Disney+ Hotstar Data Analysis
Analysed Disney+ Hotstar's movie catalogue across genres, runtimes, release years, age ratings, and content types to surface insights for content strategy, audience segmentation and marketing teams
Tools :
Power BI
Techniques :
Power Query, DAX
Dataset :
8,000+ records across multi-outlet retail data
Tools :
Power BI, Power Query, DAX
Dataset :
6,874 titles (1928–2023)
Techniques :
DAX measures, Genre segmentation, Runtime binning, Correlation analysis
Problem :
Hotstar's catalogue spanning 37 genres, multiple content types, and 95 years of releases existed in raw form. Without a unified view, patterns in audience preferences, content gaps and release trends remained hidden.
Transformed fragmented restaurant data into strategic insights using Power BI accelerating decisions across vendor onboarding, delivery optimization, and customer engagement.
Approach :
Profiled 6,874 titles, resolving inconsistencies across genre, runtime, age rating, and episode fields; created runtime bins for distribution analysis.
Built dedicated deep-dive pages for Action (596 titles) and Comedy (787 titles) with genre-specific runtime, year and age rating breakdowns.
Completed all 12 business tasks from top 10 longest movies and genre popularity over time to runtime distribution and age rating segmentation.
Insights :
U/A 13+ dominates with 2,980 titles (43% of catalogue); U-rated content accounts for only 1,251, revealing a significant underserved kids and family segment.
Drama leads with 2,005 distinct titles, the single largest genre, giving acquisition teams a clear catalogue depth benchmark.
Average runtime is 98.75 minutes; distribution analysis reveals where content length aligns or conflicts with engagement patterns across age groups.
37 genres tracked across 95 years; genre-year heatmap identifies which categories have grown, plateaued, or declined for long-term portfolio strategy.
Comedy (787) outnumbers Action (596) by 32%; deep-dive pages surface runtime and age rating differences that generic genre charts miss.
growth note :
Next iteration: correlate runtime and age rating against actual watch time from catalogue analysis to achieve true engagement measurement.
github link
More Projects
Power BI
8,000+ restaurant records
Restaurant Analysis of Swiggy
Analysed 8,680 restaurant records to reveal that only 3.73% score above 4.5, exposing a vendor quality gap across cities and cuisines.
Techniques: DAX measures, Correlation analysis, Geographical mapping
Outcome:
Delivered insights and business recommendations with direct impact on vendor strategy and delivery optimization
Power BI
8,000+ records across multi-outlet retail data
BlinkIt – A Retail Intelligence
Transformed 8,000+ retail records to find newer outlets generate 68% higher sales, redefining outlet benchmarking and inventory strategy.
Techniques: DAX measures, KPI design, Dynamic slicers
Outcome:
Delivered strategic insights and recommendations impacting sales, inventory, and customer strategy
More Projects
Power BI
8,000+ restaurant records
Restaurant Analysis of Swiggy
Analysed 8,680 restaurant records to reveal that only 3.73% score above 4.5, exposing a vendor quality gap across cities and cuisines.
Techniques: DAX measures, Correlation analysis, Geographical mapping
Outcome:
Delivered insights and business recommendations with direct impact on vendor strategy and delivery optimization
Power BI
8,000+ records across multi-outlet retail data
BlinkIt – A Retail Intelligence
Transformed 8,000+ retail records to find newer outlets generate 68% higher sales, redefining outlet benchmarking and inventory strategy.
Techniques: DAX measures, KPI design, Dynamic slicers
Outcome:
Delivered strategic insights and recommendations impacting sales, inventory, and customer strategy
More Projects
Power BI
8,000+ restaurant records
Restaurant Analysis of Swiggy
Analysed 8,680 restaurant records to reveal that only 3.73% score above 4.5, exposing a vendor quality gap across cities and cuisines.
Techniques: DAX measures, Correlation analysis, Geographical mapping
Outcome:
Delivered insights and business recommendations with direct impact on vendor strategy and delivery optimization
Power BI
8,000+ records across multi-outlet retail data
BlinkIt – A Retail Intelligence
Transformed 8,000+ retail records to find newer outlets generate 68% higher sales, redefining outlet benchmarking and inventory strategy.
Techniques: DAX measures, KPI design, Dynamic slicers
Outcome:
Delivered strategic insights and recommendations impacting sales, inventory, and customer strategy
Disney+ Hotstar Data Analysis
Analysed Disney+ Hotstar's movie catalogue across genres, runtimes, release years, age ratings, and content types to surface insights for content strategy, audience segmentation and marketing teams
Tools :
Power BI
Techniques :
Power Query, DAX
Dataset :
8,000+ records across multi-outlet retail data
Tools :
Power BI, Power Query, DAX
Dataset :
6,874 titles (1928–2023)
Techniques :
DAX measures, Genre segmentation, Runtime binning, Correlation analysis
Problem :
Hotstar's catalogue spanning 37 genres, multiple content types, and 95 years of releases existed in raw form. Without a unified view, patterns in audience preferences, content gaps and release trends remained hidden.
Transformed fragmented restaurant data into strategic insights using Power BI accelerating decisions across vendor onboarding, delivery optimization, and customer engagement.
Approach :
Profiled 6,874 titles, resolving inconsistencies across genre, runtime, age rating, and episode fields; created runtime bins for distribution analysis.
Built dedicated deep-dive pages for Action (596 titles) and Comedy (787 titles) with genre-specific runtime, year and age rating breakdowns.
Completed all 12 business tasks from top 10 longest movies and genre popularity over time to runtime distribution and age rating segmentation.
Insights :
U/A 13+ dominates with 2,980 titles (43% of catalogue); U-rated content accounts for only 1,251, revealing a significant underserved kids and family segment.
Drama leads with 2,005 distinct titles, the single largest genre, giving acquisition teams a clear catalogue depth benchmark.
Average runtime is 98.75 minutes; distribution analysis reveals where content length aligns or conflicts with engagement patterns across age groups.
37 genres tracked across 95 years; genre-year heatmap identifies which categories have grown, plateaued, or declined for long-term portfolio strategy.
Comedy (787) outnumbers Action (596) by 32%; deep-dive pages surface runtime and age rating differences that generic genre charts miss.
growth note :
Next iteration: correlate runtime and age rating against actual watch time from catalogue analysis to achieve true engagement measurement.
github link
More Projects
Power BI
8,000+ restaurant records
Restaurant Analysis of Swiggy
Analysed 8,680 restaurant records to reveal that only 3.73% score above 4.5, exposing a vendor quality gap across cities and cuisines.
Techniques: DAX measures, Correlation analysis, Geographical mapping
Outcome:
Delivered insights and business recommendations with direct impact on vendor strategy and delivery optimization
Power BI
8,000+ records across multi-outlet retail data
BlinkIt – A Retail Intelligence
Transformed 8,000+ retail records to find newer outlets generate 68% higher sales, redefining outlet benchmarking and inventory strategy.
Techniques: DAX measures, KPI design, Dynamic slicers
Outcome:
Delivered strategic insights and recommendations impacting sales, inventory, and customer strategy
More Projects
Power BI
8,000+ restaurant records
Restaurant Analysis of Swiggy
Analysed 8,680 restaurant records to reveal that only 3.73% score above 4.5, exposing a vendor quality gap across cities and cuisines.
Techniques: DAX measures, Correlation analysis, Geographical mapping
Outcome:
Delivered insights and business recommendations with direct impact on vendor strategy and delivery optimization
Power BI
8,000+ records across multi-outlet retail data
BlinkIt – A Retail Intelligence
Transformed 8,000+ retail records to find newer outlets generate 68% higher sales, redefining outlet benchmarking and inventory strategy.
Techniques: DAX measures, KPI design, Dynamic slicers
Outcome:
Delivered strategic insights and recommendations impacting sales, inventory, and customer strategy
More Projects
Power BI
8,000+ restaurant records
Restaurant Analysis of Swiggy
Analysed 8,680 restaurant records to reveal that only 3.73% score above 4.5, exposing a vendor quality gap across cities and cuisines.
Techniques: DAX measures, Correlation analysis, Geographical mapping
Outcome:
Delivered insights and business recommendations with direct impact on vendor strategy and delivery optimization
Power BI
8,000+ records across multi-outlet retail data
BlinkIt – A Retail Intelligence
Transformed 8,000+ retail records to find newer outlets generate 68% higher sales, redefining outlet benchmarking and inventory strategy.
Techniques: DAX measures, KPI design, Dynamic slicers
Outcome:
Delivered strategic insights and recommendations impacting sales, inventory, and customer strategy
Disney+ Hotstar Data Analysis
Analysed Disney+ Hotstar's movie catalogue across genres, runtimes, release years, age ratings, and content types to surface insights for content strategy, audience segmentation and marketing teams
Tools :
Power BI
Techniques :
Power Query, DAX
Dataset :
8,000+ records across multi-outlet retail data
Tools :
Power BI, Power Query, DAX
Dataset :
6,874 titles (1928–2023)
Techniques :
DAX measures, Genre segmentation, Runtime binning, Correlation analysis
Problem :
Hotstar's catalogue spanning 37 genres, multiple content types, and 95 years of releases existed in raw form. Without a unified view, patterns in audience preferences, content gaps and release trends remained hidden.
Transformed fragmented restaurant data into strategic insights using Power BI accelerating decisions across vendor onboarding, delivery optimization, and customer engagement.
Approach :
Profiled 6,874 titles, resolving inconsistencies across genre, runtime, age rating, and episode fields; created runtime bins for distribution analysis.
Built dedicated deep-dive pages for Action (596 titles) and Comedy (787 titles) with genre-specific runtime, year and age rating breakdowns.
Completed all 12 business tasks from top 10 longest movies and genre popularity over time to runtime distribution and age rating segmentation.
Insights :
U/A 13+ dominates with 2,980 titles (43% of catalogue); U-rated content accounts for only 1,251, revealing a significant underserved kids and family segment.
Drama leads with 2,005 distinct titles, the single largest genre, giving acquisition teams a clear catalogue depth benchmark.
Average runtime is 98.75 minutes; distribution analysis reveals where content length aligns or conflicts with engagement patterns across age groups.
37 genres tracked across 95 years; genre-year heatmap identifies which categories have grown, plateaued, or declined for long-term portfolio strategy.
Comedy (787) outnumbers Action (596) by 32%; deep-dive pages surface runtime and age rating differences that generic genre charts miss.
growth note :
Next iteration: correlate runtime and age rating against actual watch time from catalogue analysis to achieve true engagement measurement.
github link
More Projects
Power BI
8,000+ restaurant records
Restaurant Analysis of Swiggy
Analysed 8,680 restaurant records to reveal that only 3.73% score above 4.5, exposing a vendor quality gap across cities and cuisines.
Techniques: DAX measures, Correlation analysis, Geographical mapping
Outcome:
Delivered insights and business recommendations with direct impact on vendor strategy and delivery optimization
Power BI
8,000+ records across multi-outlet retail data
BlinkIt – A Retail Intelligence
Transformed 8,000+ retail records to find newer outlets generate 68% higher sales, redefining outlet benchmarking and inventory strategy.
Techniques: DAX measures, KPI design, Dynamic slicers
Outcome:
Delivered strategic insights and recommendations impacting sales, inventory, and customer strategy
More Projects
Power BI
8,000+ restaurant records
Restaurant Analysis of Swiggy
Analysed 8,680 restaurant records to reveal that only 3.73% score above 4.5, exposing a vendor quality gap across cities and cuisines.
Techniques: DAX measures, Correlation analysis, Geographical mapping
Outcome:
Delivered insights and business recommendations with direct impact on vendor strategy and delivery optimization
Power BI
8,000+ records across multi-outlet retail data
BlinkIt – A Retail Intelligence
Transformed 8,000+ retail records to find newer outlets generate 68% higher sales, redefining outlet benchmarking and inventory strategy.
Techniques: DAX measures, KPI design, Dynamic slicers
Outcome:
Delivered strategic insights and recommendations impacting sales, inventory, and customer strategy
More Projects
Power BI
8,000+ restaurant records
Restaurant Analysis of Swiggy
Analysed 8,680 restaurant records to reveal that only 3.73% score above 4.5, exposing a vendor quality gap across cities and cuisines.
Techniques: DAX measures, Correlation analysis, Geographical mapping
Outcome:
Delivered insights and business recommendations with direct impact on vendor strategy and delivery optimization
Power BI
8,000+ records across multi-outlet retail data
BlinkIt – A Retail Intelligence
Transformed 8,000+ retail records to find newer outlets generate 68% higher sales, redefining outlet benchmarking and inventory strategy.
Techniques: DAX measures, KPI design, Dynamic slicers
Outcome:
Delivered strategic insights and recommendations impacting sales, inventory, and customer strategy