Blog

<h2>Introduction</h2>In today’s competitive health and wellness market, brands must leverage data-driven insights to optimize their marketing strategies. For a leading men’s health and general wellbeing brand, measuring the effectiveness of Above-the-Line (ATL) and Below-the-Line (BTL) campaigns was a significant challenge. With fragmented data, overlapping audience segments, and a need for real-time measurement, the brand partnered with More Than Data to build a granular Marketing Mix Model (MMM) that quantified media performance, promotional synergy, and cross-product impact.This article explores how More Than Data’s solution helped the brand:<ul><li>Measure and quantify ATL &amp; BTL media performance </li><li>Track the synergy between TV/Radio and digital promotions </li><li>Optimize media mix for different audience groups </li><li>Analyse cross-product bundle sales impact </li><li>Enable real-time marketing planning &amp; forecasting</li></ul>The Challenge: Fragmented Data &amp; Complex Audience TargetingThe men’s health brand primarily targeted three key audience segments:<ul><li>Men aiming to lose body weight </li><li>Men looking to grow muscle </li><li>Men seeking a balanced everyday diet</li></ul>Each segment responded differently to media channels, making it crucial to tailor the marketing mix. However, the brand faced several challenges:<h3>1. Disorganized &amp; Fragmented Data</h3><ul><li>Data from Meta, Google Ads, TikTok, Snapchat, TV, and Radio was mismanaged. </li><li>Manual data collection took 2-3 weeks, delaying insights. </li><li>Mismatched reporting led to version control issues.</li></ul><h3>2. Measuring ATL &amp; Promotional Synergy</h3><ul><li>The brand needed to measure both standalone and combined effects of ATL (TV, Radio) and BTL (social, search, influencers). </li><li>They also wanted to track how promotions (online vouchers) interacted with media spend.</li></ul><h3>3. Cross-Product Purchase Behaviour</h3><ul><li>Many customers bought product bundles (e.g., weight loss + muscle growth). </li><li>Traditional MMM struggled to attribute sales correctly when promotions overlapped.</li></ul><h3>4. Real-Time Decision-Making Needed</h3><ul><li>The in-house marketing team required a live measurement system to adjust campaigns dynamically.</li></ul><h2>The Solution: Automated Data Pipelines &amp; Granular MMM</h2><h3>1. Automated &amp; Semi-Automated Data Collection</h3><ul><li>Fully Automated: APIs from Google Ads, Meta, TikTok, Snapchat pulled real-time ad performance data. </li><li>Semi-Automated: TV &amp; Radio data was ingested from publisher reports in just 3 days (vs. 2-3 weeks manually).</li></ul><h3>2. Granular Marketing Mix Modelling</h3><ul><li>Measured ATL &amp; BTL contributions separately and their synergistic effects. </li><li>Modelled bundle promotions to understand cross-product sales impact. </li><li>Built response curves to predict sales based on media spend.</li></ul><h3>3. Optimized Media Mix for Each Audience Segment</h3><ul><li>Weight Loss Audience: Best responded to online video, influencers, TikTok, and social ads. </li><li>Muscle Growth Audience: More influenced by search and performance marketing. </li><li>Diet Balancers: Engaged via ATL (TV/Radio) + educational promotions.</li></ul><h3>4. Real-Time Planning &amp; Forecasting</h3><ul><li>The brand could simulate budget scenarios before launching campaigns. </li><li>Incremental testing helped validate media effectiveness without overspending.</li></ul><h2>The Results: Higher Sales, Better ROAS &amp; Smarter Budgeting</h2>✅ Identified High-Performing Bundle Promotions<ul><li>Certain product bundles (e.g., weight loss + diet balancers) drove higher conversion rates.</li></ul>✅ Optimized Media Mix per Audience<ul><li>Weight loss ads performed best on TikTok &amp; influencer marketing. </li><li>Diet-focused campaigns benefited from TV ads + voucher promotions.</li></ul>✅ Sales Uplift: 2% In-Store, 8% Online<ul><li>In-store sales grew 2% YoY due to better-targeted ATL campaigns. </li><li>Online sales surged 8% from optimized digital promotions.</li></ul>✅ 16% ROAS Improvement<ul><li>Synergy between ATL + digital promotions amplified returns without increasing budget.</li></ul><h2>Testimonial from the Brand</h2>"Working with More Than Data transformed our marketing measurement approach. Their automated data pipelines eliminated weeks of manual work, and their MMM provided crystal-clear insights into how ATL, digital ads, and promotions work together. We now know exactly which media mix works for each audience segment and how bundle promotions drive sales. Most importantly, we’ve seen an 8% boost in online sales and 16% higher ROAS—all without increasing our budget. Their real-time planning tools empower our in-house team to make faster, smarter decisions."<h2>Conclusion: Data-Driven Decisions = Higher ROAS</h2>For men’s health brands, precision in media measurement is key. By leveraging automated data collection, granular MMM, and response curve modelling, More Than Data helped this brand:<ul><li>Eliminate guesswork in media planning </li><li>Maximize ATL + BTL synergy </li><li>Boost sales &amp; ROAS efficiently</li></ul>The future of marketing is real-time, data-driven, and audience-specific—and this case study proves it.

More Than Data Transformed a Men’s Health Brand’s Marketing Measurement & Strategy

<h2>Understanding the Basics of a Marketing Mix Model</h2>
A Marketing Mix Model (MMM) is an analytical tool used to evaluate the impact of various marketing tactics on sales and other performance metrics. By understanding this model, you can allocate your budget more effectively and optimize your marketing strategies. The MMM considers four key marketing elements: Product, Price, Place, and Promotion, often referred to as the 4Ps.

Before diving into building your own model, it's essential to get familiar with these components. The Product refers to what you are selling, Price is how much you charge, Place involves distribution channels, and Promotion covers advertising and other marketing efforts. Mastering these elements will be critical in creating an effective model.

<figure id="figure-dclpgek5vbm9b7kp60"><img id="dclpgek5vbm9b7kp60" src="https://media.gettyimages.com/id/629303228/photo/im-happy-to-explain.jpg?b=1&s=2048x2048&w=0&k=20&c=WEmzS8V0sMIhn9i-_wyF5Cs9Iyk4Ye2AfzGK4jmkLt4=" alt="marketing strategy" class="object-cover bg-gray-100" style="aspect-ratio: 16/9"></figure>

<h2>Setting Up Your Data</h2>
The first step in building a Marketing Mix Model is gathering the right data. You will need historical data on sales, marketing spend, and any external factors that might influence your results, such as economic conditions or seasonal trends. Accurate data is crucial as it forms the foundation of your model.

Once you have collected your data, organize it in a spreadsheet. Ensure that each variable is clearly labeled and that the data covers a sufficient time period to allow for meaningful analysis. Ideally, you should have at least two years of data to identify trends and patterns.

<figure id="figure-soaylopau5m9b7kp60"><img id="soaylopau5m9b7kp60" 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>Choosing the Right Tools</h2>
With your data in place, the next step is selecting the appropriate tools for building your model. There are several software options available, ranging from Excel to more advanced analytics platforms like R or Python. For beginners, starting with Excel might be the easiest way to get a grip on the basics.

If you're more experienced or willing to learn, using R or Python can provide greater flexibility and more robust modeling capabilities. These tools offer powerful statistical packages that can handle complex data sets and provide more accurate results.

<figure id="figure-4kasi2gznbm9b7kp60"><img id="4kasi2gznbm9b7kp60" src="https://media.gettyimages.com/id/2174071798/photo/maintenance-repair-and-improvement-concept-tools-3d-icon-on-the-glossy-futuristic-surface.jpg?b=1&s=2048x2048&w=0&k=20&c=kTE5mxyqtZOeO86BdxSdo3hShGKpODKO1sjCoG-1EaA=" alt="software tools" class="object-cover bg-gray-100" style="aspect-ratio: 16/9"></figure>

<h2>Building Your Model</h2>
Now that you're equipped with data and tools, it's time to start building your Marketing Mix Model. Begin by inputting your data into the chosen software and setting up a basic regression model. Regression analysis will help you understand the relationship between different marketing variables and sales outcomes.

Pay attention to key metrics like Return on Investment (ROI) and Elasticity. These will help you evaluate which marketing activities are driving the most value and where adjustments can be made for better performance. Remember, the goal is to identify which elements of your mix are working and which need improvement.

<h3>Testing and Refining Your Model</h3>
Once your model is built, it's crucial to test its accuracy by comparing its predictions against actual results. This step will help you identify any discrepancies and refine your model further. Don't be discouraged if your first attempt isn't perfect; iterative testing and tweaking are part of the process.

Consider running simulations to predict future outcomes based on different marketing scenarios. This can provide valuable insights and guide decision-making for upcoming campaigns.

<figure id="figure-ryilvgsg2qm9b7kp60"><img id="ryilvgsg2qm9b7kp60" src="https://media.gettyimages.com/id/2041488351/photo/artificial-intelligence-artificial-general-intelligence-neural-science-computing-node.jpg?b=1&s=2048x2048&w=0&k=20&c=jNywzXLhgT6WVvFTQGGkeLZ98BCiT-4hPWCRl1D2Y_M=" alt="testing model" class="object-cover bg-gray-100" style="aspect-ratio: 16/9"></figure>

<h3>Interpreting Results</h3>
Interpreting the results of your Marketing Mix Model is perhaps the most critical part of the process. Look for trends and insights that can inform your strategy moving forward. For instance, if promotions consistently show high ROI, you might consider increasing your investment in this area.

Share these insights with your team to ensure everyone is aligned on strategic changes. Effective communication of your findings can lead to more informed decisions and ultimately improve overall marketing success.

<h3>Continuous Improvement</h3>
The world of marketing is ever-evolving, so it's important to continually update and refine your Marketing Mix Model. As new data becomes available or market conditions change, revisit your model to ensure it remains relevant and accurate.

Remember, a well-maintained MMM can be a powerful tool in your marketing arsenal, helping you make data-driven decisions that enhance performance and drive growth.

Quick Guide to Building Your First Marketing Mix Model in Under 10 Minutes

Marketing Mix Modelling (MMM) has been around for decades. It’s the OG of marketing measurement—older than Facebook, older than Google Ads, older than that one Excel formula you keep forgetting.Yet, despite its proven ability to answer critical questions like "Is my TV ad actually working?" or "Why am I spending so much on LinkedIn ads that only my ex-colleagues see?", many marketers still treat MMM like a mysterious black box.So why aren’t strategists, media planners, account managers, and brand managers using MMM more? Let’s break it down—with a side of brutal honesty and a dash of humor.<h3>Reason 1: “We Don’t Have the Data… Or Do We?” </h3>The Problem:Many marketers believe they need perfect data to run MMM. Newsflash: No one has perfect data.<ul><li>Media Planners: "Our agency reports are a mess—some are in PDFs, some in Excel, and some are just scribbled on a napkin from last year’s media lunch." </li><li>Brand Managers: "We don’t track in-store sales properly, so MMM won’t work for us." </li><li>Media Investment Managers: "Half our digital spend is dark social—how do we even measure that?"</li></ul>The Reality: MMM doesn’t need perfect data—it just needs enough data. More Than Data has worked with:<ul><li>Brands that thought their data was unusable (spoiler: it wasn’t). </li><li>Companies still using spreadsheets from 2015 (yes, really). </li><li>Teams who didn’t even know where their media spend reports were stored (we found them).</li></ul>Lesson:&nbsp; If you’re waiting for "perfect" data, you’ll be waiting forever. Start messy, refine later.<h3>Reason 2: “MMM Sounds Complicated… Like, ‘Call-IT-Support’ Complicated” </h3>The Problem: Somewhere along the way, MMM got a reputation for being:<ul><li>Too technical (What’s a Bayesian model? Is that like a Star Wars thing?) </li><li>Too slow (We need answers now, not in six weeks!) </li><li>Too academic (If I see one more regression analysis, I’m switching careers.)</li></ul>The Reality: Modern MMM isn’t your grandpa’s econometrics project. At More Than Data, we:<ul><li>Explain results in plain English (No jargon, just "This is what’s working, this is what’s not.") </li><li>Deliver insights fast (Weekly updates, not quarterly post-mortems.) </li><li>Make it visual (Dashboards &gt; 100-page PDFs.) </li></ul>Lesson:&nbsp; If your MMM provider makes you feel like you’re back in stats class, fire them.<h3>Reason 3: “But Last Time We Did MMM, Nothing Changed!” </h3>The Problem: Some marketers have tried MMM before… and then promptly ignored the findings.<ul><li>Account Managers: "The model said to cut radio spend, but the client loves radio!" </li><li>Marketing Managers: "The report said influencers don’t work, but our CEO’s daughter is an influencer!" </li><li>Strategists: "The data says we should shift budget, but we’ve always done it this way!"</li></ul>The Reality: MMM isn’t a magic wand—it’s a flashlight. It shows you where the money’s working (and where it’s disappearing into a black hole). But if you don’t act on it, nothing changes.Lesson:&nbsp;MMM only works if you actually use it. Shocking, we know.<h3>Reason 4: “We’re Too Busy Fighting Fires to Think Long-Term” </h3>The Problem: Most marketers are stuck in:<ul><li>Last-minute campaign tweaks ("The creatives aren’t performing—panic!") </li><li>Endless meetings ("Let’s discuss the meeting about the meeting.") </li><li>ROI justification ("The CFO wants to know why we spent $500K on TikTok.")</li></ul>Who has time for strategic measurement when you’re putting out daily fires?The Reality:MMM actually saves you time. Instead of guessing:<ul><li>You’ll know exactly which channels drive sales. </li><li>You’ll stop wasting money on underperforming tactics. </li><li>You’ll have data to shut down pointless budget debates.</li></ul>Lesson:&nbsp; You’re not too busy for MMM—you’re too busy NOT using it.<h3>Reason 5: “We’re Addicted to Last-Click Attribution - Help Us!” </h3>The Problem: Many marketers are hooked on:<ul><li>Digital attribution ("Look, this Google Ad led to a sale!") </li><li>Vanity metrics ("Our Instagram post got 10K likes!") </li><li>Short-term thinking ("Sales dipped this week—ABORT THE STRATEGY!")</li></ul>The Reality: Last-click attribution is like judging a movie by its last scene. MMM looks at the whole story, including:<ul><li>How TV builds brand awareness that later converts via search. </li><li>Why radio drives in-store purchases that never get tracked online. </li><li>When social media buzz leads to sales three months later.</li></ul>Lesson:&nbsp; If you’re only measuring what happens right before a sale, you’re missing 90% of the picture.<h3>The Bottom Line: Stop Making Excuses, Start Using MMM </h3>For Media Planners:Stop guessing which channels work best. Use MMM to allocate budgets with confidence.For Brand Managers:Stop relying on gut feel. Use MMM to prove your campaigns actually drive brand awareness.For Marketing Managers:Stop wasting time on pointless debates. Use MMM to shut down budget arguments with data.For Everyone Else:Stop pretending you don’t need MMM. You do.<h3>What’s Your Excuse?</h3>✅ "Our data is messy." → We’ve seen worse.✅ "It’s too complicated." → Not the way we do it.✅ "We tried it once and nothing changed." → Then you did it wrong.✅ "We don’t have time." → You don’t have time NOT to.<h3>Final Thought </h3>The brands winning right now aren’t the ones with the biggest budgets—they’re the ones using MMM to spend smarter.So… when are you starting?<h3>Key Takeaways - For Those Who Skipped to the End </h3><ul><li>No one has perfect data. Start with what you’ve got. </li><li>MMM shouldn’t feel like a math exam. If it does, get a better provider. </li><li>Insights are useless if you ignore them. Actually use the findings. </li><li>Last-click attribution is lying to you. MMM tells the full story. </li><li>The best time to start MMM was last year. The second-best time is now.</li></ul>Ready to stop guessing and start measuring? Let’s talk.

5 Real Reasons Marketers Avoid Marketing Mix Modeling - Why They're Wrong

<h3>The Challenge: Untangling a Complex Full-Funnel Strategy</h3>In crowded insurance market, where consumers shop across multiple brands before selecting a policy, one major car and home content insurer faced a pressing question:"How can we generate more insurance quotes without significantly increasing our marketing budget?"The brand was running one of the industry's most sophisticated media mixes, spanning:<ul><li>Traditional channels: TV, radio, outdoor, print, cinema </li><li>Digital channels: Search, social, display, online video, affiliates</li></ul>But three critical challenges stood in their way:1. Data Disorganization at ScaleDespite working with established media agencies, the brand's media data was trapped in:<ul><li>Inconsistent Excel files with mismatched date formats </li><li>PDF reports that required manual extraction </li><li>Disconnected affiliate tracking systems</li></ul>The marketing team was spending 40% of their time on data wrangling rather than strategy.2. Granular Measurement RequirementsThe key metric—insurance quotes—needed to be analyzed across multiple dimensions:<ul><li>Product type: Car insurance vs. home &amp; content </li><li>Demographics: Different conversion patterns for 25-40 vs. 41-65 age groups </li><li>Geography: Varying performance across states</li></ul>Traditional marketing mix models couldn't handle this level of detail.3. Budget Optimization Under ConstraintsWith a mandate to limit budget growth to just 3% annually, the team needed to:<ul><li>Identify the most efficient channels for each product / age group / state </li><li>Predict how reallocations would impact quote volume </li><li>Maintain brand-building while driving immediate quote submission</li></ul><h3>The Solution: A Custom Hierarchical Measurement Framework </h3>1. Streamlining the Data Foundation More Than Data implemented a robust data processing system that:<ul><li>Automated ingestion of agency reports using Python-based cleaning scripts </li><li>Established consistent cost tracking across all channels </li><li>Integrated affiliate data with owned media performance</li></ul>This reduced data preparation time from weeks to days.2. Multi-Dimensional Performance ModellingThe custom-built marketing mix model accounted for:<ul><li>Channel effectiveness by product type (e.g., search worked better for car insurance) </li><li>Regional variations (OOH performed best in metro areas) </li><li>Age-based conversion patterns (social drove younger quotes, radio influenced older demographics)</li></ul>3. Key Insights Uncovered <ul><li>Cinema ads generated 3% more car insurance quotes among 25-40 year old </li><li>Radio drove 8% higher conversion rates in regional Queensland </li><li>Affiliate partnerships accounted for 18% of high-intent quotes</li></ul>4. Dynamic Budget Allocation System The team could now:<ul><li>Test scenarios like "What happens if we shift 15% from print to YouTube?" </li><li>See predicted quote impact before making changes </li><li>Set budget rules and constraints to protect brand-building channels</li></ul><h3>The Results: Doing More With the Same Budget</h3>✅ 11% increase in marketing efficiency (surpassing the 6% target)✅ 5% more quotes generated with just 3% additional spend✅ Ongoing partnership as the brand's marketing effectiveness advisor"Before working with More Than Data, we were making million-dollar decisions based on incomplete information. Their hierarchical modelling approach finally gave us the clarity we needed to optimize across products, regions and age groups. The 11% efficiency gain wasn't just a number—it translated directly to our bottom line."<h3>Why This Matters for Insurance Services Marketers </h3><ul><li>Insurance Purchases Are Complex – Different products appeal to different demographics through different channels </li><li>Every Dollar Counts – In a low-margin industry, small efficiency gains create big impacts </li><li>Static Models Aren't Enough – Consumer behavior changes constantly </li></ul><h3>The Real Competitive Advantage </h3>What set this solution apart wasn't flashy technology, but rather:<ul><li>Deep understanding of insurance purchase journeys </li><li>Rigorous methodology for hierarchical measurement </li><li>Practical tools that marketers could actually use</li></ul><h3>Key Lessons for Marketing Leaders</h3><ul><li>Clean data comes first – No model can overcome poor inputs </li><li>Granularity matters – Average performance hides important variations </li><li>Scenario testing is essential – The market changes constantly</li></ul>For brands ready to move beyond guesswork, this is how modern marketing measurement works.

Precision Marketing Measurement for an Insurance Leader

<h3>The Challenge: Untangling a Complex Web of Media and Non-Media Drivers</h3>In competitive beauty and aesthetics industry, one leading skin and laser treatment brand stood out for its rapid growth—attracting young, professional women with cutting-edge treatments and strategic partnerships. However, as the brand expanded its franchise network across major cities, its marketing team faced mounting pressure to:<ul><li>Prove which campaigns actually drove clinic bookings (not just engagement). </li><li>Measure the true impact of bundle promotions with other fashion brands. </li><li>Optimize spend across a full-funnel mix of ATL (TV, Radio), BTL (social, search, display, video, influencers, local SEO).</li></ul>1. The Data Hurdle: Fragmented Media Spend &amp; Third-Party DependenciesWhile sales data was neatly stored in a third-party data warehouse, media performance metrics were scattered across multiple agency reports, Excel files, and ad platforms. More Than Data faced three core challenges:<ul><li>Inconsistent Media Data: Each agency reported metrics differently—some tracked clicks, others focused on impressions, and a few couldn’t provide accurate cost breakdowns. </li><li>Non-Media Variables Were Critical: Factors like inbound calls, store openings, bundle discounts, and influencer promo codes are expected to contribute to 11% of sales—but weren’t being measured cohesively. </li><li>Real-Time Tracking Gaps: The brand needed to test dynamic bundle offers (e.g., "20% off laser treatments with Partner X’s fashion purchase") but lacked a system to measure cross-brand impact.</li></ul>2. The Boardroom Pressure: "Show Us the ROI" With new clinics opening and partnerships multiplying, the executive team demanded:<ul><li>"How much revenue comes from our fashion brand collabs?" </li><li>"Should we spend more on Instagram influencers or Google Ads?" </li><li>"Which bundle offers should we scale—and which should we kill?"</li></ul>Without answers, the marketing team was making million-dollar decisions based on hunches.<h3>The Solution: A Next-Gen MMM with Real-Time Bundle Tracking </h3>1. Automated Data Pipelines: From Chaos to ClarityMore Than Data built a fully integrated data infrastructure, including:<ul><li>Automated ingestion of sales data from the third-party warehouse. </li><li>Python-powered cleaning scripts to standardize messy agency reports. </li><li>UTM-tagged tracking for all bundle-driven traffic (e.g., clicks from partner sites).</li></ul>This eliminated weeks of manual reconciliation and provided a single source of truth.2. Full-Funnel MMM: Quantifying Every Sales DriverThe custom model didn’t just measure media—it mapped the entire customer journey, including:<table style="border-collapse: collapse; width: 100%;" border="1" data-mce-style="border-collapse: collapse; width: 100%;"><tbody><tr><td style="width: 50%;" data-mce-style="width: 50%;">Factor</td><td style="width: 50%;" data-mce-style="width: 50%;">Impact on Sales</td></tr><tr><td style="width: 50%;" data-mce-style="width: 50%;">TV &amp; Radio (ATL)</td><td style="width: 50%;" data-mce-style="width: 50%;">Brand lift, but delayed conversions</td></tr><tr><td style="width: 50%;" data-mce-style="width: 50%;">Instagram Influencers</td><td style="width: 50%;" data-mce-style="width: 50%;">High-intent clinic bookings</td></tr><tr><td style="width: 50%;" data-mce-style="width: 50%;">Bundle Discounts</td><td style="width: 50%;" data-mce-style="width: 50%;">23% higher AOV vs. standalone treatments</td></tr><tr><td style="width: 50%;" data-mce-style="width: 50%;">Inbound Calls</td><td style="width: 50%;" data-mce-style="width: 50%;">17% conversion rate (vs. 4% from web forms)</td></tr><tr><td style="width: 50%;" data-mce-style="width: 50%;">New Store Openings</td><td style="width: 50%;" data-mce-style="width: 50%;">3% sales surge in first 6 months</td></tr></tbody></table>3. The Bundle Optimization EngineA real-time dashboard tracked:<ul><li>Which partner drives the most traffic (e.g., Fashion Brand Y’s referrals converted 2.1x better than Z’s). </li><li>Promo code redemption rates by campaign. </li><li>ROI of each bundle combo (e.g., "Laser + Skincare packs outperformed Laser + Fashion vouchers").</li></ul>4. Scenario Testing for Maximum Profit The team could now simulate:<ul><li>"What if we shift 15% of TV spend to TikTok influencers?" </li><li>"How does closing the metro Sydney clinic for renovations affect regional bookings?" </li><li>"Which partner should we prioritize for Q3’s promo?" </li></ul><h3>The Results: Smarter Spending, Scalable Growth</h3>✅ 4% sales increase despite 23% lower wasted ad spend.✅ 5 new clinics opened in secondary markets with confidence.✅ Bundle partnerships now drive 18% of revenue, with Fashion Brand X’s collab alone generating 2,200+ bookings/month."More Than Data didn’t just give us reports—they built a decision engine. Now, we know exactly which influencers convert, which bundles profit, and even how weather impacts bookings. Their MMM isn’t a luxury; it’s our growth blueprint."<h3>Why This Matters for Beauty &amp; Aesthetics Brands </h3><ul><li>Non-Media Factors Are Revenue Levers – Store locations, partnerships, and even call center staffing directly impact sales. </li><li>Bundles Require Precision Tracking – Without UTM tagging and promo code analytics, you’re leaving money on the table. </li><li>Real-Time &gt; Retrospective – Monthly reports are too slow; live dashboards let you optimize between board meetings.</li></ul><h3>The Bottom Line </h3>In an industry where trends shift weekly and partnerships make/break margins, generic MMM isn’t enough. Winning requires a custom, commerce-aware model—exactly what More Than Data delivered.<h3>Key Takeaways for Franchise Beauty Brands </h3><ul><li>Track every touchpoint—even phone calls and local events. </li><li>Automate data flows to avoid "spreadsheet hell." </li><li>Model bundle offers like media campaigns—they’re just as critical.</li></ul>Ready to transform guesswork into growth? That’s what More Than Data does.

How Data-Driven Insights Transformed a Skin & Laser Brand’s Marketing Strategy

<h3>The Challenge: Data Disarray and a CMO Under Fire</h3>In fiercely competitive auto components sector, where profit margins are razor-thin and customer loyalty is hard-won, one homegrown retail leader found itself at a crossroads. Despite holding a strong market position, its marketing leadership was struggling with a crisis of confidence. The executive team could not answer fundamental questions that determined the success of their campaigns:<ul><li>Which marketing initiatives were actually driving sales, and which were just generating vanity metrics? </li><li>How should budgets be allocated across TV, radio, digital, and sponsorships for maximum impact? </li><li>Why did every executive presentation turn into a defensive debate rather than a strategic discussion?</li></ul>1. The Data Abyss: A Fragmented NightmareThe root cause of these challenges became immediately apparent: marketing and media data was scattered across 37 different Excel files, managed by two competing media agencies. These files were riddled with version conflicts, missing fields, and incompatible format, making historical marketing performance analysis nearly impossible. More Than Data’s analysts discovered that 72% of their initial phase was spent simply validating basic campaign dates and spend figures—time that should have been allocated to strategic insights.The disorganization was staggering:<ul><li>Some files contained duplicate entries, while others had incomplete campaign data. </li><li>Key metrics like impressions, clicks, and conversions were defined differently across reports. </li><li>There was no centralized repository, forcing the team to manually cross-reference data from emails attachments and shared drives.</li></ul>2. The Fallout from an Agency HandoffA mid-year transition between media agencies had compounded the chaos. The two agencies used entirely different measurement frameworks:<ul><li>Agency A measured digital conversions using a last-click attribution model. </li><li>Agency B used a 7-day view-through attribution window. Neither agency had properly tracked the in-store sales impact of out-of-home (OOH) campaigns, despite significant investment in billboards and transit ads.</li></ul>This inconsistency meant that year-over-year comparisons were unreliable, and optimization decisions were being made based on flawed assumptions.3. Leadership Under the MicroscopeThe CMO found himself under fire at every quarterly review, facing relentless scrutiny from the executive panel. Sceptical board members demanded proof of marketing’s contribution to revenue, asking pointed questions such as:<ul><li>"Why are we allocating 20% of our budget to radio when we can’t trace a single sale back to it?" </li><li>"Prove that this super car racing sponsorship is doing more than just generating social media likes." </li><li>"How do we know our TV ads are driving purchases and not just brand awareness?"</li></ul>Without clean, reliable data, the CMO’s responses were based on intuition rather than evidence—eroding trust and putting future budgets at risk.<h3>The Solution: Surgical-Precision Marketing Analytics</h3>1. The Data Triage (Weeks 1-4)Before any strategic decisions could be made, More Than Data had to cleanse and structure the chaos. This required a forensic approach to data validation:<ul><li>Campaign Reconciliation: Aligning 14 months of mismatched agency reports into a single, coherent dataset. </li><li>Promotion and Special Offer Mapping: Checking promotional and price discount activities with point-of-sale (POS) system spikes to identify true drivers of revenue.</li></ul>This phase was grueling but necessary—without accurate data, any insights would be meaningless.2. Granular Marketing Mix Modelling (MMM)With a reliable dataset in place, More Than Data built a highly detailed MMM that quantified the impact of every marketing dollar. The model revealed several uncomfortable truths:<ul><li>Digital display ads had a 63% lower ROI than search campaigns, despite higher spend. </li><li>Tuesday morning radio slots outperformed traditional drive-time placements by 17% in revenue driving. </li><li>OOH ads near racetracks drove 6x more in-store visits than CBD billboards, justifying the brand’s racing sponsorships.</li></ul>The analysis also uncovered surprising synergies:<ul><li>Combining TV ads with targeted Facebook retargeting boosted campaign ROI by 28%. </li><li>Catalogue drops performed best when timed with radio promotions, increasing redemption rates by 33%.</li></ul>3. The Decision Engine: From Guesswork to Predictive PowerTo ensure the brand could sustain these insights, More Than Data developed a dynamic budget optimization tool. This allowed the marketing team to:<ul><li>Simulate "what-if" scenarios (e.g., "What if we shift 20% of radio spend to YouTube?"). </li><li>Predict sales impact before committing funds, with a 2.3% margin of error. </li><li>Generate boardroom-ready justification decks in minutes, not days.</li></ul>No more guesswork—just quantifiable, defensible decisions.4. Building a Sustainable Data FoundationTo prevent future chaos, More Than Data implemented a semi-automated data pipeline that:<ul><li>Ingested media logs, sales data, and external factors (e.g., competitor pricing, weather impacts). </li><li>Standardized reporting across marketing team and collaborated media agencies. </li><li>Flagged anomalies in real-time, reducing manual validation work by 65%.&nbsp;</li></ul><h3>The Transformation: From Reactive to Proactive</h3>12-Month Results✅ 5.2% sales lift in a contracting market.&nbsp;✅ 27% improvement in media efficiency, freeing up budget for high-ROI channels.&nbsp;✅ First-ever unanimous budget approval from the executive panel.The most telling moment came in the final quarterly review. Instead of a 50-slide deck defending past decisions, the CMO presented two important slides:<ul><li>"Here’s exactly how we’ll spend next quarter’s budget." </li><li>"Here’s the 9.1% revenue growth it will generate."</li></ul>For the first time, the discussion shifted from "Can we trust these numbers?" to "How fast can we scale this?"<h3>The Lesson for Modern Marketers </h3><ul><li>Data Debt Compounds – Every month of unvalidated reports makes recovery harder. </li><li>Agencies Are Partners, Not Owners – Measurement accountability must stay in-house. </li><li>Boardrooms Demand Artillery, Not Anecdotes – Predictive analytics replace gut feelings.</li></ul><h3>Final Insight</h3>This wasn’t just a marketing case study—it was a corporate governance wake-up call. The board wasn’t questioning the CMO’s creativity; they were demanding proof of fiscal responsibility. In today’s environment, that proof only comes through ruthless data discipline.The Bottom Line: If you can’t prove it, you shouldn’t spend it. And if your data is a mess, you’re flying blind in a storm.

How Data-Driven Decision Making Supported Market Leadership for an Auto Component Retailer

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

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

Blog

More Than Data Transformed a Men’s Health Brand’s Marketi...

Quick Guide to Building Your First Marketing Mix Model in...

5 Real Reasons Marketers Avoid Marketing Mix Modeling - W...

Precision Marketing Measurement for an Insurance Leader

How Data-Driven Insights Transformed a Skin & Laser Brand...

How Data-Driven Decision Making Supported Market Leadersh...