Elea McDonnell Feit

@eleafeit

Marketing Analytics Prof . Bayesian. Philadelphian.

Philadelphia
eleafeit.com
Vrijeme pridruživanja: siječanj 2010.
189 fotografija ili videozapisa Fotografije i videozapisi

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  1. Prikvačeni tweet
    27. sij

    Have you ever calculated the sample size for an and come up with a sample size that is bigger than you can ever practically get? Does this mean you shouldn't run the test? No! A paper thread for 1/17

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  2. 1. velj

    Saturday night at the Feit’s

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  3. 31. sij

    Don't forget that the deadline to submit to 's Advanced Research Techniques Forum is approaching on 2/9. If you are doing cool data science work in marketing, we would love to hear about it. Conference will be in Rochester June 3-5.

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  4. 29. sij

    Wouldn’t even weak evidence in favor of the null be worrisome in a randomization check?

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  5. 29. sij

    I think I know the answer to this, but why do randomization checks or placebo tests use the cutoff p=0.05 instead of something more conservative?

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  6. 29. sij

    Do I know anyone that works with one of the private ad exchanges in the US? E.g., Concert (VOX Media), Technorati, CBS, IDG TechNetwork, NBCUniversal

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  7. 29. sij

    Very pretty!

    ...gradient + interval plots...
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  8. 27. sij

    TFW you post a thread on your recent paper and misspell your co-authors twitter handle. Sorry,

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  9. 27. sij

    What else will you find in the paper? We show how to compute profit-maximizing sample sizes when one treatment is likely to perform better than the other. That is, we can tell you the optimal size of holdout in media incrementally tests. Read the paper for the details. 17/17

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  10. 27. sij

    The paper is called “Test & Roll: Profit Maximizing A/B tests” and just came out @MarketingScience: . wrote more about it at . 16/17

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  11. 27. sij

    Those familiar with multi-armed bandits may be wondering how this compares to a dynamic approach. We looked into that and found that an optimal-size test & roll (orange in plots) achieves nearly the same level of expected profit as Thompson Sampling (purple). 15/17

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  12. 27. sij

    Computing a test & roll sample size requires information about the range of average response you expect from your treatments. Using data on the past performance of similar treatments, you can estimate priors with an HB model using tools like and . 14/17

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  13. 27. sij

    Our sample size formula also scales with the size of your population. So, if you have lots of customers/traffic, then you should run a bigger test, but marketers with small populations should still run tests. Tests always improve profit! 13/17

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  14. 27. sij

    A test & roll usually deploys the wrong treatment at a greater rate than an NHST. Is this bad? No, because the sample size has been optimized for profit. Errors are most likely to happen when the difference between treatments is small and doesn't affect profit much. 12/17

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  15. 27. sij

    Why should stop doing hypothesis tests? Profit-maximizing tests are much smaller and generate more profit! In one of our case studies a hypothesis test requires a sample size of 18,468 per group while the profit-maximizing size is 2,284 and makes more profit. 11/17

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  16. 27. sij

    developed a similar approach with encouragement from . He frames the problem around discounting, but the idea of trading opportunity cost against deployment errors is essentially the same. 10/17

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  17. 27. sij

    So, what do you do when you get the results of the test? Simple: pick the treatment with the higher average response. No inconclusive tests! We test then roll in an intuitive way. 9/17

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  18. 27. sij

    If you don’t like formulas, you can compute the profit-maximizing test & roll sample size at . We also provide R code at . 8/17

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  19. 27. sij

    So, how big is the profit-maximizing test & roll? @marketsensi and I came up with a new sample size formula that maximizes expected profit. Here it is: 7/17

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  20. 27. sij

    In a test & roll, there is a fundamental trade-off between the opportunity cost of the test and the potential to deploy the wrong treatment. With a few distributional assumptions, we can compute the expected profit of a test & roll for different test sizes. 6/17

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  21. 27. sij

    Marketers do this type of experiment all the time. For example, here is an email A/B testing tool from . , and all have similar A/B testing tools. 5/17

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