Marketing Mix Modeling (MMM) is a statistical analysis method for evaluating the impact of marketing activities on sales and key performance indicators (KPIs). It helps you understand which elements of your marketing mix are most effective and optimize your marketing investments.

For more information about MMM : What is MMM (Marketing Mix Modeling)? Explaining its features, procedures, examples, etc.

In recent years, as many companies have introduced or are considering introducing MMM, the "transparency" of the analysis process has become a major concern. One common issue behind this is that the assumptions and details of the model are black boxes, making it difficult for management and marketing staff to understand the results because they cannot see the process or what is behind them.

A transparent MMM is not just a way to increase the reliability of analysis results, but also a key to refine your marketing strategy. In this article, we will explore the risks of a lack of transparency in MMM and best practices for ensuring transparency.

How much business risk does a lack of transparency create?

MMM has often been called a "black box" in the past. While it makes use of complex statistical techniques, it is difficult for non-experts to understand and verify the results, and decision-making often proceeds without clarity on "why a conclusion was reached." This lack of transparency has led to the following issues:

Decreasing decision-making accuracy

Lack of transparency will lead to doubts when using MMM results for decision-making. If the assumptions and processes behind the model are not clear, questions such as "To what extent did the investment in this campaign contribute to sales?" cannot be answered with confidence, and there is a risk that re-evaluation of investment allocation will be delayed or budgets will continue to be allocated to inefficient measures.

Declining trust from stakeholders

If MMM looks like a black box, it will affect the credibility of the project among stakeholders. If there is even the slightest doubt about whether the analysis results can be trusted to make decisions, the approval process and decision-making will be delayed, and proposals that utilize the analysis results will be rejected, increasing the risk.

Decreasing frequency of use

Lack of transparency can make it time-consuming to interpret complex results, and can reduce the frequency with which they can be used to improve strategies and programs. As a result, there is a risk that investments in MMM will not realize their full value and will have a low return on investment.

The Importance of Transparency at MMM

As such, many marketers who have tried to use MMM may have been skeptical due to its complexity. However, the transparency of MMM overcomes these barriers, making the entire process understandable for all stakeholders and building trust within the organization. Here are some examples:

Improved alignment with business goals

By ensuring transparency, it becomes clear how the analysis results are connected to business goals. This increases the consistency between marketing strategies and the company's business goals, allowing you to plan and execute effective marketing measures that lead to better business results.

Improved stakeholder engagement

The more transparent you are, the more your stakeholders will understand and agree with you. Also, by using visual data displays and concise explanations, you can effectively communicate your findings to non-technical stakeholders, which will increase their trust in your analysis and help you gain their cooperation.

Improved adaptability to market changes

A transparent model means a model that allows you to clearly understand how each factor affects the results. Because you can understand the structure, you can quickly determine the selection and incorporation of variables (for example, variables related to changes in competitors or consumer behavior) to reflect changes in market trends and obtain insights to respond immediately to changes in the market.

However, it is not enough to just pursue transparency. We must not forget that what is required in the field of marketing is "usable analysis." For example, it is important to consider the following points:

• Is it specific enough to be used for investment and other decision-making?
• Are there any suggestions that marketers in the field can use immediately?

Transparency is a vital element in building trust, but it should be premised on business-impacting analysis. It's not just about ensuring accountability, but also about helping stakeholders understand the results and take action based on them. Providing simple, clear insights will bring real value to the field, rather than spending a lot of time on complex reports and detailed technical explanations.

Key approaches to improving MMM transparency

Below are some specific approaches we are taking to increase transparency when operating MMM.

Building the right model

Transparency in model construction is a key factor in making MMMs trustworthy and understandable.

• Clear criteria and rationale for variable selection
• Identifying appropriate data collection methods and reliable sources
• Description of statistical techniques and algorithms used
• Rigorous model validation and proper testing procedures

For those who want to know more about statistical modeling techniques in marketing:List of statistical models in marketing: overview, usage, issues and requirements explained in an easy-to-understand manner (no equations)

Proper Data Management

The transparency and reliability of MMM also depends on the quality and handling of the data used. It is a good idea to pay attention to the following points.

• Explain the data pre-processing, cleansing and transformation process
• Dealing with missing values ​​and outliers (mean imputation, outlier trimming, etc.)
• Explanation of limitations and restrictions on the data and how potential biases may affect the results of the analysis

For more information on data integration and consistency:Introduction to Data Modeling | Difficult technical terms explained in an easy-to-understand way

Understand the model's assumptions and limitations

It is also important to appreciate the inherent characteristics and limitations of the model.

• Explanation of the underlying assumptions of the model
• Understand the specific advantages and disadvantages of your chosen approach
• Identifying areas where the model accuracy is limited or uncertain (e.g. R2 score, prediction error)

Making the results easier to interpret

It is important to understand the somewhat specialized areas mentioned above, but in order to utilize the results, it is also important to devise and share complex analysis results in a way that makes them easy to interpret.

• Aligning objectives, issues, hypotheses, etc. that are the premise of the analysis
• Use of visually easy-to-understand graphs and charts
• Avoid technical jargon and use simple, easy-to-understand language

Here are some data analysis basics to remember:[Data Analysis from Scratch 1] "8 steps of analysis" that beginners in data analysis should know first

For those who want to learn basic knowledge of data visualization:Basics of data visualization and characteristics of each graph

Challenges in ensuring MMM transparency

While these approaches promise to increase transparency in MMM, they also pose challenges that must be overcome. Some common challenges include:

Difficulty in understanding due to lack of specialized knowledge

Improving the transparency of MMM may require basic knowledge of statistical models and data analysis. However, as this area of ​​data science is not something that many marketers deal with on a daily basis, they often find it difficult to fully understand the analysis results and model structures. This can lead to marketers being unable to trust the results and use them in their decision-making.

Workaround

• Basic training:
  We provide training on the basics of data science and MMM. It is more effective to use e-learning and workshops that can be understood in a short time.
• Q&A session:
  Model creators and data analysts will hold regular sessions to answer marketers' questions, using concrete examples to help them deepen their understanding.

Time and resource constraints

Increasing transparency can require significant resources, such as model validation and detailed explanatory materials, but this may not be feasible in projects where results must be achieved quickly or where resources are limited.

Workaround

• Clarifying priorities:
  Identify the most important items for transparency early in the project (e.g., model assumptions, data sources, etc.) and focus your time and resources there.
• Improving work efficiency through partial automation:
  Utilize tools that automate the processes of report creation and data preparation (BI tools and data processing tools) to streamline the necessary work.

Communication gap with management

If the results of MMM analysis are not sufficiently transparent, there is a risk of communication gaps between the marketing department and management. In particular, if the intent of the analysis and how the results relate to business goals are not clear, there is a risk that the importance of the analysis results will be overlooked.

Workaround

• Emphasize relevance to business goals:
  Clearly explain how the results of MMM specifically tie to business objectives (e.g., increased sales, reduced costs) Demonstrate results using metrics that management cares about, such as ROI and KPIs.
• Providing decision-making simulation:
  The system simulates multiple budget allocation scenarios based on the analysis results of MMM and presents specific options that are useful for decision-making. It is more effective if the content is made intuitively understandable by eliminating technical terms.

Lack of control due to vendor dependency

When outsourcing MMM to external solution providers or consultants, companies may have limited control over the construction and management of the model, which may result in insufficient information being provided to ensure transparency of the analysis, and as a result there is a risk of trust being lost within the company.

Workaround

• Clarify vendor selection criteria:
  Choose a vendor with a policy of transparency, including whether they are willing to explain in detail the logic and assumptions behind their model.
• Internal talent development:
  In the long term, we will develop the talent within our team to be able to operate MMM in-house, thereby reducing our dependency on vendors.

In conclusion

As companies continue to seek more sophisticated ways to understand their marketing effectiveness, the demand for clear, understandable analytical models will only increase. The future of marketing analytics lies not in opaque algorithms, but in open, explainable, and adaptable methods.

On the other hand, while it is important to pursue transparency in MMM, it is not an end in itself, and the analysis results must actually be useful for management decision-making and on-site strategies. Ultimately, what is important is to provide clear, specific insights based on transparency and to have a real impact on business.

XICA's MMM solution, MAGELLAN, is designed to ensure transparency. By disclosing information through a collaborative process with clients in model design, providing detailed reports and actionable insights, XICA helps marketers make decisions with confidence.

For more information about MMM Solution MAGELLAN, here.Or if you would like to contact XICA, here.please use.