Marketing Mix Modeling (MMM) Tutorial in Google Spreadsheets

May 27, 2024 at 3:56:55 PM

TL;DR Marketing Mix Modeling (MMM) is a statistical technique to optimize ad spend by understanding each ad network's sales contribution. Due to iOS14 and privacy concerns, MMM has gained popularity. This tutorial offers a free Google Sheets template for linear regression to predict sales, making MMM accessible without a data scientist. It includes a 4-step process, discusses limitations, and compares MMM with Multi-Touch Attribution (MTA).

Marketing Mix Modeling (MMM) Tutorial in Google Spreadsheets

Marketing Mix Modeling (MMM) is a statistical technique for understanding the sales contribution of each ad network and optimizing advertising spend.

Due to the unreliability of digital tracking post-iOS14, MMM has gained popularity for its privacy-friendly approach.

MMM in Google Spreadsheets Tutorial:

  • Objective: Use a Google Sheets template to Predict future sales with different budget allocations.
  • Method: Linear regression via Google Sheets’ LINEST function, estimating the incremental performance of each marketing channel without needing a data scientist.

Steps to Build a Marketing Mix Model:

  1. Data Collection:

    • For Tenjin customers: Export data using Tenjin’s Data Exporter.
    • For others: Export daily spend and revenue data from ad platforms and analytics tools.
  2. Data Preparation:

    • Paste data into the RAW tab.
    • Configure variables and add a constant term if needed.
  3. Model Building:

    • Choose variables and adjust carryover and saturation rates.
    • Measure accuracy using MAPE and NRMSE.
    • Evaluate plausibility with RSSD, significance, uncertainty, skew, and kurtosis.
  4. Visualization and Action:

    • Use charts on the MODEL page to visualize accuracy and contributions.
    • Forecast future spending allocations using the PLAN tab.

Limitations:

  • Simple linear regression assumes constant performance over time.
  • Potential data insufficiency for a valid model.
  • Risk of incorrect model leading to poor decisions.

MMM vs. MTA:

  • MMM: Uses aggregate data to match sales spikes with marketing events.
  • MTA: Requires user-level data for deterministic attribution.

Explore Bayesian MMM tutorials or automated MMM solutions on the Recast website for more advanced analysis.

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