Why Traditional SaaS Solutions for Marketing Mix Modelling Have Limitations

In our previous article, Why Small and Medium-Sized Media Agencies Haven’t Used MMM, 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.

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.

Limited Customization

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.

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.

In our previous article, Building MMM the Traditional Way: Why It’s So Time-Consuming, 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.

Hidden Backend Processes

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.

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.

Data Integration Challenges

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.

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.

Self-Service Tools Are Too Complicated

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.

Steep Learning Curve

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.

We browsed through customer and user feedback on MASS Analytics, 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.

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.

Limited Support and Guidance

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.

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.

The Expense of Current SaaS Solutions

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.

Subscription Fees and Hidden Costs

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.

Resource Allocation

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.

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.

Conclusion: Rethinking the Approach to MMM

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.

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.

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.

If you're looking for an easy-to-use, customizable, DIY-enabled, and cost-efficient solution for MMM, please contact us!