Clarifying Questions:
Is the drop in likes consistent across all types of content (e.g. posts, photos, videos)?
Are there any changes in user behavior or trends that could be contributing to the drop in likes?
Are there any changes in the algorithm that could be affecting the number of likes?
Product/Feature Description and Objective:
The Facebook app is a social networking platform that allows users to connect with friends and family, share content such as posts, photos, and videos, and engage with other users' content through likes, comments, and shares. The objective is to understand why the average number of likes has dropped by 10% over the past year and identify potential solutions to address the issue.
Hypotheses:
"Algorithm changes hypothesis" - Changes in Facebook's algorithm have decreased the visibility of posts and made it harder for users to discover new content, resulting in fewer likes.
"Content quality hypothesis" - The quality of content posted on Facebook has decreased, leading to lower engagement from users.
"Feature changes hypothesis" - Changes in Facebook's features or user interface have made it more difficult for users to engage with content, resulting in fewer likes.
"Competitor hypothesis" - The rise of competing platforms such as TikTok or Instagram has drawn users away from Facebook, resulting in fewer likes.
"Demographic shift hypothesis" - A shift in the demographic makeup of Facebook's user base has resulted in a different mix of users who are less likely to engage with content through likes.
"User behavior hypothesis" - Changes in user behavior, such as spending less time on Facebook or engaging with content in different ways, have resulted in fewer likes.
Operationalizing Hypotheses:
Algorithm changes hypothesis: Analyze the engagement metrics (e.g., likes, comments, shares) for posts before and after significant algorithm changes and compare them to the overall trend.
Content quality hypothesis: Analyze the engagement metrics for high-quality posts (e.g., those with more likes or shares than average) and compare them to low-quality posts to identify any patterns.
Feature changes hypothesis: Analyze the engagement metrics for posts before and after significant feature changes and compare them to the overall trend.
Competitor hypothesis: Analyze user behavior metrics (e.g., time spent on Facebook, frequency of visits) for users who also use competing platforms to identify any correlations.
Demographic shift hypothesis: Analyze engagement metrics for different demographic groups to identify any changes in the mix of users engaging with content.
User behavior hypothesis: Analyze user behavior metrics to identify any changes in the way users engage with content and identify any correlations with changes in the number of likes.
Conclusion and Recommendation:
After analyzing the data, it is recommended that Facebook focus on addressing the content quality hypothesis and the user behavior hypothesis. Improving the quality of content posted on Facebook could lead to higher engagement, while understanding and adapting to changes in user behavior could help Facebook stay relevant and engaging to users. Additionally, further investigation into the algorithm changes hypothesis and the competitor hypothesis could provide additional insights into the drop in likes and potential solutions.