Network's popular titles include Ragnarok Online Mobile: Eternal Love, Girls' Frontline, Sausage Man, and Muse Dash. Network is a Hong Kong-listed online game developer/distributor, running 38 titles under its online community and distribution platform TapTap. network break out of its niche by reaching a broader audience on its platform.įounded in 2002, X.D. Network's game fans to its video streaming rooms, while Bilibili can help X.D. Network presents a win-win situation Bilibili can promote its games through TapTap's distribution channels and attract X.D. However, the gaming sector showed flat growth over the past year, declining from 71% and 53% in 20.Īccording to Bilibili's financial report, its game sector generated total revenue of CNY1.13 billion in the fourth quarter of 2020, down 11.7% month-over-month.Īs one of the cash cows of Bilibili, slowing revenue growth of the gaming sector is not a good sign, especially for a loss-making company like Bilibili, which has been in the red for 13 consecutive quarters. Their ambition is to become a comprehensive and vibrant community for all kinds of users and video creators.ĭespite its more diversified business, Bilibili's games are still its largest revenue-generating sector, which accounted for 40% of the total revenue in 2020. Initially positioned as a playground for China's ACG (anime, comics, and games) community, Bilibili has been expanding its offerings to a wide range of categories, like fashion, lifestyle, beauty, music, and technology. The investment aims to enrich the video platform's content that caters to the diverse tastes of animation fans, gamers, and merchants. Under the agreement, Hong Kong and Nasdaq listed Bilibili has purchased a 4.72% stake of Taptap's owner X.D. TapTap challenges the common unfair taxation of the Chinese game distributors, and will help Bilibili reach more gamer audience.Ĭhinese video sharing and streaming platform Bilibili has invested HKD9.6 billion in domestic game distribution platform TapTap. Any experiment that involves later statistical inference requires a sample size calculation done BEFORE such an experiment starts.Bilibili's Investment in TapTap Is A Win-Win, And Here Is Why - PingWest English 中文īilibili's Investment in TapTap Is A Win-Win, And Here Is Why The significance level for the experiment: A 5% significance level means that if you declare a winner in your AB test (reject the null hypothesis), then you have a 95% chance that you are correct in doing so.The null hypothesis is tested against the alternative hypothesis which is that the two conversion rates are not equal:īefore we start running the experiment, we establish three main criteria: In every AB test, we formulate the null hypothesis which is that the two conversion rates for the control design ( ) and the new tested design ( ) are equal: Calculating the minimum number of visitors required for an AB test prior to starting prevents us from running the test for a smaller sample size, thus having an “underpowered” test. The test power : the probability of detecting that difference between the original rate and the variant conversion rates.Minimum detectable effect : The desired relevant difference between the rates you would like to discover.It also means that you have significant result difference between the control and the variation with a 95% “confidence.” This threshold is, of course, an arbitrary one and one chooses it when making the design of an experiment. Using the statistical analysis of the results, you might reject or not reject the null hypothesis. There is a difference between the two conversion rates but you don’t have enough sample size (power) to detect it.The difference between the two conversion rates is too small to be relevant.There is no difference between the two conversion rates of the control and the variation (they are EXACTLY the same!).Not rejecting the null hypothesis means one of three things: Rejecting the null hypothesis means your data shows a statistically significant difference between the two conversion rates. The first case is very rare since the two conversion rates are usually different. The second case is ok since we are not interested in the difference which is less than the threshold we established for the experiment (like 0.01%).
The worst case scenario is the third one. You are not able to detect a difference between the two conversion rates although it exists. Because of the data, you are completely unaware of it. To prevent this problem from happening, you need to calculate the sample size of your experiment before conducting it.
It is important to remember that there is a difference between the population conversion rates and the sample size conversion observed rates r.
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