[HN Gopher] Real-world uplift modelling with significance-based ...
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       Real-world uplift modelling with significance-based uplift trees
       [pdf]
        
       Author : luu
       Score  : 15 points
       Date   : 2024-08-25 22:20 UTC (5 days ago)
        
 (HTM) web link (www.stochasticsolutions.com)
 (TXT) w3m dump (www.stochasticsolutions.com)
        
       | rrherr wrote:
       | Here's a plain language explanation of why uplift modeling is
       | useful, written by the same author as the paper:
       | 
       | https://stochasticsolutions.com/uplift/
       | 
       | > It is normally assumed that the worst outcome direct marketing
       | activity can have is to waste money. In fact, some direct
       | marketing provably drives away business within certain segments,
       | and it is not unknown for it to drive away more business in total
       | than it generates. This is especially true in retention activity.
       | 
       | > [Non-Uplift] Churn and attrition models prioritize customers
       | whose probability of leaving is highest. Such customers tend to
       | be dissatisfied, so are usually hard to retain. To make matters
       | worse, in many cases, the only thing currently keeping them is
       | inertia, and interventions run a serious risk of back-firing,
       | triggering the very defections they seek to avoid.
       | 
       | > It is more profitable to focus retention activity on those
       | people who ... will leave without an intervention, but who can be
       | persuaded to stay. Uplift models allow you to target them, and
       | them alone. At all costs, you want to avoid targeting the ... so-
       | called Sleeping Dogs, whose defection you are likely to trigger
       | by your intervention. Again, uplift models can direct you away
       | from those customers.
        
       | abhgh wrote:
       | Interesting to see this paper here! Many years ago when working
       | on a problem of offering online campaigns of some form, I had
       | stumbled onto this paper, and it entirely changed my perspective.
       | Eventually, I built an internal library based on the paper with a
       | d3-based tree visualizer.
       | 
       | If memory serves right, my primary takeaway was that it isn't a
       | good idea to make "customer retention" kind of offers to visitors
       | (to a website) based on probability of purchase, because a
       | fraction of them would have purchased irrespective of the
       | discount offer. Of course,loyal customers should be rewarded in
       | other ways, e.g., loyalty points, and this discussion is strictly
       | for customer retention campaigns. In terms of model building this
       | translates to: features that predict probability of purchase
       | don't necessarily predict the _difference_ in the probability of
       | purchase _for the same person given an offer_. Of course, the
       | second quantity is what we want. Its challenging to get to this
       | since, in your data, for a given person, you would have made an
       | offer to them or not - so you can 't directly model this
       | difference for them. Or model it in a statistically significant
       | way. This paper provides a way to do so.
       | 
       | Great read. A little verbose for my taste. But lots of good
       | ideas.
        
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       (page generated 2024-08-30 23:00 UTC)