What is MMM (Marketing Mix Modeling)? A comprehensive summary of the basics, features, analysis procedures, implementation points, and points to note.

"Was this really the optimal allocation for this year's marketing budget?"
Which measures really contributed to the final sales?
"What evidence do I need to predict next year's results and confidently request a budget?"

This is a question that many marketing managers and executives constantly ask themselves.

Marketing Mix Modeling (MMM) is a powerful analytical approach that uses data as an objective basis to derive answers to these fundamental business questions.

In this article, we will explain the essential points, from the basics of what MMM is, to why so many companies are paying attention to it now, and how the insights gained from its analysis can be used to grow your business.

[Quick Reference] Marketing Mix Modeling (MMM) in 30 seconds

This article provides a condensed overview of the "MMM" series. Please use it as a guide before continuing reading.

The three phases that you should especially keep in mind in this article

MMM is not just a calculation, but a way of thinking that solves business problems through the following process.

• 1. design: Formulate a hypothesis and draw up a blueprint for your analysis (this is the most important step)
• 2. Execution: Structuring data and building statistical models.
• 3. Improvement: Interact with the model and incorporate on-site knowledge to improve accuracy

To further deepen our understanding and put it into practice

This article will explain the details, but if you would like more specific examples and practical guides to keep at hand, please also use the free materials below.

What is MMM (Marketing Mix Modeling)? An easy-to-understand explanation of the basics

MMM is a method of statistically analyzing and visualizing the contribution of various marketing initiatives, such as television commercials, digital advertising, sales activities, and promotions, to business results (KGI) such as sales and conversions.

To put it in perspective, it's like a company's "health checkup of business results." Just as a health checkup looks for "causes" such as diet and exercise habits from "results" such as weight and blood pressure, MMM quantitatively analyzes the effects of each component measure from the result of "sales."

MMM's characteristics: Breaking down the "structure" of results

The true value of MMM is that it can break down and visualize the complex structure of business results. MMM breaks down results into two main components:

• Base Effect:These results are not directly related to marketing strategies. They are the "fundamental strength" of a business, such as the brand power that has been built up over many years, store locations, and seasonality.
• Incremental Effects: These are the results added by the marketing measures implemented. MMM further breaks this down into each measure, clarifying how much each measure contributed (direct effect) and how measures influenced each other (indirect effect/synergistic effect).

Another major feature is the ability to isolate and analyze the impact of "external factors" that are beyond a company's control. By statistically isolating the impact of factors such as large-scale campaigns by competitors, economic fluctuations, and weather, the "pure effect" of marketing initiatives can be more accurately evaluated.

By breaking down the structure of results in this way, you can accurately understand the ROI (return on investment) of each initiative and make data-driven decisions.

Three reasons why MMM is gaining attention: cookieless, offline integration, and management perspective

MMM is by no means a new concept. It has long been used by large corporations in the United States, a country with a strong marketing leadership. However, in recent years, it has rapidly gained attention in Japan as well. Behind this is three major, unavoidable changes facing the modern business environment.

1. The Cookieless Era is Here

Global trends in personal information protectionAs a result, the use of cookies, which had previously been the mainstream method for measuring the effectiveness of online advertising, is being gradually restricted. As traditional methods of tracking the behavior of individual users become more difficult, MMM is gaining renewed attention as a sustainable method for measuring effectiveness that respects privacy because it does not rely on cookie information but instead uses macro data (aggregated data that does not identify individuals) such as the amount of advertising and sales of each campaign.

2. The need for overall optimization, including offline measures

For many Japanese companies, especially large ones, offline measures such as television commercials and transit advertising continue to play an important role. However, it has been difficult to measure their effectiveness using the same standards as digital advertising. MMM allows the effectiveness of these offline measures to be evaluated alongside digital measures in terms of "contribution to sales," thereby enabling optimal allocation of the overall marketing budget.

3. Accountability for marketing investments from a management perspective

As market uncertainty increases, management is increasingly viewing marketing not simply as a cost, but as an "investment" for business growth. Marketers are being asked to provide objective data to answer the question, "How much return will this investment generate?" MMM is a powerful tool for clearly showing the rationale for budget allocation and building a common language with management.

What you can learn and do with MMM: From visualizing contributions to optimal budget allocation

What specifically will be possible by introducing MMM? Its range of applications is wide-ranging, from evaluating the past to predicting the future.

What you can learn ①: The pure contribution of each measure (visualization of ROI)

We calculate how much each marketing initiative contributed to sales on a monetary basis. You can accurately grasp the ROI of each initiative, such as "TV commercials contributed XX billion yen, and web advertising contributed △△ billion yen in sales." This makes it clear which initiatives are highly effective and which are less effective.

What we can learn ②: The impact of external factors

You can numerically understand the impact that competitors' advertising, seasonal trends, weather, etc. have on your company's sales. This allows you to conduct analyses such as "during the week that competitor A increased its advertising, our company's sales decreased by XX%," which is useful for planning strategies that respond to changes in the market environment.

What you can do ①: Simulate optimal marketing budget allocation

Using analytical models, you can run simulations such as, "If the budget for next fiscal year is 10 billion yen, how should we allocate it to which channels to maximize sales?" You can create optimal budget allocation plans based on data, rather than relying on past experience or intuition.

What you can do ②: Improve the accuracy of your business plans

The relationship between marketing investment and expected sales returns becomes clear, enabling more accurate sales forecasts and business plans to be developed.

Free download of case study

What is "MMM" that all marketers should know about?
~ Benefits of Implementation: Insights from Three Company Case Studies ~

Download Now

Changes brought about by MMM in organizations: A "common language" that connects management to the field

MMM doesn't end with just looking at an analytical report. Its value is realized when the insights gained are used in decision-making at all levels of the organization. The data presented by MMM becomes a "common language" that transcends departments and positions, helping to improve the accuracy of marketing activities across the entire organization.

Here, we will look at how MMM can be used at each level.

Management: Supporting data-driven investment decisions

For management, MMM serves as a basis for making decisions when steering the business.

• Use as a "business simulator"
  MMM's model functions as a simulator for predicting business results. It can calculate scenarios such as "How will sales fluctuate if the marketing budget is increased by 10% next fiscal year?" or "How will ROI change if the TV commercial budget is shifted to online advertising?" before they are executed. This is useful for considering more accurate budget allocation based on past data.
• Business portfolio review
  For companies with multiple businesses and brands, applying MMM can help with complex decision-making, such as which business to invest limited management resources in to most likely lead to growth for the entire company.

Middle-level (department heads and managers): Clarify the results of team activities

For middle-level managers and department heads, MMM can be a useful tool for leading teams and improving results in their areas of responsibility.

• Visualize the contribution of your area of responsibility
  MMM clarifies the overall marketing structure, allowing you to objectively understand how the channels and measures your team is responsible for ultimately contribute to sales. It allows you to provide specific explanations, such as "our team's activities moved awareness indicators by this much, and as a result contributed this much to sales," which is effective when quantitatively reporting the results of your team's activities.
• Data-driven team management
  MMM's analysis results are useful for aligning team members' goals (KPIs) with business results. For example, if the data shows that an increase in branded searches contributes to sales, you can set that as one of your team's KPIs. This makes it easier for team members to recognize the connection between their daily work and business results.
• Promoting cross-departmental collaboration
  MMM data functions as an objective common language when negotiating budgets or collaborating with other departments. For example, the advertising manager can now have a data-based conversation with the sales manager, saying, "We predict that this commercial placement will have this much impact on the conversion rate three weeks from now," which is expected to lead to smoother collaboration between departments.

Execution team (field staff): Increase the effectiveness of daily operations

For those responsible for implementing measures on a daily basis in the field, MMM is a useful tool for confirming the value of their own work and improving the accuracy of improvement actions.

• Lead to creative and tactical improvements
  "Creative content that appeals to A tends to be more effective at sending visitors to websites than content that appeals to B," "Press releases released at this time are more likely to spread on social media." The feedback obtained from MMM serves as a basis for considering specific daily actions, such as improving advertising creative, reviewing media plans, and timing social media posts. Trial and error turns into "data-based improvements," leading to increased effectiveness of measures.
• Cultivate a sense of satisfaction with your work
  By showing the strategic intentions behind why a certain measure is being implemented through data, it becomes easier for the person in charge to understand how their work contributes to the larger goal. Understanding the connection, such as "clicks on this banner ad contribute to a portion of the company's sales," leads to a sense of satisfaction in their daily work.
• Developing a perspective for proposing improvements based on data
  Exposure to the MMM approach encourages managers to put data-driven thinking into practice. It fosters a culture of formulating hypotheses and proposing improvements, such as, "According to the model, this measure has residual effects. Therefore, perhaps implementing additional measures at this timing would increase the effectiveness." This not only contributes to the growth of individuals, but also to improving the marketing capabilities of the entire organization.

For more information on why we introduced MMM, please see ourMMM Service MAGELLANPlease take a look at our case study interviews where our clients speak about the service.

Successful cases of MMM analysis and interviews on the introduction of MMM solutions

Three Key Points for Successful MMM Implementation: Data, Expertise, and Organizational Structure

MMM is a powerful technique, but there are several key elements to its successful implementation and use.

Point 1: Collecting and organizing high-quality data

The accuracy of MMM's analysis is directly related to the quality of the data you input. At least two to three years' worth of weekly or monthly data is required.

• Internal data:Sales, advertising volume and costs (all channels including TV, digital, newspapers), promotion information, price data, etc.
• External Data:Competitor advertising data, seasonal indexes, market trend data, etc.

This data is often scattered across various departments within a company, so even when a specialized company like XICA provides support, they usually start by collecting and organizing data across the entire company.

Point 2: Expertise in statistical modeling

MMM analysis requires advanced expertise in statistics and econometrics. Failure to properly incorporate complex factors such as correlations between campaigns and delays and saturation of advertising effects into the model could lead to erroneous analysis results. Because the analysis results are directly linked to business decision-making, collaboration with trusted experts and partners is essential.

Point 3: Organizational structure that translates analysis results into action

There's no point in just creating an analysis report and leaving it at that. Based on the suggestions gained from MMM, the next budget allocation and marketing strategy should be formulated and implemented. The results should then be accumulated as data and the analysis model updated. The most important thing is to build a culture and system that will keep this "analysis → execution → evaluation" cycle going as an organization.

Advantages and Cautions of MMM

Here, let's summarize the benefits of introducing MMM and the points to be aware of in advance.

Benefit

• Overall optimization:All marketing initiatives, whether online or offline, can be evaluated using the same indicator (sales contribution) to optimize budget allocation.
• Objectivity:Objective evaluation based on data makes it easier to reach consensus within the company and explain the results to management.
• Predictive ability:It allows you to simulate the sales impact of future budget allocations, enabling strategic decision-making.
• Cookieless support:Since it does not rely on personal information, it can respond to the trend toward privacy protection.

Points to note (disadvantages)

• Data preparation load:Analysis requires a wide variety of data over a long period of time, and collecting and organizing this data can be time-consuming and costly.
• Limitations of short-term policy evaluation:Because the analysis is done on daily, weekly, or monthly data, it is difficult to get a detailed picture of the effects of shorter-term measures such as daily creative improvements and A/B testing.
• Expertise required:Building and operating highly accurate analytical models requires advanced statistical knowledge.

MMM is not a panacea. It is important to use it appropriately in conjunction with other analytical methods, such as using digital tools for short-term policy evaluations and MMM for medium- to long-term strategic decision-making.

Three Phases of MMM Analysis: Organizing Procedures and Thinking

MMM analysis is not just a data processing task. It is a "thought process" for solving business problems. Here, we will explain this process by dividing it into three strategic phases: (1) Design, (2) Execution, and (3) Improvement.

Phase 1 "Design": Draw a "blueprint" for your analysis

Before starting the analysis, it is most important to draw up a detailed blueprint of what you want to clarify. In this phase, it is important to involve not only analysts but also other stakeholders such as marketing, sales, and management from an early stage.

1. Define the purpose and hypotheses of your analysis

First, clearly define the objective of the analysis, such as "maximizing the number of new customers acquired" or "increasing sales." Next, create a hypothesis about the relationship between factors that will lead to achieving that objective. For example, a hypothesis such as "increasing investment in television commercials will improve brand awareness, resulting in an increase in new customers" will form the framework of the analysis.

There are various approaches to MMM's analytical logic, but this approach of defining a causal hypothesis in advance, such as "which measures lead to which indicators and ultimately to results," is extremely important in linking the analysis results to concrete actions. This hypothesis-driven approach is what we at XICAMMM service called "MAGELLAN"It is also the core of the analytical thinking we practice.

2. Identify the influencing "internal and external factors"

Next, based on the defined objectives and hypotheses, we comprehensively identify all factors that affect the results of the analysis.

• Internal factors:These are factors that can be controlled by the company (e.g., advertising expenses for each media, product prices, and promotion implementation status).
  What's particularly important here is to identify the measures at a granularity that matches the purpose of the analysis. For example, if you are running multiple appeal axes and campaigns in TV commercials and your goal is to visualize the effects of each appeal axis, you need to identify the measures at the granularity of "TV commercials with appeal axis A" and "TV commercials with appeal axis B" rather than at the granularity of "TV commercials."
• External factors:These are factors outside your control (e.g., competitor activity, macroeconomic indicators, seasonality, market trends).
  This refers to factors that are beyond the company's control but have a significant impact on results. For example, whether a competitor's TV commercial is broadcast or not cannot be controlled, but if it is thought to affect the results of the product or service being analyzed, then the "competitor's TV commercial" should be identified as an external factor.

Interviews with field staff and department managers are useful in this step. By incorporating on-site knowledge that cannot be seen from data alone, such as "Actually, a competitor was running a major campaign around this time" or "Sales of this product are affected by certain weather conditions," you can incorporate variables that are often overlooked into the model and improve the accuracy of your analysis. A good blueprint is one that brings together the knowledge of the entire organization.

3. "Model" customer purchasing behavior

Finally, model the process a customer goes through from learning about a product to making a purchase. The famous "AIDMA" model can be used as a reference, but the important thing is to build a model that suits your company's products and services. Link each of the marketing measures you hypothesized above to each stage of this model (e.g., awareness, interest, comparison, purchase). This will visualize the relationship between which measures affect which stage.

This meticulous blueprint determines the quality of the subsequent analysis.

Phase 2 "Execution": Structuring the data and building a model

Once the blueprint is complete, the next step is the execution phase, where data is collected based on the blueprint and an analytical model is built.

4. Collect and organize data

We collect past data for all identified factors (at least one year's worth is required if seasonal fluctuations are to be taken into account). We collect data from various sources, such as internal databases and advertising platforms, and integrate and organize it into an analyzable format.

5. Analyze with statistical modeling

Using the collected and organized data, we build a model that statistically verifies the hypotheses (analysis logic) established in the design phase. This step requires specialized knowledge, such as taking into account marketing-specific phenomena such as delays and saturation of advertising effects.

Phase 3 "Improvement": Interact with the model to improve accuracy

The initial analysis results are just the starting point. We continually improve the accuracy of the model by comparing the results with business reality.

6. Verify and improve analytical accuracy

To improve the accuracy of analytical results, it is important to always question and interact with the model.

• Data quality:We always ensure the quality of the data by checking for missing or noisy data.
• Factors to consider:If the influence of external factors that were not initially anticipated is discovered, the model is rebuilt by adding new data on them.
• Cooperation with experts:Because this requires advanced statistical knowledge, it is effective to collaborate with experts to objectively evaluate the validity of the model.

By repeating this process, the analysis becomes more realistic and produces powerful implications for decision-making.

MMM's limitations and areas where it is not strong

MMM is a powerful strategic tool, but it is not a panacea. Its value can be maximized by properly understanding its limitations and combining it with other analytical techniques.

It cannot measure the "quality" of creatives. MMM can show that "the ROI of campaign A was higher than B," but it cannot directly prove that this was because "the creative message was superior." It is merely a tool to measure the effectiveness of media investment, and is not a tool to evaluate the quality of the creative itself.

• Not suitable for real-time tactics
  MMM is a strategic "telescope" that uses macro historical data from several months to several years. Its role is different from that of a tactical "microscope" (e.g., various ad operation dashboards) that is used for real-time optimization such as daily bid adjustments. It is used to determine quarterly and annual budget allocation, and daily improvements are made with a separate tool.
• It is difficult to predict the effectiveness of completely new measures
  Because models are built based on past data, it is difficult to accurately predict the effectiveness of completely new initiatives that have never been implemented before (e.g., running TikTok ads in Japan for the first time). MMM excels at finding the "optimal combination" of existing activities.
• Doesn't track individual customer behavior
  MMM analyzes macro aggregate data such as overall advertising expenses and sales. Therefore, it cannot track individual behavioral history, such as "Mr. A saw a TV commercial, then performed a personalized search and made a purchase." This is a major difference from MTA (multi-touch attribution), which tracks the behavior of individual customers, and is also one of MMM's strengths in the cookie-less era.

Conclusion: Creating the future of marketing through data-driven decision-making

In this article, we have explained MMM, from its basics to the background to its popularity and specific ways to use it in organizations.

In today's increasingly complex marketing environment, accurately understanding the contribution of each measure to results is an important challenge for business growth. MMM is one effective approach to addressing this challenge. Finally, let's review the main points of this article.

• Visualizing the "structure" of results
  MMM reveals the "pure effect" of marketing activities by breaking down the additional effect of measures (incremental effect) from basic sales (base effect), and also by separating the influence of external factors.
• Improving the "accuracy" of future investment decisions
  By using the analytical model as a simulator, it is possible to predict "how much investment will be needed in which measures and what results can be expected." This enables strategic budget allocation based on data, rather than relying on intuition or rules of thumb.
• Creating a common language for the organization
  The objective data provided by MMM supports decision-making at different levels, such as management, middle management, and field personnel, and serves as a "common language" for the entire organization to work toward the same goal.

If your company is still stuck with suboptimal policy evaluations and budget allocations based on past practices, now may be the time to consider introducing MMM. Taking a firm step based on data will be the driving force that propels your business to the next stage.

If you want to learn more about MMM, check out our comprehensive guide to what marketers need to understand and pay attention to in order to make their MMM projects successful and lead to real action and contributions to their companies.MMM Practical Guide .

For those looking for a partner to successfully implement and operate MMM

As explained in this article, MMM is a powerful analytical method, but its success requires advanced statistical knowledge and continuous data preparation and model improvement.

XICA is a group of professionals who combine data science and consulting to support not only the production of analytical results but also the implementation that leads to results.

MAGELLAN, the MMM solution with the most implementation record in Japan

MAGELLAN, the MMM analysis platform provided by XICA, is not just an analysis tool. It is an MMM service that allows you to carry out analysis based on the "hypothesis thinking" that we have emphasized in this article.

Please contact us first to find out how MMM can be used to address your company's challenges.

We can also provide advice even at a stage when you don't have all the data or you don't know which analysis to start with.

FAQ: Frequently Asked Questions about MMM (Marketing Mix Modeling)

The following FAQ focuses on points that marketing managers and field staff who have read the article will be particularly concerned about when applying the article to their work. The main points are explained briefly but at a level that can be used in practice.

Basic

Q1: In one sentence, please tell us what you can learn from MMM.
A: It quantifies the incremental contribution of marketing measures (TV, digital, sales promotion, etc.) to business results such as sales, and shows the interactions between measures and external factors separately. It's like creating a "business simulator" that can be used for management decisions and budget simulations.

Q2 Who is MMM analysis for?
A: It can be used for a wide range of purposes, from investment decisions by management, budget allocation and KPI design by department heads and managers, to tactical improvements on the front lines. However, it is not good at precise optimization (individual recommendations, etc.) that follows the behavior of individual users (customers).

Q3: How is MMM different from consumer surveys and MTA (contact-based attribution)?
A: Consumer surveys measure "changes in psychology and attitudes," while MTA tracks the causality of individual contact. MMM evaluates contributions to the final outcome of sales from a macro perspective across channels. Because they have different roles, it is realistic to use them in a complementary manner. In the cookie-less era, the measurement scope of MTA is limited, so the strategic value of MMM, which can evaluate offline and online in an integrated manner, is increasing.

Data and Practice

Q4 What kind of data do you need?
A: Mainly (1) the amount and cost of measures invested (TV advertising GRP, advertising expenses, advertising volume, etc.), (2) performance data (sales and number of contracts), and (3) external factors (price, promotions, competition, seasonality, weather, etc.). Data is prepared on a daily, weekly, or monthly basis. The granularity is determined according to the purpose of the analysis (e.g., if you want to know by appeal axis, prepare advertising data for each appeal axis).

Q5 Can this be done even if the data is incomplete?
A: It is possible, but the accuracy will be reduced. The important thing is to understand missing data and aggregation rules. First, you need to take stock of data availability and design a system to supplement it with alternative indicators or external data.

Q6 Is individual-level (user-identifying) data necessary?
A: No. MMM works on macro (aggregated) data, which makes it less susceptible to privacy regulations and cookie restrictions.

Q7 What if there is a "lag" or "carryover" in the effects of the measures?
A: We incorporate lag and decay into the model. For example, television tends to have a mixture of immediate and residual effects, so we express these in mathematical formulas and separate them.

Model Design and Interpretation

Q8 Are the results of MMM the “absolute truth”?
A: No. A model is a "quantification of a hypothesis." Conscientious operation involves always presenting contributions and optimization proposals with uncertainty (error rate, confidence interval, and sensitivity analysis), verifying them with small verifications (experiments) before implementation, and fine-tuning the model based on any discrepancies found.

Q9 Can you measure synergy?
A: Yes, it can be measured. By incorporating interaction terms and path analysis structures, it is possible to numerically show cases where one channel increases the effectiveness of another channel (for example, TV increases the number of web searches, which increases the effectiveness of advertising there).

Q10 How do you evaluate the accuracy of the model?
A: We evaluate the contribution of explanatory variables, holdout/backtesting (prediction accuracy), sensitivity analysis (how results change when external factors are added or removed), etc. What's important is not just accuracy, but also whether it can be used in business (interpretability and causal validity).

Q11 Is it possible to view multiple products or regions at the same time?
A: It is possible, but looking at them simultaneously will make the model complicated. Depending on the scale and purpose, choose between an "individual model (by product)" or a "hierarchical model (with regions and products as layers)." Clarify business priorities at the design stage.

Utilization and Decision-Making

Q12 How can MMM's reports be presented in a way that will impress management?
A: It will be more persuasive if you show a set of (1) "Incremental sales (amount) by measure," (2) "Return on investment (ROAS/ROMI or CPA/cost per acquisition)," (3) "Scenario/simulation (change in sales when budget is changed)," and (4) "Uncertainty (prediction accuracy rate, confidence interval)." In addition to the numbers, be sure to include "what should be changed in the decision (action)."

Q13 Will the results of MMM be the answer to how to allocate the budget?
A: MMM provides powerful suggestions, but the final decision must be made taking into account organizational issues (contractual constraints, brand strategy, sales channel constraints). MMM is a "material for decision-making," and the "final decision" should be made in a business context.

Q14 Which do you prioritize: MMM or experiments (A/B and geographic targeting)?
A: They are complementary. MMM shows global optimization and channel interactions, while experiments more strictly verify specific hypotheses (causality). Confirming with small-scale experiments before making important investment decisions reduces the execution risk.

Limitations and pitfalls

Q15 What are common failure patterns?
A: Typical examples are (1) data that is too rough to perform meaningful analysis, (2) overestimating the effectiveness of measures without taking into account external factors, (3) blindly believing in the model without verifying it after implementation, and (4) proceeding with analysis without reaching consensus with stakeholders. It is important to create governance and verification plans in advance.

Q16 Is multi-collinearity (strong correlation between explanatory variables) a problem?
A: Yes, it is a problem. Channels with similar advertising patterns (e.g., multiple digital campaigns launched simultaneously) make it difficult to determine the contribution of each. You need to combine variables, change the granularity of your data, or separate them using statistical regularization or domain knowledge.

Q17 Can I not use MMM for the first time for a new product or campaign?
A: It's not completely unusable, but there is a lot of uncertainty. It's best to supplement it with the history of similar products, external benchmarks, or through experimentation.

Introduction decision/operation edition

Q18 Who should be the owner within the company?
A: Ideally, the marketing analysis team or data science department should take the lead, working closely with finance, sales, and field marketing (or the person in charge of the marketing organization if outsourcing). To trust and use the results, it is essential that all stakeholders have a common language.

Q19 What are the key points to continue doing after implementation?
A: Monitoring results, periodically reevaluating the model (re-learning if there are changes in the external environment or policies), and looping through execution and verification (confirming the model's suggestions through small experiments).

Q20 What reporting items must be checked when outputting MMM?
A: 1) Incremental sales (by channel), 2) ROAS/CPA estimates, 3) Breakdown of base sales and incremental sales, 4) Residual effects and synergistic effects, 5) Model prediction accuracy/error rate, 6) Simulation results (multiple scenarios including optimal budget allocation, etc.).

Q21 Is it okay to just look at the return on investment (ROI)?
A: ROI is important, but brand strategy and long-term fundamentals (base sales) also need to be evaluated. Judging solely on short-term ROI can damage long-term brand value. XICA's MMM service, "MAGELLAN," has the following features:Brand equity analysis (medium- to long-term brand accumulation effect)can be visualized.

↓ Click here for a list of carefully selected articles to deepen your understanding of MMM

• What is MMM (Marketing Mix Modeling)? Explaining its features, procedures, examples, etc.
• Why you should use Marketing Mix Modeling (MMM) to measure the effectiveness of your advertising
• 10 Common Misconceptions About Marketing Mix Modeling (MMM)
• What are the limitations and areas in which MMM (Marketing Mix Modeling) does not excel?
• Latest Trends and Future Outlook of MMM (Marketing Mix Modeling) (2024 Edition)）
• “Modern MMM (Marketing Mix Modeling)” Practical Guide for Marketers

Author information

XICA Co., Ltd. Marketing Team