An Actionable Checklist for Marketing Mix Modeling
Data Gathering and Preparation
- Identify and document all the relevant data sources that are needed for the analysis.
- Include sales data, advertising data, pricing data, and any other data sources that are relevant to the marketing mix modeling analysis.
- Cleanse the data by removing any duplicate or irrelevant records.
- Validate the data by checking for errors, inconsistencies, and missing values.
- Implement data quality checks and processes to ensure the accuracy and integrity of the data.
- Identify the appropriate database or data warehouse to store and consolidate the data.
- Transfer the data from the various data sources into the chosen database or data warehouse.
- Ensure that the data is structured and organized in a way that facilitates analysis and reporting.
- Normalize the data by removing any redundancies and inconsistencies.
- Standardize the data by applying consistent formats, units, and definitions.
- Ensure that all variables and metrics are defined and calculated consistently across the data.
Analysis Methodology
Modeling Execution
- Identify the methodology to be used for the marketing mix modeling
- Define the variables that will be included in the model
- Collect and organize the necessary data for the model
- Create multiple scenarios with different assumptions
- Run the model for each scenario
- Analyze and compare the results to identify the impact of different assumptions
- Analyze the sensitivity of the model to different parameters and coefficients
- Adjust the parameters and coefficients to improve the model's accuracy
- Iterate the process until the model produces satisfactory results
- Compare the model's predictions with actual historical data
- Analyze the accuracy and reliability of the model's results
- Validate the model's performance against real-world observations
- Create a detailed document describing the methodology used in the model
- List all the assumptions made during the modeling process
- Document the limitations and potential errors of the model
Use in Manifestly