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"Honey, we blew the data - to build a Market Mix Model" : Modeling the Market Mix under unfavourable data conditions
Submitted by Anindya Neogi on Saturday, 30 June 2012
Section: Data Analytics Technical level: Intermediate Session type: Lecture
The term Market mix modeling is widely used and applied indiscriminately to a broad range of marketing models used to evaluate different components of marketing plans, such as advertising, promotion, packaging, media weight levels, sales force numbers, etc. These models can be of many types, but multiple regression is the workhorse of most marketing mix modeling. Regression is based on a number of inputs (or independent variables) and how these relate to an outcome (or dependent variable) such as sales or profits or both. Once the model is built and validated, the input variables (advertising, promotion, etc.)can be manipulated to determine the net effect on a company’s sales or profits. Market mix modeling can assist in making specific marketing decisions and tradeoffs, but it can also create a broad platform of knowledge to guide strategic planning. But how to build a reliable model in an unfavorable data conditions involving incomplete data, non uniform spend and lack of reliable measuring variables corresponding to different media activities? The objective of the session is to touch base on the advanced statistical techniques those can be used to get rid of such situations.
Maintaining quality data is utmost important for any enterprise to take strategic decision in a highly competitive environment. But when the condition of the data is far from an ideal situation, should companies utilize their data in a more efficient manner utilizing sophisticated analytical techniques or they will simple dishonor the data? The session will focus on advanced analytical techniques to build a near robust marketing mix modeling under unfavorable data conditions.
Anindya currently holds the position of Analytical Project Manager in advanced analytical projects team for HP Global Analytics. He has over 10 years of experience in the areas of Statistical Modeling, Data Mining, Predictive Analytics and related areas across verticals like Insurance, Banking and Technology. Anindya received his Bachelors and Masters in Statistics from University of Kalyani. He also holds a Masters degree in Computer Applications.
Debayan currently holds the position of Business Analyst in advanced analytical projects team for HP Global Analytics. Debayan received his Bachelors in Engineering from Jadavpur University and Masters in Technology in Quality Control and Operations Research from Indian Statistical Institute.