EditFlow Intelligence

A data analytics project focused on analyzing video production efficiency, content performance, client value, and operational workflow in a social media marketing agency.

The project is inspired by real-world video editing and agency workflows, including short-form content, motion graphics, long-form YouTube videos, revisions, deadlines, and content performance.

Project Objective

The goal of this project is to answer business questions such as:

• Which video types require the most production time?
• Does higher editing complexity lead to better content performance?
• How do revisions affect editing workload?
• Which video types have the highest late-delivery rates?
• Which content formats generate the most views and leads?
• Which clients require the most production effort?
• How does content performance compare with operational effort?

Dataset

The project uses a simulated dataset modeled around realistic social media agency operations.

The datasets include:

• Client information
• Video production projects
• Editor workload
• Content performance metrics
• Monthly client performance metrics

The analysis covers approximately:

• 1,800 video projects
• 24 clients
• Multiple video formats
• 25M+ total views
• 4K+ leads generated

Tools & Technologies

• Python
• Pandas
• Matplotlib
• Jupyter Notebook
• Power BI

Data Analysis Process

1. Data Cleaning

• Inspected dataset structure and data types
• Checked for missing values and duplicates
• Removed duplicate project records
• Handled missing engagement metrics using median imputation
• Converted date columns to proper datetime format
• Standardized categorical values
• Created derived metrics such as:
  • Turnaround days
  • Delay days
  • Late delivery status

2. Exploratory Data Analysis

The analysis explored:

• Video performance by content type
• Editing workload by video type
• Content performance by editing complexity
• Impact of revisions on editing hours
• Late delivery rates
• Client-level production workload
• Revenue and lead generation
• Content source performance

Key Insights

• Standard short-form content provided strong production efficiency, achieving similar average views and leads to motion graphics content while requiring fewer editing hours.
• Higher editing complexity did not automatically result in better content performance.
• Long-form YouTube videos required significantly more editing time than short-form content.
• Revision count showed a relationship with increased production workload.
• Late-delivery rates varied across different video formats.
• Client-level analysis revealed significant differences in production effort and content outcomes.

Power BI Dashboard

An interactive Power BI dashboard was created to visualize key operational and content performance metrics.

Dashboard KPIs

• Total Projects
• Total Views
• Total Leads Generated
• Average Engagement Rate

Dashboard Visualizations

• Editing Hours by Video Type
• Average Views by Complexity
• Late Delivery Rate by Video Type
• Editing Hours vs. Revisions

Project Structure

EditFlow-Intelligence/
│
├── data/
│   ├── raw/
│   └── cleaned/
│
├── notebooks/
│   ├── 01_data_cleaning.ipynb
│   └── 02_exploratory_analysis.ipynb
│
├── EditFlow_Intelligence_Dashboard.pbix
├── requirements.txt
└── README.md