{"id":4357,"date":"2014-11-23T11:17:23","date_gmt":"2014-11-23T05:47:23","guid":{"rendered":"http:\/\/ucanalytics.com\/blogs\/?p=4357"},"modified":"2016-09-14T14:52:27","modified_gmt":"2016-09-14T09:22:27","slug":"next-best-action-retail-case-study-example-part-11","status":"publish","type":"post","link":"https:\/\/ucanalytics.com\/blogs\/next-best-action-retail-case-study-example-part-11\/","title":{"rendered":"Next Best Action – Retail Case Study Example (Epilogue)"},"content":{"rendered":"
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\"Next<\/a>

Next Best action\u00a0& Free Will – by Roopam<\/p><\/div>\n

This article is the epilogue to our case study example about campaign and marketing analytics for an online retail company. You could read the previous parts at<\/p>\n

Problem definition: \u00a0Part 1<\/a>\u00a0&\u00a0Part 2<\/a><\/div>\n
Description: Part 3<\/a><\/div>\n
Association: Part 4<\/a><\/div>\n
Classification: Part 5<\/a>, Part 6<\/a>,\u00a0Part 7<\/a>,\u00a0Part 8<\/a><\/div>\n
Estimation: Part 9<\/a>, Part 10<\/a><\/div>\n

 <\/p>\n

In this part we will synthesise our learning from the previous parts and explore some utilities of response propensity & profit estimation models for business purposes. We will also talk about next best action or next best offer\u00a0as one of the advanced use cases of customer propensity modelling.<\/p>\n

Free Will & Next Best Action<\/span><\/h2>\n

“I am the master of my actions and destiny<\/em>” – it’s a good feeling to think this way. We all want to be in control of our lives, and want to make independent decisions. However, the new evidence that is\u00a0emerging from neuroscience\u00a0contradicts this belief. These are early days but still the evidence is\u00a0building fast. Benjamin Libet pioneered experiments in the 1970s to question free will. You can\u00a0find a version of Libet’s\u00a0experiment in this video<\/a>.<\/p>\n

Recently, far more advanced experiments were designed with the use of an advanced brain imaging technique called fMRI.\u00a0In one of these experiments, participants were asked to make a binary (yes \/ no) choice (e.g. press a button or not). FMRI collects the data for pre-decision activities in the brain and the researcher attempts to correlate these activities to decision making. Based on the data from fMRI a multivariate model was developed to predict the choice participants will\u00a0make.\u00a0The predictive model, with high statistical significance, was able to correctly predict the choice that the participant made\u00a07 seconds before they\u00a0were consciously aware of his or her choice. \u00a0Now this is totally freaky, a mathematical model could predict what you are going to do\u00a0before you are aware of your own actions.<\/p>\n

Let’s assume that these brain activities are the cause of the decision making process. Then we can think of another way to look at the above results. If I could ignite the right\u00a0areas in your brain before you will make a decision, I could control your decisions. I guess smart marketers are using this trick for a long time through the usage of powerful messaging and advertisements. Luckily only one in a million ads have this kind of power over us.<\/p>\n

On some level, propensity models we have discussed in this case study for marketing analytics were also trying to predict human actions. Predictive models may not have access to brain imaging data but they try to model\u00a0customers’ behaviour and predict customers’ chances\u00a0to buy a product.\u00a0However, to make these models useful it is essential that they are deployed properly for business purposes. They need to create the right environment to facilitate decision makers. In order to create useful decision support systems you need to have good understanding of end-user \/ decision makers\u00a0and their dilemma.<\/p>\n

Decision Maker’s Dilemma and Next Best Action<\/span><\/h2>\n

\"Next<\/a>Imagine you are\u00a0a telemarketer with a list of 20 products to sell to every customer you call. You are overloaded with information and questions about the products to sell. The following is a small list of questions sales persons have:<\/p>\n