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<p>As the field of marketing analytics evolves, the role of marketing mix modeling (MMM) has become increasingly prominent in helping businesses optimize their marketing strategies. Two open-source packages—<a data-mce-href="https://facebookexperimental.github.io/Robyn/" href="https://facebookexperimental.github.io/Robyn/" target="_blank" rel="noopener"><strong>Meta’s Robyn</strong></a> and <a data-mce-href="https://github.com/google/lightweight_mmm" href="https://github.com/google/lightweight_mmm" target="_blank" rel="noopener"><strong>Google’s Lightweight</strong></a> MMM—have gained significant attention among analytics professionals. While both tools have empowered marketers with free, flexible solutions, they are not without limitations. This article explores the advantages, drawbacks, and areas for improvement of these popular MMM packages, especially from the perspective of media agencies and marketers without technical expertise.</p><h3>Meta Robyn: A Robust Yet Complex Tool</h3><p><a data-mce-href="https://facebookexperimental.github.io/Robyn/" href="https://facebookexperimental.github.io/Robyn/" target="_blank" rel="noopener"><strong>Meta Robyn</strong></a>, developed by Meta (formerly Facebook), has earned praise for its robustness and scalability. Built on <a data-mce-href="https://www.r-project.org/" href="https://www.r-project.org/" target="_blank" rel="noopener"><strong>R</strong></a>, this package leverages <a data-mce-href="https://en.wikipedia.org/wiki/Evolutionary_computation" href="https://en.wikipedia.org/wiki/Evolutionary_computation" target="_blank" rel="noopener"><strong>evolutionary computing</strong></a> to model the impact of various marketing channels. It provides advanced features like multi-objective optimization, which allows users to balance ROI with incremental sales.</p><p><strong>Advantages of Meta Robyn</strong></p><ul><li><strong>Advanced Modeling Capabilities</strong>: Meta Robyn supports complex modeling scenarios, making it suitable for businesses with diverse and intricate marketing data.<br></li><li><strong>Optimization Features</strong>: The multi-objective optimization functionality helps identify the best marketing mix for achieving various goals.<br></li><li><strong>Transparency and Customization</strong>: As an open-source tool, Robyn’s algorithms and processes are transparent, allowing experienced data scientists to customize and adapt it to their specific needs.</li></ul><p><strong>Drawbacks of Meta Robyn</strong></p><ul><li><strong>Steep Learning Curve</strong>: The reliance on R programming and evolutionary computing can be daunting for marketers without a technical background.<br></li><li><strong>Performance Concerns</strong>: Meta Robyn’s advanced calculations often require significant computational resources, leading to long running times for large datasets.<br></li><li><strong>Difficulty Aligning with Commercial and Marketing Criteria</strong>: Meta Robyn’s algorithm, based on <a data-mce-href="https://en.wikipedia.org/wiki/Evolutionary_computation" href="https://en.wikipedia.org/wiki/Evolutionary_computation" target="_blank" rel="noopener"><strong>evolutionary computing</strong></a>, struggles to incorporate domain knowledge or pre-determined variable contribution percentages. Users must run the model, check whether variable contributions meet expected criteria, and adjust transformation parameters to re-run as needed.<br></li><li><strong>Limited Accessibility for Non-Technical Users</strong>: Smaller media agencies and independent marketers may find it challenging to implement and interpret results without dedicated data professionals.</li></ul><p>Meta Robyn uses multiple-objective optimization driven by evolutionary computing to minimize the difference between share-of-spend and share of variable effect. However, this method has limitations: (1) If the optimized results still do not yield a satisfactory share of effect, how should users address this issue? (2) Non-media variables, such as socio-economic factors, are not tied to spend and cannot be quantified in dollars—how should users handle the share of effect for these non-media variables? The following screenshot illustrates how closely the share-of-spend and share-of-effect align, highlighting the optimization process.</p><figure id="figure-jwy873nb2m67srndb"><img id="jwy873nb2m67srndb" src="https://cdn.durable.co/blocks/14pJ9V7wwDJT34GB9v4mwV7DM6qckS9LbgfDWI1qr4tJqQr2WvNRbI1hIWasoEpm.png" alt="meta-robyn" title="meta-robyn" class="object-cover bg-gray-100 " style="aspect-ratio: 16/9; object-position: undefined undefined;" data-corners="default" data-aspect="16:9" data-caption="Source: https://facebookexperimental.github.io/Robyn/docs/analysts-guide-to-MMM" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/14pJ9V7wwDJT34GB9v4mwV7DM6qckS9LbgfDWI1qr4tJqQr2WvNRbI1hIWasoEpm.png" data-mce-style="aspect-ratio: 16/9; object-position: undefined undefined;"><figcaption class="body-normal text-gray-500 mt-2">Source: https://facebookexperimental.github.io/Robyn/docs/analysts-guide-to-MMM</figcaption></figure><h3>Google Lightweight MMM: Simplicity with Limitations<br></h3><p><a data-mce-href="https://github.com/google/lightweight_mmm" href="https://github.com/google/lightweight_mmm" target="_blank" rel="noopener"><strong>Google’s Lightweight</strong></a> MMM is another popular option in the open-source MMM space. Developed in <a data-mce-href="https://www.python.org/" href="https://www.python.org/" target="_blank" rel="noopener"><strong>Python</strong></a>, this package focuses on providing a simpler, more accessible approach to marketing mix modeling while maintaining the accuracy of its results, based on <a data-mce-href="https://en.wikipedia.org/wiki/Bayesian_linear_regression" href="https://en.wikipedia.org/wiki/Bayesian_linear_regression" target="_blank" rel="noopener"><strong>Bayesian regression</strong></a>.</p><p><strong>Advantages of Google Lightweight MMM</strong></p><ul><li><strong>Ease of Use</strong>: With its focus on simplicity, Lightweight MMM offers a lower barrier to entry for users familiar with Python.<br></li><li><strong>Efficiency</strong>: Designed for faster calculations, it delivers results more quickly compared to Meta Robyn, making it suitable for agile marketing environments.<br></li><li><strong>Open-Source Flexibility</strong>: As an open-source package, it allows for customization to meet unique business requirements.</li></ul><p><strong>Drawbacks of Google Lightweight MMM</strong><br></p><ul><li><strong>Steep Learning Curve</strong>: The reliance on Python programming and Bayesian regression can be daunting for marketers without a technical background.<br></li><li><strong>Performance Concerns</strong>: Advanced calculations, particularly for <a data-mce-href="https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo" href="https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo" target="_blank" rel="noopener"><strong>Markov Chain Monte Carlo simulations</strong></a>, often require significant computational resources, leading to long running times for large datasets.<br></li><li><strong>Difficulty Aligning with Commercial and Marketing Criteria</strong>: The algorithm’s reliance on prior distribution of variable coefficients makes it challenging to align variables and their contribution percentages with expected criteria. Users must iterate by adjusting prior distributions and re-running the model to meet predefined goals.<br></li><li><strong>Technical Barrier</strong>: Although easier to use than Robyn, it still requires Python expertise, leaving non-technical marketers at a disadvantage.</li></ul><p>Google Lightweight adjusts the prior distribution, including its shape and parameters, to influence the variable share effect. This can be a challenging setting, even for data analytics professionals. The following screenshot demonstrates how a Python file needs to be modified using a half-normal distribution to ensure the media variable has a positive impact. This method also requires multiple attempts to achieve the final satisfactory results.</p><figure id="figure-l4fyld7dr8m67sxzmw"><img id="l4fyld7dr8m67sxzmw" src="https://cdn.durable.co/blocks/35eJB3OF9CuzpHlnxZYT0q1eBSEQf89dmLB8bxfK6nISiEq5aMRB3s1VTFh0f8cY.png" alt="google-lightweight" title="google-lightweight" class="object-cover bg-gray-100 " style="aspect-ratio: 16/9; object-position: undefined undefined;" data-corners="default" data-aspect="16:9" data-caption="Source: https://getrecast.com/google-lightweightmmm/" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/35eJB3OF9CuzpHlnxZYT0q1eBSEQf89dmLB8bxfK6nISiEq5aMRB3s1VTFh0f8cY.png" data-mce-style="aspect-ratio: 16/9; object-position: undefined undefined;"><figcaption class="body-normal text-gray-500 mt-2">Source: https://getrecast.com/google-lightweightmmm/</figcaption></figure><h3>Challenges for Smaller Media Agencies and Non-Technical Marketers</h3><p>Despite their open-source nature, both Meta Robyn and Google Lightweight MMM face significant barriers when it comes to adoption by smaller media agencies and marketers without technical expertise. These challenges include:</p><ul><li><strong>Technical Expertise Requirements</strong>: Both tools require proficiency in programming languages (R for Robyn and Python for Lightweight MMM) and statistical modeling, which are not typically part of a marketer’s skill set.<br></li><li><strong>Running Time</strong>: Meta Robyn’s resource-intensive computations and Google Lightweight MMM’s relatively faster but still technical processes can make adoption challenging for teams with limited computational power or time.<br></li><li><strong>Customization Challenges</strong>: While both packages are customizable, doing so often requires a level of technical skill that smaller agencies and independent marketers lack.<br></li><li><strong>Alignment with Business Goals</strong>: Neither tool is designed with an out-of-the-box capability to accommodate specific commercial and marketing criteria, which can limit their usability for strategic decision-making.</li></ul><h3>Opportunities for Improvement<br></h3><p>To make these tools more accessible and practical for a wider range of users, several enhancements could be considered:</p><ul><li><strong>No-Code Solutions for Non-Technical Users</strong>: Developing tools that do not require programming knowledge would significantly expand accessibility.<br></li><li><strong>User-Friendly Interfaces</strong>: GUI-based versions of these tools could lower the barrier to entry for non-technical users.<br></li><li><strong>Integrated Training Resources</strong>: Comprehensive tutorials, case studies, and user guides can help users gain confidence in using these tools.<br></li><li><strong>Faster Computation Solutions</strong>: Migrating to cloud platforms can reduce running times and computational resource requirements, making the tools more efficient for smaller agencies.<br></li><li><strong>Built-In Business Rules</strong>: Incorporating features that allow users to easily apply commercial and marketing criteria would improve alignment with business objectives. For example, enabling users to set variable contribution percentages before modeling and guiding the model evolution to meet predefined criteria.</li></ul><h3>Conclusion<br></h3><p>Meta Robyn and Google Lightweight MMM represent significant advancements in open-source marketing mix modeling. Their capabilities have empowered many marketing analytics professionals to build robust models and gain valuable insights. However, their limitations in terms of running time, technical accessibility, and alignment with commercial and marketing criteria cannot be overlooked.</p><p>For smaller media agencies and marketers without technical expertise, the complexity and resource requirements of these tools remain significant hurdles. By addressing these challenges through user-friendly enhancements, cloud-based solutions, and improved training resources, these open-source packages could become even more impactful. Until then, businesses must carefully weigh the benefits and drawbacks to determine if these tools are the right fit for their marketing analytics needs.</p><p>We are <a data-mce-href="https://morethandata.biz/" href="https://morethandata.biz/" target="_blank" rel="noopener"><strong>More Than Data</strong></a>, and we are here to help media agencies fulfill the need for user-friendly, fast-to-run, easy-to-use tools that align with commercial and marketing criteria. Our sophisticated software, <a data-mce-href="https://morethandata.biz/sign-up" href="https://morethandata.biz/sign-up" target="_blank" rel="noopener"><strong>Minute MMM</strong></a>, is designed to make media measurement accessible and effective for all.</p>

Exploring Meta Robyn and Google Lightweight: Open-Source MMM Packages

<h2>Introduction</h2><p>In the fast-paced world of startups, the ability to quickly adapt and leverage technological advancements is crucial for success. One Sydney-based startup has made waves by harnessing the power of SaaS marketing analytics to fuel its rapid growth. This case study explores their journey and the strategies they employed to achieve impressive results.</p><h2>Understanding SaaS Marketing Analytics</h2><p>SaaS marketing analytics involves the use of cloud-based software tools to track, analyze, and optimize marketing efforts. By utilizing these tools, businesses can obtain valuable insights into customer behavior, campaign performance, and market trends. For this Sydney startup, embracing these analytics was a game-changer.</p><figure id="figure-28hmwcwv4um65zuqjl"><img id="28hmwcwv4um65zuqjl" src="https://media.gettyimages.com/id/1477183258/photo/woman-holding-ai-icons-with-laptop.jpg?b=1&amp;s=2048x2048&amp;w=0&amp;k=20&amp;c=RMuuMiUVQrLzQOCbvpypnXZ96kPsoXpsCqjznaate0A=" alt="saas marketing analytics" class="object-cover bg-gray-100" style="aspect-ratio: 16/9;" data-mce-src="https://media.gettyimages.com/id/1477183258/photo/woman-holding-ai-icons-with-laptop.jpg?b=1&amp;s=2048x2048&amp;w=0&amp;k=20&amp;c=RMuuMiUVQrLzQOCbvpypnXZ96kPsoXpsCqjznaate0A=" data-mce-style="aspect-ratio: 16/9;"></figure><h2>Identifying Key Metrics</h2><p>The first step for the startup was identifying key performance indicators (KPIs) that aligned with their business goals. They focused on metrics such as customer acquisition cost (CAC), customer lifetime value (CLV), and conversion rates. By closely monitoring these KPIs, they were able to make informed decisions that drove growth.</p><h3>Setting Up a Robust Analytics Framework</h3><p>To effectively leverage SaaS marketing analytics, the startup set up a robust framework that integrated seamlessly with their existing systems. This involved choosing the right analytics tools and ensuring they were properly configured to collect and report data accurately. The integration allowed for real-time insights and facilitated agile decision-making.</p><figure id="figure-03bzxx2l1m65zuqjl"><img id="03bzxx2l1m65zuqjl" src="https://media.gettyimages.com/id/1954841243/photo/data-analysis-chart-graph-3d-statistics-background.jpg?b=1&amp;s=2048x2048&amp;w=0&amp;k=20&amp;c=JqlWDrRuqpKotSJiWlw9XEKfqmlar9qw3g5TGMGWdVk=" alt="analytics dashboard" class="object-cover bg-gray-100" style="aspect-ratio: 16/9;" data-mce-src="https://media.gettyimages.com/id/1954841243/photo/data-analysis-chart-graph-3d-statistics-background.jpg?b=1&amp;s=2048x2048&amp;w=0&amp;k=20&amp;c=JqlWDrRuqpKotSJiWlw9XEKfqmlar9qw3g5TGMGWdVk=" data-mce-style="aspect-ratio: 16/9;"></figure><h2>Data-Driven Decision Making</h2><p>With a solid analytics framework in place, the startup embraced a data-driven approach to decision-making. They regularly analyzed data to identify trends and patterns, allowing them to optimize marketing campaigns and improve targeting. This approach led to more effective use of resources and a better understanding of their audience.</p><h3>Personalizing Marketing Efforts</h3><p>By leveraging insights from SaaS marketing analytics, the startup was able to personalize their marketing efforts. They segmented their audience based on behavior and preferences, tailoring content and offers to meet individual needs. This personalization increased engagement and conversion rates significantly.</p><figure id="figure-vchbug9nfm65zuqjl"><img id="vchbug9nfm65zuqjl" src="https://media.gettyimages.com/id/1889510378/photo/ai-enhanced-social-networking.jpg?b=1&amp;s=2048x2048&amp;w=0&amp;k=20&amp;c=9qP8sQkkkRHC2roYuoQ4gVejwcqrLOyWVADVOd-iUZY=" alt="personalized marketing" class="object-cover bg-gray-100" style="aspect-ratio: 16/9;" data-mce-src="https://media.gettyimages.com/id/1889510378/photo/ai-enhanced-social-networking.jpg?b=1&amp;s=2048x2048&amp;w=0&amp;k=20&amp;c=9qP8sQkkkRHC2roYuoQ4gVejwcqrLOyWVADVOd-iUZY=" data-mce-style="aspect-ratio: 16/9;"></figure><h2>Continuous Improvement</h2><p>The journey didn't stop with initial success. The startup established a culture of continuous improvement, regularly refining their strategies based on data insights. This iterative process ensured that they remained competitive and adaptable in a constantly evolving market.</p><h3>The Results</h3><p>The impact of implementing SaaS marketing analytics was profound. Within a year, the startup experienced a substantial increase in customer acquisition and retention. Their ability to quickly adapt strategies based on real-time data insights proved invaluable in achieving rapid growth.</p><h2>Conclusion</h2><p>This case study illustrates the transformative power of SaaS marketing analytics for startups. By embracing data-driven decision-making and continuously refining their approach, the startup not only achieved rapid growth but also established a strong foundation for sustained success. For other startups looking to scale, investing in SaaS marketing analytics can be a pivotal step towards achieving business objectives.</p>

Case Study: How a Startup Leveraged SaaS Marketing Analytics for Rapid Growth

<h2>Understanding the Importance of Budget Allocation</h2>
<p>In the realm of financial planning and management, budget allocation plays a pivotal role. It's the blueprint guiding where and how resources are utilized, ensuring that every dollar is spent effectively. By mastering advanced budget allocation techniques, businesses can uncover hidden opportunities for growth and efficiency.</p>
<p>Traditional budgeting methods often overlook nuanced financial dynamics, leading to suboptimal resource distribution. Understanding these dynamics and implementing strategic allocation can significantly impact a company's bottom line.</p>
<figure id="figure-bisulosqcm65zty78"><img id="bisulosqcm65zty78" src="https://media.gettyimages.com/id/1168618923/photo/japanese-man-calculating-financial-bills-at-home.jpg?b=1&s=2048x2048&w=0&k=20&c=C0gKVZstqh-Gj_O611ytQKOTmRqU7Symac-mlAXzy7Q=" alt="budget planning" class="object-cover bg-gray-100" style="aspect-ratio: 16/9"></figure>

<h2>Advanced Techniques for Effective Budget Allocation</h2>
<h3>Zero-Based Budgeting</h3>
<p>One innovative technique is Zero-Based Budgeting (ZBB). Unlike traditional methods that adjust previous budgets, ZBB requires justifying every expense from scratch. This fresh evaluation helps identify unnecessary expenditures, reallocating funds to areas with higher returns.</p>
<h3>Activity-Based Budgeting</h3>
<p>Another approach is Activity-Based Budgeting (ABB), which focuses on activities that incur costs. By analyzing these activities, businesses can better understand cost drivers, allowing for more precise budgetary adjustments. This method promotes efficiency by aligning budgets with strategic business activities.</p>
<figure id="figure-avi62jqc9m65zty78"><img id="avi62jqc9m65zty78" 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="financial analysis" class="object-cover bg-gray-100" style="aspect-ratio: 16/9"></figure>

<h2>Leveraging Data Analytics for Better Budget Decisions</h2>
<p>Data analytics has revolutionized budget allocation by providing deeper insights into spending patterns. By leveraging big data, businesses can forecast future trends, adjust budgets dynamically, and capitalize on emerging opportunities. This proactive approach minimizes risks and maximizes resource utilization.</p>
<p>Moreover, advanced analytics tools can identify hidden correlations between various budget items, offering a more comprehensive view of financial health. This holistic perspective enables smarter, data-driven decisions that align with long-term objectives.</p>
<figure id="figure-mp8ww2imxmm65zty78"><img id="mp8ww2imxmm65zty78" 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 analytics" class="object-cover bg-gray-100" style="aspect-ratio: 16/9"></figure>

<h2>Integrating Scenario Planning into Budgeting</h2>
<p>Scenario planning is a powerful tool for navigating uncertainties in budget allocation. By simulating various financial scenarios, businesses can anticipate potential challenges and devise strategies to mitigate them. This foresight ensures that budgets remain flexible and resilient in the face of unforeseen events.</p>
<p>Effective scenario planning involves considering both positive and negative outcomes, allowing businesses to prepare for a range of possibilities. This comprehensive approach helps maintain stability and seize opportunities even in turbulent times.</p>

<h2>Continuous Monitoring and Adjustment</h2>
<p>The effectiveness of budget allocation does not end with planning; it requires continuous monitoring and adjustment. Regularly reviewing financial performance against the budget helps identify discrepancies early, enabling timely corrective actions.</p>
<p>Implementing a robust feedback loop ensures that budgetary adjustments are data-driven and aligned with evolving business goals. This dynamic approach fosters a culture of agility and responsiveness, crucial for thriving in today's fast-paced business environment.</p>
<figure id="figure-vcl3o3c0qhm65zty78"><img id="vcl3o3c0qhm65zty78" src="https://media.gettyimages.com/id/1887449067/photo/confident-businessman-in-modern-office.jpg?b=1&s=2048x2048&w=0&k=20&c=t7lPpv8khkhF5ZXW1w5wQLekaIqG_8Tb_LJ87_mFLz0=" alt="business growth" class="object-cover bg-gray-100" style="aspect-ratio: 16/9"></figure>

<h2>Conclusion: Embracing Advanced Techniques</h2>
<p>The landscape of budget allocation is evolving, driven by new techniques and technologies. By embracing advanced methods like Zero-Based Budgeting, Activity-Based Budgeting, data analytics, and scenario planning, businesses can unlock hidden opportunities and drive sustainable growth.</p>
<p>Ultimately, effective budget allocation is about more than just numbers; it's about creating a strategic roadmap that aligns resources with organizational objectives. By continuously refining these strategies, businesses can position themselves for success in an ever-changing economic landscape.</p>

Unlocking Hidden Opportunities: Advanced Budget Allocation Techniques

<p>In a previous article, "<a data-mce-href="https://morethandata.biz/blog/why-small-and-medium-sized-media-agencies-haven-t-used-mmm" href="https://morethandata.biz/blog/why-small-and-medium-sized-media-agencies-haven-t-used-mmm" target="_blank" rel="noopener"><strong>Why Small and Medium-Sized Media Agencies Haven’t Used MMM</strong></a>", we discussed the key barriers that prevent smaller agencies from adopting Marketing Mix Modeling (MMM). In this article, we aim to dive deeper into the underlying internal constraints these agencies face when attempting to build their own in-house analytics capabilities.</p><p>While MMM has proven to be a valuable tool for optimizing marketing effectiveness, implementing it requires a level of organizational support, resources, and expertise that smaller agencies often struggle to achieve. By examining the advantages that large, enterprise-level media agencies possess, we can better understand the specific challenges smaller agencies encounter and why bridging this gap is crucial for their long-term success.</p><h3>The Analytics Advantage of Large Media Agencies</h3><p>Large media agencies like <a data-mce-href="https://www.wpp.com/en-au" href="https://www.wpp.com/en-au" target="_blank" rel="noopener"><strong>WPP</strong></a>, <a data-mce-href="https://www.omnicomgroup.com/" href="https://www.omnicomgroup.com/" target="_blank" rel="noopener"><strong>Omnicom</strong></a>, <a data-mce-href="https://www.interpublic.com/" href="https://www.interpublic.com/" target="_blank" rel="noopener"><strong>Interpublic</strong></a>, and <a data-mce-href="https://www.publicis.com.au/" href="https://www.publicis.com.au/" target="_blank" rel="noopener"><strong>Publicis </strong></a>have built their dominance partly by establishing robust in-house analytics capabilities. These global giants not only provide traditional marketing and media services but also operate specialized analytics arms designed to meet the increasing demand for data-driven solutions.</p><p>These internal analytics teams handle a wide array of tasks, including marketing and media effectiveness measurement, reporting, dashboard creation, audience targeting and retargeting, customer segmentation, and much more. Notably, these analytics operations are often housed within subsidiaries that serve as dedicated partners to the parent company. For instance:</p><ul><li>WPP operates <a data-mce-href="https://www.acceleration.biz/" href="https://www.acceleration.biz/" target="_blank" rel="noopener"><strong>Acceleration </strong></a>and <a data-mce-href="https://www.choreograph.com/" href="https://www.choreograph.com/" target="_blank" rel="noopener"><strong>Choreograph </strong></a>as its analytics arms.<br></li><li>Omnicom relies on <a data-mce-href="https://www.annalect.com/" href="https://www.annalect.com/" target="_blank" rel="noopener"><strong>Annalect </strong></a>for its data-driven solutions.<br></li><li>Interpublic utilizes <a data-mce-href="https://kinesso.com/" href="https://kinesso.com/" target="_blank" rel="noopener"><strong>Kinesso</strong></a>, .which brings Matterkind and Reprise together as a newly unified entity.<br></li><li>Publicis has invested in <a data-mce-href="https://www.publicissapient.com/" href="https://www.publicissapient.com/" target="_blank" rel="noopener"><strong>Sapient </strong></a>as its analytics powerhouse.</li></ul><p>These specialized divisions hire highly skilled data professionals and invest heavily in cutting-edge analytics tools and software. They function as internal service providers for their parent companies, delivering analytics solutions to enhance marketing strategy and decision-making. For example, Omnicom’s <a data-mce-href="https://www.annalect.com/" href="https://www.annalect.com/" target="_blank" rel="noopener"><strong>Annalect </strong></a>supports other agencies within the group, such as <a data-mce-href="https://www.omd.com/" href="https://www.omd.com/" target="_blank" rel="noopener"><strong>OMD </strong></a>and <a data-mce-href="https://www.phdmedia.com/australia/" href="https://www.phdmedia.com/australia/" target="_blank" rel="noopener"><strong>PHD</strong></a>, ensuring that even the client-facing teams have access to high-quality data insights.</p><h3>Why Smaller Agencies Struggle to Compete</h3><p>In stark contrast to these enterprise-level operations, small and medium-sized media agencies face significant hurdles when trying to build their own analytics capabilities.</p><p>Smaller agencies typically operate with far fewer resources. In markets like Sydney and Melbourne, these agencies often employ only 20–30 professionals, with annual revenues rarely exceeding $10 million. A realistic estimate would be closer to $7–8 million. With such tight margins, these agencies must carefully allocate their budgets to maintain profitability. Unfortunately, this often leads to data analytics roles being deprioritized.</p><p>Instead, smaller agencies focus on hiring client-facing professionals, such as account executives, account managers, media buyers, media planners and strategists. These roles directly contribute to revenue generation by managing client relationships and expanding the agency’s business. By contrast, analytics professionals are often viewed as cost centers—essentially an expense rather than a profit-driving asset.</p><h3>The Value of Analytics: Lessons from the Largest Agencies</h3><p>The marginalization of analytics professionals in smaller agencies does not mean that analytics itself is unimportant. On the contrary, the largest agencies globally—<a data-mce-href="https://www.wpp.com/en-au" href="https://www.wpp.com/en-au" target="_blank" rel="noopener"><strong>WPP</strong></a>, <a data-mce-href="https://www.omnicomgroup.com/" href="https://www.omnicomgroup.com/" target="_blank" rel="noopener"><strong>Omnicom</strong></a>, <a data-mce-href="https://www.interpublic.com/" href="https://www.interpublic.com/" target="_blank" rel="noopener"><strong>Interpublic</strong></a>, and <a data-mce-href="https://www.publicis.com.au/" href="https://www.publicis.com.au/" target="_blank" rel="noopener"><strong>Publicis</strong></a>—have demonstrated the immense value of investing in analytics capabilities. Their continued investment in analytics underscores the importance of marketing measurement in driving effectiveness, enhancing service offerings, and staying competitive.</p><p>However, smaller agencies often find themselves falling behind for several reasons:</p><ul><li><strong>Talent Acquisition Challenges</strong> Analytics professionals are more likely to pursue opportunities at larger, well-known agencies that offer a clear pathway for career development and access to advanced resources. Smaller agencies struggle to attract such talent due to their limited reputations and lack of infrastructure.<br data-mce-bogus="1"></li><li><strong>Budgetary Limitations</strong> Hiring a skilled marketing analytics professional can cost over $100,000 per year. For smaller agencies, this is a significant investment—one that is often deemed unsustainable. Even when budgets are available, these agencies tend to prioritize roles that directly contribute to revenue generation.<br></li><li><strong>Cultural Misalignment</strong> Smaller agencies are predominantly focused on client relationships and business development, which can make analytics professionals feel undervalued. Their technical expertise is often overlooked, and their contributions may not be fully understood or appreciated. This disconnect leads to frustration and disengagement.<br></li><li><strong>Limited Career Development Opportunities </strong>Smaller agencies rarely have structured pathways for skill development, performance evaluation, or career progression for analytics professionals. Without guidance, recognition, or opportunities for advancement, these professionals often feel stagnant, leading to high turnover rates. On average, analytics professionals stay at smaller agencies for less than eight months—a concerning trend highlighted by feedback from More Than Data team members who have worked in similar environments.</li></ul><h3>The Growing Divide: AI and Digital Transformation<br></h3><p>The challenges for smaller agencies are further compounded by the rapid rise of AI and digital technologies. Larger agencies are accelerating their adoption of these innovations, leveraging AI to enhance efficiency and improve their service offerings. In contrast, smaller agencies face growing difficulties in keeping up, particularly in the post-COVID era, where many struggle with client retention and shrinking revenues.</p><p>To illustrate this gap, consider the analogy of a cyclist attempting to compete with a car. The cyclist, representing smaller agencies, may work tirelessly to keep up, but the car—symbolizing larger agencies—can effortlessly accelerate with the press of a pedal, leaving the cyclist far behind.</p><h3>How More Than Data Can Help</h3><p>At <a data-mce-href="https://morethandata.biz/" href="https://morethandata.biz/" target="_blank" rel="noopener"><strong>More Than Data</strong></a>, we understand the unique struggles faced by smaller media agencies. Our mission is to bridge the gap by providing tailored analytics solutions that empower these agencies to compete effectively in an increasingly data-driven industry.</p><p>Through our technology, software, and expertise, we enable smaller agencies to embrace marketing analytics without the need for extensive in-house resources. By partnering with us, smaller agencies can unlock the benefits of data-driven insights, enhance their service offerings, and stay competitive in an ever-evolving landscape.</p>

In-House Constraints for Small and Medium-Sized Media Agencies in Adopting MMM

<p>In our previous article, we explored <a data-mce-href="https://morethandata.biz/blog/why-small-and-medium-sized-media-agencies-haven-t-used-mmm" href="https://morethandata.biz/blog/why-small-and-medium-sized-media-agencies-haven-t-used-mmm" target="_blank" rel="noopener"><strong>why small and medium-sized media agencies struggle to adopt Marketing Mix Modeling (MMM)</strong></a>. High costs, lack of expertise, and opaque processes are common barriers. Here, we uncover the deeper causes and evaluate the implications of outsourcing MMM to external vendors or freelancers.</p><h3>Outsourcing to Service Vendors</h3><p>Engaging a service vendor—whether a consulting firm like Analytic Partners or a SaaS platform such as <a data-mce-href="https://mass-analytics.com/" href="https://mass-analytics.com/" target="_blank" rel="noopener"><strong>Mass Analytics</strong></a> or <a data-mce-href="https://www.mutinex.co/" href="https://www.mutinex.co/" target="_blank" rel="noopener"><strong>Mutinex</strong></a>—might seem logical, but significant drawbacks exist:</p><ul><li><strong>Loss of Control</strong> By outsourcing MMM, agencies surrender control over their marketing measurement process and data-driven strategies. Although agencies retain data ownership, they often lack influence over or understanding of the modeling process, limiting their ability to fully utilize insights.<br></li><li><strong>Recurring Costs </strong>Vendors charge service fees, whether for consulting or SaaS. While SaaS platforms may seem cost-effective, subscription fees and customization expenses accumulate, burdening smaller agencies.<br></li><li><strong>Inconsistent Outcomes</strong> Switching vendors often results in discrepancies due to differing methodologies. This inconsistency can confuse agencies, leading to doubts about the reliability of past and present insights.<br></li><li><strong>Weak Internal Data Strategy</strong> Reliance on external providers prevents agencies from developing strong internal data strategies and capabilities, which are essential for modern marketing.</li></ul><h3>Outsourcing to Freelancers<br></h3><p>Freelancers may appear more affordable, but they share many drawbacks of vendors and add new concerns:</p><ul><li><strong>Data Security Risks</strong> Sharing sensitive marketing data with freelancers introduces potential risks. Unlike established vendors, freelancers may lack robust security measures, increasing the chance of breaches.<br></li><li><strong>Variable Quality</strong> The effectiveness of MMM depends on the freelancer’s skills. Expertise varies widely, and some freelancers may lack the technical know-how to meet an agency’s specific needs.<br></li><li><strong>Operational Vulnerability </strong>Relying on a single person for critical insights is risky. If the freelancer becomes unavailable, agencies may face disruptions or lose access to valuable methodologies.</li></ul><p>We explored <a data-mce-href="https://www.fiverr.com/" href="https://www.fiverr.com/" target="_blank" rel="noopener"><strong>Fiverr</strong></a>, one of the most well-known platforms for freelancers and outsourcing services. When searching for "Marketing Mix Model," we found several freelancers offering their services, with rates ranging from $100 to over $2,000.</p><figure id="figure-djq5q6yxxm64pqs4s" class=""><img id="djq5q6yxxm64pqs4s" src="https://cdn.durable.co/blocks/8qhI5Cy2I7E6D06c8x7a129JA9waZozMjlgTEOIHH9jSdLdtp7vhFXFC9eO5OKcL.png" alt="" title="" class="object-cover bg-gray-100 " style="aspect-ratio: 16/9; object-position: undefined undefined;" data-corners="default" data-aspect="16:9" data-caption="Source: https://www.fiverr.com/search/gigs?query=marketing%20mix%20model&amp;source=main_banner&amp;ref_ctx_id=9579bc55f72b4322be6f7c8606d5f4f0&amp;search_in=everywhere&amp;search-autocomplete-original-term=marketing%20mix%20model" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/8qhI5Cy2I7E6D06c8x7a129JA9waZozMjlgTEOIHH9jSdLdtp7vhFXFC9eO5OKcL.png" data-mce-style="aspect-ratio: 16/9; object-position: undefined undefined;"><figcaption class="body-normal text-gray-500 mt-2">Source: https://www.fiverr.com/search/gigs?query=marketing%20mix%20model&amp;source=main_banner&amp;ref_ctx_id=9579bc55f72b4322be6f7c8606d5f4f0&amp;search_in=everywhere&amp;search-autocomplete-original-term=marketing%20mix%20model</figcaption></figure><h3>Broader Challenges of Outsourcing MMM<br></h3><p>Whether agencies outsource to vendors or freelancers, these overarching challenges persist:</p><ul><li><strong>Inconsistent Methodologies</strong> Each provider employs different approaches, creating inconsistencies that hinder long-term planning and erode trust in MMM.<br></li><li><strong>Financial Strain</strong> Even cost-efficient outsourcing requires recurring expenses, which can stretch limited budgets.<br></li><li><strong>Missed Opportunities for Growth</strong> Outsourcing prevents agencies from building internal data management and analytical capabilities, leaving them at a disadvantage in an increasingly data-driven industry.</li></ul><h3>A Call to Action for Media Agencies<br></h3><p>To overcome these challenges, media agencies must shift toward self-reliance:</p><ul><li><strong>Adopt Scalable Tools</strong> Scalable, user-friendly MMM tools enable agencies to manage marketing measurement independently and cost-effectively.<br></li><li><strong>Develop Robust Data Strategies</strong> Agencies should focus on centralizing data storage, enhancing security, and fostering a data-driven culture. Building strong internal capabilities ensures sustainable growth and competitive advantage.</li></ul><p>By addressing these issues, small and medium-sized media agencies can reduce outsourcing dependencies and unlock the full potential of MMM for effective, data-driven marketing strategies.</p><p>If you’re facing challenges or need support in building a data-driven strategy and business model, <a data-mce-href="https://morethandata.biz/sign-up" href="https://morethandata.biz/sign-up" target="_blank" rel="noopener"><strong>contact us</strong></a>. We are <a data-mce-href="https://morethandata.biz/" href="https://morethandata.biz/" target="_blank" rel="noopener"><strong>More Than Data</strong></a>, and we’re here to help.</p>

The Challenges of Outsourcing Marketing Mix Modeling for Media Agencies

<h2>Understanding Media Mix Modelling</h2><p>Businesses are constantly seeking innovative ways to enhance their marketing strategies. One such method that has gained traction is Media Mix Modelling (MMM). This analytical approach helps marketers determine the effectiveness of various marketing channels and campaigns. By analyzing historical data, businesses can allocate their resources more efficiently and improve their overall marketing outcomes.</p><figure id="figure-4l0c720ce5m61piavz"><img id="4l0c720ce5m61piavz" src="https://media.gettyimages.com/id/801269928/photo/aerial-view-of-sydney-city.jpg?b=1&amp;s=2048x2048&amp;w=0&amp;k=20&amp;c=RmJZcx3dPL9nwa14EscpHTAJrq3XgnNGa7FUwajpCE0=" alt="sydney skyline" class="object-cover bg-gray-100" style="aspect-ratio: 16/9;" data-mce-src="https://media.gettyimages.com/id/801269928/photo/aerial-view-of-sydney-city.jpg?b=1&amp;s=2048x2048&amp;w=0&amp;k=20&amp;c=RmJZcx3dPL9nwa14EscpHTAJrq3XgnNGa7FUwajpCE0=" data-mce-style="aspect-ratio: 16/9;"></figure><h3>The Importance of Data-Driven Decisions</h3><p>In today's competitive market, relying on intuition alone is not enough. Media Mix Modelling provides a solid foundation for making data-driven decisions. By analyzing past performances across different channels like TV, radio, digital, and print, marketers can gain insights into what works best for their target audience. This information is crucial for optimizing future campaigns and achieving better returns on investment.</p><h3>How MMM Works</h3><p>Media Mix Modelling involves collecting extensive data from various marketing channels and using statistical techniques to understand the impact of each channel on sales or other key performance indicators. This process typically involves:</p><ul><li>Data Collection: Gathering historical data from all marketing activities.</li><li>Data Analysis: Using advanced statistical methods to analyze the data.</li><li>Model Development: Building a predictive model to simulate different scenarios.</li><li>Optimization: Identifying the optimal mix of media channels for maximum impact.</li></ul><h2>Benefits of Implementing MMM</h2><p>Sydney's diverse and dynamic market presents unique challenges and opportunities for marketers. Implementing Media Mix Modelling can provide several benefits:</p><figure id="figure-jmtsrd46fkm61piavz"><img id="jmtsrd46fkm61piavz" src="https://media.gettyimages.com/id/1954841243/photo/data-analysis-chart-graph-3d-statistics-background.jpg?b=1&amp;s=2048x2048&amp;w=0&amp;k=20&amp;c=JqlWDrRuqpKotSJiWlw9XEKfqmlar9qw3g5TGMGWdVk=" alt="data analysis" class="object-cover bg-gray-100" style="aspect-ratio: 16/9;" data-mce-src="https://media.gettyimages.com/id/1954841243/photo/data-analysis-chart-graph-3d-statistics-background.jpg?b=1&amp;s=2048x2048&amp;w=0&amp;k=20&amp;c=JqlWDrRuqpKotSJiWlw9XEKfqmlar9qw3g5TGMGWdVk=" data-mce-style="aspect-ratio: 16/9;"></figure><h3>Enhanced Marketing Efficiency</h3><p>By understanding which channels deliver the most value, businesses can allocate their budgets more effectively. This ensures that marketing dollars are spent on strategies that yield the highest returns, reducing waste and maximizing impact.</p><h3>Adaptation to Market Trends</h3><p>Sydney's market is constantly evolving, with new trends emerging regularly. MMM allows businesses to stay ahead by adapting their strategies based on real-time insights. This agility is crucial in maintaining a competitive edge in a fast-paced environment.</p><h2>Challenges and Considerations</h2><p>While the benefits of Media Mix Modelling are substantial, there are challenges to consider. Gathering accurate and comprehensive data can be difficult, especially when integrating multiple sources. Additionally, developing an effective model requires expertise in both analytics and marketing strategy.</p><h3>Choosing the Right Tools and Partners</h3><p>To successfully implement MMM, businesses need the right tools and partners. Choosing a reliable analytics platform and working with experienced professionals can make a significant difference in the accuracy and usefulness of the model.</p><figure id="figure-qbo7psxosm61piavz"><img id="qbo7psxosm61piavz" src="https://media.gettyimages.com/id/629303228/photo/im-happy-to-explain.jpg?b=1&amp;s=2048x2048&amp;w=0&amp;k=20&amp;c=WEmzS8V0sMIhn9i-_wyF5Cs9Iyk4Ye2AfzGK4jmkLt4=" alt="marketing strategy" class="object-cover bg-gray-100" style="aspect-ratio: 16/9;" data-mce-src="https://media.gettyimages.com/id/629303228/photo/im-happy-to-explain.jpg?b=1&amp;s=2048x2048&amp;w=0&amp;k=20&amp;c=WEmzS8V0sMIhn9i-_wyF5Cs9Iyk4Ye2AfzGK4jmkLt4=" data-mce-style="aspect-ratio: 16/9;"></figure><h2>Conclusion</h2><p>Media Mix Modelling offers a powerful way for businesses to enhance their marketing strategies. By leveraging data-driven insights, companies can make informed decisions that drive growth and improve efficiency. As the market continues to evolve, adopting MMM can be a game-changer in staying competitive and achieving long-term success.</p>

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