Data specifications for Marketing Mix Models depend on the modeling scope. For example, Sellforte's Digital plan targeted for ecom companies includes only online media and online conversions using standard Sellforte data schema, whereas Growth and Enterprise plans can incorporate offline media and offline sales as well. However, all Marketing Mix Models leverage three broad categories of data (shared below) and have following characteristics:
Time-series format;
As granular as possible (daily data is typically sufficient);
Structured, machine-readable format.
1. Sales data
The main objective of Marketing Mix Modeling is to understand how marketing activities impact sales, which is why granular and high quality sales data is the foundation for all models. Following considerations are important when deciding on the data to be used:
Dimensions of the sales data. Typically dimensions include:
Geography (for example, 'country')
Sales channel ('brick & mortar', 'online', ..)
Product group
For FMCG/brands with multiple brands: Brand
Unit of the sales data: Volume or value
Source of the sales data. There are a three main options to consider
Conversion data from online platforms (e.g., Google Analytics): This data can typically be easily automated using data connector services, such as Supermetrics
Sales data from an ERP or similar company-owned system: This data is often very granulary but requires a custom data connector to connect to the MMM model
Sales data collected by a 3rd party: This data has the lowest granularity (weekly, in some cases daily) and is often collected for brands without direct consumer channel
2. Marketing activity data
Marketing Mix Models use data on marketing activities to estimate the impact of different marketing activities on sales. Thus, the accuracy of data for a specific media has big impact for the model's ability to estimate the impact for that media. Following are typically dimensions that should be considered:
Media hierarchy
Media Type: Online media, Offline media, Own media
Media Groups within Media Types: TV, OOH, Radio, Display, Search, etc.
Individual media channels with Media Groups
Campaign hierarchy
Campaign types: brand, tactical, seanoal, etc.
Campaign names of individual campaigns (different for each company)
The dataset should include for all media:
Investments
Exposure metrics (such as TRPs for TV, Impressions for Display)
Others metrics relevant to the medium (e.g., clicks)
3. Non-marketing variables
There can be variables which have large impact on company sales, but are not related to marketing. It is important to control for the effect of these variables, so that the Marketing Mix Model doesn't attribute their positive or negative effects to media. These variables can include for example the following:
Pricing & discounts: Decrease in prices typically increase the volume of products sold
Distribution network: Having products more accessible to consumers has a positive impact on their sales
Macroeconomics: Downturn can have negative impact on sales of some products and positive on others
Weather: There can be products whose sales is positively or negatively impacted for example by rain or tempertature
Competitor activities: In certain industries, competitive activity can impact sales
COVID-19: In recent years COVID-19 related restrictions imposed by governments has had impact on many companies' sales
Holidays: Certain holidays could have positive or negative impact to some products
Seasonality: Different product have different seasonal patterns (this is often observed from the data)
This is not a comprehensive list, and including non-marketing variables is a model is always a case-by-case consideration.