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<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.</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/contact-us" href="https://morethandata.biz/contact-us" target="_blank" data-mce-selected="inline-boundary"><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>

How Media Mix Modelling Can Elevate Your Marketing Strategy

<p>In our 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 explored the challenges faced by media agencies in adopting MMM. These challenges range from limited resources and expertise to the high costs associated with implementing MMM solutions. In this follow-up article, we delve deeper into one of the core hurdles that many agencies face when attempting to leverage MMM: the limitations of traditional Software-as-a-Service (SaaS) platforms used to build and execute MMM.</p><p>While SaaS platforms promise convenience, scalability, and cost-effectiveness, they often fall short when it comes to providing the flexibility, transparency, and support that small and medium-sized agencies need to succeed with MMM. Below, we examine the key limitations of these traditional SaaS solutions and why they may not always be the best fit for agencies seeking to maximize the impact of their marketing strategies.</p><h3>Limited Customization<br></h3><p>Customization is a significant challenge when building MMM, as it requires modelers to adjust and recalibrate the model to align with both commercial and marketing practices, as well as statistical assumptions and tests. While SaaS platforms may allow users to tweak certain inputs or settings, the core logic and algorithms are often hidden. This lack of visibility prevents businesses from fully tailoring the model to their specific needs and unique marketing nuances. Consequently, they are often forced to rely on a one-size-fits-all approach, which may not be well-suited for complex or specialized marketing strategies.</p><p>Without a clear understanding of the underlying model, making informed, data-driven adjustments or fine-tuning the analysis becomes increasingly difficult. This ultimately hinders the ability to achieve optimal, actionable results. When customization is restricted, businesses miss the opportunity to optimize the model to suit their unique needs and leverage data more effectively.</p><p>In our previous article, <a data-mce-href="https://morethandata.biz/blog/building-mmm-the-traditional-way--why-it-s-so-time-consuming" href="https://morethandata.biz/blog/building-mmm-the-traditional-way--why-it-s-so-time-consuming" target="_blank" rel="noopener"><strong>Building MMM the Traditional Way: Why It’s So Time-Consuming</strong></a>, we discussed the reasons why building an MMM in the traditional way can be time-consuming and inefficient. One of the key challenges is that the model often requires iterative adjustments to align the media contribution with the sales outcomes. Achieving a satisfactory percentage-wise (%) media contribution to sales typically takes considerable time, as it involves multiple rounds of recalibration and fine-tuning. This iterative process is a typical example and representation of customization in action.</p><h3>Hidden Backend Processes</h3><p>Another key limitation of traditional SaaS platforms for MMM is the obscurity of critical backend processes. These platforms often make it challenging for users to understand how their data is being processed, integrated, and analyzed. Data cleaning, normalization, and transformation are essential steps for ensuring the accuracy and quality of the data that feeds into the model. However, when these processes are not transparent, users lack insight into how their data is being handled.</p><p>As a result, it becomes difficult to verify that the data is reliable and aligned with business objectives. Without visibility into the data flow, businesses risk working with flawed or inconsistent data, leading to inaccurate models and misguided decisions. The lack of transparency ultimately undermines the trust users have in the platform and the insights it provides, which can have a detrimental effect on marketing decisions and outcomes.</p><h3>Data Integration Challenges</h3><p>Effective MMM requires the integration of various data sources, such as media spend, sales data, competitor activity, and external factors like weather or economic conditions. While many SaaS platforms offer pre-built integrations with popular data sources, users often have limited control and visibility into how this data is processed once it’s uploaded.</p><p>For agencies managing large volumes of diverse data, ensuring seamless integration and alignment with the business’s marketing objectives becomes a significant challenge. Lack of control over how data is managed within these platforms can lead to inefficiencies, errors, and missed opportunities to draw meaningful insights.</p><h3>Self-Service Tools Are Too Complicated</h3><p>One of the primary appeals of SaaS platforms is their promise of self-service tools that allow users to manage and execute complex processes without needing specialized expertise. However, many traditional SaaS platforms offering MMM have self-service tools that can overwhelm non-technical users.</p><p><em><strong>Steep Learning Curve</strong></em></p><p>While SaaS platforms are marketed as user-friendly, the reality is that the complexity of MMM requires a certain level of expertise to operate effectively. Non-technical users, such as marketers or media planners, often struggle to navigate the sophisticated analytics features, configure the right settings, or interpret the results. The steep learning curve associated with these tools can lead to frustration, inaccurate analysis, and underutilization of the platform’s capabilities.</p><p>We browsed through customer and user feedback on <a data-mce-href="https://www.capterra.com/p/178761/MassTer-Predictive-Marketing-Analytics/" href="https://www.capterra.com/p/178761/MassTer-Predictive-Marketing-Analytics/" target="_blank" rel="noopener"><strong>MASS Analytics</strong></a>, and while many users offered positive comments about the software, they also mentioned some challenges. Some users pointed out that the software is not particularly convenient to use. They highlighted that the numerous features can be overwhelming, and the process often feels like a back-and-forth, requiring constant adjustments and navigating between different buttons, screens, and windows. This feedback indicates that while the tool offers valuable functionality, the user experience could be improved. Streamlining the interface for greater ease of use and efficiency would be particularly beneficial for users without extensive technical expertise, allowing them to leverage the software’s full potential without unnecessary complexity.</p><figure id="figure-2977aurgdm5z6xi77" class=""><img id="2977aurgdm5z6xi77" src="https://cdn.durable.co/blocks/22BtABrqPr3hPWdSEFgcK9XCvNqhNK11S40bP1RmdMsXrgqfSuqJD5BQ42ph3LMj.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.capterra.com/p/178761/MassTer-Predictive-Marketing-Analytics/" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/22BtABrqPr3hPWdSEFgcK9XCvNqhNK11S40bP1RmdMsXrgqfSuqJD5BQ42ph3LMj.png" data-mce-style="aspect-ratio: 16/9; object-position: undefined undefined;"><figcaption class="body-normal text-gray-500 mt-2">Source: https://www.capterra.com/p/178761/MassTer-Predictive-Marketing-Analytics/</figcaption></figure><p>We also noticed that most of the users of MASS Analytics software are analytics professionals, such as analysts, analytics managers, consultants, and lead data scientists. This indicates that the tool is more suited for technical users who have the expertise to navigate its complex features and functionalities. However, for non-technical users, this software may present a challenge, making it a difficult choice for those without specialized data skills. To truly unlock the power of MMM, non-technical users need a more intuitive, user-friendly solution that allows them to manage the process without the steep learning curve and technical complexities.</p><p><em><strong>Limited Support and Guidance</strong></em></p><p>Most traditional SaaS platforms offer limited or no personalized support for users, relying instead on automated tutorials or knowledge bases. While these resources can be helpful, they often fall short for non-technical users who may require more hands-on assistance. In the absence of effective support, users can become stuck or make errors in their analysis, which can ultimately undermine the value of the platform.</p><p>For businesses that do not have dedicated data science teams or specialized personnel, the complexity of self-service tools can become a significant barrier. Without proper assistance, users may avoid using the platform to its full potential or, worse, misuse it entirely.</p><h3>The Expense of Current SaaS Solutions</h3><p>While SaaS platforms are often marketed as cost-effective alternatives to traditional software or custom-built solutions, they can still be quite expensive, especially for businesses that require more advanced features or higher levels of support.</p><p><em><strong>Subscription Fees and Hidden Costs</strong></em></p><p>Many SaaS platforms operate on a subscription-based pricing model, where businesses pay recurring fees for access to the software. While this can be more affordable upfront compared to building an in-house solution, the costs can quickly add up over time. In addition to basic subscription fees, many platforms charge extra for advanced features, increased data storage, or additional users. These hidden costs can make the overall expense of using a SaaS platform much higher than initially anticipated.</p><p><em><strong>Resource Allocation</strong></em></p><p>In addition to subscription fees, businesses must also factor in the time and resources required to fully utilize a SaaS platform. For non-technical teams, the need for additional training or external support services can increase operational costs. Companies may need to hire third-party consultants or data experts to manage the platform and ensure accurate modeling, further increasing the overall expense.</p><p>For smaller businesses or those with limited budgets, these cumulative costs can make traditional SaaS solutions less cost-effective in the long term, particularly when compared to more tailored or in-house approaches that offer greater flexibility and control.</p><h3>Conclusion: Rethinking the Approach to MMM</h3><p>While traditional SaaS platforms offer a convenient way to build and execute MMM, they come with several limitations that may hinder the effectiveness of businesses trying to optimize their marketing strategies. The lack of customization, hidden backend processes, complex self-service tools, and high costs are significant challenges, particularly for businesses without specialized technical expertise.</p><p>As marketing continues to become more data-driven, businesses must carefully assess whether traditional SaaS solutions are truly the right fit for their needs. The limitations of these platforms—ranging from restricted flexibility to the challenges of working with opaque processes—suggest that alternative approaches may offer more tailored solutions, greater customization, and a more cost-effective path toward effective MMM.</p><p>Ultimately, the future of MMM will likely lie in solutions that strike a better balance between ease of use, customization, and cost-efficiency. Such solutions will empower businesses to unlock the full potential of their data, making informed, data-driven decisions without being constrained by the limitations of traditional SaaS platforms. By embracing more flexible, transparent, and user-friendly models, businesses can stay ahead in an increasingly competitive marketing landscape.</p><p>If you're looking for an easy-to-use, customizable, DIY-enabled, and cost-efficient solution for MMM, please <a data-mce-href="https://morethandata.biz/minute-mmm" href="https://morethandata.biz/minute-mmm" target="_blank" data-mce-selected="inline-boundary"><strong>contact us</strong></a>!</p>

Why Traditional SaaS Solutions for Marketing Mix Modelling Have Limitations

<h2>Understanding the Importance of AI in Marketing</h2>
<p>In today's rapidly evolving digital landscape, <strong>Artificial Intelligence (AI)</strong> is no longer a futuristic concept but a present-day necessity. For Sydney marketers, leveraging AI can transform how they understand and engage with their customers. The integration of AI in marketing strategies not only enhances customer experience but also drives business growth. By harnessing the power of AI, marketers can gain deeper insights into consumer behavior, enabling them to tailor their campaigns more effectively.</p>

<figure id="figure-uoqqi3nglbm5xf6qst"><img id="uoqqi3nglbm5xf6qst" src="https://media.gettyimages.com/id/1889510378/photo/ai-enhanced-social-networking.jpg?b=1&s=2048x2048&w=0&k=20&c=9qP8sQkkkRHC2roYuoQ4gVejwcqrLOyWVADVOd-iUZY=" alt="ai marketing" class="object-cover bg-gray-100" style="aspect-ratio: 16/9"></figure>

<h2>Enhancing Customer Segmentation with AI</h2>
<p>One of the most significant advantages of using AI in marketing is the ability to enhance customer segmentation. Traditional segmentation methods often rely on broad categories, but AI allows for more precise and granular segmentation. By analyzing data patterns and customer interactions, AI can identify specific segments based on behavior, preferences, and demographics. This allows Sydney marketers to craft personalized messages that resonate with individual customers, increasing the likelihood of conversion.</p>

<p>AI-powered tools can process vast amounts of data in real-time, offering insights that were previously unattainable. As a result, marketers can create highly targeted campaigns that speak directly to the needs and desires of their audience. This level of personalization not only improves customer satisfaction but also fosters brand loyalty.</p>

<h2>Predictive Analytics for Better Decision Making</h2>
<p>Predictive analytics is another area where AI can significantly benefit marketers. By analyzing past customer behavior and predicting future actions, AI helps marketers make informed decisions that drive results. This capability is especially valuable in planning marketing strategies and allocating resources efficiently.</p>

<figure id="figure-g5penx9etim5xf6qst"><img id="g5penx9etim5xf6qst" src="https://media.gettyimages.com/id/2160041298/photo/a-financial-analyst-using-ai-powered-predictive-analytics-to-forecast-market-trends-and.jpg?b=1&s=2048x2048&w=0&k=20&c=sFmaE98koZXGDJSOF5sSu5TtHGG440z3F3KnDGdha5U=" alt="predictive analytics" class="object-cover bg-gray-100" style="aspect-ratio: 16/9"></figure>

<p>For example, by understanding which products a customer is likely to purchase next or which channels they prefer for communication, marketers can optimize their strategies accordingly. Predictive analytics can also help in identifying trends and patterns that might not be evident through traditional analysis methods.</p>

<h2>Improving Customer Engagement Through Chatbots</h2>
<p>AI-driven chatbots have revolutionized how businesses interact with their customers. These intelligent virtual assistants provide instant support and information, enhancing customer engagement and satisfaction. For Sydney marketers, implementing chatbots can streamline customer service processes and provide 24/7 assistance without the need for human intervention.</p>

<p>Chatbots can handle various tasks, from answering frequently asked questions to assisting with product recommendations. By integrating chatbots into their marketing strategy, businesses can ensure a seamless and responsive customer experience, ultimately leading to higher customer retention rates.</p>

<figure id="figure-eo6ct4yu5qm5xf6qst"><img id="eo6ct4yu5qm5xf6qst" src="https://media.gettyimages.com/id/1933417108/photo/ai-chatbot-artificial-intelligence-concept.jpg?b=1&s=2048x2048&w=0&k=20&c=8GDNbLpA9t0cu-nQwo2NRJBl2_Rf5jtBx92JzpvWA50=" alt="chatbot customer service" class="object-cover bg-gray-100" style="aspect-ratio: 16/9"></figure>

<h2>Leveraging AI for Content Creation</h2>
<p>AI is also making waves in content creation, offering tools that can generate high-quality content quickly and efficiently. For Sydney marketers, this means they can produce a steady stream of engaging content without overwhelming their creative teams. AI tools can assist in creating everything from blog posts to social media updates, ensuring consistency in messaging and tone.</p>

<p>Moreover, AI can analyze which types of content perform best with specific audiences, allowing marketers to refine their content strategies for maximum impact. By understanding what resonates with their audience, marketers can create content that drives engagement and fosters a deeper connection with their brand.</p>

<h2>Conclusion: Embracing AI for Future Success</h2>
<p>The integration of AI into marketing strategies is no longer optional for Sydney marketers who wish to remain competitive in today's market. By leveraging AI, businesses can gain valuable insights into their customers' needs and preferences, enabling them to deliver more personalized and effective marketing campaigns.</p>

<p>As AI technology continues to advance, its applications in marketing will only grow more sophisticated. Marketers who embrace these innovations will be well-positioned to lead the way in delivering exceptional customer experiences and achieving long-term success.</p>

How Sydney Marketers Can Leverage AI for Better Customer Understanding

<p>In our 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 some barriers preventing agencies from adopting Marketing Mix Modeling (MMM). Another significant reason is the time-consuming nature of building MMM using traditional methods. Let’s explore why this process takes so much time.</p><h3>Slow Data Collection</h3><p>Data collection is one of the biggest bottlenecks in traditional MMM processes. For digital channels like search, social media, and online advertising, gathering data is relatively quick and straightforward. However, offline channels—such as TV, Out-of-Home (OOH), and radio—are a completely different story.</p><p>Offline data often arrives in inconsistent formats, mingled across Excel and PDF files with ever-changing table structures and headings. For example, one file might label OOH data as “outdoor,” while another uses “out of home.” While the meaning is clear, the lack of standardization creates delays as teams must spend significant time reconciling and cleaning the data.</p><p>If the data comes in a PDF format, it often leaves us questioning—how do we even begin to extract the numbers accurately? Take this example from a client case: A media agency received a 90-page PDF from Channel 9 after booking TV ads. Despite requesting an Excel version, none was provided, and attempts to use online PDF-to-Excel converters failed to produce accurate results, leaving the agency frustrated and overwhelmed. That’s where we stepped in. Leveraging our software, we transformed the PDF into a clean, structured Excel file, turning an otherwise daunting task into a seamless process.</p><figure id="figure-tzsl2y716nm5x9wwno"><img id="tzsl2y716nm5x9wwno" src="https://cdn.durable.co/blocks/2224sjxX2Gfa4OaACQMEzHuLRVXv0h8yILUw65isBdD9clEhboBGI9JRO0xZsW1q.png" alt="90-page-tv-pdf" title="90-page-tv-pdf" class="object-cover bg-gray-100" style="aspect-ratio: 16/9; object-position: center center;" data-corners="default" data-aspect="16:9" data-caption="" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/2224sjxX2Gfa4OaACQMEzHuLRVXv0h8yILUw65isBdD9clEhboBGI9JRO0xZsW1q.png" data-mce-style="aspect-ratio: 16/9; object-position: center center;"></figure><h3>Slow Data Processing</h3><p>Even after the data is collected, processing it can be another hurdle. Many media agencies rely on Excel for this step, using pivot tables and manual indexing. This manual work is prone to errors and inefficiencies. While some advanced users leverage VBA (Visual Basic for Applications) for automation, most marketers find it too complex and time-consuming to learn and implement effectively.</p><p><a data-mce-href="https://learn.microsoft.com/en-us/office/vba/library-reference/concepts/getting-started-with-vba-in-office" href="https://learn.microsoft.com/en-us/office/vba/library-reference/concepts/getting-started-with-vba-in-office" target="_blank" rel="noopener"><strong>Excel VBA</strong></a> enables powerful data processing through coding and workflow design. While it can present a learning curve, many non-technical users and media agencies have successfully leveraged it to streamline their data operations with the right guidance and resources. Should additional support be needed, we are always here and happy to assist.</p><figure id="figure-u3tpnhjz6km5xa5hkx" class=""><iframe id="u3tpnhjz6km5xa5hkx" src="https://www.youtube.com/embed/G05TrN7nt6k" class="w-full bg-gray-100 " style="aspect-ratio: 16/9;" allowfullscreen="" data-url="https://www.youtube.com/watch?v=G05TrN7nt6k" data-corners="default" data-fullwidth="false" data-mce-src="https://www.youtube.com/embed/G05TrN7nt6k" data-mce-style="aspect-ratio: 16/9;"></iframe></figure><p>Furthermore, while <a data-mce-href="https://www.r-project.org/" href="https://www.r-project.org/" target="_blank" rel="noopener"><strong>R</strong></a> and <a data-mce-href="https://www.python.org/" href="https://www.python.org/" target="_blank" rel="noopener"><strong>Python</strong> </a>are widely used in data analytics and processing, the key consideration is how non-technical users and media agencies can be empowered to effectively leverage these programming languages to achieve their goals. And, if additional support is needed, we are always here and ready to assist.</p><h3>Tedious Data Quality Checks and Validation</h3><p>Data validation often becomes an endless loop of back-and-forth updates. Agencies frequently revise datasets multiple times, signing off on one version only to find errors that require new updates.</p><p>In one of our experiences with a media agency, we collaborated on tracking 10+ iterations of a dataset over six months. These updates reflected the evolving nature of their operations, including adjustments to media spend, special offers, competitor activity, and even KPI sales figures, which were refined four times. This dynamic environment underscores the complexity of maintaining a stable foundation for effective MMM while adapting to real-world changes.</p><h3>Complex Model Building Process</h3><p>Once the data is processed and validated, building the MMM itself introduces additional complexities. The model must satisfy both commercial and marketing criteria, as well as adhere to statistical assumptions and tests.</p><p>In this article, we focus solely on commercial and marketing practice criteria. Readers interested in the statistical aspects can refer to the previous article, <strong><a data-mce-href="https://morethandata.biz/blog/building-mmm-the-traditional-way--why-it-s-way-too-expensive" href="https://morethandata.biz/blog/building-mmm-the-traditional-way--why-it-s-way-too-expensive" target="_blank" rel="noopener">Building MMM the Traditional Way: Why It’s Way Too Expensive</a></strong>.&nbsp;</p><p>For example, in the FMCG industry, agencies might estimate that TV contributes 5% of sales, paid social 3%, paid search 1–2%, and OOH 6%. Meanwhile, discounts and promotions account for 12% and 11%, respectively. These percentage-based criteria require the modeler to validate each media variable one by one, transforming raw media spend data using techniques like diminishing returns and adstocking.</p><p>However, the process is highly sensitive: adjusting one variable often disrupts the balance of others, requiring constant recalibration. This iterative cycle can feel like a frustrating game of "whack-a-mole," where solving one issue creates new ones.</p><figure id="figure-u54nfnrdijm5xaldlj"><img id="u54nfnrdijm5xaldlj" src="https://cdn.durable.co/blocks/214Q12WTgiQ64HKkFf6gtu6elZDK6LNtSxSYglWdrRXwT2QHJp8x1joT5GWo1Z99.png" alt="whack-a-mole" title="whack-a-mole" class="object-cover bg-gray-100 " style="aspect-ratio: 16/9; object-position: center center;" data-corners="default" data-aspect="16:9" data-caption="" data-positionx="undefined" data-positiony="undefined" data-fullwidth="false" data-mce-src="https://cdn.durable.co/blocks/214Q12WTgiQ64HKkFf6gtu6elZDK6LNtSxSYglWdrRXwT2QHJp8x1joT5GWo1Z99.png" data-mce-style="aspect-ratio: 16/9; object-position: center center;"></figure><h3>Why This Matters</h3><p>Traditional MMM methods are simply too slow and inefficient for fast-paced scenarios. For instance, if a media agency needs to create a small-scale pitch model to win a new client, the lengthy MMM process makes it impossible to meet tight deadlines.</p><p>As a result, many agencies opt to partner with freelancers for specialized expertise or focus on highlighting their strengths in creative capabilities and the experience of their teams in their pitch decks. While data-driven insights may not always be the focal point, agencies often incorporate carefully selected data visuals or dashboard screenshots to demonstrate their understanding of analytics and provide a comprehensive view of their approach.&nbsp;</p><h3>Conclusion</h3><p>The traditional way of building MMM is plagued by inefficiencies at every step—data collection, processing, validation, and modeling. For media agencies to truly embrace MMM, they need faster, more streamlined solutions that align with the time-sensitive nature of their work.</p><p>If this resonates with your challenges, why not chat with us at <a data-mce-href="https://morethandata.biz/" href="https://morethandata.biz/" target="_blank" rel="noopener"><strong>More Than Data</strong></a>? We’re here to help! Our solutions streamline the MMM process, addressing inefficiencies in data collection, processing, validation, and modeling. With our expertise, you can focus on delivering impactful insights without the headaches of traditional methods.</p><p>Let’s work together to make your data-driven marketing a reality—quickly and effectively. <a data-mce-href="https://morethandata.biz/contact-us" href="https://morethandata.biz/contact-us" target="_blank"><strong>Contact us today</strong></a>!</p>

Building MMM the Traditional Way: Why It’s So Time-Consuming

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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Building MMM the Traditional Way: Why It’s So Time-Consuming