Unlocking the Hidden Potential of Marketing Data: A Journey from Confusion to Clarity

As someone who doesn't wait around for answers to find me, I've always been the type to dive in and uncover the truth myself. While this approach hasn't always served me well—often making others uncomfortable with my discoveries—it's led to some of the most valuable lessons in my career.

The Early Days: Learning the Hard Way

Back when I was the Tourism Director for a county in North Alabama, I was initially hired for a marketing role. This quickly evolved into navigating an unfamiliar industry. True to form, I sought out the most knowledgeable colleagues, immersing myself in a mentorship-like fashion to learn and grow. Unfortunately, my goals clashed with those who were misusing tax dollars. When I finally spoke up regarding my discovery, I was asked to leave.

A New Challenge: Making Sense of Marketing Data

Today, as a marketer inundated with data, I face a different challenge: making clear sense of the overwhelming information available. The inability to quantify decisions made with marketing budgets often leads to marketing roles being excluded from critical business conversations.

The Importance of Quantifiable Results

A recent statistic reveals that 72% of marketing executives feel they are excluded from key business decisions because they cannot quantify the impact of their spending . As someone responsible for significant budgets, I strive to be a good steward of these resources. I believe in the importance of confidently identifying the results of our expenditures and determining where to double down in our decision-making processes.

Understanding Marketing Mix Modeling

Marketing Mix Modeling (MMM) is a statistical analysis technique used to measure the effectiveness of marketing activities and their impact on sales. By analyzing various marketing channels and their interactions, MMM helps marketers understand which activities are driving the most value and how to optimize their budgets accordingly.

Example of Basic MMM in Action

For instance, a company might use MMM to evaluate the impact of its digital advertising, TV commercials, and promotional events on overall sales. By identifying which channels contribute the most to revenue, the company can allocate its budget more effectively, reducing waste and maximizing ROI.

Testing Basic MMM in Real Life

It's possible to upload flat documents like Word or Excel files to ChatGPT and ask it to help make sense of multiple data points to develop custom models that provide great outcomes. Not wanting to wait to make sense of the mountain of data ReTreet is sitting on in our HubSpot account, I thought I'd try it out.

For ReTreet, there are a few questions I would like our data to help me determine in order to spend wisely and achieve better results with my 2025 budget. But you have to start somewhere, so I began with three questions that I thought the data would pair well with to help tell a more precise story:

1. How are my marketing channels generating leads over time?
2. What types of interactions happened before a contact was created?
3. How are my marketing assets generating leads over time?

First, I loaded the data and verified the structure was right before moving on. Next, I ensured my goals were in line with the data model.

For ReTreet there are a few questions I would like our data to help me determine in order to spend wisely and achieve better results with my 2025 budget. But you have to start somewhere, so I began with three questions that I thought the data would pair well with to help tell a more precise story:

1. How are my marketing channels generating leads over time?
2. What types of interactions happened before a contact was created?
3. How are my marketing assets generating leads over time?

First, it loaded the data and verified the structure was right before moving on.

Next, it verified the steps to ensure my goals are in line with the data model.

And because this is all experimental, I took ChatGPT's word for it, and went for it...

And, because I'm not a programmer (not even close) I asked it to summarize my MMM results.

Summary of Marketing Mix Modeling (MMM) Results

Step 1: How Are My Marketing Channels Generating Leads Over Time?

Model Summary:

• Independent Variables (Marketing Channels): Email, Social Media, Paid Search, Organic Search, Direct Mail
• Dependent Variable: Total Leads
• Model Fit: The regression model showed a significant relationship between the marketing channels and the total leads generated.

Key Insights:

• Email Marketing: This channel had the highest positive impact on lead generation, suggesting that email campaigns are highly effective.
• Social Media: Also contributed positively but to a lesser extent compared to email marketing.
• Paid Search: Showed a moderate positive impact, indicating it's a valuable channel but with a smaller return on investment compared to email and social media.
• Organic Search: Had a smaller, yet significant positive impact, emphasizing the importance of SEO efforts.
• Direct Mail: Showed the least impact among the channels analyzed.

Step 2: What Types of Interactions Happened Before a Contact Was Created?

Sequence Analysis:

• Common Interaction Types Leading to Contact Creation: Website Visit, Downloaded Content, Email Opened, Social Media Engagement
• Most Frequent Sequence: Website Visit > Downloaded Content > Email Opened > Social Media Engagement > Contact Created

Key Insights:

• Website Visits and Content Downloads: These interactions are critical in the journey towards contact creation, highlighting the need for engaging and valuable content on the website.
• Email Engagement: Plays a pivotal role in converting interactions into contacts, stressing the importance of effective email marketing strategies.
• Social Media Engagement: Supports the lead generation process, although not as prominently as other interactions.

Step 3: How Are My Marketing Assets Generating Leads Over Time?

Model Summary:

• Independent Variables (Marketing Assets): Blog Posts, Whitepapers, Case Studies, Webinars, E-books
• Dependent Variable: Total Leads
• Model Fit: The regression model indicated a strong relationship between the marketing assets and lead generation.

Key Insights:

• Blog Posts: Had the highest impact on lead generation, suggesting frequent and high-quality blog content drives significant leads.
• Whitepapers and E-books: Showed a substantial positive impact, indicating that in-depth, downloadable content is highly effective.
• Case Studies: Also contributed positively, highlighting the importance of demonstrating real-world success stories.
• Webinars: Had the least impact among the assets analyzed, although still significant, suggesting live and interactive content can attract leads but may require more promotion to be highly effective.

The MMM analysis provided valuable insights into the effectiveness of various marketing channels and assets. Email marketing and blog posts emerged as the top performers, while website visits and content downloads were critical interactions leading to contact creation.

These findings are interesting because talking to guests, I would have thought social media was the leading driver of revenue but now I know more clearly how to refine marketing strategies, optimize budget allocation, and improve overall marketing effectiveness. And this is just the beginning...

Bringing MMM to Web3

As we transition into the Web3 era, the principles of MMM can be adapted to this new landscape. With the increased transparency and data availability inherent in blockchain technology, marketers can track and analyze their efforts with greater precision. For example, smart contracts can automatically collect and analyze data from various marketing campaigns, providing real-time insights into their performance and helping marketers make data-driven decisions.

In our quest to make sense of the vast amounts of data at our disposal, it's crucial to embrace tools like Marketing Mix Modeling. This proactive approach not only justifies our marketing expenditures but also drives more effective strategies that align with our business goals. By continuously seeking out the truth and leveraging the insights provided by these tools, we can navigate the complexities of the Web3 landscape and pave the way for a more data-driven and accountable marketing future.

As a marketer, it's becoming increasingly clear that our ability to adapt and innovate with these tools is not just beneficial—it's essential for relevancy.