In this article we will cover marketing mix modeling and how it is used in various domains. We will also touch upon what questions marketing mix modeling answers along with basic model implementation in SAS, R and Python.
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Table of Contents
Introduction : Marketing Mix Modeling
Marketing Mix Modeling (MMM) is one of the most popular analysis under Marketing Analytics which helps organisations in estimating the effects of spent on different advertising channels (TV, Radio, Print, Online Ads etc) as well as other factors (price, competition, weather, inflation, unemployment) on sales. In simple words, it helps companies in optimizing their marketing investments which they spent in different marketing mediums (both online and offline).
Uses of Marketing Mix Modeling
It answers the following questions which management generally wants to know. - Which marketing medium (TV, radio, print, online ads) returns maximum return (ROI)?
- How much to spend on marketing activities to increase sales by some percent (15%)?
- Predict sales in future from investment spent on marketing activities
- Identifying Key drivers of sales (including marketing mediums, price, competition, weather and macro-economic factors)
- How to optimize marketing spend?
- Is online marketing medium better than offline?
Imagine you are a chief strategist and your role requires you to come up with strategies that can boost company's revenue growth and profitability. With the use of insights from marketing mix model, you know sales can be increased by 50 million dollars for every 5 million dollars you spend on advertising. MMM would also help you to determine how much to spend on each advertising medium to get maximum return on investment (ROI).