trends, or anomalies you’ve identified in your data. For instance, if you’ve noticed that your website traffic peaks on weekends, you might hypothesize that launching campaigns over the weekend could increase engagement.
2. Make it Testable: A good hypothesis is specific and testable. It should clearly state what you expect to happen as a result of a specific change or strategy.
3. Use the ‘If-Then’ Structure: A practical way to frame your hypothesis is using the ‘If-Then’ structure. For example, “If we target our ads to demographic X, then we will see an increase in conversions.”

Example Template for Crafting a Hypothesis: “Based on our observation of [Specific Data Trend], we hypothesize that implementing [Specific Change] for [Target Audience/Aspect] will result in [Expected Outcome]. We will confirm this hypothesis by observing [Specific Metric or Outcome].”

Crafting a well-thought-out hypothesis is more than just an academic exercise; it sets the stage for strategic experimentation and focused analysis. It turns your data analysis efforts into actionable insights and directs your marketing experiments towards measurable outcomes.

Step 7: Putting Your Hypothesis to the Test

The true test of any hypothesis in marketing is in its validation — putting it to the test in the real world. This is where you find out if your insights and strategic changes based on data analysis hold up under practical conditions.

Key Steps in Testing Your Hypothesis:

1. Define Your Testing Parameters: Clearly outline what you are testing and the metrics you will use to measure success. This could be an increase in conversion rates, higher engagement on social media, or improved email open rates.
2. Use A/B Testing: A/B testing is a powerful way to test hypotheses. It involves comparing two versions of a campaign to see which performs better. By only changing one element at a time (like the headline, image, or call-to-action), you can accurately assess the impact of that specific change.
3. Collect and Analyze Data: As you run your tests, collect data in real-time and analyze it continuously. This will help you understand if the changes you made are moving the needle in the right direction.
4. Draw Conclusions: Based on the data collected, determine whether your hypothesis was correct. Did the changes you implemented result in the expected outcome?
5. Iterate and Optimize: Regardless of whether your hypothesis was proven or disproven, use the findings to refine your approach. Every test provides valuable insights that can guide future strategies.

Example Scenario: If your hypothesis was that sending emails at a specific time would increase open rates, you would send two sets of emails at different times to similar audience segments. By comparing the open rates of these two sets, you can validate or refute your hypothesis.

Testing your hypothesis is not just about proving if you were right or wrong; it’s a learning process. Each test provides a deeper understanding of your audience and what resonates with them, helping you to continuously refine and improve your marketing strategies.

Step 8: Report & Shine

The final step in your data analysis journey as a marketer is to consolidate and communicate your findings. Effective reporting turns your data analysis and hypothesis testing into actionable insights that can inform broader marketing strategies and decision-making.

Essentials of Effective Reporting:

1. Consolidate Your Findings: Gather all the key data points, insights, and outcomes from your analysis and testing. Make sure your report is comprehensive, covering all aspects of the process from hypothesis to conclusion.
2. Use Visual Aids: Leverage charts, graphs, and infographics to make your data more digestible and engaging. Visual representations can help convey complex data in a more understandable and impactful way.
3. Highlight Key Insights: Clearly articulate the main takeaways from your analysis. What did you learn? What worked and what didn’t? How can these insights be applied to future marketing strategies?
4. Make Recommendations: Based on your findings, suggest actionable next steps. This could involve scaling up successful strategies, discontinuing ineffective ones, or further testing to refine your approach.
5. Share with Stakeholders: Present your report to key stakeholders in your organization. This includes not just the results of your tests but also the strategic implications and recommended actions.

Example Format: Start with an executive summary that gives an overview of the objectives, methods, and key findings. Follow with detailed sections on each stage of your analysis, supported by visuals. Conclude with a section on conclusions and recommendations.

Remember, the purpose of reporting is not just to showcase the work you’ve done but to provide valuable insights that can drive your organization forward. It’s your opportunity to shine as a data-driven marketer and demonstrate the power of informed decision-making.

Conclusion

As we wrap up this journey through the process of analyzing data as a marketer, it’s clear that the path to mastering data analysis is both challenging and rewarding. The ability to sift through data, extract meaningful insights, and translate them into effective marketing strategies is what sets apart successful marketers in today’s data-driven world.

Remember, the key to effective data analysis lies in asking the right questions, understanding the context of your data, meticulously grooming and exploring your datasets, formulating testable hypotheses, and, most importantly, implementing and validating your findings. The cycle of continuous learning and adapting is what will keep you ahead in the rapidly evolving landscape of digital marketing.

By following the structured approach outlined in this blog, you’re not just analyzing data; you’re unlocking the stories and insights hidden within it. This process empowers you to make informed decisions, tailor your strategies to meet your audience’s needs, and ultimately, achieve your marketing goals with precision and confidence.

So, take these steps, apply them to your unique marketing challenges, and watch as your data analysis skills transform the impact of your marketing efforts. The world of data-driven marketing is yours to conquer.

Need expert help in making sense of your marketing data? Well-Oiled Marketing offers a variety of data services, including data housing & cleaning, data analysis, and data visualization. Contact us today, where data-driven insights meet creative marketing solutions.