Blog

<h2>Introduction: The Illusion of Success in Digital Marketing</h2><p>In the fast-paced world of digital marketing, brands often fall into the trap of chasing surface-level metrics—likes, shares, impressions, and follower counts—without understanding whether these numbers translate into real business growth. This was the unfortunate reality for an online flower and rose brand that spent half a million dollars on social media campaigns, influencer partnerships, and sponsored magazine content, only to see sales decline year after year.</p><p>The brand’s experience serves as a cautionary tale about the dangers of vanity metrics, prestige ad placements, and poorly structured campaign measurement. Despite pouring money into TikTok, Facebook Reels, influencer collaborations, and high-end lifestyle magazines, the company struggled to answer a fundamental question: Did any of these efforts actually drive flower sales?</p><h2>The Problem: Vanity Metrics Masking Poor Performance</h2><h3>Superficial Engagement vs. Real Sales Impact</h3><p>The brand’s marketing team celebrated high engagement rates on Instagram Reels and TikTok videos, assuming that viral content would naturally lead to conversions. However, vanity metrics—such as views, likes, and shares—only measure visibility, not purchasing intent.</p><ul><li><strong>Short-form video ads (Reels/TikTok) failed to convert</strong> because they lasted only seconds, leaving no lasting impression or clear call-to-action.<br></li><li><strong>Influencer partnerships yielded inconsistent results</strong>, with some generating 5X sales uplifts while others delivered near-zero ROI.<br></li><li><strong>Sponsored magazine articles</strong> looked prestigious but lacked direct attribution to sales.</li></ul><h3>Seasonal Campaigns: Overlapping Efforts, No Clear Attribution</h3><p>Before major holidays (Valentine’s Day, Mother’s Day, International Women’s Day), the brand launched multi-channel campaigns:</p><ul><li>Paid social media ads<br></li><li>Sponsored magazine features<br></li><li>Influencer collaborations<br></li><li>Email marketing<br></li><li>Direct mail drops</li></ul><p>While these efforts created a temporary buzz, seasonality made it impossible to isolate which tactics actually worked. The overlapping campaigns muddied performance data, leaving the brand with no way to distinguish between effective and wasteful spending.</p><h3>Rising Marketing Spend, Declining Sales</h3><p>Over five years, the brand’s ad budget grew, yet sales steadily declined. If the campaigns were successful—and even suppressing competitors—why weren’t revenues increasing?</p><p><strong>The answer: The brand was optimizing for the wrong metrics.</strong></p><p><strong>The Solution: Data-Driven Marketing Accountability</strong></p><p>To fix this, the brand partnered with More Than Data, specializing in attribution modelling and performance optimization.</p><h2>Extracting Reliable Data from Fragmented Sources</h2><ul><li><strong>Magazines &amp; Blogs</strong>: Many publishers focus on content creation, not tracking performance. More Than Data worked directly with them to extract visitor behaviour data from Google Analytics.<br></li><li><strong>Influencers</strong>: Performance varied wildly—some drove sales, others didn’t. Tracking required deep analysis of promo codes and transaction histories.<br></li><li><strong>CRM Limitations</strong>: The brand’s system couldn’t link promo codes to sales. More Than Data manually mapped discounts to transactions to measure true ROI.</li></ul><h2>Marketing Mix Modelling: Identifying True Drivers</h2><p>By analysing all touchpoints, More Than Data built response curves for each channel, revealing:</p><ul><li>Which platforms actually drove sales<br></li><li>Optimal budget allocation<br></li><li>"What-if" scenario testing before campaign launches</li></ul><h2>The Results: What Actually Worked (And What Didn’t)</h2><h3>Instagram &amp; TikTok Reels Were Nearly Useless</h3><ul><li>Short attention spans meant viewers rarely acted on promotions.<br></li><li>Quarterly retainer contracts with content creators failed—engagement ≠ sales.</li></ul><h3>Influencers Were a High-Risk, High-Cost Gamble</h3><ul><li>Performance depended on timing, personality, and audience fit.<br></li><li>Some influencers 5x'd sales, others wasted budget entirely.<br></li><li>Not a reliable long-term strategy due to unpredictability.</li></ul><h3>Magazine Placements Were Mid-Funnel, Not Sales Drivers</h3><ul><li>Sponsored articles raised awareness but didn’t close sales efficiently.<br></li><li>Better suited for brand prestige than direct response.</li></ul><h3>Email Marketing Was the Hidden Winner</h3><ul><li>Targeted existing customers, leading to repeat purchases.<br></li><li>High ROI compared to other channels.</li></ul><h3>Direct Mail Was a Waste</h3><ul><li>Most recipients discarded it immediately.<br></li><li>Low response rates made it ineffective.</li></ul><h3>Blogs Were Expensive and Ineffective</h3><ul><li>Long-form articles cost thousands but drove minimal sales.<br></li><li>Not cost-efficient for performance marketing.</li></ul><h2>Conclusion: Lessons for Future Campaigns</h2><p>This case study highlights <strong>three critical mistakes</strong> the brand made:</p><ul><li><strong>Focusing on vanity metrics</strong> (likes, shares) instead of sales attribution.<br></li><li><strong>Overlapping seasonal campaigns</strong> without proper tracking.<br></li><li><strong>Assuming prestige placements (magazines, influencers) guaranteed sales</strong>.</li></ul><p>The solution? Data-driven decision-making. By leveraging marketing mix modelling, promo code tracking, and response curve analysis, More Than Data helped the brand:</p><ul><li>Stop wasting budget on underperforming channels<br></li><li>Reallocate spend to high-ROI tactics (email, select influencers)<br></li><li>Implement ongoing optimization for future campaigns</li></ul><p>For any e-commerce brand, the lesson is clear: If you can’t measure it, you shouldn’t spend on it.</p><h2>Testimonial from the Online Flower &amp; Rose Brand</h2><p><em>"We spent $500,000 on marketing but saw declining sales—until More Than Data stepped in. Their team uncovered the harsh truth: our Instagram Reels, influencer deals, and magazine features weren’t driving real revenue. By implementing rigorous tracking and attribution modeling, they helped us identify what actually worked (email marketing) and what was just vanity (short-form video ads). Now, we allocate budget based on data, not guesswork. More Than Data didn’t just save us money—they transformed our entire marketing strategy. Highly recommended for any brand tired of wasting ad spend!"</em></p><h2>Final Thought: Are You Measuring the Right Things?</h2><p>Many brands fall into the same trap—spending on flashy, trendy channels without verifying ROI. Before launching your next campaign, ask:</p><ul><li>Can I track exactly how each dollar contributes to sales?<br></li><li>Am I relying on vanity metrics instead of real business impact?<br></li><li>Do I have a system to optimize spend continuously?</li></ul><p>If not, you might be wasting half a million dollars too.</p>

The Pitfalls of Vanity Metrics: How an Online Flower Brand Wasted $500,000 on Misguided Marketing

<h2>Introduction to DIY Marketing Mix Modelling</h2>
<p>In the ever-evolving world of marketing, understanding the impact of various channels on your sales is crucial. Marketing Mix Modelling (MMM) offers a data-driven approach to measure the effectiveness of different marketing strategies. With the advent of tools like Minute MMM, DIY marketing mix modelling has become more accessible than ever before.</p>

<figure id="figure-9fksguuvslm9js7kan"><img id="9fksguuvslm9js7kan" src="https://media.gettyimages.com/id/1145882183/photo/financial-and-technical-data-analysis-graph.jpg?b=1&s=2048x2048&w=0&k=20&c=1P4qIn2D6k6OG4XDmSk21rltJjPZs1t1vXEIbJD-uZk=" alt="marketing analysis" class="object-cover bg-gray-100" style="aspect-ratio: 16/9"></figure>

<h2>What is Marketing Mix Modelling?</h2>
<p>Marketing Mix Modelling is a statistical analysis technique used to estimate the impact of various marketing tactics on sales and ROI. By analyzing historical data, businesses can identify which channels are driving results and allocate resources more effectively.</p>
<p>MMM considers different factors such as advertising spend, pricing strategies, distribution channels, and external influences like economic conditions. This comprehensive view helps businesses make informed decisions to optimize their marketing strategies.</p>

<h2>Benefits of Using Minute MMM</h2>
<p>Minute MMM is designed to simplify the complex process of marketing mix modelling. It offers an intuitive interface that allows marketers to input their data and receive actionable insights quickly. Here are some key benefits:</p>
<ul>
    <li><strong>User-Friendly Interface:</strong> You don't need to be a data scientist to use Minute MMM.</li>
    <li><strong>Real-Time Results:</strong> Get insights faster and adjust your marketing strategies promptly.</li>
    <li><strong>Cost-Effective:</strong> Save money by avoiding expensive consultancy fees.</li>
</ul>

<figure id="figure-mde4g0zjkm9js7kan"><img id="mde4g0zjkm9js7kan" src="https://media.gettyimages.com/id/1954841243/photo/data-analysis-chart-graph-3d-statistics-background.jpg?b=1&s=2048x2048&w=0&k=20&c=JqlWDrRuqpKotSJiWlw9XEKfqmlar9qw3g5TGMGWdVk=" alt="data analysis" class="object-cover bg-gray-100" style="aspect-ratio: 16/9"></figure>

<h2>Steps to Create Your Own Marketing Mix Model</h2>
<p>Creating your own marketing mix model with Minute MMM involves a few straightforward steps:</p>
<ol>
    <li><b>Gather Data:</b> Collect historical data on sales, marketing spend, and other relevant factors.</li>
    <li><b>Input Data:</b> Use Minute MMM to input your data into the system.</li>
    <li><b>Analyze Results:</b> Review the model’s output to understand the impact of different marketing channels.</li>
    <li><b>Adjust Strategies:</b> Use the insights to refine your marketing tactics for better performance.</li>
</ol>

<h2>Common Challenges and Solutions</h2>
<p>While DIY marketing mix modelling offers many advantages, it does come with challenges. One common issue is data quality. Ensure that your data is accurate and comprehensive for the best results. Additionally, interpreting complex results can be daunting for beginners, so consider leveraging online tutorials or support forums provided by Minute MMM.</p>

<figure id="figure-m9vv29lznpm9js7kan"><img id="m9vv29lznpm9js7kan" src="https://media.gettyimages.com/id/2151309406/photo/worried-senior-businessman-listening-presentation-with-coworkers-during-business-meeting-at.jpg?b=1&s=2048x2048&w=0&k=20&c=74u64V5FDlaKala40zi_hVbfrRKG4wDiOSoaz7ykwgs=" alt="business meeting" class="object-cover bg-gray-100" style="aspect-ratio: 16/9"></figure>

<h2>Case Study: Successful Implementation</h2>
<p>Consider a mid-sized retail company that used Minute MMM to analyze its marketing strategies. By inputting data from various channels such as TV, online ads, and promotions, they discovered that social media campaigns were driving significant ROI. This insight allowed them to reallocate their budget effectively, leading to a 20% increase in overall sales within six months.</p>

<h2>Conclusion: Embrace the Power of DIY MMM</h2>
<p>Incorporating DIY marketing mix modelling into your strategy can transform how you approach marketing decisions. With tools like Minute MMM, businesses have the power to unlock valuable insights without the complexity or high costs traditionally associated with marketing analytics.</p>
<p>Whether you're a small business owner or a seasoned marketer, embracing MMM can lead to more strategic resource allocation and improved business outcomes. Start exploring this powerful tool today and see how it can revolutionize your marketing efforts.</p>

The Ultimate Guide to DIY Marketing Mix Modelling with Minute MMM

<h2>Introduction to Media Mix Modeling</h2>
<p>Media mix modeling (MMM) is a powerful tool that helps marketers understand the effectiveness of their advertising efforts. By analyzing various channels and their impact on sales, businesses can optimize their marketing strategies for better ROI. This tutorial will guide you through creating a media mix model in under 10 minutes, using simple steps and tools.</p>

<figure id="figure-k8abliapmcm9fhvwen"><img id="k8abliapmcm9fhvwen" src="https://media.gettyimages.com/id/1170678949/photo/monitors-woman-red-light.jpg?b=1&s=2048x2048&w=0&k=20&c=LeRpmb4bsLzq4beVXEyrEDbNcFXvqfPsxUJ4XxKZBzI=" alt="media analysis" class="object-cover bg-gray-100" style="aspect-ratio: 16/9"></figure>

<h2>Gathering Data</h2>
<p>The first step in building a media mix model is collecting the necessary data. You will need historical data on sales and marketing spend across different channels such as TV, radio, digital, print, and more. Ensure that the data is clean and organized in a spreadsheet for easy analysis.</p>

<h3>Tools for Data Collection</h3>
<p>There are several tools available to help streamline data collection. Some popular options include Google Analytics for digital channels, Nielsen for TV data, and various CRM platforms for sales information. Combining data from these sources will provide a comprehensive view of your marketing efforts.</p>

<h2>Setting Up Your Model</h2>
<p>Once you have your data ready, it's time to set up your media mix model. You can use simple tools like Excel or Google Sheets to create your model. Start by organizing your data by channel and over time, ensuring that each entry is clearly labeled.</p>

<figure id="figure-fvvtft25m9fhvwen"><img id="fvvtft25m9fhvwen" src="https://media.gettyimages.com/id/635969404/photo/hand-point-on-data-report-show-on-screen.jpg?b=1&s=2048x2048&w=0&k=20&c=9HHJz_4VlONpThC7jqE1KFwpqkR_swhbrQoUCdFD_7E=" alt="spreadsheet analysis" class="object-cover bg-gray-100" style="aspect-ratio: 16/9"></figure>

<h3>Choosing the Right Metrics</h3>
<p>Select key performance indicators (KPIs) that are relevant to your business goals. These could include metrics like cost per acquisition (CPA), return on ad spend (ROAS), or customer lifetime value (CLV). These KPIs will help you assess the impact of each media channel on your overall performance.</p>

<h2>Building the Model</h2>
<p>With your data and KPIs in place, you can now build the model. Use regression analysis to determine the relationship between your marketing spend and sales performance. This statistical technique helps identify which channels are driving the most value and where adjustments might be needed.</p>

<h3>Using Software Tools</h3>
<p>For those less familiar with regression analysis, software tools like R or Python can automate this process. These platforms offer powerful libraries for statistical analysis, making it easier to create accurate media mix models quickly.</p>

<figure id="figure-2vujs6xdk9m9fhvwen"><img id="2vujs6xdk9m9fhvwen" src="https://media.gettyimages.com/id/1954841243/photo/data-analysis-chart-graph-3d-statistics-background.jpg?b=1&s=2048x2048&w=0&k=20&c=JqlWDrRuqpKotSJiWlw9XEKfqmlar9qw3g5TGMGWdVk=" alt="data analysis tools" class="object-cover bg-gray-100" style="aspect-ratio: 16/9"></figure>

<h2>Interpreting Results</h2>
<p>After running your regression analysis, you'll receive output showing the effectiveness of each channel. Look for patterns in the data that indicate which channels contribute most to sales and which may need reevaluation. The coefficients from your regression analysis will guide these insights.</p>

<h3>Optimizing Your Media Mix</h3>
<p>Based on your findings, adjust your marketing strategy to optimize channel performance. This might involve reallocating budgets towards more effective channels or experimenting with new tactics to boost underperforming ones. Continual optimization is key to maintaining a successful media mix model.</p>

<h2>Conclusion</h2>
<p>Building a media mix model doesn't have to be time-consuming or complex. By following these steps, you can create a basic model in under 10 minutes that provides valuable insights into your marketing efforts. Remember, the goal is to constantly refine and adjust your strategy based on data-driven decisions for maximum impact.</p>

How to Build a Media Mix Model in Under 10 Minutes: A Step-by-Step Tutorial

<h2>The Open-Source Gold Rush in Marketing Mix Modelling</h2><p>In recent years, a growing number of SaaS businesses have entered the Marketing Mix Modelling (MMM) space by taking a shortcut: wrapping open-source packages like Google Meridian, Google Lightweight, and Meta Robyn into commercial products.</p><p>At first glance, this seems like a smart play—why build from scratch when you can leverage free, publicly available code? These companies quickly repackage existing models, add a UI/UX layer, and sell it as a premium SaaS solution. No heavy R&amp;D, no complex algorithm development—just a fast path to market.</p><p>But while this approach saves time and cost for the vendor, it fails to solve the real pain points of marketers. The fundamental flaws of these open-source tools—slow computation, tedious parameter tuning, and unintuitive outputs—remain unaddressed. The result? Frustrated marketing teams stuck with inefficient tools, just now in a prettier interface.</p><p>At More Than Data, we refuse to take this shortcut. Instead of wrapping flawed open-source models, we’ve built our own marketer-first MMM solution—one that prioritizes speed, usability, and real-world business impact over technical vanity.</p><h2>The Wrapper MMM SaaS Trend: Fast Money, Lasting Problems</h2><h3>Why MMM SaaS Companies Are Taking the Open-Source Shortcut</h3><ul><li><strong>No Need for Heavy R&amp;D</strong><br></li></ul><ol><li style="list-style-type: none;" data-mce-style="list-style-type: none;"><ol><li>Google and Meta have already done the core statistical work.<br></li><li>Vendors minimise or even skip algorithm development and focus on UI/UX.</li></ol></li></ol><ul><li><strong>Faster Time-to-Market</strong><br></li></ul><ol><li style="list-style-type: none;" data-mce-style="list-style-type: none;"><ol><li>No need to build a model from scratch—just integrate existing code.<br></li><li>Launch a "new" MMM product in few weeks, not years.<br></li></ol></li></ol><ul><li><strong>Lower Development Costs</strong></li></ul><ol><li style="list-style-type: none;" data-mce-style="list-style-type: none;"><ol><li>Avoid hiring expensive data scientists to design proprietary models.<br></li><li>Focus resources on sales and marketing instead.</li></ol></li></ol><h3>The Hidden Flaws of Wrapper MMM SaaS</h3><p>While this approach benefits vendors, marketers end up with the same underlying problems:</p><ul><li><strong>Slow &amp; Inefficient Computation</strong></li></ul><ol><li style="list-style-type: none;" data-mce-style="list-style-type: none;"><ol><li>Google Meridian &amp; Lightweight MMM rely on Bayesian MCMC sampling, which is computationally heavy.</li><li>Meta Robyn uses evolutionary computing, which can take hours (or days) for large datasets.<br></li><li>Wrapper SaaS products inherit these inefficiencies—adding a UI doesn’t make the models faster.</li></ol></li></ol><ul><li><strong>Tedious Parameter Adjustments</strong></li></ul><ol><li style="list-style-type: none;" data-mce-style="list-style-type: none;"><ol><li>Media curves, adstock decay, saturation points—all require detailed tuning.<br></li><li>Non-technical marketers can’t easily modify these—these “simplified” interfaces don't eliminate the need for technical expertise; they merely relocate the problem from code editors to dropdown menus. Frustrating back-and-forth re-runs just to get usable outputs.</li></ol></li></ol><ul><li><strong>Opaque "Black Box" Results</strong></li></ul><ol><li style="list-style-type: none;" data-mce-style="list-style-type: none;"><ol><li>Many wrapper SaaS tools hide the model workings behind a sleek dashboard.<br></li><li>Marketers get outputs without understanding how they were derived.<br></li><li>No transparency into why certain channels are deemed more effective.</li></ol></li></ol><ul><li><strong>Same Old Problems, Just in Another New Package</strong></li></ul><ol><li style="list-style-type: none;" data-mce-style="list-style-type: none;"><ol><li>If the core model is flawed, wrapping it in SaaS doesn’t fix it.<br></li><li>Marketers still struggle with slow, inflexible, and hard-to-trust outputs.&nbsp;</li></ol></li></ol><h3>The AI Wrapper Parallel: A Cautionary Tale</h3><p>This trend mirrors what’s happening in the AI SaaS space, where many startups simply:</p><ul><li>Call OpenAI’s API (or another LLM provider).<br></li><li>Build a UI on top.<br></li><li>Sell it as their own "AI solution."</li></ul><p>The problem?</p><ul><li>Clients don’t realize they’re just paying for a UI over someone else’s model.<br></li><li>No real innovation—just repackaging.<br></li><li>When the underlying model has limitations, the wrapper inherits them.</li></ul><p>The same is happening in MMM SaaS today.</p><h3>More Than Data’s Approach: Solving Real Pain Points, Not Just Wrapping Code</h3><p>We refuse to take the easy path. Instead of repackaging flawed open-source models, we built our own marketer-first MMM solution from the ground up.</p><p>How We’re Different:</p><ul><li><strong>Speed That Matches Marketing Cycles</strong></li></ul><ol><li style="list-style-type: none;" data-mce-style="list-style-type: none;"><ol><li>Proprietary engineering delivers MMM at unprecedented speed.<br></li><li>Get results in minutes, not days.<br></li></ol></li></ol><ul><li><strong>Built for Non-Technical Marketers</strong></li></ul><ol><li style="list-style-type: none;" data-mce-style="list-style-type: none;"><ol><li>No coding or data science skills needed.<br></li><li>Free data prep tools to easily format inputs.<br></li></ol></li></ol><ul><li><strong>Business-Driven, Not Statistically Obsessed</strong></li></ul><ol><li style="list-style-type: none;" data-mce-style="list-style-type: none;"><ol><li>Set your own commercial expectations (e.g., Anchor the model to your business intuition - if TV should drive 12% of sales, we'll deliver results that respect your expertise).<br></li><li>Adjust media impact assumptions intuitively—no Bayesian priors required.<br></li></ol></li></ol><ul><li><strong>Transparent &amp; Actionable</strong></li></ul><ol><li style="list-style-type: none;" data-mce-style="list-style-type: none;"><ol><li>No black-box outputs—see and trust how decisions are made.<br></li><li>Clear, decision-making friendly reporting.<br></li></ol></li></ol><ul><li><strong>Listening to Real Marketers</strong></li></ul><ol><li style="list-style-type: none;" data-mce-style="list-style-type: none;"><ol><li>We don’t self-enjoy as data scientists.<br></li><li>We solve real frontline marketing challenges.<br>&nbsp;<br></li></ol></li></ol><h2>Conclusion: The Future of MMM Belongs to True Innovators</h2><p>The wrapper MMM SaaS trend is a shortcut, not a solution. While it helps vendors make quick money, it does little to advance the field or serve marketers better.</p><p>At More Than Data, we believe MMM should:</p><p>✅ Be fast enough for real-time decisions.<br>✅ Empower marketers, not restrict them.<br>✅ Deliver clear, actionable insights—not just statistical outputs.</p><p>We’re not here to repackage old problems in a new UI. We’re here to solve them.</p><h2>Final Thought<br></h2><p>If your MMM provider’s "innovation" is just a wrapper around free open-source code—are you really getting a better solution, or just a more expensive version of the same flaws (The secret sauce? Same free, questionable open-source recipe - now with a 500% markup 😊)?</p><p>At More Than Data, we’re building the alternative.&nbsp;</p>

The Rise of "Wrapper" MMM SaaS: A Shortcut That Falls Short for Marketers

<h2>The Gap Between Promise and Reality</h2><p>In an effort to democratize marketing mix modelling (MMM), tech leaders like Google Meridian, Google Lightweight and Meta Robyn have released free, open-source tools. The stated goal? To make MMM more transparent, customizable, and accessible.</p><p>Yet despite their technical sophistication, these tools remain out of reach for the very people who need them most: non-technical marketers.</p><p>This article examines:</p><ul><li><strong>Why these tools were developed</strong>—and why their design overlooks real marketing needs.<br></li><li><strong>Key limitations</strong> of Google Meridian, Google Lightweight, and Meta Robyn—based on hands-on user frustrations.<br></li><li><strong>Why MMM success hinges on marketer adoption</strong>—not just statistical metrics.<br></li><li><strong>What a truly marketer-centric MMM solution should offer</strong>.<br></li></ul><h2>Why Google &amp; Meta Released Open-Source MMM Tools</h2><h3>The Stated Vision</h3><ul><li>Reduce reliance on costly vendors (e.g., Nielsen, Analytic Partners).<br></li><li>Encourage customization for unique business needs.<br></li><li>Promote transparency in an often opaque field.</li></ul><h3>The Unspoken Realities</h3><ul><li><strong>Data ecosystem lock-in</strong>: Tools subtly encourage reliance on Google/Meta platforms.<br></li><li><strong>Talent recruitment</strong>: Open-source projects attract top data science minds.<br></li><li><strong>Industry influence</strong>: Framing their methods as "standard" shapes market preferences.</li></ul><p>While these motivations aren’t inherently problematic, they’ve led to tools that serve data teams far better than marketing teams.</p><h2>The Reality: Why Open-Source MMM Tools Struggle with Adoption</h2><h3>Designed for Coders, Not Marketers</h3><ul><li><strong>Google Meridian &amp; Lightweight MMM</strong>: Python-based Bayesian frameworks requiring statistical expertise.<br></li><li><strong>Meta Robyn</strong>: R-based evolutionary computing models that operate like a "black box."<br></li><li><strong>No intuitive interfaces</strong>—just code repositories and command-line operations.</li></ul><p><strong>Result</strong>: Most marketers cannot use these tools independently, forcing reliance on data scientists.</p><h3>Ignoring Critical Business Needs</h3><ul><li><strong>Inflexible Adjustments</strong></li></ul><ol><li style="list-style-type: none;"><ol><li><strong>Media channel tuning</strong>: No easy way for marketers to adjust expected media performance (e.g., "TV should have higher impact than social").<br></li><li><strong>Prior distributions</strong>: Even data scientists struggle to define Bayesian priors in Meridian/Lightweight.<br></li><li><strong>Re-running models</strong>: Robyn’s evolutionary approach requires restarting the entire process for minor tweaks.</li></ol></li></ol><ul><li><strong>Data Preparation Hurdles</strong></li></ul><ol><li style="list-style-type: none;"><ol><li style=""><strong>No plug-and-play connectors</strong>: Marketers must manually assemble data from disparate sources (Google Ads Manager, or other ad platforms).<br></li><li style=""><strong>Opaque formatting rules</strong>: Only data scientists understand how to structure inputs correctly.</li></ol></li></ol><ul><li><strong>Delayed Insights</strong></li></ul><ol><li style="list-style-type: none;"><ol><li><strong>Meta Robyn’s evolutionary computing</strong> is slow for large datasets.<br></li><li><strong>MCMC (Markov chain Monte Carlo) sampling process</strong> inherent in Bayesian methods also creates significant computational bottlenecks.&nbsp;</li></ol></li></ol><h3>Community Pain Points</h3><ul><li><strong>Google Meridian &amp; Lightweight MMM</strong></li></ul><ol><li style="list-style-type: none;"><ol><li><strong>"Documentation is overly technical"</strong>: Filled with intimidating equations, lacking practical guidance.<br></li><li><strong>"Adjusting priors is guesswork"</strong>: No clear way to align assumptions with business reality.<br></li><li><strong>"Lightweight is too simplistic"</strong>: Lacks nuance for multi-channel strategies.</li></ol></li></ol><ul><li><strong>Meta Robyn</strong></li></ul><ol><li style="list-style-type: none;"><ol><li><strong>"Uncontrollable model-building"</strong>: Evolutionary algorithms optimize without user constraints.<br></li><li><strong>"Adstock and diminishing return transformation tweaks are tedious and time consuming"</strong>: Changing decay rates requires re-running the entire model.</li></ol></li></ol><h2>The Root Problem: Built for Data Teams, Not Marketing Decision-Makers</h2><h3>The "Lab vs. Field" Disconnect</h3><ul><li><strong>These tools prioritize:</strong></li></ul><ol><li style="list-style-type: none;"><ol><li><strong>Statistical novelty</strong> (e.g., Robyn’s evolutionary optimization).<br></li><li><strong>Methodological purity</strong> (e.g., Meridian’s Bayesian rigor).</li></ol></li></ol><ul><li><strong>But marketers need:</strong></li></ul><ol><li style="list-style-type: none;"><ol><li><strong>Speed </strong>(days, not weeks).<br></li><li><strong>Control </strong>(adjust assumptions without coding).<br></li><li><strong>Clarity </strong>(insights that align with business intuition).</li></ol></li></ol><h3>MMM Has No "Source of Ground Truth Answer"—So Why Obsess Over Metrics?</h3><p>Unlike controlled experiments, MMM relies on interpretation. Yet open-source tools fixate on:</p><ul><li><strong>R², MSE, back-testing error</strong> (withholding some data points and using past observations to predict the withheld values)<br>Instead of:<br></li><li><strong>"Do these results feel reasonable?"</strong><br></li><li><strong>"Can we act on them tomorrow?"</strong></li></ul><h3>The Forgotten Success Metric: Marketer Trust</h3><p>A model’s real value isn’t its statistical score—it’s whether:</p><p style=""><strong>✅ Marketers trust it.</strong><br><strong>✅ Marketing Stakeholders believe it.</strong><br><strong>✅ It improves marketing decisions.</strong></p><p>Most open-source tools fail this test. Consider the case of a Fortune 500 company where internal data scientists employed Google Meridian for MMM - the marketing team has repeatedly questioned both the methodology and business relevance of the outputs.</p><h2>What a Marketer-Centric MMM Tool Would Offer</h2><p>A truly effective MMM solution would:</p><p>✅ <strong>Require zero coding</strong>—button-click workflows, easy data prepare, and auto transformation parameter tuning.<br>✅ <strong>Let marketers set goals</strong> (e.g., "Prioritize sales activation within short-term").<br>✅ <strong>Obtain what marketers expect</strong> (achieve expected media impact without back-and-forth running models).<br>✅ <strong>Deliver insights in hours</strong>—not weeks of iterations.<br>✅ <strong>Speak the language of business</strong>—not p-values and confidence intervals.</p><h2>Conclusion: A Call for Truly Accessible MMM</h2><p>Google and Meta’s tools represent progress—but they’re stuck in the data science lab. Until they address:</p><ul><li><strong>Usability </strong>(for non-technical users).<br></li><li><strong>Flexibility </strong>(to match business requests).<br></li><li><strong>Speed </strong>(to keep pace with marketing cycles).</li></ul><p>They’ll remain academic curiosities, not practical solutions.</p><p>The future of MMM belongs to tools that empower marketers—not just impress data scientists.</p><h2>Final Thought<br></h2><p>If MMM is meant to guide marketing decisions, why are the tools designed for people who don’t make them?</p><p>The industry needs a shift—from model-centric to marketer-centric.</p>

The Open-Source MMM Paradox: Why Google and Meta’s Tools Miss the Mark for Real-World Marketers

<h2>Introduction</h2><p>Marketing Mix Modelling (MMM) is a powerful tool for measuring the effectiveness of marketing campaigns, optimizing budgets, and improving ROI. Yet, despite its potential, many non-technical marketers—both on the brand side and within media agencies—have been slow to adopt it.</p><p>The co-founder of More Than Data has worked closely with both groups and understands their unique challenges. While they share common pain points, their perspectives on MMM differ. This article explores why MMM hasn’t gained widespread traction among non-technical marketers, the key differences between agency and brand-side marketers, and how More Than Data facilitates a technical-to-non-technical conversation to drive adoption.</p><h2>The Challenge: Why Non-Technical Marketers Struggle with MMM</h2><p>Marketing Mix Modelling is often seen as:</p><ul><li><strong>Too complex</strong> – Many marketers lack statistical expertise and find MMM intimidating.<br></li><li><strong>Time-consuming</strong> – Traditional MMM requires extensive data collection and long analysis cycles.<br></li><li><strong>Lack of clear communication</strong> – Technical teams struggle to translate insights into actionable strategies for marketers.</li></ul><p>As a result, many marketers—especially those without a data background—rely on simpler (but less accurate) metrics like last-click attribution or platform-reported performance.</p><p>But this is changing. With advancements in advanced analytics and automation, MMM is becoming more accessible. The key is bridging the gap between data science and marketing execution—which is exactly what More Than Data does.</p><h2>Media Agency Marketers vs. Brand-Side Marketers: Similarities and Differences</h2><p>While both groups share the same end goal—driving effective marketing campaigns—their priorities, constraints, and perspectives differ.</p><h3>1. Media Agency Marketers: The Performance-Driven Partners</h3><p><strong>Who they are:</strong></p><p>Media agency marketers work across multiple clients, managing ad spend, optimizing campaigns, and proving Return-on-ads-spend (ROAS). They operate in fast-paced environments where agility is key.</p><p><strong>Their challenges with MMM:</strong></p><ul><li><strong>Short-term focus</strong> – Clients demand quick results, making long-term MMM analysis seem impractical.<br></li><li><strong>Multi-client complexity</strong> – Each brand has different KPIs, making one-for-all MMM difficult.<br></li><li><strong>Pressure to justify spend</strong> – Without clear MMM insights, they rely on platform metrics that may overstate performance.</li></ul><p><strong>What they need from MMM:</strong></p><ul><li>Faster, real-time insights to adjust campaigns on the fly.<br></li><li>Clear visualizations (not raw data) to present to clients.<br></li><li>Proof that their media mix recommendations are optimal.</li></ul><h3>2. Brand-Side Marketers: The Strategic Decision-Makers</h3><p><strong>Who they are:</strong></p><p>Brand marketers focus on long-term growth, brand equity, and customer loyalty. They work closely with leadership to align marketing with business objectives.</p><p><strong>Their challenges with MMM:</strong></p><ul><li><strong>Budget constraints</strong> – MMM is often seen as a luxury, not a necessity.<br></li><li><strong>Internal silos</strong> – Sales, finance, and marketing teams may not share data seamlessly.<br></li><li><strong>Fear of complexity</strong> – Many assume MMM requires a data science team.</li></ul><p><strong>What they need from MMM:</strong></p><ul><li>A clear link between marketing investment and business outcomes (e.g., sales, brand awareness, market share).<br></li><li>Guidance on budget allocation across channels (traditional vs. digital).<br></li><li>Simplified reporting for C-level stakeholders.</li></ul><p><strong>Where They Overlap</strong></p><p>Despite their differences, both groups:</p><ul><li>Need actionable insights, not just data.<br></li><li>Want faster, more transparent measurement.<br></li><li>Struggle with attribution in a multi-channel world.<br></li></ul><h2>How More Than Data Bridges the Gap</h2><p>The co-founder of More Than Data has worked with both agency and brand-side marketers, recognizing that MMM adoption isn’t just about better models—it’s about better accessibility and communication.</p><p>Here’s how they make MMM accessible:</p><h3>1. Speaking Their Language (No Jargon Allowed)</h3><ul><li>For agencies: Focus on speed, flexibility, and client-facing reports.<br></li><li>For brands: Tie MMM to business outcomes (e.g., “This is how TV ads impact quarterly revenue”).</li></ul><h3>2. Automating the Heavy Lifting</h3><ul><li>Traditional MMM takes months; More Than Data delivers insights in minutes (our product Minute MMM can do so).<br></li><li>No need for a PhD in statistics—marketers get clear dashboards, not complex equations.</li></ul><h3>3. Aligning with Their Goals</h3><ul><li>Agencies → Prove your media mix is optimal.<br></li><li>Brands → Justify budget driving business performance to leadership.</li></ul><h3>4. Building Trust Through Education</h3><ul><li>Free tools as high-value offers, simplified guides, and case studies to demystify MMM.<br></li><li>Showing real-world examples of how MMM optimized media mix and delivered business outcomes.&nbsp;</li></ul><h2>The Future: MMM as a Standard Tool for All Marketers</h2><p>The gap between technical MMM and non-technical marketers is closing. With tools like Minute MMM and Budget Optimizer, agencies can measure media performance and optimize future campaigns faster, and brands can make data-driven budget decisions with confidence.</p><p>The key is not just better models, but better conversations. By translating MMM into practical, marketer-friendly insights, More Than Data is helping both agency and brand-side marketers unlock the full potential of their marketing.</p><h2>Final Thought</h2><p>Marketing Mix Modelling shouldn’t be reserved for data scientists. Whether you’re an agency marketer juggling multiple clients or a brand leader making strategic budget calls, MMM can—and should—work for you.</p><p>The question isn’t “<strong>Why hasn’t MMM been adopted?</strong>” but rather “<strong>How can we make MMM work for non-technical marketer?</strong>”</p><p>And that’s exactly what More Than Data is solving.</p><p>Would you like to explore how MMM can fit into your marketing strategy? Let’s start the conversation—no technical expertise required.&nbsp;</p>

Why Marketing Mix Modelling Hasn’t Been Adopted by Non-Technical Marketers—And How We Bridge the Gap

More Than Data

An Australia Sydney-based pioneer in marketing analytics, offering a free, fast, and user-friendly marketing mix modelling platform. We help marketers build actionable marketing mix models in under 10 minutes for instant insights. Our software Minute MMM is free, DIY, and simple—no costly consultants, complex tools, or data expertise needed. Optimize marketing investments with ease!

Mulish

Sora

Merriweather

<p>Thank You to Our First 100 Clients and Users.</p>
<p>Your support helped shape our products and services—built to make media impact measurement accessible and affordable.</p>
<p>Starting 1 July 2025, Minute MMM and Budget Optimizer move to a paid model with monthly subscription and upfront payment options.</p>
<p>We're no longer free—but we're still powerful, affordable, and focused on your success.</p>

Blog

The Pitfalls of Vanity Metrics: How an Online Flower Bran...

The Ultimate Guide to DIY Marketing Mix Modelling with Mi...

How to Build a Media Mix Model in Under 10 Minutes: A Ste...

The Rise of "Wrapper" MMM SaaS: A Shortcut That Falls Sho...

The Open-Source MMM Paradox: Why Google and Meta’s Tools ...

Why Marketing Mix Modelling Hasn’t Been Adopted by Non-Te...