Cleaning & Preprocessing Your Marketing Data

Why Cleaning & Preprocessing Matter in Marketing Analytics:

Marketing data is messy; there's no polite way to put it. Whether you’re pulling reports from Google Ads, Facebook, CRM systems, or offline sales, the data usually comes with duplicates, missing values, inconsistent formats, or even outright errors.

Before you can run attribution models, Marketing Mix Modelling (MMM), or customer segmentation, your data needs to be clean, structured, and reliable. Think of it as prepping ingredients before cooking. If the vegetables aren’t washed and chopped properly, the final dish won’t turn out right.

Data preprocessing ensures that your insights are accurate, trustworthy, and actionable. Without it, you risk making decisions on flawed data, which could waste budgets or misguide strategies.

Common Data Issues in Marketing:

When dealing with marketing datasets, here are the most frequent problems:

1. Missing Values:

•  Example: Impressions are logged, but clicks are missing for certain campaigns.
• Fix: Use imputation (example: fill with averages or forward-fill) or flag for exclusion.

2. Inconsistent Formats:

• Example: One dataset records dates as YYYY-MM-DD, while another uses MM/DD/YYYY.
• Fix: Standardise formats at the preprocessing stage.

3. Outliers:

• Example: A sudden spike in clicks due to bot traffic.
• Fix: Detect with statistical thresholds and decide whether to cap, remove, or investigate further.

4. Unstructured Data:

• Example: Customer feedback stored as raw text in multiple languages.
• Fix: Apply NLP techniques, translation, or categorisation before analysis.
  

Steps in Cleaning & Preprocessing Marketing Data:

1. Data Integration:

• Combine data from different sources: ad platforms, CRM, analytics, and offline sales.
• Ensure consistency in naming conventions (e.g., "Google Ads" vs "AdWords").
  

2. Handling Missing Data:

• Drop irrelevant rows if the missing data is too high.
• Apply interpolation or model-based imputation for important fields.
  

     3. Standardization & Normalization:

• Convert currencies into one standard unit.
• Scale numerical values of feeding into ML models.
  

     4. Deduplication & Identity Resolution:

• Merge records across devices and touchpoints to get a single customer view.
• Use unique identifiers, or probabilistic matching if IDs are missing.
  

    5. Feature Engineering:

• Create derived fields like Cost per Lead (CPL), Customer Lifetime Value (CLV), or Return on Ad Spend (ROAS).
• Encode categorical variables for model readiness.
  

     6. Validation & Quality Checks:

• Run automated scripts to detect anomalies.
• Validate against benchmarks (e.g., CTR ranges) before final use.

Why Preprocessing is Critical for MMM & Attribution:

Models like MMM, attribution, or churn prediction rely heavily on clean input.

• Garbage In, Garbage Out: If your impressions, spend, or conversions are misaligned, the model’s ROI estimates won’t make sense.
  
• Lag Effects: Accurate timestamps are crucial for detecting carryover effects in MMM.
  
• Multicollinearity: Preprocessing helps reduce redundancy (e.g., clicks and spend moving together).

In short, clean data makes models robust, interpretable, and trustworthy.

Tools & Techniques for Marketing Data Cleaning

• BigQuery - Ideal for deduplication, joins, and transformations at scale.
• Python (pandas, NumPy, scikit-learn) - Flexible handling of missing values, outliers, and feature engineering.
• ETL Tools (Fivetran, Airbyte, dbt) - Automate standardisation across multiple sources.
• Data Quality Checks (Great Expectations, dbt tests) - Catch anomalies early.

Challenges to Keep in Mind

• Data Volume: Marketing data grows rapidly billions of rows per year in some cases. Automation is key.
• Identity Matching: With privacy changes, connecting user journeys across platforms is harder. Probabilistic methods are increasingly important.
• Real-Time Needs: Preprocessing must be fast enough for dashboards and decision-making.
  

Cleaning and preprocessing marketing data may not be glamorous, but it’s the foundation of reliable analytics. By systematically handling missing values, duplicates, inconsistencies, and outliers, marketers ensure that models like MMM or attribution actually reflect reality.

In today’s privacy-first, multi-channel world, data trustworthiness is non-negotiable. Whether you’re building advanced models or simply reporting ROAS, well-prepared data is the difference between confident decisions and costly mistakes.