How to Run Marketing Mix Modeling Without Code
Step 1: Prepare Your CSV
Create a CSV file with a Date column, a Sales (or conversions) column, and one column per marketing channel spend (e.g., Facebook_Ads, Google_Ads, TV_Ads). Each row should represent one time period — daily, weekly, or monthly.
Step 2: Upload and Run the Model
Upload your CSV to CheapMMM. The tool automatically applies geometric adstock transformations, Hill-function saturation curves, and Joint MAP estimation with Ridge regression to model channel contributions. No configuration or coding required — results appear in seconds.
Step 3: Read Your Results
Review ROAS (Return on Ad Spend) by channel with 90% credible intervals, feature importance scores showing each channel's relative impact, actual vs. predicted sales charts, carryover insights, and budget allocation recommendations to optimize your spend. Data quality warnings alert you to any issues like correlated channels or sparse data.
Sample CSV Format
| Date | Sales | Facebook_Ads | Google_Ads | TV_Ads |
|---|---|---|---|---|
| 1/1/25 | 12500 | 360 | 175 | 338 |
| 1/2/25 | 10000 | 300 | 150 | 270 |
| 1/3/25 | 19000 | 516 | 240 | 513 |
Frequently Asked Questions
What is Marketing Mix Modeling?
Marketing Mix Modeling (MMM) is a statistical method that measures how each marketing channel — paid search, social ads, TV, etc. — contributes to sales or conversions. It uses historical spend and outcome data to quantify channel effectiveness and guide budget allocation decisions.
Do I need to know statistics or Python to use CheapMMM?
No. CheapMMM is a fully no-code tool. You upload a CSV and the model runs automatically. There is no setup, no scripting, and no statistical knowledge required to get actionable results.
What data format does CheapMMM accept?
CheapMMM accepts CSV files with at minimum three columns: Date (MM/DD/YY or YYYY-MM-DD), Sales (or another conversion metric), and one or more channel spend columns. Each row represents one time period (daily, weekly, or monthly).
How does CheapMMM calculate ROAS?
ROAS is calculated as total modeled contribution divided by total spend per channel, using coefficients from Ridge regression on adstock- and saturation-transformed spend. Uncertainty is quantified via Laplace approximation, producing 90% credible intervals around each channel's ROAS estimate.
Is my data private and secure?
Yes. Uploaded data is stored temporarily (1 hour) for analysis and automatically deleted. Your marketing data is never permanently stored and is never used for any purpose other than your immediate analysis session.
How much data do I need for reliable results?
We recommend at least 3-6 months of weekly or monthly data (minimum 8 time periods). Each channel needs at least 8 non-zero spend observations — channels below this threshold are automatically excluded. The model adapts regularization strength based on your data-to-parameter ratio, so even thinner datasets can produce directional insights with appropriate warnings.