More Than Data

Prepare Clean, Organized, and Structured Data for Your Marketing Mix Modeling

Jan 23, 2025By More Than Data
More Than Data

Marketing Mix Modeling (MMM) is a powerful tool for media agencies to optimize marketing strategies and measure the impact of their campaigns. However, one of the most significant barriers to its adoption is the complexity of data preparation. Over the years, it has become evident that clean, hygienic, structured, and well-organized data is essential for MMM to deliver actionable insights. Yet, the process of preparing such data remains a challenge.

This article explores why data preparation is a hurdle for media agencies and offers practical steps to overcome it. By understanding the data sources, MMM input requirements, appropriate tools, and quality assurance measures, media agencies can streamline the process and achieve better results.

The Challenge of Data Preparation for MMM

The difficulty in adopting MMM lies in the intricate process of collecting, processing, and preparing data to meet specific input requirements. Unlike other marketing tools, MMM demands data from multiple sources, often involving:

  • Key Performance Indicators (KPIs): Data from the agency's client, such as sales, leads, or brand awareness.
  • Media and Marketing Spend: Data managed by the media agency, including spend, impressions, and clicks.
  • Operating and Business Factors: Inputs from the client, like pricing, promotions, and new product launches.
  • Socioeconomic Factors: Third-party data, such as consumer confidence, CPI, weather, and unemployment rates.

Given the diversity of these data sources, integrating and standardizing them into a format suitable for MMM is no small task.

Step 1: Understand the Data Sources

The first step in preparing data for MMM is to identify and understand all relevant data sources. Each source serves a unique purpose:

  • KPIs measure the effectiveness of marketing efforts and form the primary outcome variables.
  • Media and Marketing Spend data reflect the investments made in various channels and campaigns.
  • Operating and Business Factors provide context about internal decisions that influence performance.
  • Socioeconomic Factors capture external influences that may impact the outcomes.

A thorough understanding of these sources allows agencies to capture the right data and ensure nothing critical is overlooked.

Step 2: Align with MMM Input Requirements

Once the data sources are clear, the next step is to align the data with MMM’s input requirements. This involves understanding:

  • Time Scale: Whether the data needs to be aggregated weekly, monthly, or quarterly.
  • Variable Names: Ensuring consistent and meaningful naming conventions.
  • Values: Verifying that all values align with the required format and range.

Clear communication between the media agency, client, and data providers is crucial at this stage. Any misalignment can lead to errors, delays, and inefficiencies.

Step 3: Select Appropriate Data Processing Methods and Tools

Data processing is about transforming raw data into a format ready for MMM analysis. While advanced tools and techniques are available, they are not always necessary. Instead, focus on methods and tools that:

  • Are suitable for the data source and MMM input requirements.
  • Are user-friendly and familiar to the team handling the data.

For instance, if a media agency is proficient in Excel, leveraging its functions and VBA scripting for data cleaning and aggregation may suffice. On the other hand, if the team is comfortable with automated solutions, APIs can streamline data collection from platforms like Google Ads or Facebook.

Step 4: Ensure Data Quality

Data quality assurance is a critical but often overlooked step. Without it, agencies risk submitting inaccurate or incomplete data, leading to repeated rework and delays. To ensure high-quality data:

  • Conduct thorough checks to validate accuracy, completeness, and consistency.
  • Compare processed data with raw inputs to identify discrepancies.
  • Involve relevant stakeholders to review and sign off on the data before submission.

This stage should be a collaborative effort between the media agency and other parties involved. By agreeing on the final data quality, agencies can minimize the need for revisions and build confidence in the results.

Common Pitfalls to Avoid

  • Ignoring Input Requirements: Skipping the step of aligning data with MMM requirements can lead to significant rework later.
  • Overcomplicating Tools: Using tools that are too advanced or unfamiliar can create more challenges than they solve.
  • Neglecting Data Quality Checks: Failing to review and validate the data can result in errors that undermine the entire analysis.
  • Poor Communication: A lack of collaboration between the media agency, client, and data providers can lead to misaligned expectations and delays.

Conclusion

Preparing clean, hygienic, structured, and well-organized data for MMM doesn’t have to be an insurmountable challenge. By understanding data sources, aligning with input requirements, selecting appropriate tools, and ensuring data quality, media agencies can overcome the hurdles of data preparation.

When done right, MMM can provide valuable insights that drive better marketing decisions and ROI. The key is to invest time and effort upfront in the data preparation process. This not only saves time and resources in the long run but also ensures the success of MMM adoption for media agencies.

At More Than Data, we are committed to supporting every media agency in making the most of MMM. Let us help you unlock the full potential of your marketing and media strategies. If this resonates with you and you need our expertise, don’t hesitate to reach out to us!