Social-Buzz-Content-Analysis

I participated in the open-access Accenture Data Analytics Virtual Experience Program with Forage. I worked as a data analyst to help an organization named “Social Buzz” analyze their data and help them understand how they can leverage their massive amount of data.

Client’s Background and Business Problem

1. Social Buzz is a fast-growing social media organization and over the past 5 years, Social Buzz has reached over 500 million active users each month. They have scaled quicker than anticipated and need the help of an advisory firm to oversee their scaling process effectively.

2. Due to the rapid growth and digital nature of their core product, the amount of data they create, collect and analyze is huge. Every day over 100,000 pieces of content, ranging from text, images, videos and GIFs are posted. All this data is highly unstructured and requires extremely sophisticated and expensive technology to manage and maintain.

3. Up until this point, they have not relied on any third-party firms to help them get to where they are. However, one of the main reasons why they are looking at bringing in external expertise is because they want to learn data best practices from a large corporation. Due to the nature of their business, they have a massive amount of data so they are keen on understanding how the world’s biggest companies manage the challenges of big data.

Project Understanding

The first section of the project focused on establishing the deliverables and facts about the business, Social Buzz.  This aspect of the project is crucial as it was instrumental in understanding the task and relevant columns in the data presented, and those that were necessary for the deliverables. In the end, it was established that Social Buzz had the following expectations:
-An analysis of their content categories that highlight the top 5 categories with the largest aggregate popularity.
-An audit of their big data practices.
-Recommendations for successful IPO.

Data Cleaning

After the project had been fully understood and the deliverables established, it was time to get my hands dirty. I was presented with different CSV files, with a data model which depicted the relationship between the data contained in the CSV files. During this cleaning process, I did the following -:
Removed unwanted columns and those that are not relevant to the task.
Eliminated duplicates.
Changed datatypes of some values within a column.
Eliminates rows that have values which are missing.

Data Exploration

There were seven datasets for this project-:

• User.
  
• Profile.
  
• Location.
  
• Session.
  
• Content.
  
• Reaction.
  
• ReactionTypes.
  I followed these steps during the exploration of the datasets:
  

1. I created a final data set by merging the last three tables containing relevant columns using VLOOKUP.
2. Figured out the Top 5 performing categories using a pivot table.

Visualization

After data cleaning and transformation were done in Microsoft Excel, the cleaned dataset was loaded into Microsoft Power BI Desktop for visualization.

This session of the project was mainly about bringing the data to life. It was about creating a dashboard that at a glance tells the story of Social Buzz and their activities over the period. It was about telling the business story using the data they have collected over time. 

Insights and Recommendation

Social Buzz has experienced remarkable growth, evident in the vast volume of content posted daily across various categories. This analysis identified key insights that can drive strategic content refinement, enhancing audience engagement and platform performance.

1. There are 16 unique categories. The top 5 Popular Categories by aggregate popularity score are:
   

• Animals
  
• Science
  
• Healthy Eating
  
• Technology
  
• Food
  

2. May Content Surge: May witnessed the highest volume of content posts with a total of 2,138 posts, This spike presents an opportunity to capitalize on increased user activity, possibly tied to seasonal trends, holidays, or special events. 
3. Animal Content Dominance: Animals emerged as the category with the highest audience reaction, with 1897 reactions. This suggests a strong emotional connection among users towards animal-related content. Social Buzz should prioritize this category, doubling down on content creation and curation to capitalize on heightened engagement. Incorporating user-generated content, interactive features, and storytelling elements can further amplify the impact of animal-related posts.
4. Diversified Content Strategy: While animals stand out, the popularity of science, healthy eating, technology, and food signifies a diverse user base with varied interests. Social Buzz can capitalize on this diversity by implementing a balanced content strategy that caters to different audience segments. By offering a mix of content across these top categories, Social Buzz can broaden its appeal and attract a wider audience demographic.
5. Engagement Initiatives: Building upon the success of animal-related content, Social Buzz can explore innovative engagement initiatives such as themed campaigns, contests, and partnerships with relevant organizations or influencers. These initiatives drive interaction and foster a sense of community among users with shared interests. Strategic Content Planning: The presence of both the "Food" and "Healthy Eating" categories suggests that some users may be drawn to indulgent food content, while others may prioritize health-conscious content. Social Buzz can leverage this insight to curate content that aligns with prevailing health trends, offering informative and inspiring content on nutrition, wellness, and healthy lifestyle choices. They can also explore monetization opportunities through sponsored content, affiliate marketing partnerships, or product collaborations with brands in the food and wellness industry. This diversifies revenue streams and enhances the platform's value proposition for advertisers and partners.

Tools used to carry out this project include

• Microsoft Excel: This was used to clean the datasets and generate key insights into the datasets.
  
• Microsoft Power BI: This is used to visualize the insights from my analysis in Excel.
  
• Microsoft PowerPoint: I used this tool to prepare slides for presentation purposes to the client.