GLORY AGUNWA - Whatsapp Chat Analysis

Data Analysis For Talkativ

Problem

Talkativ wants to uncover the following insights from the data gotten from their online community:

1. Topics that are of interest in their online community

2. The champions within their online community.

To give a background of the amount of data involved:

• There is a total of 4,649 words generated by community members

• A total of 375 members in the community

• A total of 590 texting times.

In this day and time, community is key but how do you capitalize on it when there is so much of unstructured data? It’s not just about having a large community. The real value is in understanding their needs and responding to them thereby providing a more personalized and enjoyable experience for each community member.

And this is where my data analytics expertise comes in. With the right insight I have uncovered from this task, you can create a content marketing strategy and as well identify the champions within your online community.

Tools Used:

Power Query
Tableau
Excel

The Process

So how did I tackle this problem? Well, I approached it in five steps:

Understanding business needs: I made sure that I knew what the business wanted me to do with their data first before starting out because the key to success on any data project is to understand the business problem.

Problem

Talkativ wants to uncover the following insights from the data gotten from their online community:

• Topics that are of interest in their online community

• The champions within their online community.

Data Extraction: after understudying the business problem, I exported data from the Designers’ WhatsApp chat (their online community).

3. Data Processing: after extracting the raw data, I needed to process it to fit into a data frame that can answer the business questions and produce insights. I was able to achieve this using Microsoft Excel Power Query where I created four tables from the raw data, cleaned the data and saved the workbook. Below are pictures of what the various data tables look like ranging from the raw data to the clean tables.

Raw Data Table

Cleaned Data Table

Word & Event Count Table

Names & Description Table

The Steps for cleaning the data are below;

Creating relationship model and visuals: with my new data sets, I used my analytical skills to uncover insights from the datasets and produce visualizations to describe the insights. These visuals were created using Tableau where I created a relationship in data model from my four data tables.

Below are some of the visuals and dashboard created from the data.

And finally, I used these insights to unlock business decisions and to make recommendations on next steps.

From the word cloud, some potential topics that could be discussed in a group that focuses on SQL, data, BI, Excel, Python, visualization, Tableau, analytics, science, Power BI, computer, remote, skills, and learning include:

Techniques for querying and analyzing large datasets using SQL
Best practices for data visualization and creating effective dashboards in Tableau or Power BI
Strategies for using Excel or Python for data cleaning and preprocessing
Tips for using advanced analytics techniques such as machine learning to gain insights from data
Ways to improve remote work skills and productivity
Discussion of the latest trends and developments in the field of data science and analytics.
How to use computer and software tools to collect and analyze data.
Sharing examples of interesting projects or case studies related to data analysis and visualization.
Techniques for self-learning and skills development.
Sharing resources and tutorials for learning new data analysis tools and technologies.

Below are the top 10 active members in the community;

THANK YOU!

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