What are your users doing on your website or in your store? How do you turn the piles of data your organization generates into actionable information? Where do you get complementary data to make yours more comprehensive? What tech, and what techniques?
The Fifth Elephant is a two day conference on big data.
Early Geek tickets are available from fifthelephant.doattend.com.
The proposal funnel below will enable you to submit a session and vote on proposed sessions. It is a good practice introduce yourself and share details about your work as well as the subject of your talk while proposing a session.
Each community member can vote for or against a talk. A vote from each member of the Editorial Panel is equivalent to two community votes. Both types of votes will be considered for final speaker selection.
It’s useful to keep a few guidelines in mind while submitting proposals:
Describe how to use something that is available under a liberal open source license. Participants can use this without having to pay you anything.
Tell a story of how you did something. If it involves commercial tools, please explain why they made sense.
Buy a slot to pitch whatever commercial tool you are backing.
Speakers will get a free ticket to both days of the event. Proposers whose talks are not on the final schedule will be able to purchase tickets at the Early Geek price of Rs. 1800.
"Honey, we blew the data - to build a Market Mix Model" : Modeling the Market Mix under unfavourable data conditions
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.