Reference: YouTube Statistics
Just a year and a half after the youtube.com domain was bought, Google acquired the video-sharing platform for $1.65 billion. At the time, Google believed they overpaid to acquire the platform. But considering YouTube ad revenue was $8.6 billion in just the last quarter, we know now that Google got a steal.
Reference: YouTube Statistics
ABOUT:We are seeking a YouTube statistics specialist with expertise in the YouTube API to join our team. The selected candidate will be responsible for working with the YouTube API to extract and analyze data for a variety of YouTube-focused projects.RESPONSIBILITIES:- Working with the YouTube API to extract and analyze data from YouTube channels, videos, and other sources- Developing scripts and tools to automate the collection and processing of YouTube data- Implementing and maintaining YouTube integrations for various projects- Providing insights and recommendations based on YouTube data analysis- Collaborating with the development team to integrate YouTube data into web applications and other projects- Staying up-to-date with the latest features and best practices for working with the YouTube API- Documenting processes and procedures related to YouTube data collection and analysis- Providing technical support and guidance to team members as needed
Daily active user statistics relate to YouTube usage on any given day. While monthly stats give a good idea of the overall user base, daily figures can show how many people log on to YouTube as part of their everyday routine.
YouTube is localized in over 100 countries. It can also be used in 80 different languages. Clearly, the video sharing platform has become a truly global phenomenon. But how do usage statistics break down by country?
These statistics reveal that the United Kingdom average the most views as a proportion of the overall population. It is the equivalent of every single British and Northern Irish person viewing 5750 videos! Canada, South Korea, Spain and the USA also have viewing figures significantly higher than the overall population.
YouTube created the technology to detect uploaded videos that infringe copyright in 2007. Content ID technology creates a ID File for copyrighted audio and video material, and stores it in a database. When a video is uploaded, it is checked against the database, and flags the video as a copyright violation if a match is found. When this occurs, the content owner has the choice of blocking the video to make it unviewable, tracking the viewing statistics of the video, or adding advertisements to the video
I have been trying to get the video list of our youtube channel and advance statistics like avg view duration, subscribers gained, watch time, etc.These statistics do exist in Youtube analytics Web GUI. But I could not find how I can implement that per video via API.
For the curious, these represent a series of numbers that boggle the mind, users counted in tens and hundreds of millions, and time in millions and billions of hours. For marketers, knowing the statistics behind the social networks can inform strategy and spend, allowing focused targeting of users.
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The YouTube app ranked 7th in revenue globally in January 2019. Its popularity is not just predicated on big brands using the platform to reach millions of people. Its growth is deeply connected to the rise of organic content from individual creators. Thanks to influencer and their videos, companies can now close the gap with their audiences and vice versa. Top categories for influencers impacting purchasing decisions include cosmetics, food & drinks, clothing, and shoes. Experts expect more virtual shops to be put up in the future. Proof of this is 19% of US adults see YouTube as important in making purchase decisions. Here are useful YouTube statistics for video marketing.
Taking the YouTube statistics and data above into consideration, it sure seems that mobile video marketing is where it is at in the future. YouTube will be a big part of it. Videos will be a part of it. Your brand should become a part of it too. But remember, YouTube is not the only video-sharing social media network at your disposal. When it comes to social selling, Facebook is still king. It just intuitively is and with a recorded GMV of 74%, it is really hard to dispute that.
Looking at statistics about YouTube users by country, we can conclude that India represents one of the most engaged markets of the Alphabet-owned platform. Seven out of 10 users in the country watch ads with the sound on. Approximately 90% of all consumption of the content in India happens in local languages.
Based on the latest statistics, the YouTube user demographics of Brazil make it one of the largest countries in the fold. Data further shows that the number of users in the country enjoying the video content platform will continue to rise to over 166 million by 2025.
In statistics and optimization, the residuals represent the deviation of an observed value of an element and its theoretical value. In regression analysis, the residual is the difference between any data point and the regression line. Sometimes they are also known as an error. An error in this context does not mean that something is wrong with the analysis; it just means that there is an unexplained difference between the observed and theoretical values. In simple words, the residual is the error that is not explained by the regression line.
This subsection deals with the application of the HTBPT-Lomax model using a data set related to the YouTube advertising data. The data are available at -statistics/. The box plot of the YouTube advertising data is provided in Figure 6 whereas the basic measures (BMs) of the data are presented in Table 3.
The part parameter serves two purposes in this operation. It identifies the properties that the write operation will set as well as the properties that the API response will include. Note that this method will override the existing values for all of the mutable properties that are contained in any parts that the parameter value specifies. For example, a video's privacy setting is contained in the status part. As such, if your request is updating a private video, and the request's part parameter value includes the status part, the video's privacy setting will be updated to whatever value the request body specifies. If the request body does not specify a value, the existing privacy setting will be removed and the video will revert to the default privacy setting. In addition, not all parts contain properties that can be set when inserting or updating a video. For example, the statistics object encapsulates statistics that YouTube calculates for a video and does not contain values that you can set or modify. If the parameter value specifies a part that does not contain mutable values, that part will still be included in the API response.
This interface returns trade statistics for goods and services based on the following parameters: type of product, frequency, classification code, commodity code, periods, reporter, partner, second partner, trade flows, modes of transport, and customs code. Users can provide more than a single criterion as the parameter.
For example, we process your information to report use statistics to rights holders about how their content was used in our services. We may also process your information if people search for your name and we display search results for sites containing publicly available information about you.
To open YouTube's analytics page, first make sure you're signed into YouTube. If you don't have a YouTube channel or haven't uploaded any videos, you won't be able to do anything with statistics yet. Follow our guide to uploading videos to YouTube and come back once your videos get some traffic.
You probably noticed from the above steps that YouTube offers a ton of data about your videos. If you're overwhelmed and not totally sure what to look into, don't worry. The Analytics homepage has some handy graphs to show you vital statistics at a glance.
Becoming familiar with your YouTube audience is of paramount importance when you analyze your channel and strategy. An important indicator to consider is the Device metric (since you need to think about production costs of modifying videos for other means of broadcasting), Age metric (to consider the differences between Millennials or Seniors, for example), and Gender (to adequately adjust the styling of the video). You can also use these statistics to validate your original target audience (as you usually think of your audience long before you create the first video). If the audience is different than you thought it would be, analyze the reasons. With datapine, it can be done with just a few clicks.
To contextualize our findings, we first provide summary user engagement data broken down by key demographic and audience characteristic variables in Tables 1 through 4. In addressing RQ1, to explore to what extent video characteristics and social endorsement cues shape user engagement with YouTube science videos, we employ negative binomial regression and hierarchical least ordinary squares regression. Negative binomial regression was the appropriate analytic choice for the analyses of video view count, comments, shares, number of subscribers gained, and number of playlists added in, given that these dependent variables are essentially count data and because of the over-dispersion of the distribution of these five variables where the variance exceeds the mean [16,58,59]. Negative binomial regression enters blocks of variables following presumed causal sequence and allows researchers to test the extent to which the additional block of variables adds to the explanatory power above and beyond the preceding model. We report likelihood-ratio test statistics (i.e., Chi-Square, degrees of freedom, p-value) to test the contribution of each variable block and goodness-of-fit measures (i.e., AIC, BIC) to assess model fit. Hierarchical ordinary least squares (OLS) regression was used in the analyses of average view duration and average percentage viewed due to the continuous nature of these outcome variables. Similar to negative binomial regression, hierarchical OLS regression also allows researchers to assess the relative explanatory power of different independent variables by entering them in blocks based on their presumed causal order.