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<h2>Introduction</h2><p>Marketing Mix Modelling (MMM) remains a cornerstone of marketing analytics, helping brands measure the effectiveness of their campaigns and optimize budgets. However, the process of tuning and refining MMM models is notoriously complex, requiring deep statistical expertise and patience.</p><p>Two of the most prominent open-source MMM tools—Google Meridian (a Bayesian MMM framework) and Meta Robyn (an evolutionary computing-based MMM solution)—promise to simplify this process. Yet, in practice, both tools suffer from inefficiencies, uncontrollable tuning mechanisms, and time-consuming iterations that frustrate data scientists and marketers alike.</p><p>In this article, we’ll dissect why these tools fail to deliver a smooth MMM tuning experience, the technical and practical challenges they introduce, and how real-world users struggle with them.</p><h2>The Problem with Google Meridian: A Bayesian Nightmare</h2><p>Google Meridian adopts a Bayesian approach to MMM, leveraging Markov Chain Monte Carlo (MCMC) sampling to estimate media effects. While Bayesian methods offer flexibility, Meridian’s implementation introduces several pain points:</p><h3>Prior Distribution Tweaking is Inefficient and Unpredictable</h3><ul><li><strong>"Spray and Pray" Methodology</strong>: Adjusting prior distributions (e.g., for media coefficients) feels like a blind optimization process. There’s no clear guidance on how changes will impact the final model.<br></li><li><strong>Unstable Adjustments</strong>: Modifying one prior often unexpectedly shifts other variables, destabilizing the model. Without constraints, coefficients can drift into unrealistic ranges (e.g., negative ROAS for TV ads).<br></li><li><strong>No Guarantee of Marketing-Friendly Outputs</strong>: Bayesian models prioritize statistical fit over business logic. Marketers expect ROAS and contribution shares to align with spend—but Meridian often produces counterintuitive results that require manual overrides.<br></li></ul><h3>MCMC is Slow and Uncontrollable</h3><ul><li><strong>Random Sampling ≠ Controlled Optimization</strong>: MCMC explores parameter space randomly, meaning convergence is slow—especially with large datasets.<br></li><li><strong>No Constraints on Realism</strong>: Unrestricted sampling can yield implausible coefficients (e.g., a tiny digital ad spend driving massive sales). Users must manually enforce constraints (e.g., via half-normal priors), but this is trial-and-error.<br></li><li><strong>Re-Runs Are Painfully Frequent</strong>: Each adjustment requires full re-sampling, leading to hours or days of wasted computation just to test a single hypothesis.</li></ul><h3>Real-World Modeller’s Frustration</h3><p><em>"I had to pre-calculate coefficients using constrained OLS and feed them into Meridian as priors—otherwise, the Bayesian model would give me nonsense. Even then, it took weeks of tweaking before stakeholders accepted the results."</em></p><p>This workaround defeats the purpose of using a Bayesian framework—if you need OLS to guide your priors, why not just use OLS?</p><h2>Meta Robyn: Smarter in Theory, Lazier in Practice</h2><p>Meta Robyn uses evolutionary computing to optimize MMM, framing model selection as a "survival of the fittest" problem. While this sounds innovative, real-world usage reveals critical flaws:</p><h3>Evolutionary Computing is a Black Box</h3><ul><li><strong>No Mid-Process Adjustments</strong>: Once Robyn starts evolving models, you can’t tweak media contributions manually. You’re stuck waiting for generations of models to evolve—hoping one aligns with business expectations.<br></li><li><strong>Fitness Trade-Offs Hurt Practicality</strong>: Robyn optimizes for two conflicting objectives:<br><ul><li><strong>Model accuracy</strong> (minimizing data fitting error).<br></li><li><strong>Spend-to-impact alignment</strong> (ensuring media contribution shares match spend shares).<br></li><li><strong>Since these are competing goals, the final model is always a compromise—rarely satisfying marketers.</strong><br></li></ul></li></ul><h3>Adstock &amp; Diminishing Returns Tweaking is Back-and-forth</h3><ul><li><strong>Manual Parameter Overrides</strong>: If results are unrealistic, users must adjust Adstock decay rates or saturation curves and re-run the entire evolution—another time sink.<br></li><li><strong>No Guarantee of Improvement</strong>: Changing parameters doesn’t guarantee better outputs. Many users report running Robyn for weeks only to find none of the evolved models are usable.<br></li></ul><h3>Real-World Modeller’s Struggle</h3><p><em>"I spent weeks running Robyn, but the marketing team kept rejecting the outputs. The evolutionary algorithm doesn’t let me enforce business rules—it just ‘hopes’ a good model emerges. Most of the time, it doesn’t."</em></p><h2>The Common Failure: Lack of Control &amp; Efficiency</h2><p>Both tools share three critical shortcomings:</p><ul><li><strong>Blind Optimization:</strong><br><ul><li>Meridian relies on random MCMC walks, while Robyn uses evolutionary trial-and-error.<br></li><li>Neither allows direct intervention to enforce business logic mid-process.<br></li></ul></li><li><strong>Time-Consuming Re-Runs:</strong><br><ul><li>Every tweak requires full computation, leading to days or weeks of wasted effort.<br></li></ul></li><li><strong>Marketing-Unfriendly Outputs:</strong><br><ul><li>Bayesian and evolutionary methods prioritize statistical fit over realism.<br></li><li>Marketers need controllable, explainable models—not black-box approximations.</li></ul></li></ul><h2>A Better Way Forward</h2><p>Given these frustrations, what should MMM practitioners do?</p><h3>Hybrid Approaches (Pre-Optimization + Bayesian / Evolutionary)</h3><ul><li>Use constrained regression (OLS/LASSO) first to get reasonable priors before running Meridian.<br></li><li>Feed business-logic bounds into Robyn’s fitness function to reduce unrealistic outputs.</li></ul><h3>Custom Constraints &amp; Post-Hoc Adjustments</h3><ul><li>Enforce ROAS floors/ceilings and contribution sanity checks after model runs.<br></li><li>Manually override implausible coefficients when necessary (even if it breaks "pure" statistical rigor).</li></ul><h3>Alternative Tools with More Control</h3><ul><li>Reimplementing core algorithms with constrained sampling (e.g., Hamiltonian Monte Carlo with boundaries) can help.</li></ul><h2>Conclusion: The Need for Marketing-Centric MMM Tools</h2><p>Google Meridian and Meta Robyn are powerful in theory but inefficient in practice. Their lack of control, slow iterations, and unrealistic outputs make them frustrating for real-world MMM projects.</p><p>Until these tools introduce:</p><p><strong>✅ Real-time tuning during model runs</strong><br><strong>✅ Stricter business-logic enforcement</strong><br><strong>✅ Faster convergence with guided sampling</strong></p><p>MMM practitioners will continue to bleed time and sanity in the trenches of model tweaking.</p><p>The industry needs better solutions—ones that respect both statistics and marketing reality. Until then, modellers will keep resorting to manual hacks, workarounds, and hybrid approaches just to deliver usable results.</p>

Why Google Meridian and Meta Robyn Fall Short in Marketing Mix Modelling Tuning and Tweaking

<h2>Introduction</h2><p>Marketing Mix Modelling (MMM) remains a critical tool for quantifying the impact of media and non-media variables on sales. Google’s Meridian, an advanced Bayesian MMM solution, has gained traction for its scalability and integration with Google’s ecosystem. However, marketers often struggle with its limitations—particularly in calibration, efficiency, and alignment with business demands.</p><p>This article explores:</p><ul><li>Key shortcomings of Google Meridian<br></li><li>Why a constrained Bayesian approach outperforms traditional Bayesian methods<br></li><li>Real-world applications and success stories<br></li></ul><h2>The Problem with Google Meridian: Key Drawbacks</h2><p>While Google Meridian offers a powerful framework for MMM, several critical issues hinder its effectiveness in real-world marketing scenarios.</p><h3>Inflexible Media &amp; Non-Media Variable Adjustments</h3><p>Meridian relies heavily on prior distribution tweaking to align model outputs with marketer expectations. However, this process is:</p><ul><li><strong>Unpredictable</strong>: Small changes in priors can drastically alter other variables effect and modelling results.<br></li><li><strong>Time-Consuming</strong>: Requires multiple iterations to reach acceptable outputs.<br></li><li><strong>Inefficient</strong>: Struggles with large datasets (common in enterprise marketing).<br></li></ul><p><strong>Example</strong>: A FMCG brand spent weeks adjusting priors in Meridian, only to find that TV ad contributions were still overestimated, while digital channels were undervalued.</p><h3>Difficulty in Meeting Marketer Demands for Impact Share &amp; ROAS</h3><p>Marketers need precise control over:</p><ul><li>Media Impact Share (% contribution to sales)<br></li><li>Return on Ad Spend (ROAS)<br></li></ul><p>Google Meridian’s unconstrained Bayesian approach makes it difficult to enforce these business requirements. Without constraints, the model may produce unrealistic media effects (e.g., negative ROAS for high-performing channels).</p><h3>Forward-Thinking vs. Backward-Thinking Methodology</h3><ul><li><strong>Forward-Thinking (Meridian’s Approach):</strong><br><ul><li>Adjust priors → Run model → Check results → Repeat.<br></li><li>Highly iterative, slow, and inefficient.<br></li></ul></li><li><strong>Backward-Thinking (Constrained Bayesian Approach):</strong><br><ul><li>Start with desired ROAS/impact share → Apply constraints → Run model once.<br></li><li>Faster, more predictable, and aligned with business goals.</li></ul></li></ul><h3>Why Backward-Thinking Wins</h3><p>✅ Reduces rework (no endless tweaking of priors).<br>✅ Scales efficiently (handles 100+ variables seamlessly).<br>✅ Guarantees business-aligned outputs.</p><h3>Computational Inefficiency in MCMC Sampling</h3><p>Meridian uses Markov Chain Monte Carlo (MCMC) for Bayesian regression, which:</p><ul><li>Involves random sampling, leading to slow convergence.<br></li><li>Lacks constraints, making results less controllable.<br></li></ul><p>For large datasets (e.g., 200+ media and marketing variables), this becomes computationally impractical.</p><h2>The Solution: Constrained Bayesian Regression</h2><p>To overcome these limitations, More Than Data developed a constrained Bayesian MMM framework that:</p><ul><li>Embeds business rules directly into the model.<br></li><li>Uses Gibbs Sampling (MCMC) with modified conditional distributions.<br></li><li>Ensures outputs align with marketer expectations.<br></li></ul><h2>How It Works</h2><h3>Applying Impact Share Constraints</h3><ul><li>Each media / non-media variable is assigned lower and upper bounds for its contribution.<br></li><li>Example:<br><ul><li>TV Ads: Must contribute 8-12% of total sales impact.<br></li><li>Competitor Discounts: Must have a negative impact (non-positive coefficient).</li></ul></li></ul><h3>Modified Gibbs Sampling for Efficient Convergence</h3><ul><li>Traditional MCMC: Random walks lead to unstable results.<br></li><li>Constrained Gibbs Sampling:<br><ul><li>Truncates distributions to enforce constraints (e.g., only positive coefficients for paid media).<br></li><li>Speeds up convergence by reducing the sampling space.<br></li></ul></li></ul><h3>Technical Advantage</h3><ul><li>If a paid ads must be non-negative, its conditional distribution is truncated to exclude negative values.<br></li><li>If competitor media must hurt sales, its coefficient sampling is forced to be non-positive.</li></ul><h3>Guaranteed Business-Aligned Outputs</h3><p>Unlike Meridian’s trial-and-error approach, this method:</p><p>✅ Delivers ROAS and impact shares within expected ranges.<br>✅ Eliminates unrealistic media effects (e.g., negative impact and negative ROAS).<br>✅ Scales to hundreds of variables without losing accuracy.</p><h2>Real-World Success: A Furniture Brand Case Study</h2><h3>Challenge</h3><p>A leading online &amp; in-store furniture brand needed to measure:</p><ul><li>174 media variables (ATL/BTL, full-funnel channels).<br></li><li>23 promotions (online/in-store).<br></li><li>48 product-level discounts.<br></li><li>12 bundled offers.<br></li><li>9 competitor campaigns<br></li><li>11 key seasonality factors (e.g. mid-year clearance sales)</li></ul><p>Google Meridian failed to handle this complexity, producing unstable and unrealistic results.</p><h3>Solution: More Than Data’s Constrained Bayesian MMM</h3><ul><li>Defined constraints for each variable (e.g., "Social ads must drive 4-6% of sales").<br></li><li>Applied Gibbs Sampling with truncated distributions.<br></li><li>Delivered actionable insights in days (vs. weeks with Meridian).</li></ul><h3>Results:</h3><p>✅ Identified top-performing channels (e.g., integrated TV + Social + Promotions Deliver 3x Returns vs. Single-Channel Ads).<br>✅ Quantified competitor impact (e.g., a rival’s 20% discount reduced sales by 12%).<br>✅ Optimized budget allocation, boosting marketing efficiency by 35%.</p><h3>Testimonial:&nbsp;</h3><p><em>"We struggled for months with Google Meridian—spending countless hours tweaking priors only to get unrealistic media contributions. More Than Data’s constrained Bayesian approach was a game-changer. Their model incorporated our business rules from the start, delivering reliable ROAS and impact shares in just one run. For the first time, we had a clear, actionable view of what was really driving sales. This isn’t just an upgrade to Meridian; it’s a fundamentally better way to do MMM."</em></p><h2>Conclusion: Why Constrained Bayesian MMM is the Future</h2><p>Google Meridian has limitations that make it unsuitable for enterprise MMM. By adopting a constrained Bayesian framework, marketers can:</p><p>✅ Eliminate guesswork with business-driven constraints.<br>✅ Speed up analysis (no more endless re-runs).<br>✅ Handle large datasets (100+ variables effortlessly).</p><p>For brands seeking accurate, scalable, and marketer-friendly MMM, the choice is clear: Ditch unconstrained models and embrace backward-thinking Bayesian methods.</p><h2>Ready to Upgrade Your MMM?</h2><p>If you’re tired of wrestling with Meridian’s limitations, More Than Data’s constrained Bayesian MMM delivers the precision and efficiency you need.</p><p>Contact us today for a <a data-mce-href="https://morethandata.biz/minute-mmm" href="https://morethandata.biz/minute-mmm" title="constrained Bayesian marketing mix modelling free consultation" target="_blank" rel="noopener" data-mce-selected="inline-boundary"><strong>free consultation</strong></a>!</p>

Upgrading Google Meridian: A Constrained Bayesian Approach for Robust Marketing Mix Modelling

<h2>Introduction</h2><p>Influencer marketing has become a cornerstone of modern digital strategies, with brands investing heavily in collaborations to boost visibility, engagement, and sales. However, many businesses fail to measure the real impact of these campaigns, treating them as a "spray and pray" tactic rather than a performance-driven investment.</p><p>Without proper measurement, you risk:</p><ul><li>Wasting budget on ineffective influencers<br></li><li>Missing opportunities to optimize campaigns<br></li><li>Failing to justify future investments</li></ul><p>This article will guide you through a structured approach to measuring influencer marketing ROI, using real-world examples and proven methodologies.</p><h2>Why You Must Measure Influencer Marketing</h2><p>Many marketers assume that influencer success is purely based on engagement rates or follower counts. However, vanity metrics alone don’t translate to sales. Here’s why measurement is non-negotiable:</p><h3>Influencer Marketing is Performance Marketing</h3><p>Just like paid ads, influencer campaigns should be tied to business outcomes—whether it’s sales, leads, or brand lift. If you can’t track ROI, you’re essentially gambling with your marketing budget.</p><p><strong>Example</strong>: A DTC skincare brand invested $50,000 in micro-influencers but saw no sales lift. Upon analysing, they discovered that while engagement was high, the influencers’ audiences didn’t align with their target demographic. Without tracking conversions, they wasted resources.</p><h3>Justify Future Investments</h3><p>Not all influencers deliver equal value. Measuring performance helps you:</p><ul><li>Identify top-performing creators<br></li><li>Optimize budget allocation<br></li><li>Discontinue partnerships that don’t drive results</li></ul><p><strong>Example</strong>: A fashion retailer worked with 10 influencers but only three generated measurable sales. By reallocating budget to the best performers, they increased ROI by 40% in the next campaign.</p><h2>Avoid Vanity Metrics – They’re Misleading</h2><p>Vanity metrics (likes, shares, impressions) might look impressive, but they don’t guarantee sales. Worse, they can be manipulated.</p><h3>The Problem with Vanity Metrics</h3><ul><li><strong>Fake Engagement</strong>: Some agencies use bots or click farms to inflate numbers.<br></li><li><strong>No Direct Sales Correlation</strong>: High impressions don’t always mean high conversions.</li></ul><h3>Real-World Case</h3><p>A TikTok influencer agency was caught using hundreds of devices to artificially boost live-stream engagement. The campaign showed 200K+ views, but actual sales were near zero.</p><h3>What to Track Instead</h3><ul><li><strong>Conversion Rate</strong> (% of viewers who purchase)<br></li><li><strong>Click-Through Rate (CTR)</strong> (traffic driven to your site)<br></li><li><strong>Cost Per Acquisition (CPA)</strong> (how much you spend to gain a customer)<br></li><li><strong>Attributable Sales</strong> (direct purchases from promo codes or tracked links)</li></ul><h2>Step 1: Set Up Proper Tracking Before Launching Campaigns</h2><p>Without accurate tracking, you’ll never know which influencers drive sales.</p><h3>Key Tracking Methods</h3><p><strong>1. Unique Promo Codes &amp; Affiliate Links</strong></p><ul><li>Assign each influencer a unique discount code or affiliate link.<br></li><li>Track redemptions in your e-commerce platform.<br></li></ul><p><strong>Example</strong>: A flower e-commerce brand failed to link promo codes to specific influencers, making it impossible to measure which partnerships were profitable. After fixing this, they identified that 70% of sales came from just two influencers.</p><p><strong>2. UTM Parameters &amp; Google Analytics</strong></p><ul><li>Embed UTM tags in influencer-shared URLs (e.g., ?utm_source=Instagram&amp;utm_medium=InfluencerX).<br></li><li>Monitor traffic sources in Google Analytics.<br></li></ul><p><strong>Best Practice</strong>: Use consistent naming conventions (e.g., Campaign=SummerSale_InfluencerY). Track not just clicks but also on-site behaviour (lead form submission, cart additions).</p><p><strong>3. Conversion API (CAPI) for Reliable Tracking</strong></p><ul><li>Traditional pixel tracking is limited due to cookie restrictions.<br></li><li>CAPI uses server-side tracking for more accurate data.<br></li></ul><p><strong>Example</strong>: A beauty brand using Meta’s CAPI saw a 21% increase in tracked conversions compared to pixel-only tracking.</p><h2>Step 2: Separate Trackable vs. Non-Trackable Channels</h2><p>Not all marketing efforts can be directly measured. Here’s how to categorize them:</p><h3>Non-Trackable Channels (ATL Marketing)</h3><ul><li>TV, radio, billboards, print ads<br></li><li>Hard to attribute directly to sales<br></li></ul><p><strong>Solution</strong>: Use Marketing Mix Modelling (MMM) to estimate their impact.</p><h3>Trackable Channels (Digital &amp; Influencer Marketing)</h3><ul><li>Paid social ads, influencer campaigns, email marketing<br></li><li>Should be precisely measured via UTM, promo codes, and analytics<br></li></ul><p><strong>Best Practice</strong>: Ensure all digital campaigns are properly tagged. Compare influencer-driven sales against other channels (e.g., paid ads).<br></p><h2>Step 3: Use Marketing Mix Modelling (MMM) for Holistic Measurement</h2><p>MMM helps quantify the impact of both trackable and non-trackable marketing efforts.</p><h3>How MMM Works</h3><ul><li><strong>Define Business Goals</strong> (e.g., sales, revenue, new customers).<br></li><li><strong>Input Historical Data</strong> (marketing spend, sales, external factors).<br></li><li><strong>Analyse Contribution</strong> of each channel (including influencer marketing).</li></ul><h3>Real-World Application<br></h3><p>A soda water brand used MMM to discover that:</p><ul><li>Influencers contributed 0.3% of total sales (much higher than projected, yet almost no measurable influence).<br></li><li>TV ads had minimal short-term impact but boosted long-term brand recognition.</li></ul><h3>Key Considerations for MMM</h3><ul><li>Calibrate with Trackable Data: Ensure MMM outputs align with digital metrics (e.g., Google Analytics).<br></li><li>Avoid Overestimating ATL Channels: Traditional media rarely drives immediate sales spikes.<br></li></ul><h2>Final Thoughts: Turning Data into Action</h2><p>Measuring influencer marketing isn’t just about proving ROI—it’s about optimizing future campaigns.</p><h3>Actionable Steps</h3><p><strong>✅ Set up tracking before launching campaigns</strong> (promo codes, UTMs, CAPI).<br><strong>✅ Focus on business outcomes, not vanity metrics</strong>.<br><strong>✅ Use MMM to measure both digital and traditional channels</strong>.<br><strong>✅ Continuously refine influencer selection based on performance data</strong>.</p><p>By adopting a data-driven approach, you’ll ensure every dollar spent on influencer marketing delivers measurable results.</p><h3>Need Help Measuring Your Influencer Campaigns?</h3><p>If tracking and analytics feel overwhelming, consider partnering with performance marketing experts who specialize in attribution modelling and ROAS optimization. Would you like a <a data-mce-href="https://morethandata.biz/minute-mmm" href="https://morethandata.biz/minute-mmm" title="free social media influencer measure and audit" target="_blank" rel="noopener"><strong>free audit</strong></a> of your current influencer tracking setup?&nbsp;</p>

How to Measure Social Media Influencers’ Impact on Your Sales

<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

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!

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<p><strong>This website delivers the media measurement &amp; budget optimization tools that big MMM providers charge premium prices for—only now, they're free.</strong></p>

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