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<h3>Introduction</h3>At More Than Data, we pride ourselves on delivering data-driven solutions that help businesses optimize their marketing strategies and achieve growth. However, not every engagement ends in success, and there’s much to learn from the ones that don’t. This article shares the story of an unsuccessful project with a waste management brand, highlighting the challenges we faced, the lessons we learned, and how we’re using this experience to improve our processes and better serve future clients.<h3>The Challenge </h3>The waste management brand was introduced to us by a middle person who had been working with the business for some time. The brand was struggling with declining revenue, high operating costs, and difficulty retaining staff. Their marketing efforts had been inconsistent, and they had cycled through multiple marketing and media agencies without seeing meaningful results. Here’s a breakdown of the challenges we encountered:<ul><li>Financial and Operational Pressure: the brand was under significant financial strain, leading to staff layoffs and a general sense of urgency to cut costs and stabilize revenue. This pressure created a challenging environment for implementing new strategies or investing in marketing optimization. </li><li>Ineffective Marketing Efforts: despite engaging with several agencies for paid social, paid search, and SEO services, the brand had not seen measurable improvements in customer acquisition or revenue. They were frustrated and skeptical about the value of marketing investments. </li><li>Poor Data Management: the brand’s data management and governance were disorganized and disconnected. Key performance metrics and marketing data were scattered across different systems, making it difficult to access and analyze.</li></ul><h3>The Solution: A Free Marketing Mix Modelling Project</h3>Understanding the brand’s financial constraints, we decided to offer a free marketing mix modelling (MMM) project to help them measure the effectiveness of their historical marketing efforts and identify opportunities for optimization. Our approach included:<ul><li>Measuring Historical Marketing Performance We aimed to analyze the brand’s past marketing efforts across four channels: SEM, social media (LinkedIn and Meta), Reddit, and Quora. The goal was to understand how these channels contributed to customer acquisition and ROI. </li><li>Building a Budget Allocation System We developed a budget allocation tool to help the brand test different scenarios and optimize their marketing spend across channels.&nbsp;</li></ul><h3>The Results: Why the Project Failed</h3>Despite our best intentions and efforts, the project had to be terminated after a few weeks due to several critical issues:<ul><li>Slow and Difficult Data Collection The brand took nearly three weeks to collect and provide the necessary marketing data, despite only having four channels to analyze. This delay significantly slowed down the project and highlighted the brand’s lack of readiness for data-driven initiatives. </li><li>Misalignment with HubSpot Data The brand treated HubSpot as their single source of truth for customer acquisition metrics. When our MMM results didn’t align with HubSpot’s numbers, the brand owner dismissed our findings, creating a fundamental disconnect. </li><li>Skepticism About the Budget Allocation System While the brand’s marketing manager found our budget allocation tool useful, the owner doubted the underlying math and theory. This lack of trust made it impossible to move forward with recommendations.</li></ul><h3>Key Learnings and Improvements</h3>While this engagement didn’t achieve the desired outcome, it provided valuable insights that are shaping our approach to future projects:<ul><li>Data Readiness is Critical Data is the foundation of any successful MMM project. We’ve since introduced a lead-qualification step to assess a client’s data readiness before committing to a project. This helps us identify potential challenges early and ensure smoother collaboration. </li><li>Positioning Ourselves Clearly We learned the importance of clearly positioning ourselves as data-driven marketing strategy partners, not competitors to tools like HubSpot. By setting this expectation upfront, we can avoid confusion and align better with clients’ existing systems. </li><li>Focusing on Clients with Diverse Marketing Channels Marketing mix modelling (MMM) is most effective for businesses with a rich mix of Above-the-Line (ATL) and Below-the-Line (BTL) channels. Whether a brand is small or large, the key is having a diverse and measurable media mix. We now focus on clients who invest in multiple channels, as this allows us to provide more comprehensive insights and recommendations. </li><li>Building Trust Through Transparency Trust is essential for any successful partnership. We’ve improved our communication processes to ensure clients understand the methodology behind our recommendations. By being transparent about our approach, we aim to build stronger relationships and foster confidence in our solutions.</li></ul><h3>Conclusion: Turning Failure into Opportunity</h3>While the engagement with the waste management brand didn’t go as planned, it reinforced the importance of data readiness, clear positioning, and client alignment. By learning from this experience, we’re better equipped to serve future clients and deliver impactful results.At More Than Data, we believe that every challenge is an opportunity to grow and improve. This experience is a reminder that even the best intentions and efforts can fall short without the right foundation and alignment. Moving forward, we’re committed to applying these lessons to ensure that every engagement—regardless of the client’s size—delivers value and drives success.<h3>Key Takeaways for Businesses </h3><ul><li>Invest in Data Management: Clean, organized data is essential for effective marketing optimization. </li><li>Align Expectations Early: Clearly define roles, goals, and deliverables at the start of any project. </li><li>Choose the Right Partners: Select partners who understand your unique needs and challenges and can work with your existing systems. </li><li>Learn from Failure: Unsuccessful projects can provide valuable insights that drive future success.</li></ul>By embracing these principles, businesses can build stronger partnerships, optimize their marketing efforts, and achieve sustainable growth.

Case Study: Learning from an Unsuccessful Engagement with a Waste Management Brand

Every great company has its origin story, and More Than Data is no exception. But behind the success lies a series of laugh-out-loud moments from the early days of its co-founder, who was a passionate (but occasionally clueless) data enthusiast in the marketing and media industry. Here’s a funny recount of his journey, filled with statistical mishaps, questionable recommendations, and a few too many cups of coffee.<h3>1. The P-Value Fanatic </h3>The co-founder had one golden rule: worship the p-value. If it was statistically significant, it was gospel—no matter what the actual results said. Positive coefficient for TV? Great! Negative coefficient for OOH? Who cares? The p-value was significant, and that’s all that mattered. His mantra: “If the p-value is low, let it glow!” Needless to say, this blind faith in p-values led to some eyebrow-raising conclusions.<h3>2. TV: The Sales Superhero </h3>One of his early models claimed that TV contributed to 72% of sales. Yes, 72%! While the marketing team was thrilled at first, they quickly realized it was too good to be true. Turns out, the model had some quirks (read: flaws), and TV wasn’t quite the superhero he made it out to be. But hey, who doesn’t love a little over-optimism?<h3>3. OOH: The Invisible Channel </h3>On the flip side, his model declared that Out-of-Home (OOH) advertising had almost zero impact on the brand. The marketing team was furious. “What do you mean our billboards are useless?!” they cried. The co-founder shrugged and pointed to his beloved p-value. Spoiler: OOH wasn’t useless—it was just misunderstood.<h3>4. Share-of-Market vs. Share-of-Voice: The 10x Miracle </h3>In another head-scratching moment, his model showed that the brand’s share-of-market was 10 times higher than its share-of-voice. Basically, the brand was punching way above its weight. The team was skeptical, to say the least. “Are we secretly a global megabrand?” they joked. The co-founder just smiled and said, “The numbers don’t lie.” (Spoiler: They did.)<h3>5. The Digital-Only Disaster </h3>In a bold (read: reckless) move, the co-founder suggested that the brand should stop investing in all offline channels and shift the entire budget to digital. The marketing team nearly had a collective heart attack. “No TV? No OOH? Are you trying to get us fired?!” they yelled. Let’s just say this recommendation didn’t go over well, and the co-founder almost lost his job.<h3>6. The Statistically Boring Presentation </h3>When presenting his findings to non-technical stakeholders, the co-founder made the classic mistake of using jargon-heavy statistical language. As he droned on about coefficients, confidence intervals, and heteroscedasticity, the room grew quieter and quieter. By the end, everyone was either asleep or plotting to throw him out the window. Lesson learned: Keep it simple, silly!<h3>7. The Dashboard from Hell </h3>In an attempt to impress, the co-founder created a dashboard with over 100 charts and widgets. It was a data visualization nightmare. The media planning team took one look at it and felt like they were staring into the abyss. “What is this? A dashboard or a modern art installation?” they groaned. One team member even joked, “I need a dashboard just to navigate this dashboard!”<h3>8. The Budget Allocation Maze </h3>He built a user interface for budget allocation but forgot one tiny detail: a filter to select specific media channels. The media planning team had to manually go through every single variable, one by one. It was like climbing a high-rise building without an elevator. “Next time, can you at least install a lift?” they begged.<h3>9. The Coffee Catastrophe </h3>In a misguided attempt to bond with his marketing buddies, the co-founder bought everyone coffee. Unfortunately, he forgot to ask about dietary preferences. One lactose-intolerant team member drank his latte and spent the rest of the day glued to the toilet. “Thanks for the coffee,” he said weakly, “but next time, maybe ask about lactose-free milk?”<h3>The Silver Lining </h3>Despite these hilarious missteps, the co-founder’s passion for data and relentless curiosity eventually paid off. He learned from his mistakes, refined his models, and built More Than Data into a trusted partner for marketing analytics. Today, he laughs about his early blunders and uses them as cautionary tales for aspiring data scientists.<h3>Moral of the Story </h3><ul><li>P-values aren’t everything: Don’t let statistical significance blind you to common sense. </li><li>Keep it simple: Avoid jargon and overcomplicating things—especially in presentations. </li><li>Ask about lactose-free milk: Always consider your audience’s needs (and dietary restrictions).</li></ul>And that, folks, is how a data-obsessed co-founder turned his early fumbles into a thriving business. Cheers to learning, laughing, and not taking yourself too seriously!&nbsp;

The Hilarious Misadventures of a Data-Driven Co-Founder

<h3>Introduction</h3>In the highly competitive meat and poultry industry, effective marketing strategies are essential for driving brand awareness, customer loyalty, and sales growth. However, measuring the effectiveness of marketing efforts can be challenging, especially when dealing with complex media mixes and granular data requirements. This case study explores how More Than Data helped a management consulting firm optimize its marketing strategy for a prominent meat and poultry brand in Australia. By addressing critical challenges and implementing advanced solutions, the consultancy enabled the brand to minimize marketing costs, improve execution efficiency, and achieve measurable results.<h3>The Challenge </h3>The management consulting firm was tasked with improving the marketing strategy and efficiency of a meat and poultry brand in Australia. The brand’s marketing efforts included a mix of Above the Line (ATL) and Below the Line (BTL) channels, such as TV, Out-of-Home (OOH), and digital campaigns. However, the consulting firm faced several significant challenges:1. Inadequate Marketing Effectiveness MeasurementThe consulting firm recognized the importance of measuring marketing effectiveness but struggled with the tools and methodologies available. They initially used Meta Robyn, a marketing mix modelling (MMM) tool, but encountered several limitations:<ul><li>Limited Scalability: Meta Robyn could not handle a large number of marketing and non-marketing variables without becoming excessively slow. </li><li>Lack of Granularity: The tool could not provide the detailed breakdowns required by the brand’s marketing team. For example, the brand wanted granular insights into specific OOH sub-channels (e.g., roadside billboards, street furniture, in-transit ads) and TV sub-channels (e.g., Sydney Metro, Melbourne Metro, Brisbane Metro across different networks like Channel 7 and Channel 9). </li><li>Monthly Refresh Requirements: The brand required monthly updates to the marketing mix model to inform ongoing strategy adjustments. This frequency placed immense pressure on the consulting firm’s team, leading to burnout and dissatisfaction.</li></ul>2. Data Collection and Processing ChallengesThe brand’s sales data was highly segmented, with categories such as fresh meat, processed meat, and different types of meat (e.g., pork, beef, chicken, lamb). Additionally, offline media data (e.g., TV and OOH) was difficult to collect and process efficiently. The consulting firm lacked the tools and infrastructure to automate data collection and reconciliation, leading to inefficiencies and delays.3. Client DissatisfactionThe brand had grown increasingly unhappy with the consulting firm’s inability to deliver actionable insights and meet its expectations. This dissatisfaction threatened the consulting firm’s relationship with the client and its reputation in the industry.<h3>The Solution: A Comprehensive Approach by the Data Analytics Consultancy </h3>To address these challenges, the consulting firm partnered with More Than Data specializing in marketing mix modelling and data-driven decision-making. The consultancy implemented a multi-faceted solution:1. Replacing Meta Robyn with a Robust MMM SolutionThe first critical decision was to stop using Meta Robyn and adopt a more advanced and scalable marketing mix modelling solution. The new tool could handle a large number of variables, provide granular insights, and deliver results quickly, even with monthly updates.2. Automating Data Collection and ProcessingMore Than Data built an automated data pipeline to streamline the collection and processing of sales and media data. This pipeline included:<ul><li>Sales Data Automation: Automated collection and categorization of sales data by product type (e.g., fresh vs. processed meat) and meat category (e.g., pork, beef, chicken, lamb). </li><li>Semi-Automated Media Data Processing: A semi-automated pipeline for online and offline media data (e.g., TV, OOH and Digital), developed in collaboration with the consulting firm’s data engineers.</li></ul>3. Granular Marketing Mix ModellingThe consultancy implemented a comprehensive MMM approach that measured all marketing and non-marketing variables at the required granularity level. This included:<ul><li>Media Sub-Channel Analysis: Detailed breakdowns of OOH sub-channels (e.g., roadside billboards, street furniture) and TV sub-channels (e.g., Sydney Metro, Melbourne Metro, Brisbane Metro across different networks). </li><li>Non-Marketing Variables: Incorporation of social-economic factors such as the Consumer Confidence Index, Retail Index, and calculated FMCG Turnover Index to provide a holistic view of external influences on sales.</li></ul>4. Dynamic Budget Allocation and Scenario Testing The consultancy developed a user-friendly interface that allowed the consulting firm and the brand’s marketing team to:<ul><li>Test different budget allocation scenarios across media channels. </li><li>Make real-time predictions based on specific media mixes. </li><li>Justify marketing investments and optimize media planning.</li></ul><h3>The Results</h3>The partnership between the consulting firm and More Than Data delivered transformative results:1. Saved the Consulting Firm’s Relationship with the ClientThe consultancy’s solutions addressed the brand’s dissatisfaction and enabled the consulting firm to deliver actionable insights that met the client’s expectations. This turnaround secured the consulting firm’s position and reputation with the brand.2. Improved Marketing Efficiency and EffectivenessThe brand achieved significant improvements in marketing efficiency, including:<ul><li>Optimized Media Spend: Better allocation of budgets across media channels based on granular insights. </li><li>Increased ROI: Higher return on investment (ROI) for marketing campaigns due to data-driven decision-making. </li><li>Monthly Insights: Regular updates to the marketing mix model provided ongoing opportunities for strategy refinement.</li></ul>3. Enhanced Team Morale and RetentionThe consultancy’s solutions alleviated the pressure on the consulting firm’s team, reducing burnout and improving morale. This led to better team retention and a more positive work environment.4. Secured Additional Clients for the ConsultancyThe success of this project led to the onboarding additional clients from the consulting firm. These clients also required advanced MMM solutions to optimize their marketing strategies.<h3>Conclusion </h3>This case study highlights the transformative impact of data-driven marketing strategies in the meat and poultry industry. By partnering with More Than Data, the consulting firm was able to:<ul><li>Overcome the limitations of inadequate tools like Meta Robyn. </li><li>Automate data collection and processing for greater efficiency. </li><li>Deliver granular insights that met the brand’s specific requirements. </li><li>Save its relationship with the client and secure additional business.</li></ul>For brands and consulting firms alike, adopting advanced marketing mix modelling solutions is essential for staying competitive in today’s data-driven landscape. By leveraging the right tools and expertise, organizations can unlock the full potential of their marketing efforts and achieve measurable, long-term success.<h3>Key Takeaways for Marketers and Consultants</h3><ul><li>Choose the Right Tools: The success of marketing mix modelling depends on using scalable and flexible tools that can handle complex data requirements. </li><li>Automate Data Processes: Automation streamlines data collection and processing, enabling faster and more accurate insights. </li><li>Focus on Granularity: Detailed breakdowns of media channels and sales categories provide actionable insights for optimizing marketing strategies. </li><li>Leverage External Factors: Incorporating social-economic variables offers a holistic view of market dynamics and their impact on sales. </li><li>Invest in User-Friendly Solutions: Tools that enable dynamic scenario testing and real-time predictions empower marketing teams to make informed decisions.</li></ul>By following this example, brands and consulting firms can transform their marketing strategies and achieve measurable, long-term success.

Case Study: How a Data Analytics Consultancy Transformed Marketing Strategy for a Meat and Poultry Brand in Australia

<h3>Introduction</h3>In the competitive world of e-commerce, an Australian online retailer specializing in pet food, supplies, and accessories faced significant challenges in measuring the effectiveness of its marketing efforts. The brand employed a Through the Line (TTL) strategy, combining Above the Line (ATL) media channels like TV, Out-of-Home (OOH), and Radio with Below the Line (BTL) tactics such as Paid Social, Paid Search, and voucher promotions. However, the brand struggled to quantify the synergy between these channels, justify its marketing investments, and prove the financial impact of its campaigns.This case study explores how a leading data analytics and marketing effectiveness consultancy used Marketing Mix Modelling (MMM) to help the retailer measure and optimize the combined effect of its ATL and BTL channels, ultimately driving online sales growth and improving return on ad spend (ROAS).<h3>The Challenges </h3>The retailer’s marketing strategy aimed to balance brand awareness (ATL) with direct sales activation (BTL). However, the brand faced several critical challenges:1. Fragmented Media Data Despite being an e-commerce business, the retailer’s media data was fragmented and poorly managed. The lack of a centralized data governance system made it difficult to track and analyze the performance of its campaigns.2. Data Quality and Version Control Issues The ATL media channels (TV, OOH, and Radio) had significant data quality issues. Media investment data was inconsistent, with multiple versions of the same data provided to the consultancy. This lack of reconciliation between media plans, actual buying reports, and financial invoices created confusion and inefficiencies.3. Inability to Justify Marketing StrategyThe Chief Marketing Officer (CMO) believed in the power of a TTL strategy to drive both brand awareness and sales activation. However, they struggled to prove the financial impact of this approach to the Chief Financial Officer (CFO) and the board. The CMO needed to demonstrate the synergy between ATL and BTL channels—how combining these efforts delivered a combined effect greater than the sum of their individual impacts.4. Lack of Data-Driven InsightsThe CMO lacked the tools and methodologies to measure the effectiveness of their campaigns. Without clear data-driven insights, they couldn’t optimize marketing spend or defend their strategy to stakeholders.<h3>The Solution: A Data-Driven Approach</h3>The consultancy stepped in to address these challenges using Marketing Mix Modelling (MMM), a statistical analysis technique that quantifies the impact of various marketing activities on sales and other key performance indicators (KPIs). Here’s how the consultancy helped the retailer:1. Calculating Synergy Across ATL and BTL ChannelsThe consultancy used advanced methodologies to measure the synergy—the combined effect—of all possible combinations of ATL and BTL channels. The retailer’s media mix included:<ul><li>ATL Channels: TV, OOH, Radio </li><li>BTL Channels: Paid Social, Paid Search, Online Voucher Promotions</li></ul>The possible combinations of ATL and BTL channels were:<ol><li>TV + Paid Social </li><li>TV + Paid Search </li><li>TV + Voucher Promotions </li><li>OOH + Paid Social </li><li>OOH + Paid Search </li><li>OOH + Voucher Promotions </li><li>Radio + Paid Social </li><li>Radio + Paid Search </li><li>Radio + Voucher Promotions</li></ol>By analyzing these combinations, the consultancy quantified how each pair of channels worked together to drive sales and brand awareness.2. Building a Unified Marketing Mix ModelThe consultancy integrated data from all ATL and BTL channels into a single MMM framework. This allowed the retailer to:<ul><li>Measure the individual and combined impact of each channel. </li><li>Identify the optimal allocation of marketing spend across channels. </li><li>Understand the time lag effects of ATL channels (e.g., how TV ads influenced sales weeks later).</li></ul>3. Automating Data Collection and ReconciliationTo address the retailer’s data quality issues, the consultancy built an automation pipeline that streamlined media data collection and reconciliation. This pipeline:<ul><li>Integrated data from media plans, buying reports, and financial invoices. </li><li>Ensured version control and data accuracy. </li><li>Provided real-time insights to the marketing team, enabling faster decision-making.</li></ul>4. Proving the TTL StrategyUsing the MMM results, the consultancy demonstrated the effectiveness of the retailer’s TTL strategy. The analysis showed that combining ATL and BTL channels created a synergistic effect, where the combined impact was greater than the sum of individual channel impacts. For example:<ul><li>TV ads increased the effectiveness of Paid Social campaigns by 15%. </li><li>OOH advertising boosted the ROI of Voucher Promotions by 20%.</li></ul><h3>The Results</h3>The collaboration between the retailer and the consultancy delivered significant results:1. Quantified Synergy and Justified TTL StrategyThe CMO was able to present clear, data-driven evidence to the board, proving the effectiveness of the TTL strategy. The synergy between ATL and BTL channels was quantified, showing how combined efforts drove greater results than individual channels.2. 3% Growth in Online SalesThe retailer achieved a 3% increase in online sales compared to the same period in the previous year. This growth was directly linked to the optimized allocation of marketing spend across ATL and BTL channels.3. Improved Brand AwarenessConsumer surveys showed a 2% increase in top-of-mind awareness and a 1.5% increase in unaided awareness during the campaign period. This demonstrated the success of ATL channels in building brand recognition.4. 2.3x Increase in ROASThe return on ad spend (ROAS) increased by 2.3 times compared to the non-treatment period, validating the efficiency of the optimized marketing mix.5. Long-Term PartnershipImpressed by the results, the CMO signed a whole-year contract with the consultancy to continue leveraging data-driven insights for marketing optimization.<h3>Conclusion </h3>This success story highlights the power of Marketing Mix Modelling in measuring and optimizing the synergy between ATL and BTL channels. By partnering with a data analytics consultancy, the retailer was able to:<ul><li>Overcome data fragmentation and quality issues. </li><li>Quantify the combined effect of its TTL strategy. </li><li>Drive online sales growth and improve ROAS. </li><li>Justify marketing investments to executive-level stakeholders.</li></ul>For e-commerce brands, adopting a data-driven approach to marketing is no longer optional—it’s essential. By leveraging advanced analytics and automation, brands can unlock the full potential of their marketing efforts and achieve sustainable growth in a competitive market.<h3>Key Takeaways for Marketers: </h3><ul><li>Synergy Matters: Combining ATL and BTL channels can deliver greater results than individual efforts. </li><li>Data Quality is Critical: Invest in data governance and automation to ensure accurate and consistent data. </li><li>Prove Your Impact: Use MMM to quantify the financial impact of your marketing strategy. </li><li>Embrace Data-Driven Marketing: Partner with experts to unlock the full potential of your campaigns.</li></ul>By following this example, brands can transform their marketing strategies and achieve measurable, long-term success.

Case Study: Measuring and Quantifying Synergy Across ATL and BTL Channels Using Marketing Mix Modelling

<h2>Understanding DIY Marketing Mix Models</h2>
In the rapidly evolving world of marketing, understanding which strategies yield the best results can be challenging. This is where Marketing Mix Models (MMM) come into play. These models provide a data-driven approach to evaluating the effectiveness of various marketing channels and tactics. With the rise of accessible tools and data, even beginners can now create DIY Marketing Mix Models to gain instant insights.

At its core, a Marketing Mix Model is a statistical analysis technique used to measure the impact of different marketing activities on sales. By analyzing historical data, these models can help businesses allocate resources more efficiently and optimize their marketing strategies.

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<h2>The Benefits of DIY Marketing Mix Models</h2>
Creating your own Marketing Mix Model offers several advantages. Firstly, it gives you a deeper understanding of your marketing ecosystem. You'll be able to identify which channels are driving the most value and adjust your budget accordingly. Additionally, a DIY approach can be more cost-effective compared to hiring external consultants or agencies.

Moreover, having control over the data and analysis process allows for greater flexibility. You can tailor the model to suit your specific business needs and goals. This personalized approach ensures that the insights you gain are directly applicable to your marketing strategy.

<h2>Getting Started: The Basics</h2>
To begin constructing a DIY Marketing Mix Model, start by gathering relevant data. This includes sales figures, marketing spend across different channels, and any external factors that might influence sales, such as seasonal trends or economic conditions.

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Next, choose a statistical software or tool that suits your level of expertise. There are several user-friendly options available that don't require advanced knowledge in statistics. These tools can help you clean and organize your data, making it easier to conduct the analysis.

<h3>Building Your Model</h3>
Once your data is prepared, it's time to build the model. Start by defining the key variables you want to analyze. Typically, these include various marketing channels like TV, radio, digital advertising, and more. Your goal is to understand how changes in spend across these channels impact overall sales.

Use regression analysis to establish relationships between your variables. This statistical method will help you identify which channels have the most significant impact on sales and how they interact with each other.

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<h3>Interpreting Results and Making Adjustments</h3>
After running your analysis, you'll be presented with a set of results indicating the effectiveness of each channel. It's crucial to interpret these results accurately to make informed decisions. Look for patterns and trends that suggest which marketing efforts are yielding the best returns.

Based on these insights, adjust your marketing strategy accordingly. Allocate resources to high-performing channels while reconsidering investment in less effective ones. Regularly updating your model with fresh data ensures that your strategy remains agile and responsive to market changes.

<h2>Maintaining Your Model</h2>
Creating a DIY Marketing Mix Model is not a one-time task but an ongoing process. As market dynamics change, it's important to continuously refine and update your model. This practice will help you stay ahead of trends and maintain a competitive edge in your industry.

Regularly revisiting your assumptions and incorporating new data will keep your insights relevant and actionable. By maintaining your model, you ensure sustained success in optimizing your marketing mix.

<figure id="figure-acxhwghnxm88moaq7"><img id="acxhwghnxm88moaq7" src="https://media.gettyimages.com/id/1686465179/photo/arrow-going-up-on-the-stairs.jpg?b=1&s=2048x2048&w=0&k=20&c=ZGayH2xAQLD2QLpua9o8upv6Z5ORtEB6MVxRbsvio1E=" alt="marketing optimization" class="object-cover bg-gray-100" style="aspect-ratio: 16/9"></figure>

DIY Marketing Mix Models: A Beginner's Guide to Instant Insights

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!<h3>The "Aha!" Moment with Unexpected Results</h3>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.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.<h3>The Great Data Clean-Up</h3>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.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.<h3>The Power of Collaboration</h3>As our co-founder neared the end of his probation period, he noticed something interesting: the marketing team wasn’t just looking for data—they were looking for insights that aligned with their goals. 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.”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.<h3>Coffee, Lunch, and Building Bridges</h3>Realizing the importance of collaboration, our co-founder went into full relationship-building mode. 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.These conversations revealed something important: the marketing team wasn’t just looking for numbers—they were looking for actionable insights 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.<h3>From Probation to Recognition</h3>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.<h3>What’s Your MMM Story?</h3>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.So, tell us: What’s your MMM story? 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!

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

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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