Introduction

Marketing mix modeling (MMM) plays a crucial role in optimizing marketing efforts and maximizing ROI. A key decision when developing an MMM is determining the granularity level: daily or weekly. The choice between daily and weekly granularity can significantly impact the accuracy and forecast capabilities of the model. Cassandra, a cutting-edge marketing mix modeling software, offers flexibility and support in building effective MMMs with the appropriate granularity level.

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Daily vs Weekly Granularity in MMM

When building a marketing mix model (MMM), the choice between daily and weekly granularity is a common question among clients. The level of granularity can significantly impact the accuracy (R^2) and forecast capabilities of the model.

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Using a daily dataset, you can expect lower accuracy compared to a weekly dataset. This is due to unexplained variance that reduces model accuracy. However, this is not necessarily a bad thing. A lower historical R^2 does not impact the model's forecasting capabilities if you are predicting future performances on a weekly or monthly basis.

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There is a rule of thumb to follow when building an MMM: you should have 10 rows per variable in the model. For example, if you have two years of historical weekly data, that would be 52 rows per year. In this case, you should have a maximum of 10 variables in your media mix. If you have more variables, it is recommended to use daily granularity in your dataset.

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However, some variables, such as offline media data, cannot be exported in daily granularity and are usually exported in weekly granularity. There is no standard rule for handling such cases. One simple fix is to divide each row value by seven, generating small variance in your input data while allowing you to include this data in your MMM dataset. This methodology may result in a trained model with wide confidence intervals for certain variables, like the TV channel. Yet, having wide confidence intervals is better than not measuring the ROI at all.

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In conclusion, choosing between daily and weekly granularity in MMM depends on factors such as the number of variables and the type of data available. Balancing granularity with model accuracy and complexity is crucial for optimizing your marketing efforts and maximizing ROI.

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Pros and Cons of Daily Granularity

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Choosing between daily and weekly granularity in marketing mix modeling (MMM) is a common question among clients. Daily granularity has its advantages and challenges, which we will explore in this section.

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Higher data granularity leading to better results:  Daily granularity offers more detailed data, capturing the day-to-day fluctuations in marketing performance. This increased granularity can lead to more accurate and insightful analysis of marketing efforts, helping businesses make better data-driven decisions.

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Aligning with the pace of performance marketing:  With the rapid pace of digital marketing, using daily granularity can help businesses stay agile and responsive to changes in market conditions. Performance marketing campaigns often require quick adjustments and optimizations, and daily granularity can provide the insights needed to make those adjustments in real-time.

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Capturing the nuances of advertising effects:  Daily granularity can help reveal the subtle effects of advertising campaigns on consumer behavior, such as short-term response patterns and seasonal trends. This level of detail can be crucial for understanding the true impact of marketing efforts and making informed decisions on future campaigns.

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Challenges of daily granularity and handling unexplained variance:  Despite its advantages, daily granularity comes with some challenges. As mentioned in the additional text, using a daily dataset can result in lower accuracy (R^2) compared to a weekly dataset. This is due to unexplained variance that reduces model accuracy. However, this is not necessarily a bad thing, as it has no impact on the forecast capabilities of the model when forecasting future performances on a weekly or monthly basis.

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In conclusion, daily granularity offers several benefits in marketing mix modeling, including better insights, responsiveness, and capturing subtle advertising effects. However, it also comes with challenges in handling unexplained variance and lower historical R^2. Businesses need to weigh these pros and cons when deciding on the granularity level for their MMM dataset.

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Pros and Cons of Weekly Granularity

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When considering whether to use daily or weekly granularity in marketing mix modeling (MMM) datasets, it is essential to weigh the pros and cons of each option. In this section, we will discuss the advantages and challenges associated with using weekly granularity in MMM.

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Easier data handling and aggregation:  Weekly granularity simplifies the data handling process, as there are fewer data points to manage compared to daily granularity. This reduced complexity can make it easier to aggregate and analyze data, especially for businesses with limited resources or experience in handling large datasets.

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Lower unexplained variance and higher historical R^2:  As mentioned earlier, using a weekly dataset typically results in higher accuracy (R^2) compared to a daily dataset. This is because the unexplained variance, which reduces model accuracy, is lower in weekly granularity. While a lower historical R^2 is not inherently negative, having a higher R^2 can provide businesses with more confidence in their model's ability to explain past marketing performance.

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Limitations in capturing short-term effects and promotions:  One drawback of weekly granularity is its potential inability to capture the short-term effects of marketing campaigns and promotions. For businesses that run frequent promotions or have rapidly changing market conditions, daily granularity may provide more accurate insights into the impact of these marketing efforts.

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Inadequacy for handling a large number of variables:  When building an MMM, it is essential to have enough rows per variable in the dataset. For example, if a business has two years of historical weekly data, they should have a maximum of 10 variables in their media mix. If more variables are needed, daily granularity should be considered. This rule of thumb can help businesses make informed decisions on the granularity level for their MMM dataset.

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In conclusion, weekly granularity offers advantages such as simpler data handling and higher historical R^2 but may be limited in capturing short-term marketing effects and handling a large number of variables. Understanding the trade-offs between daily and weekly granularity is crucial for businesses to make data-driven decisions and optimize their marketing ROI.

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Handling Data with Mixed Granularity

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In some cases, marketing mix modeling (MMM) datasets may contain variables with different levels of granularity, such as daily and weekly data. This can happen when certain variables, like offline media data, are typically exported in weekly granularity and cannot be directly converted to daily granularity. Managing data with mixed granularity can be challenging, but there are ways to address this issue and optimize your MMM.

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Dealing with variables that cannot be exported in daily granularity:  If you want to use daily granularity in your dataset but some variables are only available in weekly granularity, it is crucial to find a solution that allows you to incorporate these variables into your MMM dataset. There is no standard rule for handling this scenario, but there are simple fixes that can be applied.

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Simple fixes for incorporating weekly data into daily granularity models:  One potential fix is to divide each weekly data row value by seven, generating small variance in your input data. This approach enables you to include the weekly data in your daily granularity model. It is important to note that applying this methodology may result in a trained model with wide confidence intervals for certain variables, such as the TV channel. However, having wide confidence intervals is better than not measuring the ROI at all.

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Understanding the impact of wide confidence intervals on the ROI:  As highlighted in the additional text, using daily granularity may result in lower accuracy (R^2) compared to weekly granularity due to unexplained variance. However, this is not necessarily a bad thing, as it does not impact the model's forecasting capabilities when predicting future performance on a weekly or monthly basis. Businesses should be aware of the consequences of using mixed granularity data in their MMM and make informed decisions on whether to use daily or weekly granularity based on their specific goals and available data.

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Maximizing Marketing Impact with the Right Granularity

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Choosing the right granularity level for your marketing mix modeling (MMM) dataset is essential to maximize the marketing impact and make data-driven decisions. The decision between daily and weekly granularity depends on various factors, including the specific needs and goals of the business, data availability and quality, and the balance between granularity and model accuracy.

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Assessing the specific needs and goals of the business

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Before deciding on the granularity level, it is crucial to understand the specific needs and goals of your business. Determine the desired level of detail in your analysis, the pace at which you need to respond to market changes, and the type of data you have available. For instance, businesses with frequent promotions or rapidly changing market conditions might benefit more from daily granularity, whereas businesses with more stable marketing efforts may find weekly granularity sufficient.

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Considering the data availability and quality

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Another critical factor to consider when choosing granularity is the availability and quality of your data. As mentioned in the additional text, there is a rule of thumb for building an MMM: you should have 10 rows per variable in the model. If your dataset does not meet this requirement, you might need to consider changing the granularity level. Additionally, if certain variables cannot be exported in daily granularity, such as offline media data, you may need to find a way to incorporate these variables into your MMM dataset, like dividing each row value by seven.

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Balancing granularity with model accuracy and complexity

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Lastly, it is essential to strike a balance between granularity and model accuracy. Using daily granularity can provide more detailed insights but may result in lower accuracy (R^2) due to unexplained variance. On the other hand, weekly granularity may offer higher historical R^2 but might not capture short-term marketing effects and promotions. Finding the optimal balance between granularity and model accuracy is crucial for optimizing your marketing efforts and maximizing ROI.

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In conclusion, selecting the appropriate granularity level for your MMM dataset is a critical decision that impacts the effectiveness of your marketing efforts. By assessing your business's specific needs and goals, considering data availability and quality, and balancing granularity with model accuracy, you can ensure that your marketing mix modeling is as effective as possible in driving data-driven decisions and maximizing marketing impact.

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How Cassandra Supports Granularity in MMM

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Choosing the right granularity level for your marketing mix modeling (MMM) dataset is essential for maximizing marketing impact and making data-driven decisions. The decision between daily and weekly granularity depends on various factors, including the specific needs and goals of the business, data availability and quality, and the balance between granularity and model accuracy. Cassandra, a cutting-edge marketing mix modeling software, offers flexibility and support in building effective MMMs with the appropriate granularity level.

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Cassandra provides flexibility in choosing daily or weekly granularity, allowing businesses to select the granularity level that best meets their specific needs and goals. With its powerful features like the Budget Allocator and Media Mix Effectiveness Modeling, Cassandra helps businesses optimize their marketing efforts and maximize ROI by offering a comprehensive understanding of the impact of different marketing channels and tactics.

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Building an effective marketing mix model with Cassandra is made easy with its user-friendly MMM Algorithm and no-code UI. This approach enables businesses to create models without the need for advanced technical skills or coding expertise. Furthermore, Cassandra's cloud-based model training ensures efficient media planning and optimization, making the process more streamlined and accessible for businesses of all sizes.

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In conclusion, Cassandra's support for granularity in MMM enables businesses to make informed decisions and optimize their marketing efforts. By offering flexibility in granularity choice, powerful features, and an easy-to-use interface, Cassandra empowers businesses to maximize their marketing impact and make data-driven decisions.

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Share that we have a FREE MMM dataset builder that allows anyone to upload a csv exports and from that create a MMM dataset with ease.

They can start now registering in: https://incrementality.cassandra.app/register/

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Conclusion

In conclusion, choosing the right granularity level for your marketing mix modeling (MMM) dataset is crucial for optimizing marketing efforts and maximizing ROI. As many clients have raised this question, it is essential to weigh the pros and cons of daily and weekly granularity. The decision depends on factors such as the number of variables in the model, the type of data available, and the desired level of detail in the analysis.

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Using daily granularity can provide more detailed insights and align with the pace of performance marketing. However, it may result in lower accuracy (R^2) due to unexplained variance. On the other hand, weekly granularity simplifies data handling and offers higher historical R^2, but may not capture short-term marketing effects and promotions. When dealing with variables that cannot be exported in daily granularity, such as offline media data, businesses must find creative solutions, like dividing each row value by seven, to include this data in their MMM dataset.

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Utilizing a powerful marketing mix modeling software like Cassandra can help businesses build effective MMMs with the appropriate granularity level. With its user-friendly interface, flexible granularity options, and powerful features, Cassandra empowers businesses to make data-driven decisions and optimize their marketing impact. As a business, it is crucial to consider all the factors mentioned above and select the granularity level that best meets your specific needs and goals, ensuring the success of your marketing strategy.