BrightTV Viewership Analysis

Introduction BrightTv works in a very competitive entertainment and broadcasting market, where keeping existing customers and gaining new ones are both important fro long term growth. To meet the CEO's goal of increasing subscription this year, it is important to study how customers use the service, understand what drives their viewing choices, and find ways ti improve engagement and attract more viewers. The dataset gives useful information about viewers, like their age, gender, and location, as well as what channels they watch, when, and how often. The CVM team can use these insights to make smarter decisions that improve the customer experience and business performance.

🎯 Purpose This repository contains the analysis and deliverables for the BrightTV Viewership Analytics Case Study. The primary purpose of this project is to leverage advanced data analytics on BrightTV’s proprietary user and viewing data to derive actionable insights. These insights will directly inform the Customer Value Management (CVM) team and executive leadership, providing a strategic roadmap to achieve the company's annual subscriber growth targets.

🎯 Aim The aim is to give BrightTV's Customer Value Management Team useful insights that help grow subscriptions by improving engagement, offering targeted content and attracting new customers.

🎯 Objective The objective is to analyze user and viewing patterns to look at how viewers are spread by age, gender and province to identify which days and channels have high or low viewing. Understand what affects viewing of the study of how dempgraphics(age, gender, location, race) influence watching.Check how time of the day affects viewing and which day of the week affetc viewing. See which type of contents (sports, entertainment, kids, news,) are most popular. Suggest ways to boost low-viewership days. Recommend shows, genre, or events that can attract more viewerson slow days. Ideas to grow BrightTV's users and recommendations and loyalty programs to keep viewers engaged. The analysis is structured to answer four critical business questions: Trends: Provide comprehensive insights on user profiles and usage trends across the BrightTV platform. Drivers: Determine the key factors that influence consumption (e.g., demographics, content, time of day). Content Strategy: Recommend specific content initiatives to increase consumption during periods identified as having low viewership. Growth Strategy: Propose scalable initiatives to further grow BrightTV’s user base and enhance Customer Lifetime Value (CLV).

🎯 Tools DataBricks - Data Manipulation (SQL) Efficiently query, extract, and structure raw data. Used for initial joining of user profiles and transaction data.

Google Looker Studio Visualization & Reporting Creation of interactive dashboards to monitor key metrics.

Microsoft Advanced Excel Pivot Tables, piecharts and bar or line graphs.

MIRO used for planning of the data analysis strategy and steps to follow.