Marketing Mix Modelling (MMM) is a powerful technique that helps marketers understand the effectiveness of their marketing activities. With ChatGPT4o, you can simplify this process and gain valuable insights without needing extensive technical skills. Here's a step-by-step guide to get you started, including a dummy dataset to practice with.

Step 1: Understanding the Basics of MMM

Marketing Mix Modelling is a statistical analysis technique that helps marketers quantify the impact of various marketing inputs (such as advertising, promotions, pricing) on sales or other performance metrics. The goal is to understand which elements of the marketing mix are driving results and optimize future marketing strategies accordingly.

Step 2: Prepare Your Data

1. Gather Data: Collect historical data on your marketing activities and sales. This should include data on advertising spend, promotions, pricing, and total sales (or total conversions). Ideally, you should have data for at least a year. Aim for the data to be by week if possible.

2. Organize Data: Structure your data in a spreadsheet format (see dummy table below for reference). Each row should represent a time period (e.g., week or month), and each column should represent a different input variable (e.g., TV spend, online ad spend, emails sent). It is also crucial to include a column with an output variable (e.g., total sales, or total conversions).

Step 3: Load data into Google Sheets or Excel

Insert the data into either Google Sheets or Excel. You can use the dummy data (link), or your own data.

Step 4: Use ChatGPT4o for Analysis

• Open ChatGPT4o:

• Upload the data into ChatGPT4o: There are several ways in which you can upload the data into ChatGPT4o. You can for example Upload from computer, add from Google Drive, add from Microsoft One Drive, or simply by pasting the data into the prompt.

• Start prompting: Use a clear and detailed prompt to instruct ChatGPT4o to perform the MMM analysis. For example:

  Prompt for Marketing Mix Modeling Analysis Using ChatGPT-4:

  1. Introduction and Data Summary:

     • Start by summarizing the dataset, including the types of paid and non-paid activities, sales data, and any other relevant variables. Provide an overview of the dataset's completeness and quality.

  2. Impact Analysis:

     • Analyze the impact of both paid (e.g., Google Ads, Meta, TV, LinkedIn) and non-paid activities (e.g., social media engagement, email marketing, PR efforts) on sales.

     • Evaluate the effectiveness of each marketing activity, considering its contribution to overall sales.

  3. Visualizations:

     • Create visualizations to show the impact of each variable on sales.

     • For paid activities, provide a visualization comparing ROI or Cost per Conversion for each activity to highlight their efficiency.

  4. Insights and Recommendations:

     • Summarize the findings, highlighting the strength and reliability of the insights.

     • Provide actionable recommendations on which marketing activities are most effective and suggest optimizations to improve overall marketing performance.

  5. Data Enrichment Suggestions:

     • If the analysis reveals weak connections between input variables and sales, suggest ways to improve or enrich the dataset. This could include incorporating additional variables like customer demographics, seasonal effects, and competitive activities.

Step 5: Interpret the Results

• Review the Output: ChatGPT4o will analyze the data and provide insights. This might include regression analysis results showing the correlation between marketing spend and sales.

• Identify Key Drivers: Look for key drivers of sales. ChatGPT4o should highlight which marketing activities have the most significant impact on sales.

• Optimization Suggestions: Review any optimization suggestions provided. These might include recommendations to increase spending on high-impact channels or reduce spending on less effective ones.

Tips for Effective Marketing Mix Modelling

• Consistency: Ensure your data is consistent and covers the same time periods across all variables.

• Granularity: Use appropriate time intervals (weekly if you can, but otherwise monthly is fine)

• Validation: Validate the model with historical data to ensure accuracy before applying insights to future campaigns. In practice it typically means comparing the results of the models to other data sources (e.g. Google Analytics data, Incrementality Tests, your business knowledge).

How much historical data to use

Marketing Mix Modelling (MMM) typically requires a substantial amount of historical data to produce reliable and actionable insights. The number of data points needed can vary based on several factors, including the complexity of the model, the variability in the data, and the frequency of data collection. Here are some general guidelines:

1. Frequency of Data Collection: MMM is more effective with higher-frequency data. Monthly data is commonly used, but weekly data is often preferred.

2. Seasonality and Trends: If your business is highly sensitive to seasonality and trends, then it's crucial to have enough data to account for seasonality and long-term trends. This typically means collecting data over multiple years. For instance, having 3 years of weekly data (156 data points) can provide a robust foundation for understanding both seasonal effects and longer-term trends. If you do not have that much historical data, then you could simply use Google Trends data as an additional input variable accounting for seasonality.

3. Data Quality and Granularity: The quality of the data is as important as the quantity. Ensure that data is consistently collected and covers all relevant marketing activities (e.g., TV spend, digital advertising, promotions). More granular data can help in building more detailed and accurate models.

4. Complexity of the Marketing Mix: The more variables (marketing channels) you include in the model, the more data points you will need to ensure the model can accurately estimate the impact of each variable. As a rule of thumb, you might need an additional 3-6 months of data for each additional variable included in the model.

Be creative

Did you know that Marketing Mix Modeling (MMM) can be applied to a variety of use cases beyond the traditional analysis of paid activities on overall sales? MMM isn't just for evaluating the impact of paid advertising. You can also use it to assess the effectiveness of organic marketing activities.

For instance, suppose your marketing strategy relies solely on organic methods such as social media posts, outbound emails, and distributing leaflets. MMM can help you determine which of these activities drives the most impact. Instead of using spend data, you would analyze other metrics like the number of organic post impressions, the volume of emails sent, and the quantity of leaflets handed out.

By applying MMM in this creative way, you can gain valuable insights into the effectiveness of your organic marketing efforts and optimize your strategies accordingly.