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terestingly, progesterone has also been shown to demonstrate effects on octopus spermatozoa. [43] Progesterone is sometimes called the "hormone of pregnancy", [44] and it has many roles relating to the development of the fetus: <span>Progesterone converts the endometrium to its secretory stage to prepare the uterus for implantation. At the same time progesterone affects the vaginal epithelium and cervical mucus, making it thick and impenetrable to sperm. Progesterone is anti-mitogenic in endometrial epithelial cell

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h. Commercial aviation is a massive industry involving the flying of tens of thousands of passengers daily on airliners. Most airplanes are flown by a pilot on board the aircraft, but some are designed to be remotely or computer-controlled. <span>The Wright brothers invented and flew the first airplane in 1903, recognized as "the first sustained and controlled heavier-than-air powered flight". [1] They built on the works of George Cayley dating from 1799, when he set forth the conc

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d consider, but we are limited by the duration of the course and in practise they work well for a wide class of problems. By consistently using the caret package we give the students a scalable toolkit for exploring other model approaches. <span>Once you have a model that predicts the probability of a positive response, you score your customer base (or the subset eligible for the campaign) and sort the list by the probability from high to low. The cumulative sum of probabilities to n gives you the expected sales from contacting n customers, and the line of this is the lift curve . The way marketing people tend to use it is that they have a budget for N contacts, for example direct mailings, and they read off the curve how many responses they are going to get. Or if they need M responses (typically sales), they can find the number of contacts needed by starting on M on the y-axis and finding the corresponding N on the x-axis. Simple. And wrong . The right way to do it The lost sales from using the traditional lift curve approach; in this example it amounts to nearly 20% less sales in the worst case.

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approach; in this example it amounts to nearly 20% less sales in the worst case. Or at least the approach is not optimal in the setting where the product you are selling (or the behaviour you are encouraging) is readily available to all. <span>The problem is that some of the people who buy are people who would buy anyhow, without your marketing effort, so you are wasting some of your budget. You want to target the customers whose behaviour you are most likely to influence. It should not be a surprise that some of the people who purchased during your test campaign, the test campaign you are using to create the model, would have bought from you anyhow. Y

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to create the model, would have bought from you anyhow. You have both a treatment and a control group and it seems a real shame not to use the data from both. The treatment group obviously gives you the model for the propensity to buy. <span>We teach our students to make a baseline model from the control group to predict who would buy without any stimuli. The difference in the probability from the two models is the net response probability and this is the number you want to sort from high to low to decide on your campaign list. The two lessons are Never throw away good data. In this case, do not ignore the response from the control group. The outcome of marketing activities is to change h

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e control group to predict who would buy without any stimuli. The difference in the probability from the two models is the net response probability and this is the number you want to sort from high to low to decide on your campaign list. <span>The two lessons are Never throw away good data. In this case, do not ignore the response from the control group. The outcome of marketing activities is to change human behaviour, so make sure you model the change from your efforts and realise that this is not the same as the overall behaviour. You are not the centre of the universe. Learn more If you want to learn more, consider signing up for our training course Marketing Analytics Using R or feel free to contact us for an informal conversation.

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Once you have a model that predicts the probability of a positive response, you score your customer base (or the subset eligible for the campaign) and sort the list by the probability from high to low. The cumulative sum of probabilities to n gives you the expected sales from contacting n customers, and the line of this is the lift curve . The way marketing people tend to use it is that they have a budget for N contacts, for example direct mailings, and they read off the curve how many responses they are going to get.

d consider, but we are limited by the duration of the course and in practise they work well for a wide class of problems. By consistently using the caret package we give the students a scalable toolkit for exploring other model approaches. <span>Once you have a model that predicts the probability of a positive response, you score your customer base (or the subset eligible for the campaign) and sort the list by the probability from high to low. The cumulative sum of probabilities to n gives you the expected sales from contacting n customers, and the line of this is the lift curve . The way marketing people tend to use it is that they have a budget for N contacts, for example direct mailings, and they read off the curve how many responses they are going to get. Or if they need M responses (typically sales), they can find the number of contacts needed by starting on M on the y-axis and finding the corresponding N on the x-axis. Simple. And wrong . The right way to do it The lost sales from using the traditional lift curve approach; in this example it amounts to nearly 20% less sales in the worst case.