[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 : 8 points
Date : 2024-08-25 22:20 UTC (1 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.
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(page generated 2024-08-26 23:01 UTC)