In the world of marketing analytics, analysts are commonly surrounded by such questions as below:
" How do I manage my campaigns? Whom to target? With which campaign should I target my audience? How should I optimize my marketing budget? "
All these questions are integral to any marketeer. Some do get nightmares when none of the campaigns seem to give any uplift, in fact the numbers are turning to red. The Green zone becomes a dream and Sales is pressurizing to help and all the blames of dropping sales are coming back to the marketing team.
Analysts give great amount of thought to these questions and dissect them into separate studies which collaboratively could provide relevant guidance on a subject so complex and dynamic. Any marketing activity doesn't happen in isolation, there are multitude of factors which play actively to make or mar the game.
Flop campaigns and negative returns are painful and Marketing teams turn to data and analytical support to understand actually where should they invest their budget so as to see a positive uplift. Hence, number crunching and algorithm development become so important for any organization who want to take informed decisions.
Uplift modeling is one such new age technique to help the marketeers save money, drive positive ROI and ensure successful campaigns by controlling the target audience or respondents of the campaigns.
Uplift model will plan strategy on the hold out recommendation and drive positive incremental response. This technique has a multi-aspect approach as depicted below:
I shall talk in detail about this in next post. Till then, happy number crunching :)
