Social-Buzz-Analysis

This is Accenture Data Analytics virtual experience project with Forage. As a data analyst, The objective was to help a company called "Social Buzz" in analyzing their data and help them understand how they leverage can enormous amount of data.

Table of Contents

• Project Overview
• Analysis
• Recommendation

Project Overview

1. Social Buzz is a fast-growing social media company that has reached over 500 million monthly active users throughout the previous five years. They expanded more quickly than they anticipated, and in order to properly manage their scaling process, they require the assistance of a consulting firm.

2. A significant amount of data is created, collected, and analyzed by them because of their core product's digital nature and its rapid expansion. Each day, more than 100,000 different types of content are uploaded, including text, photos, videos, and GIFs. Managing and maintaining this vast amount of unstructured data calls for incredibly expensive and complex technology.

3. They haven't depended on external companies to assist them in getting to this position. However they are also seeking to acquire data best practices from a big company, which is one of the key reasons they are considering bringing in outside expertise. They are interested in learning how the largest corporations in the world handle the difficulties posed by big data because of the nature of their business, which generates enormous amounts of data.

Problem Statement.

The client has no access to the internal resources to handle its data because it has grown to an enormous amount in recent years.In order address to this business issue, I did the following:

Project Understanding

The first stage of the project was devoted to defining the deliverables and company information for Social Buzz. This part of the project is very important since it helped to comprehend the task and the columns in the data that were relevant and needed for the deliverables. Eventually, it was determined that Social Buzz had anticipated the following things:

1. An analysis of their content categories that highlight the top 5 categories with the Highest aggregate popularity.
2. An audit of their big data practices
3. Recommendations for successful IPO

Data Cleaning/Preparation

After understanding the business, we then cleaned the available datasets and thought about what an ideal dataset should look like for this problem. 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.
• Eliminated rows that have values which are missing.
• Data Exploration

Visualization

The Dashboard above shows that 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.

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

• Animals
• Science
• Healthy Eating
• Technology
• Food

Analysis

Animals had the highest aggregate popularity score of around 74965. It is very interesting to see both food and healthy eating within the top 5, it really shows that food is a highly engaging content category. Healthy eating ranks slightly higher than food, so perhaps your user base may be skewed towards healthy eaters and health-conscious people. its also interesting to see science and technology too. This may suggest that people enjoy consuming factual content and snippets of content that they can learn something from.

January witnessed the highest volume of content posts with a total of 3,106 posts, This spike presents an opportunity to capitalize on increased user activity, possibly tied to seasonal trends, holidays, or special events.

The percentage split of popularity among the top 5 categories may be seen in this chart. The shares of each category do not differ significantly, but the 1.1% difference between the most popular category—animals and the second, science is the biggest. From a commercial perspective, this would imply that the most popular category animals—is lagging behind the other categories and might even continue to gain popularity. It will be crucial for you to make sure that any algorithms used to manage the content on the platform give a proper balance to the content categories in order to prevent a situation where one content category takes up the entire platform.

Recommendation

The existence of the "Food" and "Healthy Eating" categories implies that although some users may be more interested in decadent food content, others might be more interested in information that promotes health. Using this knowledge, Social Buzz may curate material that reflects current health trends and provides enlightening and motivating content about wellness, nutrition, and healthy living options. They can also look for ways to make money by working with brands in the food and wellness sectors on sponsored content, affiliate marketing alliances, or product partnerships. By doing this, the platform's value offer for partners and advertising is strengthened and its revenue streams are diversified.

Tools

• Google sheet - Data Cleaning
• Microsoft Power BI - Creating dashboard reports.
• Microsoft PowerPoint - prepare slides for presentation purposes to the client.