# Quick Start Guide

### 🚀 Build Your First MMM Model in 15 Minutes

This quick start guide will walk you through creating your first Marketing Mix Model from data upload to actionable insights. By the end, you'll have a working model showing which marketing channels drive your business results.

***

### What You'll Need

#### Data Requirements

**Excel File Format**

* **.xlsx or .xls** format (CSV also supported)
* **Minimum 26 weeks** of data (52+ weeks recommended for reliable results)
* **Time series structure**: One row per time period (weekly or monthly)
* **First column MUST be named "Observation"** containing your time periods

**Required Variables**

* **1 KPI variable**: Sales, revenue, conversions, brand awareness, etc.
* **2+ Marketing variables**: TV spend, digital spend, search spend, social spend, etc.
* **Optional control variables**: Seasonality indicators, price, weather, competitor activity

#### Example Dataset Structure

**✅ Correct Format:**

```
Observation  | Sales   | TV_Spend | Digital_Spend | Radio_Spend | Holiday
2023-01-01   | 125,000 | 50,000   | 25,000        | 10,000      | 0
2023-01-08   | 134,000 | 55,000   | 27,000        | 12,000      | 0
2023-01-15   | 118,000 | 45,000   | 23,000        | 8,000       | 0
2023-01-22   | 145,000 | 60,000   | 30,000        | 15,000      | 1
```

**❌ Incorrect Format:**

```
Week | Sales   | TV_Spend | Digital_Spend
1    | 125,000 | 50,000   | 25,000
2    | 134,000 | 55,000   | 27,000
```

**Why it's wrong**: First column must be named "Observation", not "Week"

***

### Step 1: Data Upload (2 minutes)

#### Navigate to Data Upload Page

1. Open MixModeler application
2. Click **"Data Upload"** in the left sidebar
3. You'll see the upload interface with two sections

#### Download Template (Optional but Recommended)

**First time users should start with our template:**

1. Click the **"Download Excel Template"** button
2. Open the template in Excel
3. Replace sample data with your actual marketing data
4. Keep the column structure and "Observation" column name
5. Save your file

**Template includes:**

* Pre-formatted date column (Observation)
* Example marketing channels (TV, Digital, Social, Search, Radio)
* Sample KPI column (Sales)
* Example control variables (Holiday, Price)

#### Upload Your Data

1. Click **"Choose File"** or drag your Excel file into the upload zone
2. Wait for the file to be validated (usually 5-10 seconds)
3. Review the **Data Summary** that appears:
   * Number of variables detected
   * Number of observations (time periods)
   * Date range of your data
   * Preview of first 10 rows

**🔍 Verify Your Upload:**

* Check that all columns were imported correctly
* Ensure dates are in the right format
* Confirm no missing values in critical variables
* Review variable names for clarity

**⚠️ Common Upload Issues:**

* **"Observation column not found"**: Rename your date/period column to "Observation"
* **"Insufficient observations"**: You need at least 26 weeks of data
* **"Missing values detected"**: Fill in gaps or remove incomplete rows
* **"Non-numeric data"**: Ensure all data columns contain numbers only

***

### Step 2: Create Your First Model (3 minutes)

#### Navigate to Model Library

1. Click **"Model Library"** in the left sidebar
2. Click the **"+ Create New Model"** button
3. A dialog box will appear

#### Configure Basic Model

**Model Setup:**

1. **Model Name**: Enter a descriptive name
   * Good: "Sales\_MMM\_Q1\_2024" or "Revenue\_Model\_Full\_Year"
   * Avoid: "Model1" or "Test"
2. **Select KPI Variable**:
   * Choose your dependent variable from the dropdown
   * This is what you want to predict/explain
   * Examples: Sales, Revenue, Conversions, Brand\_Awareness
3. Click **"Create Model"**

**Your model is now created** with just the baseline (intercept). Next, we'll add marketing variables.

***

### Step 3: Add Marketing Variables (5 minutes)

#### Navigate to Model Builder

1. Click **"Model Builder"** in the left sidebar
2. Select your newly created model from the dropdown at the top
3. You'll see two tabs: **"Current Variables"** and **"Available Variables"**

#### Add Your First Marketing Channels

**Switch to "Available Variables" tab:**

1. Search or browse for your marketing variables
   * Examples: TV\_Spend, Digital\_Spend, Search\_Spend
2. Select 3-5 major marketing channels (start simple)
   * Focus on your largest spend channels
   * Include channels you actually want to optimize
3. **Configure Adstock for Media Variables:**
   * For each media variable, set adstock rate
   * **TV/Video**: Start with 70% (high carryover)
   * **Digital/Search**: Start with 40% (moderate carryover)
   * **Radio**: Start with 60% (moderate-high carryover)
   * **Social Media**: Start with 30% (lower carryover)
4. Click **"Add to Model"** button

#### Initial Model Results

**Review the updated model:**

* **R-squared**: How much variance your model explains (aim for above 0.60)
* **Coefficients**: Impact size of each variable
* **T-statistics**: Statistical significance (bold values are significant)

**What Good Results Look Like:**

* R-squared above 60% (0.60)
* Marketing variables with positive coefficients (green)
* T-statistics above 1.96 (shown in bold)
* Coefficients that make business sense

**If Results Look Poor:**

* R-squared below 50%: Add more relevant variables
* Negative coefficients on media: Check data quality or try different adstock rates
* Low t-statistics: Variable might not be impactful, consider removing

***

### Step 4: Run Model Diagnostics (2 minutes)

#### Navigate to Model Diagnostics

1. Click **"Model Diagnostics"** in the left sidebar
2. Select your model from the dropdown
3. Click **"Continue to Diagnostics"**

#### Run Basic Tests

**Select Tests to Run:**

* ✅ **Residual Normality** (check if errors are normally distributed)
* ✅ **Autocorrelation** (check for patterns in residuals)
* ✅ **Heteroscedasticity** (check if error variance is constant)
* ✅ **Multicollinearity** (check for correlated variables)

Click **"Run Selected Tests"**

#### Interpret Results

**Green Checkmarks (✓) = Good**

* Test passed, assumption satisfied
* Model is statistically sound

**Red Warnings (⚠) = Issues Detected**

* Click "View Details" for more information
* Some violations are acceptable in MMM
* Focus on fixing critical issues first

**Critical Issues to Address:**

* **VIF above 10**: Remove one of the correlated variables
* **Severe autocorrelation**: Add lagged variables or time trends
* **Many influential points**: Check data quality

**Acceptable Issues (Often Normal in MMM):**

* Mild autocorrelation (Durbin-Watson 1.5-2.5)
* Slight non-normality of residuals
* Minor heteroscedasticity

***

### Step 5: Set Up Contribution Groups (2 minutes)

#### Navigate to Contribution Groups

1. Click **"Contribution Groups"** in the left sidebar
2. Select your model from the dropdown
3. You'll see all variables in your model

#### Organize Variables into Business Groups

**Assign each variable to a logical group:**

**Example Groupings:**

* **Base**: Intercept/constant (baseline performance)
* **Media**: TV\_Spend, Digital\_Spend, Radio\_Spend, Social\_Spend
* **Seasonality**: Holiday, Month\_Indicators
* **Price**: Price\_Variable, Promotion\_Indicator
* **External**: Competitor\_Activity, Economic\_Indicator

**How to Assign:**

1. Click the "Group" dropdown for each variable
2. Select or type a group name
3. Use consistent names (e.g., always "Media" not sometimes "Marketing")

**Choose Colors for Each Group:**

1. Once groups are assigned, a color picker appears
2. Choose distinct, professional colors
3. Recommended: Blue for Media, Red for Price, Green for Seasonality

**Click "Save Groups"**

***

### Step 6: Run Decomposition Analysis (2 minutes)

#### Navigate to Decomposition

1. Click **"Decomposition"** in the left sidebar
2. Select your model from the dropdown
3. Click **"Run Decomposition"**

#### Understand Your Results

**Main Decomposition Chart:**

* **Stacked bars**: Each color shows a group's contribution over time
* **Black line**: Actual KPI values from your data
* **Bar height**: Total predicted value

**What to Look For:**

**Largest Contributors:**

* Which groups have the tallest bars?
* These are your primary growth drivers

**Consistency vs. Variability:**

* **Steady bars** (Base): Reliable baseline performance
* **Variable bars** (Media): Campaign-driven spikes

**Timing Patterns:**

* When did Media contributions spike?
* Do spikes align with known campaign periods?

#### Drill Down into Specific Groups

**Analyze Media Channels in Detail:**

1. Select **"Media"** from the group dropdown
2. Click **"Run Group Decomposition"**
3. See individual channel contributions (TV, Digital, Radio, etc.)

**Identify Top Performers:**

* Which channel has the largest contribution?
* Which channels show most consistent performance?
* Where are the spikes and why?

***

### Step 7: Calculate Channel ROI (1 minute)

#### Quick ROI Calculation

**For each marketing channel, calculate:**

```
ROI = (Total Incremental Contribution / Total Spend) - 1
```

**Example:**

* TV Total Contribution from decomposition: $500,000
* TV Total Spend: $200,000
* **TV ROI = ($500,000 / $200,000) - 1 = 1.5 or 150%**

**Interpretation:**

* For every $1 spent on TV, you generated $2.50 in incremental sales
* Net return: $1.50 per dollar invested

**Compare Across Channels:**

* Which channels have highest ROI?
* Which are underperforming?
* Where should you increase/decrease investment?

***

### Step 8: Export Your Results (1 minute)

#### Save Your Model

1. Go back to **Model Library**
2. Find your model in the table
3. Click the **"Export"** (📁) button
4. Your model will be saved as an Excel file containing:
   * Model coefficients and statistics
   * Diagnostic test results
   * Decomposition data by group
   * Variable transformation details

**Excel Export Includes:**

* **Model Info** sheet: Basic model details
* **Coefficients** sheet: All variable coefficients and t-stats
* **Diagnostics** sheet: Test results summary
* **Decomposition** sheet: Contribution data by time period
* **Transformations** sheet: Applied transformations (adstock, curves, etc.)

***

### Next Steps: Improving Your Model

#### Quick Wins (Do These Next)

**1. Add More Variables (if needed)**

* Include additional marketing channels you're spending on
* Add seasonality indicators (monthly dummies, holiday flags)
* Consider competitor activity or economic indicators

**2. Apply Saturation Curves**

* Navigate to **Variable Workshop**
* Create saturation curves for media variables
* Use **Curve Testing** to find optimal parameters
* Models diminishing returns more realistically

**3. Test Different Adstock Rates**

* Use **Variable Testing** page
* Test multiple adstock rates (30%, 50%, 70%)
* Choose rates with highest t-statistics
* More accurate carryover modeling

**4. Upgrade to Bayesian**

* In **Model Builder**, click "Convert to Bayesian"
* Get credible intervals and uncertainty quantification
* More robust for limited data
* Industry standard for production models

#### Advanced Features to Explore

**Variable Workshop:**

* Create lead/lag variables
* Build weighted variable combinations
* Apply AVO (Average Value Optimization) transformations
* Generate interaction terms

**Curve Testing:**

* Test S-shape vs Concave curves
* Optimize curve parameters (alpha, power)
* Visualize transformation effects
* Create variables with optimal saturation

**Variable Testing:**

* Pre-screen variables before adding to model
* Run Granger Causality tests
* Identify multicollinearity issues
* Optimize variable selection

**Model Comparison:**

* Build multiple model versions
* Compare performance side-by-side
* Test different variable combinations
* Find optimal model specification

***

### Common Quick Start Pitfalls

#### Data Issues

**❌ Wrong Column Name**

* **Problem**: First column not named "Observation"
* **Fix**: Rename your date/period column to exactly "Observation"

**❌ Insufficient Data**

* **Problem**: Less than 26 weeks of data
* **Fix**: Gather more historical data (52+ weeks ideal)

**❌ Missing Time Periods**

* **Problem**: Gaps in weekly/monthly data
* **Fix**: Fill gaps or indicate missing periods appropriately

**❌ Inconsistent Units**

* **Problem**: Mixing weekly and monthly data
* **Fix**: Ensure consistent time periods throughout

#### Model Building Issues

**❌ Too Few Variables**

* **Problem**: Model only includes 1-2 marketing channels
* **Fix**: Include all major marketing channels

**❌ No Adstock Applied**

* **Problem**: Media variables used without carryover effects
* **Fix**: Apply appropriate adstock rates (40-70% typically)

**❌ Ignoring Diagnostics**

* **Problem**: Not checking if model assumptions are met
* **Fix**: Always run diagnostics and address critical issues

**❌ Over-Interpretation**

* **Problem**: Treating estimates as exact truth
* **Fix**: Remember MMM provides estimates with uncertainty

***

### Success Checklist

Before considering your first model complete, ensure:

* ✅ **R-squared above 60%** (preferably above 70%)
* ✅ **Marketing variables have positive coefficients** (make business sense)
* ✅ **Most variables statistically significant** (t-stat above 1.96)
* ✅ **Adstock applied to all media variables** (carryover effects captured)
* ✅ **Diagnostic tests mostly passing** (especially multicollinearity check)
* ✅ **Decomposition results align with business knowledge** (reality check)
* ✅ **Contribution groups logically organized** (business-friendly)
* ✅ **Results exported and saved** (documentation)

***

### Pro Tips for Quick Start Success

**1. Start Simple, Add Complexity**

* Begin with 3-5 major channels
* Get a working model first
* Add sophistication incrementally

**2. Focus on Major Spend**

* Include channels where you spend the most
* Don't worry about small channels initially
* Cover 80% of your marketing budget

**3. Trust Your Business Knowledge**

* If results don't make sense, investigate why
* Data quality issues are common
* Model should align with experience

**4. Document Everything**

* Note your data sources and assumptions
* Keep track of model versions
* Record why you made specific choices

**5. Iterate Quickly**

* Build multiple versions
* Test different approaches
* Learn from each iteration

***

### Getting Help

**Need Assistance?**

* Check our detailed documentation sections
* Review specific feature guides
* Contact support: <support@mixmodeler.com>
* Watch tutorial videos (coming soon)

**Common Questions:**

* Data Format Requirements
* Understanding Adstock
* Interpreting Diagnostics
* Troubleshooting Guide

***

Congratulations! You've built your first Marketing Mix Model. This foundation enables you to understand which marketing channels drive results and make data-informed budget allocation decisions. Explore the advanced features to refine your models and extract even deeper insights.