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<p>Marketing Mix Modelling (MMM) is often seen as the backbone of data-driven marketing—a powerful tool that helps brands understand how their media spend drives sales. But let’s be honest, the journey to mastering MMM isn’t always smooth sailing. It’s filled with learning curves, funny moments, and yes, a lot of coffee. Today, I’m sharing a light-hearted story from one of our co-founders’ early days in the world of MMM. It’s a tale of growth, collaboration, and how data can sometimes surprise even the most seasoned marketers. So, grab your coffee, and let’s dive in!</p><h3>The "Aha!" Moment with Unexpected Results</h3><p>Our co-founder, fresh out of university and eager to make an impact, was tasked with building a regression model for a marketing department. He crunched the numbers, ran the analysis, and—wait for it—the model showed that media spend had an unexpected effect on sales. The results weren’t what the team had anticipated, but they were statistically significant.</p><p>When he presented the findings to the head of strategy, the room fell silent. The head of strategy leaned back, thought for a moment, and said, “Well, that’s interesting. Let’s dig deeper.” Instead of dismissing the results, the team used them as a springboard for discussion. It turned into a valuable learning moment for everyone, proving that sometimes, the most unexpected insights can lead to the justification of the strategies.</p><h3>The Great Data Clean-Up</h3><p>After the “aha!” moment, our co-founder thought the hard part was over. Wrong. He returned to his desk to find a shared folder filled with hundreds of Excel files and PDFs—media plans, budgets, and reports from multiple campaigns. Each file was a treasure trove of data, but none of it was in a format ready for MMM.</p><p>What followed was a data clean-up marathon. For weeks, our co-founder organized, transformed, and standardized the data into something usable. It was tedious, yes, but it was also a crash course in the importance of clean data. By the end of it, he had not only mastered Excel shortcuts but also gained a deep appreciation for the hard work that goes into preparing data for analysis. And hey, he even earned a few laughs from his colleagues for his newfound obsession with perfectly aligned columns.</p><h3>The Power of Collaboration</h3><p>As our co-founder neared the end of his probation period, he noticed something interesting: the marketing team wasn’t just looking for data—<strong>they were looking for insights that aligned with their goals</strong>. During his performance review, his manager said, “We know you’re great with data, but we also need you to help us tell the story behind the numbers.”</p><p>This feedback was a game-changer. Instead of focusing solely on the technical side of MMM, our co-founder started collaborating more closely with the team. He asked questions like, “What results are you hoping to see?” and “How can we align the model with our strategic goals?” This shift in approach not only improved the models but also strengthened the team’s trust in data-driven decision-making.</p><h3>Coffee, Lunch, and Building Bridges</h3><p>Realizing the importance of collaboration, our co-founder went into full <strong>relationship-building mode</strong>. He bought coffee, organized team lunches, and even hosted informal “data chats” to explain the intricacies of MMM in a way that resonated with everyone.</p><p>These conversations revealed something important: the marketing team wasn’t just looking for numbers—they were looking for <strong>actionable insights</strong> that supported their strategies. For example, they wanted to see TV driving 6% of sales because they had increased the budget, and they expected Paid Search to drive 4% because their campaigns were consistently ranking in the top 3 on Google. By aligning the MMM results with these expectations, our co-founder was able to deliver models that not only made sense statistically but also supported the team’s goals.</p><h3>From Probation to Recognition</h3><p>With this new collaborative approach, our co-founder turned things around. The models he delivered were not only accurate but also actionable, helping the team make smarter decisions about their media spend. By the end of his extended probation, the marketing department wasn’t just satisfied—they were impressed. In fact, they even nominated him for a VIP award for his contributions.</p><h3>What’s Your MMM Story?</h3><p>MMM is a powerful tool, but it’s also a journey filled with learning, collaboration, and yes, a few funny moments along the way. Whether it’s cleaning up messy data, presenting unexpected results, or finding creative ways to align models with business goals, we’ve all been there.</p><p>So, tell us: <strong>What’s your MMM story</strong>? Have you ever had a lightbulb moment while analyzing data? Or found a creative way to align your models with your team’s goals? Share your experiences in the comments below—we’d love to hear them!</p>

The Light-hearted Side of Marketing Mix Modelling: A Tale of Growth, Coffee, and Data-Driven Wins

<p>In the fast-paced world of Quick Service Restaurants (QSRs), where competition is fierce and consumer preferences shift rapidly, the ability to make data-driven decisions is no longer optional—it’s essential. In this article, I’ll share a success story from one of our clients, a leading QSR brand in Australia, and how we helped them transform their marketing efficiency, optimize their promotional strategies, and achieve significant financial improvements—all while keeping their identity anonymous.</p><h3>The Challenge: Navigating a Complex Promotional Landscape</h3><p>Our client operates in the highly competitive online food delivery market, engaging with consumers through platforms like Uber Eats, Menulog, and DoorDash. Like many QSR brands, they relied heavily on discounts and promotions to attract customers and drive sales. However, they faced several critical challenges:</p><ul><li><strong>Complex Discount Structures</strong>: The brand ran multiple discounts and promotions simultaneously. For example, fried chicken might be discounted by 20%, while burgers were discounted by 15%, and fries were offered as a "buy-one-get-one-free" deal. These promotions often overlapped within the same order, making it difficult to track the impact of each individual campaign.<br></li><li><strong>Frequent Changes in Promotions</strong>: The brand frequently adjusted the strength and type of discounts—sometimes weekly or fortnightly. For instance, fried chicken might be discounted at 20% one week and 25% the next. This constant variation made it nearly impossible to measure the effectiveness of each campaign.<br></li><li><strong>Lack of Data Visibility</strong>: The online ordering platforms only provided total discounted sales figures, without breaking down which items were affected by which promotions. This lack of granular data left the brand’s marketing team shooting in the dark, relying on trial-and-error methods that often led to wasted marketing spend and minimal sales growth.<br></li><li><strong>No In-House Analytics Expertise</strong>: The brand lacked the internal resources to analyze and interpret the data effectively. They reached out to their delivery platforms for help, but even Uber Eats couldn’t provide the historical breakdown they needed.</li></ul><h3>The Solution: A Data-Driven Approach to Promotional Tracking</h3><p>To address these challenges, we developed a reverse decomposition algorithm and a comprehensive tracking system that allowed the brand to measure the impact of each discount and promotion campaign with precision. Here’s how we did it:</p><p><strong>1. Reverse Decomposition Algorithm</strong></p><p>We created a sophisticated algorithm to break down the total discounted sales amount for each order into its individual components. For example, if an order totaled $23.90, our system could identify:</p><ul><li>$5.00 for normal-priced items (e.g., drinks and sauces),<br></li><li>$3.50 for fries under a "buy-one-get-one-free" promotion,<br></li><li>$12.00 for burgers with a 15% discount,<br></li><li>And an additional 5.00 saved through a "spend up to $30, save $5" promotion.</li></ul><p>We tested this algorithm on over 12,000 records from Uber Eats, achieving an accuracy rate of over 99.9% (only 2 records with discrepancies), with discrepancies of less than 5 cents attributed to rounding issues.</p><p><strong>2. Marketing Mix Modelling</strong></p><p>With the data broken down, we applied <strong>Marketing Mix Modelling (MMM)</strong> to measure the effectiveness and ROI of each promotional campaign. For example:</p><ul><li>The 15% discount on burgers was treated as a marketing cost equal to 15% of the original price.<br></li><li>The "buy-one-get-one-free" promotion was treated as a cost equal to the price of the second item.</li></ul><p>This allowed us to calculate the ROI for each campaign and identify which promotions were driving the most value.</p><p><strong>3. Response Curves and Budget Optimization</strong></p><p>Using the data from the MMM, we created response curves for each type of discount and promotion. These curves helped us identify the diminishing return point for each campaign—the point at which additional investment no longer generated significant sales growth. Based on these insights, we developed a budget allocation system that allowed the brand to reallocate their marketing spend more effectively, avoiding over-saturated campaigns and focusing on those that delivered the best ROI.</p><h3>The Results: A Transformation in Marketing Efficiency</h3><p>The impact of our data-driven approach was profound. Over a three-month testing period, the brand achieved the following results:</p><ul><li><strong>Increased Sales and Revenue</strong>: Fried chicken and burger sales volumes increased by 14% compared to the same period the previous year. Overall revenue increased by 21%, demonstrating the effectiveness of the optimized promotional strategy.<br></li><li><strong>Streamlined Promotions</strong>: The brand narrowed down their promotions to just 3 discounts and 2 promotions for specific food items, compared to their previous strategy of discounting almost every item on the menu. This focus allowed them to concentrate their efforts on the most impactful campaigns.<br></li><li><strong>Significant Cost Savings</strong>: Marketing costs were reduced by 41% compared to the same period the previous year. This reduction in spend was achieved without sacrificing sales growth, highlighting the inefficiency of their previous "spray-and-pray" approach.<br></li><li><strong>Financial Turnaround</strong>: Prior to implementing our system, the brand was operating at a break-even point, with profits barely covering staff salaries and marketing costs. Within five months of using our tracking and optimization system, the brand achieved a positive financial turnaround, moving from break-even to profitability.</li></ul><h3>Key Takeaways: Lessons in Data-Driven Marketing</h3><p>This case study offers several valuable lessons for businesses operating in competitive markets:</p><ul><li><strong>The Power of Data-Driven Decision-Making</strong>: Without clear data, marketing decisions are often based on guesswork, leading to wasted spend and missed opportunities. By leveraging advanced analytics, businesses can make informed decisions that drive real results.<br></li><li><strong>The Importance of Granular Tracking</strong>: Understanding the impact of individual promotions is critical to optimizing marketing spend. A reverse decomposition approach can unlock insights that are otherwise hidden in aggregated data.<br></li><li><strong>Avoiding Over-Saturation</strong>: Not all discounts and promotions are created equal. Some campaigns reach a point of diminishing returns, where additional investment yields little to no benefit. Identifying these points allows businesses to allocate their budget more effectively.<br></li><li><strong>The Role of Expertise</strong>: Many small and medium-sized businesses lack the in-house expertise to analyze complex data. Partnering with data analytics professionals can provide the insights needed to transform marketing strategies and drive growth.&nbsp;</li></ul><h3>Conclusion: A Blueprint for Success</h3><p>This case study demonstrates the transformative power of data-driven marketing in the QSR industry. By developing a sophisticated tracking and optimization system, we helped our client achieve significant improvements in sales, revenue, and profitability—all while reducing their marketing spend.</p><p>For businesses looking to thrive in today’s competitive landscape, the lesson is clear: Invest in data-driven strategies, and partner with experts who can help you unlock the full potential of your marketing efforts. The results speak for themselves.</p><h3>Call to Action</h3><p>If you’re facing similar challenges in your business, we would love to hear from you. How are you leveraging data to optimize your marketing strategies? What lessons have you learned along the way? Let’s continue the conversation in the comments below.</p>

Transforming Marketing Efficiency with Data-Driven Insights: A Quick Service Restaurant Case Study

<p>In today’s hyper-competitive market, where brands are vying for consumer attention and market share, the ability to leverage data-driven insights is no longer a luxury—it’s a necessity. This is especially true in industries where consumer behavior is highly unpredictable, such as healthcare and over-the-counter remedies. In this article, I’ll share a compelling case study that demonstrates how advanced marketing analytics and measurement capabilities can drive tangible business outcomes, even in the face of fierce competition and market volatility.</p><h3>The Challenge: Navigating Uncertainty in a Crowded Market</h3><p>Our client, a leading medicated throat remedy brand sold exclusively in pharmacies, faced a daunting challenge. The market was becoming increasingly crowded, with established competitors outspending them by up to three times in media investments. Additionally, new entrants with significant financial backing were entering the category, further intensifying the competition.</p><p>Compounding the issue was the unpredictable nature of the cold and flu season. The peak period for sore throat remedies—a distress purchase category—shifts significantly each year, making it difficult to time media investments effectively. For a brand in this space, being top-of-mind when symptoms arise is critical to success. The question was: How could our client maintain its market leadership and drive growth in such a volatile environment?</p><h3>The Strategy: A Data-Driven Approach to Media Planning</h3><p>To address this challenge, we developed a sophisticated, data-driven strategy that combined long-term trend analysis with short-term predictive capabilities. Here’s how we did it:</p><p><strong>1. Long-Term Trend Prediction: The Power of Advanced Analytics</strong></p><p>We collaborated with our partners to create a proprietary forecasting tool, which leveraged over 30 data sources to predict the incidence of sore throat cases across each state in Australia. This system provided a long-term trend line, enabling us to forecast the season’s length and peak period with unprecedented accuracy.</p><p>With this tool, we were able to plan less agile media channels, such as radio and out-of-home advertising, well in advance. This ensured that our client’s messaging was aligned with the anticipated rise in sore throat cases, maximizing the impact of their media investments.</p><p><strong>2. Short-Term Prediction: Agility Through Real-Time Data</strong></p><p>In addition to long-term forecasting, the system incorporated daily temperature data to predict short-term fluctuations in sore throat cases. This allowed us to work with our media partners to allocate investments by state, reflecting anticipated changes in demand on a week-to-week basis. By combining long-term trends with short-term insights, we ensured that our client’s media was live precisely when and where it mattered most.</p><h3>The Results: Driving Growth in a Challenging Market</h3><p>The results of this data-driven approach were nothing short of remarkable:</p><ul><li><strong>3% Increase in Brand Sales</strong>: Despite increased competition and a historically soft season, our client not only maintained its leading market position but also achieved a 3% year-on-year increase in sales.<br></li><li><strong>$343,000 in incremental revenue</strong>, demonstrating the tangible impact of precision media planning.<br></li><li><strong>Over 80% Accuracy</strong>: The forecasting tool achieved an over 80% accuracy between predicted and actual sore throat cases, ensuring that media investments were highly effective and efficient.</li></ul><p>These results were a testament to the power of combining advanced analytics with strategic media execution. In a market where timing is everything, our client was able to stay ahead of the curve and capitalize on emerging opportunities.</p><h3>Key Takeaways: Lessons in Innovation and Agility</h3><p>This case study offers several valuable lessons for marketers and business leaders:</p><p><strong>1. Innovation in Media Planning</strong></p><p>The development of the forecasting tool was a game-changer for our client. By leveraging a sophisticated, data-driven approach, we were able to transform media planning from a reactive process into a proactive, predictive one. This level of innovation is essential for brands looking to maintain a competitive edge in today’s fast-paced market.</p><p><strong>2. Agility Through Data</strong></p><p>The ability to combine long-term trend analysis with short-term predictive capabilities allowed us to be agile in our media planning. This ensured that our client’s messaging was always relevant and timely, maximizing the impact of their investments.</p><p><strong>3. Competitive Edge in Volatile Markets</strong></p><p>Despite the challenges posed by increased competition and market volatility, our client was able to achieve significant growth. This underscores the importance of leveraging data-driven insights to navigate uncertainty and seize opportunities.</p><h3>Visualizing Success: The Role of Infographics</h3><p>To bring this story to life, we used a series of visual infographics that highlighted the key elements of the campaign:</p><ul><li><strong>Long-Term Trend</strong>: A graph illustrating the long-term trend of sore throat cases predicted by the forecasting tool.<br></li><li><strong>Short-Term Fluctuations</strong>: A visual representation of how daily temperature data influenced short-term predictions.<br></li><li><strong>Results</strong>: A clear depiction of the 3% sales increase and the additional $343K in revenue.</li></ul><p>These visuals not only made the data more accessible but also reinforced the strategic impact of our approach.</p><h3>Conclusion: The Future of Marketing is Data-Driven</h3><p>This case study is a powerful example of how advanced marketing analytics and measurement capabilities can drive meaningful business outcomes. By leveraging data to predict consumer behavior and optimize media investments, we were able to help our client maintain its market leadership and achieve significant growth—even in the face of fierce competition and market volatility.</p><p>As we look to the future, one thing is clear: <em><strong>The brands that succeed will be those that embrace data-driven strategies and invest in the tools and expertise needed to execute them effectively</strong></em>. For our client, this approach was not just a win—it was a testament to the transformative power of precision, agility, and innovation.</p><h3>Call to Action</h3><p>If you’re facing similar challenges in your market, I’d love to hear from you. How are you leveraging data to drive your marketing strategy? What lessons have you learned along the way? Let’s continue the conversation in the comments below.</p>

Unlocking Competitive Advantage Through Advanced Marketing Analytics: A Case Study in Precision and Agility

<p>If you've ever browsed Reddit, you might have come across posts questioning whether Marketing Mix Modelling (MMM) is flawed. Take this discussion, for example: <a data-mce-href="https://www.reddit.com/r/MachineLearning/comments/utqez5/d_is_mixed_market_modelling_full_of_crap/?rdt=48909" href="https://www.reddit.com/r/MachineLearning/comments/utqez5/d_is_mixed_market_modelling_full_of_crap/?rdt=48909" target="_blank" rel="noopener"><strong>Reddit Thread</strong></a>. To be honest? We couldn’t agree more. Yes, MMM has issues. It’s not perfect. But here’s the real question—what’s the alternative?</p><h3>If Not MMM, Then What?</h3><p>Let’s explore the other options marketers have for measuring effectiveness:</p><p>1️⃣ <strong>Multi-Touch Attribution (MTA)</strong>: Once hailed as the gold standard for digital attribution, MTA allowed marketers to track the full online customer journey across multiple digital touchpoints. But here’s the problem—third-party cookies are disappearing, making cross-platform tracking nearly impossible. Yes, you can still use authenticated user IDs, but that only works within your own digital properties (your website and apps). Another major limitation? MTA only works for digital channels. What about offline media—TV, radio, out-of-home (OOH), cinema, print? MTA won’t help you there.</p><p>2️⃣ <strong>Geo Lift Studies</strong>: This method, which is like A/B testing on a geographic level, can provide insights by comparing test and control markets. But let’s be real—finding a true control market (one without any ads) is nearly impossible. If you can’t find one, you’ll have to simulate it, and that comes with its own set of challenges. In short, Geo Lift can be useful but is limited to single-channel experiments, making it hard to scale across a full marketing mix.</p><h3>MMM Is Imperfect—But It’s Still Valuable</h3><p>So, where does that leave us? Despite its flaws, MMM remains one of the most useful tools for marketers. Why? Because having a structured, data-driven approach to evaluating media performance is always better than making decisions based on gut feeling.</p><p>MMM helps brands and media agencies understand how their past campaigns performed, which channels contributed the most, and how they should allocate budgets in the future. No, it’s not flawless. Yes, it makes assumptions. But would you rather have some insight or no insight at all?</p><h3>The Biggest Challenge With MMM</h3><p>One of MMM’s biggest weaknesses is its foundational assumption: that every marketing, media, product, pricing, promotion, distribution, and external factor directly impacts the business KPI (e.g., sales). But in reality, many factors have indirect effects.</p><p>Take MTA as an example—each user’s path to purchase is unique. Some users need multiple touchpoints before converting, while others make a purchase immediately. Some paths lead to conversions, others don’t. But just because a touchpoint didn’t directly lead to a sale doesn’t mean it had no influence.</p><p>MMM, in its traditional form, flattens all these interactions into a single regression model, missing the complex web of cause and effect in real-world marketing.</p><h3>What’s the Solution? Unifying MMM and MTA</h3><p>Instead of choosing between MMM and MTA, why not combine their strengths?</p><p>At <a data-mce-href="https://morethandata.biz/" href="https://morethandata.biz/" target="_blank" rel="noopener"><strong>More Than Data</strong></a>, we’re taking MMM beyond traditional regression models. We’ve developed a causal inference modelling approach that integrates both direct and indirect impacts across online and offline media channels.</p><p>Think of it this way:</p><ul><li>MTA focuses on individual digital journeys, mapping how users interact with multiple online touchpoints before converting.<br></li><li>MMM provides a high-level view of how marketing investments impact business KPIs.<br></li><li>Our causal inference model connects these dots, recognizing that media influences are not linear—they work together in a complex network of interactions.</li></ul><p>Rather than treating digital and offline channels separately, we measure them within a unified framework. This approach provides a more realistic, holistic view of media effectiveness.</p><h3>Pushing the Boundaries of Marketing Measurement</h3><p>MMM isn’t perfect, but instead of dismissing it, we should improve it. That’s exactly what we’re doing at More Than Data. By integrating MMM and MTA into a single, more advanced framework, we’re redefining how marketers measure success.</p><p>Marketing is evolving. Measurement should evolve too.</p>

We Know Marketing Mix Modelling Isn’t Perfect—But At Least We Can Improve It

<p>In an era where data-driven decisions are paramount, businesses are increasingly turning to marketing mix modeling (MMM) to optimize their advertising strategies. While this might sound complex, DIY marketing mix modeling is now more accessible than ever, thanks to tools like Minute MMM. This guide will take you through the steps of creating your own marketing mix model using this innovative platform.</p>

<h2>Understanding Marketing Mix Modeling</h2>

<p>Marketing mix modeling is a statistical analysis technique used to determine the effectiveness of various marketing tactics and how they contribute to sales. By analyzing historical data, businesses can allocate resources more effectively and enhance their return on investment (ROI). With Minute MMM, even those without a statistical background can delve into this powerful tool.</p>

<figure id="figure-8zpe3v1izm80217dq"><img id="8zpe3v1izm80217dq" src="https://media.gettyimages.com/id/2157709105/photo/data-fabric-ai-artificial-intelligence-powered-analytics-data-data-verse-big-data-data.jpg?b=1&s=2048x2048&w=0&k=20&c=gYImeeQslIntNzWrO8iIyRALiovJOudFMtQ4GEeUQm8=" alt="data analysis" class="object-cover bg-gray-100" style="aspect-ratio: 16/9"></figure>

<h3>Step 1: Gather Your Data</h3>

<p>The first step in DIY marketing mix modeling is data collection. You'll need historical data on sales and marketing activities, including information on advertising spend, promotions, pricing, and distribution. The more detailed your data, the more accurate your model will be. It's crucial to ensure that your data is clean and consistent to avoid any errors in analysis.</p>

<p>Minute MMM simplifies this process by allowing you to upload your datasets directly into the platform. The tool offers guidance on the types of data required, making it easier for users to prepare their information effectively.</p>

<h3>Step 2: Set Up Your Model</h3>

<p>After gathering your data, the next step is to set up your model within Minute MMM. This involves defining the variables that influence your sales, such as TV ads, digital marketing efforts, and seasonal factors. The platform provides an intuitive interface where you can select and customize these variables to reflect your specific business context.</p>

<figure id="figure-6q9ffqa27pm80217dq"><img id="6q9ffqa27pm80217dq" src="https://media.gettyimages.com/id/510204243/photo/business-model-diagram-written-on-blackboard.jpg?b=1&s=2048x2048&w=0&k=20&c=UvcRz73KghGLeV9bpXeD6KM2HgsNvcvoBXTwcnS4Q-A=" alt="business model setup" class="object-cover bg-gray-100" style="aspect-ratio: 16/9"></figure>

<p>Minute MMM offers a selection of pre-built models that you can use as a starting point. This feature is particularly beneficial for beginners who may feel overwhelmed by the technical aspects of modeling. You can tweak these models as needed to better fit your business needs.</p>

<h3>Step 3: Analyze the Results</h3>

<p>Once your model is set up, it's time to run the analysis. Minute MMM processes the data and provides insights into how different marketing activities impact sales. The results are presented in easy-to-understand charts and graphs, highlighting which channels are delivering the highest ROI.</p>

<p>This step is crucial for identifying areas where you can improve your marketing strategy. By understanding which tactics are most effective, you can make informed decisions about where to allocate resources.</p>

<figure id="figure-a0pr2p4ndm80217dq"><img id="a0pr2p4ndm80217dq" src="https://media.gettyimages.com/id/517716450/photo/business-strategy-concept.jpg?b=1&s=2048x2048&w=0&k=20&c=ufvnFSCveXMKuj7RJ0IaRoXUGN3jp38v2iCbifUZ_y0=" alt="marketing insights" class="object-cover bg-gray-100" style="aspect-ratio: 16/9"></figure>

<h3>Step 4: Optimize Your Strategy</h3>

<p>With a clear understanding of your marketing mix's performance, you can begin optimizing your strategy. Use the insights gained from Minute MMM to adjust your budget allocation, focusing on high-performing channels while scaling back on less effective ones. This iterative process helps continually refine your approach for better results over time.</p>

<p>Minute MMM also allows for scenario planning, enabling you to test different strategies and predict their outcomes before implementation. This feature provides a risk-free environment to experiment with various tactics and find the most effective combination for your business.</p>

<h2>Conclusion</h2>

<p>DIY marketing mix modeling with Minute MMM empowers businesses of all sizes to harness the power of data-driven insights without requiring extensive statistical expertise. By following these steps—gathering data, setting up your model, analyzing results, and optimizing your strategy—you can enhance your marketing efforts and drive greater success. Start leveraging Minute MMM today and take control of your marketing destiny.</p>

DIY Marketing Mix Modelling: A Step-by-Step Guide Using Minute MMM

<h3>Challenge: Understanding Sales Drivers During School Holidays, Promotions, and Competitor Influence</h3><p>A media agency managing an FMCG brand, which produces a popular salad and sandwich sauce, reached out to More Than Data for insights into sales fluctuations during school holidays and promotional periods. The agency had several key questions:</p><ul><li>Sales saw a noticeable increase at the end of school holidays. The agency wanted to identify the factors responsible for this uplift from a data perspective.<br></li><li>The brand frequently runs discounts and promotions, some of which overlap with the school holiday period. The agency needed to understand whether these promotions were driving the sales increase or if other factors were at play.<br></li><li>The agency used multiple media channels during this time, including OOH (digital panels in shopping centres, street furniture), TV, paid social, and online video (YouTube). The agency wanted to distinguish and understand the sole contribution of each media channel.<br></li><li>Additionally, the agency needed to factor in the impact of competitor media spend and promotions, as these could also influence the sales results.</li></ul><h3>Solution: Integrating Data into the Causal Inference Model</h3><p>To address these challenges, More Than Data developed a comprehensive causal inference model. This model took into account not only media, promotions, and seasonality (school days vs. school holidays) but also incorporated competitor media spend and competitor promotions. Our approach was unique in that it went beyond traditional marketing mix modelling (MMM), showing not just direct links but also the indirect relationships between variables.</p><p>The model mapped out a network structure, connecting various offline and digital channels, competitor activities, pricing incentives, and seasonality. By including competitor media and promotions, we were able to measure the direct and indirect influence of competitor actions on the client’s sales. The model also quantified the unique contribution of each factor, providing the agency with a holistic understanding of what was driving the sales uptick during the back-to-school period.</p><h3>Key Insights and Results</h3><ul><li><strong>Combined Effects of Media, Pricing, and Competitor Activities</strong>: The sales uplift was not attributed to a single factor. Instead, it was a combined effect of media spend, pricing promotions, seasonality, and competitor activities. The model showed that competitor media spend and promotions had a noticeable influence on the client’s sales.<br></li><li><strong>Confirming the Role of Seasonality</strong>: A/B testing and follow-up market research surveys confirmed that seasonality was a consistent sales driver. Even without media or promotional support, housewives were purchasing the sauce for their children’s lunchboxes during school terms.<br></li><li><strong>Optimized Media Strategy</strong>: Based on these insights, the media agency adjusted their strategy for the following school holidays. They ran brand awareness campaigns via OOH and TV during the middle of the holiday period, followed by digital and paid social campaigns near the end of the school holidays, paired with in-store promotions. <strong>This shift resulted in a 7% sales uplift compared to the previous school holiday period and an 5% uplift compared to the same period the year before</strong>.<br></li><li><strong>Improved Media ROI</strong>: The changes in strategy <strong>led to a 20% improvement in media ROI, while reducing media costs to only 87% of the original media plan</strong>. Despite maintaining a full-channel campaign, the agency maximized efficiency and delivered stronger results.</li></ul><h3>Conclusion</h3><p>By leveraging <a data-mce-href="https://morethandata.biz/" href="https://morethandata.biz/" target="_blank" rel="noopener"><strong>More Than Data</strong></a>’s advanced causal inference model, which included competitor media and promotional activities, the media agency gained a deeper understanding of the factors driving sales during key periods. This data-driven approach led to an optimized media strategy, driving a significant sales increase and improving ROI while reducing media costs. The integration of multiple-source data provided a competitive edge, ensuring the agency’s strategy was both effective and efficient.</p>

Leveraging Data to Optimize FMCG Sales During Key Periods: A Case Study with More Than Data

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!

Blog

The Light-hearted Side of Marketing Mix Modelling: A Tale...

Transforming Marketing Efficiency with Data-Driven Insigh...

Unlocking Competitive Advantage Through Advanced Marketin...

We Know Marketing Mix Modelling Isn’t Perfect—But At Leas...

DIY Marketing Mix Modelling: A Step-by-Step Guide Using M...

Leveraging Data to Optimize FMCG Sales During Key Periods...