In advertising it is known that half the money spent is wasted – what is not known is which half!
There are various data science applications to help advertisers decide on the best usage of their marketing budget. Marketing Mix Modeling (MMM) is a method that helps quantifying the impact of various marketing inputs or attributes to sales or market share. The purpose of MMM is to identify how much each marketing input contributes to sales and how much should be spent on each marketing input. Market Mix Modeling is a great application of marketing analytics. MMM uses various statistical methods.
Objectives of Market Mix Modeling
The objectives of MMM are to discover the sales drivers, understand and measure sales KPIs and ROI, predict Future Sales Performance and optimize Marketing Budgets.
MMM uses statistical analyses like multivariate regressions and time-series analysis to estimate the impact of marketing inputs/tactics on sales and then forecast the impact on future marketing inputs/tactics.
Data Requirements in developing Market Mix Models
Various types of data are used while developing a marketing mix model –
- Product Data (no.of units sold of product, price of product, sub-products)
- Promotion Data – Day when promotions/offers was active & offer type (discount, cash back etc.)
- Ad data or ad spend ($) – digital spend, affiliate marketing spend, content marketing spend, social media spend etc.
- Seasonality – summer/winter, holidays, events
- Geographical data – city, state, postal code of store, serviced states
- Macroeconomic data – inflation, GDP (include to understand recession, cyclical effects)
- Sales – vol. of units sold and revenue $
Data Science methods for MMM
Market Mix Modeling (MMM) is a powerful technique in marketing analytics and data science that leverages statistical algorithms like regression (others are ridge regression and random forest) to analyze past sales data and marketing activities. By dissecting these relationships, MMM helps optimize marketing budgets and campaign allocation. Learning about MMM equips you with valuable tools to measure marketing ROI, identify high-performing channels, and ultimately drive business growth through data-driven decision making. Additionally, MMM can be used for scenario planning, allowing marketers to predict the impact of future marketing investments on sales and strategically adjust campaigns for maximum effectiveness.
When the regression model is built, sales is the dependent variable and the other input parameters are the independent variables. To begin with, univariate statistics like mean, standard deviation, quartiles etc. are analyzed for the variables and different slices as well. This initial exploration helps identify outliers, potential data quality issues, and informs decisions about data transformations needed before fitting the model.
In Market Mix Modeling (MMM), the carryover effect captures how marketing activities from one period can influence sales in future periods. Imagine advertising building brand awareness – its impact lingers even after the campaign ends. MMM accounts for this by using techniques like adstock, which considers marketing efforts as an investment with lasting influence on consumer behavior. This helps create a more holistic view of marketing’s impact on sales. Correlation matrix is used to understand the carry over effect of the ads over time. The carry over effect and the concept of diminishing return are used in the regression model.
Apart from multiple regression other methods like SUR model – Seemingly Unrelated Regression, Bayesian Modeling, Partial Least Square regression etc. are also used while developing market mix modeling in data science.
Modern tools are built in data science for market mix models. Proprietary tools are available from marketing and research agencies while models are developed in python and R languages as well. Market mix modeling is a marketing application of analytics and data science for advertisers and marketing professionals. These models and tools help marketers play around with various input parameters and optimize their marketing budget based on the sales they would like to generate.