Marketing Mix Modelling (MMM)

US$0.00

Marketing Mix Modeling: A series of AI prompts designed to optimize your marketing spend and maximize ROI. No advanced analytics experience necessary.

Key Benefits:

  • Uncover what's working: Identify the channels and tactics driving the biggest impact on sales.

  • Budget with confidence: Allocate resources strategically for maximum growth.

  • Measure incrementality: Understand the true impact of different marketing activities.

  • Visualize key insights: Easily interpret complex data with charts and reports.

  • Track marketing performance: Monitor MMM results over time to fine-tune your strategies.

  • Sample data included: Get started quickly and test the power of MMM analysis.

REQUIREMENTS:

  • ChatGPT 4 (or any other AI tool which support data uploads)

  • CSV or Excel file with your Marketing data, including for example:

    • Date (or Week)

    • New Customers (or other outcome variable that you are trying to measure, e.g. Conversions, New Sales)

    • Marketing Spend (one variable per marketing channel, e.g. facebook spend, google google ads ppc spend, performance max spend, bing ppc spend, tv spend)

    • Other

      Sample data is included in the pack which you can download below.

      Note that for the modelling to work well, you would usually need at least 12 months worth of data.

Marketing Mix Modeling: A series of AI prompts designed to optimize your marketing spend and maximize ROI. No advanced analytics experience necessary.

Key Benefits:

  • Uncover what's working: Identify the channels and tactics driving the biggest impact on sales.

  • Budget with confidence: Allocate resources strategically for maximum growth.

  • Measure incrementality: Understand the true impact of different marketing activities.

  • Visualize key insights: Easily interpret complex data with charts and reports.

  • Track marketing performance: Monitor MMM results over time to fine-tune your strategies.

  • Sample data included: Get started quickly and test the power of MMM analysis.

REQUIREMENTS:

  • ChatGPT 4 (or any other AI tool which support data uploads)

  • CSV or Excel file with your Marketing data, including for example:

    • Date (or Week)

    • New Customers (or other outcome variable that you are trying to measure, e.g. Conversions, New Sales)

    • Marketing Spend (one variable per marketing channel, e.g. facebook spend, google google ads ppc spend, performance max spend, bing ppc spend, tv spend)

    • Other

      Sample data is included in the pack which you can download below.

      Note that for the modelling to work well, you would usually need at least 12 months worth of data.

FREE PROMPT FOR Marketing Mix Modelling is available below. You can read the full article with instructions on how to prepare the data here

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.