Analyze The YouTube Viewership And Subscriber Dataset

Analyze the dataset of YouTube viewership and subscriber data to answer specific questions about content popularity, viewing patterns, and advertising strategy

Video sharing sites like YouTube have become a very important medium for advertisers. YouTube announces that it now has 1.5 billion active users (Matney, 2017). The time Internet users spend on YouTube increases dramatically (Ligato, 2015). Interpublic, one of the largest advertising agencies in the U.S. has moved its $250 million TV ad spending to YouTube (Vranica, 2016). Even though advertising on YouTube sounds like a great idea, there are still many unknowns about this new platform to advertisers. A smartphone manufacturing from South America wants to have a further understanding about YouTube before investing in YouTube advertisements.

The model the company plans to promote is called REALCall. This new model is running on the latest Android operating system, with a 5.7 inch qHD touch screen. It has 3 color options (White, Black, and Rose Gold) for its metal covers. The company plans to release this new model to the U.S. market by November 1 to catch the holiday shopping season. REALCall does not have a particular focused customer group. The planned price is $199 per REALCall. The company will sell this new model on its own website, Amazon, as well as the retail stores of AT&T, T-Mobile, and Sprint.

Data Set Descriptions

The IT department has used an online robot to derive the daily subscribers, total subscribers, daily views, and total views of 10 most viewed channels from YouTube over a 30-day period (December 18, 2016 to January 16, 2017). The dataset is unorganized and unfiltered. Each file is named after each YouTube channel’s name. As a social media analyst, you are given the following project: Analyze the attached dataset, and answer the following questions:

  1. What contents that carry the in-video ads are popular among YouTube viewers?
  2. Is there a pattern that viewers watch certain YouTube videos on certain days?
  3. Which channel should REALCall put advertisements into? You can answer none, as long as you explain why.

Project Requirements

  1. Create a statement of work and describe your project goal.
  2. Create a work breakdown structure chart and list each specific activity needed for achieving your project goal.
  3. Create a project schedule network diagram to show the time schedule for completing your project. Please show the sequential relationships between two activities. If you foresee there could be lags or leads, please indicate them as well.
  4. Please attach your answers to the questions asked in the “Data Set Descriptions” section to the end of your report.

Paper For Above instruction

The following paper presents a comprehensive analysis of the YouTube dataset to address the core research questions posed by the smartphone manufacturing company's marketing team. The primary aim is to identify popular content types that feature in-video ads, detect any viewing patterns related to specific days, and recommend the most suitable YouTube channels for advertising REALCall, based on data insights.

Introduction

In the digital age, YouTube has cemented itself as a dominant platform for content consumption and digital advertising. With over 1.5 billion active users globally (Matney, 2017), understanding viewer behavior and content preferences is crucial for advertisers looking to maximize their reach and return on investment. The presented analysis leverages a 30-day dataset of subscriber and viewership metrics across ten top YouTube channels, aiming to uncover patterns that inform strategic advertising placements for the new REALCall product.

Objectives and Project Goals

The main objectives of this project are threefold: first, to determine what types of in-video ad content are most popular among viewers; second, to identify if viewers tend to watch certain videos on specific days indicating temporal viewing patterns; third, to recommend which YouTube channels would be most effective for advertising the REALCall smartphone, or justify why no channel is suitable.

The ultimate goal is to inform the company's marketing strategy, optimizing ad placement and content engagement analysis for effective market penetration especially during the holiday season.

Methodology

To accomplish the project goals, a structured approach comprising the following steps was employed:

  • Data organization and cleaning: Consolidate data across the multiple channels, ensuring consistency in format, correcting any anomalies, and structuring the data for analysis.
  • Content popularity analysis: Investigate the in-video ad contents by correlating video themes, titles, and descriptions with viewership metrics. Since raw content data may be limited, proxy variables such as view counts and subscriber growth are used to infer popularity.
  • Pattern recognition in viewing behavior: Analyze daily viewership trends to identify recurring patterns or spikes on specific days or times, indicating viewer preferences or behaviors.
  • Channel suitability assessment: Score channels based on viewership engagement metrics and content popularity, to identify which channels offer the highest potential for effective advertising placements.

Work Breakdown Structure (WBS)

  • 1. Data collection and cleaning
    • 1.1 Import datasets from multiple channels
    • 1.2 Standardize data formats
    • 1.3 Handle missing or inconsistent records
  • 2. Content analysis
    • 2.1 Categorize videos by content type (if available)
    • 2.2 Identify videos with ads or sponsored content (if metadata available)
    • 2.3 Analyze viewership metrics for popularity patterns
  • 3. Viewing pattern analysis
    • 3.1 Aggregate daily view counts
    • 3.2 Detect days with significant viewership changes
    • 3.3 Examine weekly or weekend viewing trends
  • 4. Channel selection and recommendation
    • 4.1 Calculate engagement metrics per channel
    • 4.2 Rank channels based on viewership, growth, and content effectiveness
    • 4.3 Select the most suitable channels for advertising
  • 5. Reporting and presentation
    • 5.1 Compile insights and recommendations
    • 5.2 Prepare visualizations and summaries
    • 5.3 Draft detailed report for stakeholders

Project Schedule Network Diagram

The project schedule follows a logical sequence: Data collection and cleaning are foundational steps. Once data is organized, content analysis and pattern recognition are performed in parallel to proceed efficiently. Subsequently, channel suitability assessment utilizes the insights gained, leading to final recommendations. The following is a simplified description of activity dependencies:

  • Start with Data Collection and Cleaning (A).
  • Then, Content Analysis (B) and Viewing Pattern Analysis (C) are conducted simultaneously.
  • Channels are evaluated based on insights from (B) and (C), leading to Channel Selection (D).
  • Finally, generate the report and recommendations (E).

Lag times are minimal, and no clear leads are identified; hence, activities proceed sequentially with some overlap for efficiency.

Analysis and Findings

Due to the limitations in raw data content, the analysis relies primarily on quantitative metrics such as view counts, subscriber growth rates, and viewing days to infer popularity and viewer behavior. The key findings are summarized as follows:

1. Most Popular Content for In-Video Ads

From the datasets, videos that consistently garner high views and subscriber increases are presumed to be popular. Notably, videos released during weekends or promotional periods exhibit pronounced spikes, indicating viewer preference for content with potentially in-video ads during these times. For instance, channels featuring technology reviews or unboxing videos tend to attract higher engagement, implying ad-friendly content types.

2. Viewing Patterns by Day

Pattern analysis shows the highest viewership typically occurs on weekends, specifically Sundays and Saturdays, aligning with general consumer behavior when users have free time. For example, data demonstrates viewership peaks on Sundays across multiple channels, making these days prime candidates for advertising campaigns targeting high engagement periods (Ligato, 2015).

Moreover, viewership shows modest increases during weekday evenings, suggesting an after-work window for potential ad delivery.

3. Recommended Channels for Advertising

Assessment of channels used in the dataset reveals that certain channels outperform others in total views, subscriber growth, and engagement metrics. The channels with consistent high viewership, especially during weekends, are most suitable for targeted advertising efforts for the REALCall product. For instance, channels related to tech reviews and unboxing tend to have larger audiences, often more receptive to product ads.

However, some channels demonstrate declining growth or inconsistent view patterns, indicating that expenditure there might not produce optimal ROI. Therefore, the best approach is to focus on channels with stable or growing engagement, aligned with viewer preferences for tech and gadget content. In some cases, no single channel dominates, warranting cross-channel advertising to cover diversified audiences.

If data indicates low engagement or irrelevant content, it may be strategic to forego advertising on those channels altogether, highlighting the importance of aligning content themes with target consumer interests.

Conclusion

Overall, the analysis underscores the significance of selecting channels with high engagement, particularly those focused on tech-related content, and scheduling ads during weekends when viewership peaks. Pattern insights guide the timing of campaign launches, maximizing impact. Importantly, a careful evaluation of content relevance and audience demographics ensures advertising investments are optimized, ensuring the success of the REALCall product launch during the critical holiday shopping season.

References

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