Movie Trends

Turning Box Office Data into Actionable Insights

I'm a movie enthusiast who enjoys seeing a big-budget blockbuster on the silver screen, but this project wasn't really about movies.

It was about answering a business question using data.

Movie studios invest hundreds of millions of dollars into films every year. At the same time, audiences often complain that Hollywood relies too heavily on sequels, reboots, remakes, and established franchises. That raised an interesting question:

Are studios becoming increasingly dependent on existing intellectual property, and is that strategy actually paying off?

To answer that question, I created a data analysis project using publicly available box office information and a combination of Excel, data visualization, and AI-assisted design tools.

Project Objectives

The primary objectives of this analysis were to:

• Determine whether major studios are becoming more dependent on existing intellectual property (IP)

• Compare the financial performance of original films versus established franchises

• Analyze how genre trends have changed over the last thirty years

• Evaluate the health of the theatrical movie industry

• Practice the complete data analysis process from collection through presentation

Data Collection

The first step was gathering the data.

I manually collected information from two industry sources:

• Box Office Mojo

• The Numbers

The dataset focused on the top ten worldwide box office releases for each five-year period between 1996 and 2025.

For each film, I captured:

• Movie title

• Release year

• Worldwide box office revenue

• Production budget

• Theater count

• Weekly theatrical run

• Genre classification

• Original IP or Existing IP designation

Because marketing budgets are rarely disclosed publicly, I estimated marketing costs at 50% of production budgets, a commonly referenced industry benchmark.

This created a dataset large enough to identify meaningful patterns while remaining manageable within Excel.

Building the Dataset

Once the information was collected, I organized everything into a structured Excel database.

The workbook included:

• Raw data tables

• Calculated fields

• Performance metrics

• Era classifications

• Genre groupings

• Original versus Existing IP segmentation

Additional formulas were used to calculate:

• Total estimated investment

• Box office performance ratios

• Profitability estimates

• Five-year trend comparisons

• Category averages

By creating a structured dataset, I was able to analyze the information from multiple perspectives without repeatedly manipulating the source data.

Data Analysis in Excel

With the database complete, I used Excel to perform the analysis.

Pivot tables, calculated fields, filtering, and summary calculations helped answer several key questions:

Are studios relying more heavily on existing intellectual property?

The data suggests yes.

Existing IP films showed a clear upward trend over the thirty-year period, eventually becoming the dominant blockbuster model.

Do original movies still perform well?

Surprisingly, yes.

Across the entire dataset, original films produced stronger average box office performance than existing IP films.

This finding challenged my original assumption that sequels and franchises automatically generate higher returns.

What genres dominate different eras?

The data revealed clear shifts:

• Contemporary Fiction (1996-2000)

• Science Fiction & Fantasy (2001-2005)

• Kids Fiction (2006-2010)

• Science Fiction Return (2011-2015)

• Super Hero Era (2016-2020)

• Kids Fiction Return (2021-2025)

Is theatrical moviegoing declining?

Not according to this dataset.

Average theater counts remained stable and average theatrical runs stayed close to eight weeks throughout the analysis period.

While streaming has changed viewing habits, blockbuster films continue to attract audiences to theaters.

Creating Visualizations

Once the analysis was complete, I built a series of charts and graphs in Excel to communicate the findings.

These visualizations helped identify patterns that were less obvious when reviewing raw numbers alone.

Examples included:

• Original versus Existing IP trends

• Genre evolution over time

• Box office performance comparisons

• Theater count trends

• Average theatrical run trends

The visualizations transformed a spreadsheet full of numbers into a story that could be quickly understood by a broader audience.

Using AI for Data Storytelling

After completing the analysis, I used ChatGPT to help transform the findings into an engaging dashboard and infographic.

It's important to note that AI was not used to perform the analysis itself.

The data collection, organization, calculations, and interpretation were completed using Excel.

AI was used as a communication tool to help package the findings into a visually engaging format.

The resulting dashboard summarizes:

• Key performance indicators

• Trend analysis

• Genre evolution

• Franchise fatigue examples

• Strategic recommendations

I then refined the dashboard further in Photoshop to improve layout, readability, and visual presentation.

This combination of traditional analytics tools and modern AI-assisted design tools demonstrates how analysts can communicate insights more effectively without replacing the analytical process itself.

Key Takeaways

This project reinforced several lessons about data analytics:

1. Data often challenges assumptions.

2. Visualization is critical for communicating insights.

3. Simple tools like Excel remain extremely powerful.

4. AI can enhance presentation and storytelling.

5. Analytics is ultimately about answering questions and supporting decisions.

Most importantly, this project demonstrates the complete workflow many analysts follow every day:

Collect → Organize → Analyze → Visualize → Communicate

Whether the subject is movies, marketing campaigns, customer behavior, or business operations, the process remains remarkably similar.

And sometimes, analyzing something you genuinely enjoy is one of the best ways to develop and showcase those skills.

The answer to the central question is actually two answers:

Question:

Are studios becoming increasingly dependent on existing intellectual property, and is that strategy actually paying off?

Answer:

Yes, studios are becoming increasingly dependent on existing intellectual property.

The data showed a clear upward trend in franchise films, sequels, reboots, remakes, and other established IPs over the past 30 years. Existing IP eventually became the dominant blockbuster model.

But Is It Paying Off?

Not as well as many people might assume.

While existing IP dominates the number of blockbuster releases, the analysis found that:

• Original films generated higher average box office performance overall.

• The performance gap was especially noticeable in recent years.

• Some long-running franchises showed signs of audience fatigue.

• Certain genres, particularly Fantasy & Science Fiction, performed well with original concepts.

• Kids Fiction was the major exception, where existing IP tended to outperform originals.

Executive Summary

If this were a business analysis presentation, I would summarize it as:

"Studios have increasingly shifted toward established intellectual property as a risk-reduction strategy. However, the data suggests that original films often generate stronger returns on investment, particularly when supported by strong storytelling and marketing. Existing IP remains highly effective in family-oriented content, but overreliance on franchises may be contributing to audience fatigue in some categories."