Uplift Modeling - The lamp in the dark

As I mentioned in my last post, Uplift modeling is a new age technique to help the marketing team 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. The population base is split into a four blocker as shown in the picture:

The algorithm developed will try to split the data into four groups based on their response to the marketing activity. The effect of the treatment will be measured using this segmentation. These groups shall be individually classified by sophisticated data mining algorithms to form equations which could be thus used to create futuristic marketing strategy encompassing identification of the target audience, setting up of measurement framework i.e. the control group, optimization of the marketing budget by selection of target audience which would be comprising of the responders to the campaigns.

As seen in the picture above, marketing team would like to target persuadable or savable audience alone. Based on the price of each piece of marketing, budget optimization can be done. This will account to savings from implementation of the model.

Impact of multiple independent variables coming from purchase pattern behavior and other external data impacting the stickiness of the customer to the brand can be taken into account. In fact, the algorithm self corrects and every time new factors get incorporated, they get accounted as per their statistical significance.

The approach is unique as it combines Bayesian networks and Random forest for prediction. Both the techniques are unparalleled in accuracy and sophistication.

  

The inherent responders would be sieved out from the group of responders who converged due to the impact of the marketing activity. This approach would also help us to stop targeting the customer groups who respond negatively to any marketing activity. Also, this will minimize random targeting which could negatively impact the Dell brand value.

I shall talk in detail about both these techniques soon. So keep reading !!

Marketing Analytics - Uplift Modeling

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 :)