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6 best practices for Marketing Mix Modeling

Understanding Marketing Mix Modeling is essential for businesses as it enables data-driven decision-making, effective resource allocation, and marketing performance measurement.

MMM models guide strategic planning, provide a competitive advantage, and enhance market responsiveness. Furthermore, they offer valuable customer insights, optimize return on investment (ROI), assist in risk management, and support continuous improvement in marketing strategies.


The importance of understanding Marketing Mix Modeling

Marketing professionals face the challenge of measuring the impact of a wide variety of channels on business outcomes. This is especially complicated in strategies that span both online and offline channels. While measurement is relatively straightforward for digital media, such as "last-click," conventional models are limited and do not provide a holistic view or track offline activities.

To address this complexity, Marketing Mix Modeling (MMM) employs advanced statistics to analyze how different marketing strategies, both online and offline, affect a company's sales. MMM becomes an essential tool for making informed marketing decisions.

At Adsmurai, we have developed new attribution technology aimed at revolutionizing how companies measure the effectiveness of their advertising campaigns across multiple channels and specific advertising platforms. By incorporating Attribution into the Adsmurai Marketing Platform and complementing it with the expertise of data analysts, companies can gain a deeper understanding of their advertising performance and optimize their marketing strategies accordingly.


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Understanding these models is of vital importance because they provide a series of key benefits for marketing professionals and businesses in general:

  • Resource optimization: Understanding Marketing Mix Modeling models allows companies to allocate their marketing resources more efficiently. They can identify which channels and strategies have the greatest impact on sales and focus their investments accordingly.

  • Informed decision-making: These models provide a more comprehensive view of how marketing actions affect business outcomes. This enables making more informed decisions about which strategies to pursue and what adjustments to make.

  • Performance evaluation: MMM models offer objective metrics for evaluating the performance of marketing campaigns. Instead of relying on assumptions or perceptions, companies can base their assessments on concrete data.

  • More accurate attribution: Instead of relying on a simplified attribution model, such as "last click," MMM provides more accurate and comprehensive attribution, taking into account multiple channels and factors.

  • Strategic planning: Insights obtained through these models can inform long-term strategic planning. Companies can adjust their marketing strategies based on historical results and projections based on MMM.

  • Measuring global impact: MMM is not limited to digital channels; it also considers the impact of offline marketing activities, providing a more complete and accurate overall view.

  • Adaptation to changes: Since these models are flexible and can be updated with new data, companies can quickly adapt to changes in the market and consumer behavior.

  • Continuous improvement: Understanding MMM models encourages a continuous improvement approach to marketing strategies. Companies can identify areas of opportunity and adjust their tactics to achieve better results over time.


Best Practice #1 Establishing clear objectives

Setting clear objectives is a fundamental step in the Marketing Mix Modeling (MMM) process and is crucial for its success. Without well-defined objectives, it becomes difficult to determine what should be measured and how to interpret the results. Clear objectives serve as a roadmap that guides the entire MMM process. They help clarify the specific questions that modeling should address, such as which marketing channels are most effective, how they impact sales, and where to allocate resources for optimal ROI.

Once objectives are established, it is essential to define specific goals and metrics to measure the success of MMM efforts. Goals should be specific, measurable, achievable, relevant, and time-bound (SMART). For example, a goal could be to increase total revenue by 10% in one year through marketing mix optimization. Metrics may include key performance indicators (KPIs) such as return on investment (ROI), customer acquisition cost (CAC), or market share growth. Having well-defined goals and metrics ensures that the MMM process is focused, and results can be quantified, enabling data-driven decision-making.

The objectives and goals set for MMM should be in perfect alignment with the overall business strategy and objectives. MMM is a powerful tool to support strategic decision-making in marketing, so it is essential that the insights gained contribute to broader organizational goals. For example, if a company's strategic objective is to expand into a new market segment, MMM can help determine the most effective marketing channels and tactics to achieve this expansion efficiently. When objectives are synchronized with the business strategy, MMM becomes a valuable tool for driving growth and the company's success.


Best Practice #2 Collecting high-quality data

Collecting high-quality data is fundamental to the success of Marketing Mix Modeling (MMM) because the accuracy and reliability of the data directly impact the insights and decisions derived from the model. High-quality data ensures that the relationships between various marketing variables and sales are accurately represented, enabling companies to make informed decisions about resource allocation, strategy optimization, and performance evaluation. Without reliable data, MMM results can be flawed, leading to misguided marketing efforts and inefficient resource allocation.

Marketing Mix Modeling requires a variety of data types to comprehensively analyze the impact of marketing activities on sales. These data types include:

  • Conversion data: Information about the primary campaign goal, such as sales data, leads, etc. Having these data is crucial for accurately measuring the impact of marketing actions.

  • Advertising spend data: Information about how much money you have spent on different marketing channels and actions, such as billboard advertising, browser ads, social media campaigns, etc.

  • Relevant market data: External factors that can affect sales, such as economic indicators, competition, and consumer trends. This type of data helps understand the overall market environment and how it impacts your sales.

  • Brand context data: Information about the brand's context, including events, promotions, discount campaigns, store counts, etc.

  • Experimental data: Information that allows model calibration and improvement. Examples include Geolifts or Conversion Lifts, which help train the model and enhance its accuracy.

To ensure that the data used for MMM is of the highest quality, several best practices should be followed:

  • Accuracy: Verify the accuracy of data sources and perform regular data quality checks to identify and correct errors or inconsistencies.

  • Consistency: Ensure data consistency by using standardized formats and definitions across all relevant data sources.

  • Granularity: Collect data at an appropriate level of detail to capture meaningful insights. Avoid premature data aggregation in the process.

  • Integration: Integrate data from various sources to create a unified dataset that reflects all relevant marketing and sales factors.

  • Cleansing: Use data cleansing techniques to remove outliers, duplicates, and missing values.

  • Temporal alignment: Ensure that all data points are correctly aligned in time, as MMM heavily relies on time series data.

  • Documentation: Maintain detailed documentation of data sources, transformations, and assumptions made during the data preparation process.


Best Practice #3 Choosing the appropriate modeling technique

There are various modeling techniques available for Marketing Mix Modeling (MMM), each with its own strengths and suitability for different scenarios. Some of the modeling techniques commonly used in MMM include: Machine Learning, Regression, Time Series Analysis, Bayesian Analysis, Multichannel Attribution Modeling, Econometric Modeling, etc.

When selecting a modeling technique for MMM, several factors should be taken into account:

  1. Data complexity: Consider the complexity of your data. Linear regression may suffice for simple relationships, but complex datasets with non-linear patterns may require more advanced techniques like machine learning models.

  2. Model interpretability: Think about the level of interpretability required for your business. Linear regression models are more straightforward to interpret, making them suitable when model transparency and understanding of drivers are essential.

  3. Data availability: Evaluate data availability. Some modeling techniques may require specific types of data, and if those data are not readily available, it may limit your options.

  4. Resource constraints: Consider resources, such as computing power and expertise, available to implement and maintain the chosen model. Complex models may require more resources for development and upkeep.

  5. Business objectives: Align the choice of modeling technique with your business objectives. For example, if your goal is short-term sales forecasting, time series analysis might be the most suitable approach.

  6. Model validation: Ensure that the chosen technique can be effectively validated to assess its accuracy and reliability in predicting sales and marketing impact.

  7. Model scalability: Consider the scalability of the chosen technique. Will you be handling larger datasets and evolving business needs over time?


Best Practice #4 Regularly updating MMM models

Regularly updating and refining Marketing Mix Models (MMM) is crucial to ensure their accuracy and relevance in the ever-changing marketing landscape. Markets change, consumer behaviors evolve, and competitive dynamics fluctuate over time. To address these changes and maintain model effectiveness, organizations must incorporate new data, consider evolving market conditions, and adjust model parameters.

One of the primary ways to achieve this calibration and refinement is through Randomized Controlled Trials (RCTs), including geolifts and conversion lifts. Here's how these concepts intersect:

  1. Regular model updates: Regular model updates for MMM should include the integration of the latest data, such as sales figures, marketing expenditures, and other relevant variables. These updates ensure that the model reflects the most current market conditions and consumer trends.

  2. Model validation: RCTs serve as a robust validation mechanism for MMM models. By comparing the model's predictions with the results of RCTs, organizations can assess the accuracy of the model's forecasts. Close alignment between the model and RCT findings indicates that the model accurately captures the real-world impact of marketing.

  3. Adjustment and refinement: RCTs provide valuable data for adjusting the coefficients and parameters of the model. Insights gained from RCTs help organizations optimize the model, ensuring it accurately quantifies the incremental impact of marketing activities.

  4. Continuous improvement: Regularly conducting RCTs and updating the MMM model based on new experimental data ensures that the model remains adaptable to changing market dynamics. This iterative process allows organizations to make data-driven decisions and adapt marketing strategies accordingly.


Best Practice #5 Carefully interpreting the data


Carefully interpreting the results of Marketing Mix Modeling (MMM) is essential because the insights derived from the model can have a significant impact on marketing strategies and business decisions. MMM results guide resource allocation, budget planning, and marketing tactics. Failing to interpret the results accurately can lead to misguided actions, inefficient resource allocation, and missed opportunities. Careful interpretation ensures that organizations fully understand the implications of the model's findings and can make informed data-driven decisions that drive business success.

Every MMM model has limitations and potential biases that should be recognized and understood during the interpretation process. These limitations may stem from data quality issues, model assumptions, or simplifications made during modeling. It is crucial to acknowledge that MMM models are simplifications of the real world and may not accurately capture all nuances and interactions. For example, they may not fully account for external factors like sudden market changes or competitor actions. By understanding these limitations, companies can contextualize the results and make necessary adjustments, avoiding excessive reliance on the model's outputs.

To interpret MMM results effectively and make data-driven decisions, several best practices should be followed:

  1. Consider industry trends, market conditions, and competitive factors to ensure that results are interpreted accurately.

  2. Cross-reference model findings with real-world observations and data whenever possible to validate the accuracy of the results.

  3. Conduct sensitivity analysis to assess how changes in model inputs and assumptions affect the results.

  4. Acknowledge the potential margin of error and limitations of the model to avoid overconfidence in the conclusions.

  5. Explore various "what-if" scenarios to understand the potential consequences of different marketing strategies and budget allocations.

  6. Involve cross-functional teams, including marketing, finance, and data analytics, in the interpretation process.

  7. Recognize that interpretation is an iterative process. As new data becomes available and business conditions change, review and refine interpretations and strategies accordingly.


Best Practice #6 Communicating results effectively

Effectively communicating the results of Marketing Mix Modeling (MMM) is crucial as it ensures that the insights and recommendations derived from the model are understood and accepted by key stakeholders within an organization.

Clear and persuasive communication can bridge the gap between data analysis and concrete business decisions. When MMM results are communicated effectively, it enables teams to align their strategies, make informed marketing decisions, and optimize resource allocation based on data-driven insights.

Furthermore, transparent communication fosters trust among stakeholders and encourages their active involvement in implementing the recommended strategies, ultimately leading to improved business performance.

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