Difference between revisions of "ANLY482 AY2017-18T2 Group30 Business Objectives"

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::*For data files from <i>Facebook Insights Data Export (Post Level)</i>, the sponsor provided exported data from different periods of the year, with different metric tabs in Excel format. The tabs included are:
 
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#Key Metrics
 
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#Lifetime: Number of unique people who have created a story about your Page post by interacting with it (unique users)
 
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#Lifetime: Number of people who have clicked anywhere in your post, by type (unique users)
 
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#Lifetime: Number of people who have given negative feedback on your post, by type (unique users)
 
  
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::*Facebook Post data comprises of 4 main types (a total of 1381 rows):
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APex’s objective is to analyze and identify opportunities to improve the quality of content curation. APex went through the entire process of data preparation, data cleaning and exploratory data analysis. After which, we proposed a list of business objectives to our client and the following is the refined list of objectives.
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#Link (955 rows)
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#Photo (56 rows)
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1. What is the impact of Facebook algorithm change on TSL’s post?
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#Shared Video (103 rows)
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TSL learned that Facebook has recently changed ranking algorithms for posts in December 2017. Specifically, posts that encourage sharing and commenting to participate in competitions will be downgraded in rankings. TSL would like APex to investigate the impact of this change on their Facebook outreach, and if possible, provide recommendations.
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#Video (267 rows)
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2. Which are the Facebook videos that have high dropout rates?
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TSL would like to understand which are the top 10 videos that has high dropout rates. This would allow the management to understand which are the videos that are disliked amongst the Singapore population. In return, they will be able to focus their effort on churning out content that is of interest to Singaporeans.
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3. Which platform has better engagement for each content type?
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Having published various content types over the years, TheSmartLocal knows what posts perform well and what doesn’t. However, monitoring content engagement over different platforms such as Facebook, YouTube, Instagram and their blogs can be highly complex due to the nature of the posts such as timing, content type, authors. As such, they would like to perform a holistic, cross-platform analysis to quantify and investigate the causes of virality.
  
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4. Which Series of Youtube Videos are performing better?
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TSL produces different series of youtube videos and they would like to know which are the type of videos that are popular amongst Singaporeans. This would allow them to channel more resources in producing content that is of interest to Singaporeans.
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5. What is TSL’s golden ratio for organic reach?
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TSL prides itself as a company that did not purchase likes / followers. As such, it wants to use its organic reach as a benchmark so that it can audit against other competitors in the market that bought likes / followers. This would help increase TSL’s credibility when it is bidding for a project against a pool of competitors that flaunt “High Followership”.
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::To help us have an overview of the data throughout the year, we consolidated the various tabs, whilst concatenating the various periods of data for the same columns, into one combined file. This was carried out using the software, IBM JMP Pro, in the following steps:
 
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::* With Post ID, Permalink (permanent link of the campaign content), Post Message, Type, Countries and Posted columns as key identifiers among the different tabs for the excel files, we appended desired columns from the other tabs to the end of the Key Metrics. They included the Share, Like, Comment columns from Tab 2; Other Clicks, Link Clicks, Photo View, Video Play columns from Tab 3; Hide_Clicks , Hide_all_clicks, Unlike_page_clicks, report_spam_clicks columns from Tab 4. <br>This was conducted using the <i>Tables > Join </i>function, with “Matching Specification” as the key identifiers and “Output Columns” of the appended desired columns.  
 
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::* Next, for each period of data files (appended with new columns) from multiple tabs, we concatenate the data across different time periods to have a full year collection of data.<br>This was conducted using the <i>Tables > Concatenate </i> function, while adding multiple data tables into “Data Tables to be Concatenated”.
 
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::* Finally, we check for <b>missing data</b> in the different columns. For example, under the column Type, we have five different types, namely: Link, Photo, Shared Video, Status and Video. However, in the instances of missing data, we will cross check with the permalink of the campaign post, and check the Type of medium was posted and fill it in accordingly.
 
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::*Using JMP Pro, we can see that there is a particular post that has garnered higher than usual number of shares versus the lifetime post total reach. We will classify it as an outlier in our analysis. The outlier has higher than normal values with 400,000 total reach.
 
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[[Image:Share_vs_Lifetime_Post_Total_Reach.PNG|650px|Identification of outlier from all types of Facebook posts]]
 
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::Using Tableau for visualization, firstly, we can see that the number of Facebook posts is at the lowest at July (for the months from March to December) with only 4 posts, while there are sudden spikes in posts in April and August.
 
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[[Image:Facebook_Posts_over_Time.png|650px|From the period of March to December 2017]]
 
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::Secondly, to evaluate the effectiveness of Facebook post, we use <b>three key metrics</b> as evaluation. They are: <i>likes, comments and shares</i>. Across time (in weeks), video’s likes, comments and shares have sharp peaks in October, whereas link type performed well in the month of March with highest number of likes and shares.  On the other hand, photos performed well in the months of May to August compared to the rest, with an average 2.5K likes, comments and shares, while links and photos remained relatively constant. The outlier mentioned earlier is excluded - to prevent skewness of data.
 
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[[Image:Metrics_against_time.png|650px|Comparing Likes, Shares and Comments across types]]
 
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::Next, the dataset contains 19 missing data for the number of likes across the different post types, out of 1381 records. The data is excluded for our current analysis. We can see that Thursday’s post generally generates the most number of likes, especially for video type.<b>Video type contributes to the highest number of likes</b> (green shaded area), followed by photo type. <br/><br/>
 
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[[Image:Weekday_vs_Likes.png|450px|Comparing likes across a week]]
 
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<p>::It can also be noted that video contributes the most number of likes across the various post types with 49.21%. However, it should be noted that link type has higher percentage of engaged users than video type, accounting more than half (51.56%) of lifetime engaged users. </p>
 
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[[Image:Likes_by_Type.png|300px|Likes by Type (% of total sum of likes) ]]
 
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[[Image:Lifetime_Engaged_Users_by_Type.png|600px|Measuring lifetime engaged users by type in percentage]]
 
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Revision as of 12:13, 26 February 2018

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HOME ABOUT US PROJECT OVERVIEW EDA BUSINESS OBJECTIVES PROJECT MANAGEMENT DOCUMENTATION MAIN PAGE
Overview Objective 1 Objective 2 Objective 3 Objective 4 Objective 5


APex’s objective is to analyze and identify opportunities to improve the quality of content curation. APex went through the entire process of data preparation, data cleaning and exploratory data analysis. After which, we proposed a list of business objectives to our client and the following is the refined list of objectives.

1. What is the impact of Facebook algorithm change on TSL’s post? TSL learned that Facebook has recently changed ranking algorithms for posts in December 2017. Specifically, posts that encourage sharing and commenting to participate in competitions will be downgraded in rankings. TSL would like APex to investigate the impact of this change on their Facebook outreach, and if possible, provide recommendations.

2. Which are the Facebook videos that have high dropout rates? TSL would like to understand which are the top 10 videos that has high dropout rates. This would allow the management to understand which are the videos that are disliked amongst the Singapore population. In return, they will be able to focus their effort on churning out content that is of interest to Singaporeans.

3. Which platform has better engagement for each content type? Having published various content types over the years, TheSmartLocal knows what posts perform well and what doesn’t. However, monitoring content engagement over different platforms such as Facebook, YouTube, Instagram and their blogs can be highly complex due to the nature of the posts such as timing, content type, authors. As such, they would like to perform a holistic, cross-platform analysis to quantify and investigate the causes of virality.

4. Which Series of Youtube Videos are performing better? TSL produces different series of youtube videos and they would like to know which are the type of videos that are popular amongst Singaporeans. This would allow them to channel more resources in producing content that is of interest to Singaporeans.

5. What is TSL’s golden ratio for organic reach? TSL prides itself as a company that did not purchase likes / followers. As such, it wants to use its organic reach as a benchmark so that it can audit against other competitors in the market that bought likes / followers. This would help increase TSL’s credibility when it is bidding for a project against a pool of competitors that flaunt “High Followership”.

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