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  "documentTitle": "Qutoutiao, Inc. (QTT)",
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  "authorName": "Wolfpack Research",
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  "presentationDate": "2020-01-17 00:00:00",
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  "notes": "This is a rebuttal slide from a research report by Wolfpack Research regarding Qutoutiao (QTT).",
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      "text": "In QTT's response, they point to sample size and methodology as problems with our approach, but in doing so they inadvertently admit their platform's complete lack of technical prowess.",
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      "text": "We took many samples over a period of time... We tweaked one variable at a time... For the 50,000-ad sample... these variables were randomized... Most importantly, the resulting traffic distribution was effectively the same in every sample we ran.",
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      "text": "In our report, we showed how QTT is directing a large portion of its traffic to related parties... 88.3% of the advertisements was directed to the top 10 advertisers. Even worse, more than 35% of the total advertising traffic was directed to related parties.",
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      "text": "The total number of advertisements placed on Qutoutiao's platform per day exceeds 2,000,000,000... As our programmatic advertising system considers a wide range of parameters... any results generated by a miniscule sample through a tiny group of fake users could inevitably be skewed.",
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      "text": "\"The total number of advertisements placed on Qutoutiao's platform per day exceeds 2,000,000,000. Therefore, analyzing 50,000 advertisements (i.e., only 0.0025% of total) concentrated in a very confined period (i.e., four hours on September 12, 2019) on Qutoutiao's platform would lead to skewed results, especially considering that behind the very limited sample size is a very limited number of test phones that are not capable of replicating the diverse behaviours exhibited by our 42 million daily active users. As our programmatic advertising system considers a wide range of parameters to determine which advertisement to display, including price bid, predicted click-through rate and content relevance, any results generated by a miniscule sample through a tiny group of fake users (i.e., test phones) could inevitably be skewed. That is to say, as the test phones probably have some identical or similar characteristics, when these test phones frequently send requests to our advertising system in a short period of time, it could respond to these “fake requests” by recommending similar contents, so that the advertisements received by these fake users are concentrated in a specific type, which cannot represent the actual overall situation.\" — Qutoutiao, Inc.",
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      "text": "11. QTT has not denied the accuracy of the ad distribution in our sample. Instead, QTT used a series of qualified assumptions to imagine a scenario that could create a skewed distribution of ad traffic on its platform:",
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