No B.S. Guide to Data Analysis in Marketing

Unlock the full potential of your marketing strategies by diving into our comprehensive guide on data analysis in marketing – your roadmap to smarter decision-making!

Introduction to Marketing Analytics

Data isn't just numbers and charts. It's the voice of your customer, the pulse of the market, and the map to your destination. It tells you not just where your customers have been, but where they're likely to go next.

Marketing without data is like driving blindfolded. Scratch that. It’s like driving blindfolded in a car without a steering wheel. You may miraculously make one or two correct turns, but you will eventually crash and burn. In the real world, I have seen teams of world-class digital marketers, content writers, and creatives that have operated in businesses without a system of analyzing data. Despite the endless talent of these marketers, their team’s campaigns went nowhere, and their company’s performance ultimately struggled.

In this article, we will outline what successful marketing data analysis looks like and how you and your company can apply it. So, let’s jump in.

Why is Raw Data So Powerful?

When I mention data and databases to marketers and business owners, I often see their skin crawl as if they accidentally wandered into a high school math class. The basics of databases aren’t that hard, and you need to know very little to use them correctly. Here’s what you should know — every database in the world follows this model:

You might say, oh, that’s just a simple Excel table, right? Yes, that’s exactly how they are structured. Here’s the exact breakdown from database to a single point of raw data.

  • Database: One or more tables (i.e. Persons, Companies, Activities)
  • Table: Rows + columns (i.e. Persons)
  • Rows: All raw data associated with a unique identifier (i.e. row 1, which contains 1, Jodie, Tucker, 34)
  • Columns: All raw data associated within a specific category (i.e. Age, which contains 34, 56, 18, 56)
  • Cell: Where rows and columns intersect; this represents a single point of raw data (i.e. Tucker)

Raw data is the bedrock of your database, which means that it is the bedrock all of your analyses.  Your business operations should systematically collect the raw data and structure it into a database like this. Without this proper organization, you are simply sitting on a pile of garbage data.

TL;DR: Understanding the basic structure of a database will allow you to get started on the right foot.

What are the Most Important KPIs for Marketing?

A KPI, or a Key Performance Indicator, is a metric used to evaluate the effectiveness of marketing strategies and campaigns. The most important KPIs can vary depending on the specific goals and context of a business. However, these are the ones I have used repeatedly for thorough decision-making:

Acquisition Metrics

  • Cost Per Lead = Ad Spend / Number of Leads Generated
  • Customer Acquisition Cost (CAC) = Ad Spend / Number of New Customers
  • Customer Lifetime Value (LTV) = (Total Revenue / Number of Purchases) * (Number of Purchases / Number of Unique Customers) * Avg. Customer Lifespan
  • Return on Investment (ROI) = LTV / CAC

Lifecycle Metrics

  • Number of Leads
  • Number of MQLs (Marketing Qualified Leads)
  • Number of SQLs (Sales Qualified Leads)
  • Number of Customers
  • Number of VIPs (Very Important Persons)
  • Conversion Rate for Each Lifecycle Stage
  • Average Time in Each Lifecycle Stage

With the metrics above, you can have a pretty good thumb on if your marketing is making the company money and how quickly and efficiently leads are turning into long-term customers. That’s what matters.

When we outlined our KPIs, you may have been asking — where is the website traffic? Where is the social media growth? What about email open rates? While my teams have always tracked these metrics, they are not KPIs. They are not the most relevant metrics to sales growth. They are seldom used in decision-making. They should only be considered when drilling deeper into a concern in your top level reporting.

TL;DR: More data isn't always better; better data is better. Get started with the data that shows you if your marketing is making money and how far your leads are penetrating your marketing funnel.

What are the Best Tools for Analyzing Marketing Data?

Once you understand the structure of your databases and have outlined your KPIs, you are ready to analyze data. The data analysis process boils down to three important areas:

  1. Data Collection and Integrations
  2. Data Cleaning and Management
  3. Data Analysis and Reporting

Being familiar with these stages can help you develop a routine process for analysis that ultimately can be automated.

Data Collection

As a marketer, you use different types of platforms to collect information on your prospective buyers. These tools could be web analytics tools, CRM systems, social media platforms, email marketing tools, e-commerce platforms, User behavior tracking, etc. As result, user data is living in Google, Meta, Shopify, Hubspot, Hootsuite, and 25 different platforms.

This is a blessing and a curse. These platforms handle much of the legwork for collecting clean, accurate data, but how the heck do we get these into a single view?

Integrations and Data Management

Many of the platforms in the last section have protocol that allows systems to communicate (aka APIs) across systems. These can allow all your platforms to live in one area. Unfortunately, this can become a time suck for marketers who do not have IT teams or experience with these types of protocols. If you’re reading this, I doubt you do.

To manage this data, I often recommend marketers to export data into CSVs. If you have outlines the categories of data needed, cleaning up the data in a spreadsheet is often and easier task than tinkering with APIs and workflows. At least, at the beginnings of your analysis. Once you have a standardized process down, it may be worth the effort to look into third parties or specialists who can connect APIs and automate data collection.

Data Analysis and Reporting

There are a thousand ways to skin a cat, and 10,000 ways to visualize a single piece of data. For simplicity and consistency, I’m going to recommend ways to visualize all your KPIs. These have worked repeatedly for me, and I could explain these charts to an executive or a skinned cat.

Acquisition Metrics

  • Cost Per Lead: Bar Chart showing
  • Chart type: Bar Chart, Y-axis: Cost; X-axis: Attribution Source
  • Data Needed: Cost Per Lead; Attribution Source
  • Customer Acquisition Cost (CAC):
  • Chart type: Bar Chart, Y-axis: Cost; X-axis: Attribution Source
  • Data Needed: CAC; Attribution Source
  • Customer Lifetime Value (LTV): Bar C
  • Chart Type: Bar Chart, Y-axis: LTV; X-Axis: Date Became Customer
  • Data Needed: LTV; Date Became Customer
  • Return on Investment (ROI):
  • Chart type: Bar Chart, Y-axis: ROI; X-axis: Month of Measurement
  • Data Needed: ROI; Month of Measurement

Lifecycle Metrics

  • All KPIs in this category can be displayed in a Funnel Chart
  • Chart Type: Funnel Chart, showing conversion rate and average time in each stage
  • Data Needed: Lifecycle Stage, Date entered stage, Date exited stage

TL;DR: Start your data journey with a no-frills process that exports key marketing data into CSVs, cleans data in Excel, and visualizes using simple bar charts and funnel charts. Once this process is accurate and standardized, look into ways to automate.

10 Fundamentals of Making Decisions with Data

In the realm of data-driven decision-making, here are the 10 fundamentals that can guide you towards more effective and strategic choices.

  1. Focus on most relevant and actionable insights.
  2. Increase budget for strategies that demonstrate positive ROI
  3. Decrease budget for strategies that demonstrate negative ROI
  4. Try to balance analysis with timely action. Spending too much time analyzing data without action is as dangerous as no analysis at all
  5. Keep everything as simple as possible. This includes KPIs, reporting, database structure, etc.
  6. Regularly monitor data and adjust strategies accordingly
  7. Stay consistent with analysis. More trends will develop over time
  8. Remember the big picture of the company, so you don’t get lost in minutiae
  9. Share data with other key stakeholders. They may be able to provide insight

Conclusion and Key Takeaways

I have seen many great marketers surprised at how campaign results were widely different than our expectations. Sometimes things we think are terrible, hit home runs. Sometimes sure bets flounder. Leveraging data is not a destination, but rather a journey or a mindset. There is so much potential in analyzing unexpected metrics and having the ability to continuously learn and adapt.

If you would like to learn more about this concept, please follow me on Linkedin or sign up to receive my latest marketing guide, available at the bottom of this page. As always, it’s a pleasure to share these insights with you all, and I look forward to connecting.

Key Takeaways

  1. Data as a Navigational Tool: Understand that data is not just numbers; it's a critical insight into customer behavior and market trends, guiding marketing decisions.
  2. Importance of Database Structure: Prioritize the organization and structure of databases, as raw data is the foundation of all analysis.
  3. Key Performance Indicators (KPIs): Focus on essential KPIs like Cost Per Lead, Customer Acquisition Cost, Customer Lifetime Value, and ROI to measure marketing effectiveness.
  4. Simplicity in Data Analysis: Start with basic tools like CSV exports and Excel for data cleaning and analysis, using bar charts and funnel charts for visualization.
  5. Integration and Management of Data: Utilize APIs and third-party services for efficient data integration and management as your data processes mature.
  6. Decision-Making with Data: Make informed decisions by focusing on actionable insights, regularly monitoring data, and sharing findings with key stakeholders.
  7. Balance in Analysis and Action: Strike a balance between thorough analysis and timely action to avoid paralysis by analysis.
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