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Case Studies

At SceneSight, we’ve empowered entertainment industry leaders with cutting-edge data analytics solutions, transforming their decision-making processes and driving innovation.

Solving Content Acquisition

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Revamping TV Content Strategy

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Harnessing Big Data for Market Research

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Optimizing Pricing for Home Entertainment

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Boosting ROAS for Mobile Games

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Challenge

Content Acquisition

SVP Content Acquisition

At leading Streaming Service

WHY?

Executives at DTC platform X were bidding for movie rights from Studio Y.

WHAT?

When the price went beyond the initially sanctioned amount they had to ask for more money. How to justify the additional spending?

HOW?

Our team came to the table with data, models, insights.

Which demographics are likely to watch this movie?

Does this help our effort to launch DTC in Latin America & Southeast Asia?

Will this movie rank in Top 10 charts? Which countries?

The insights from our team provided the upper range to the executives. They decided to let go of that movie
Wise Choice – When the Movie featured on another DTC it had 35% Rotten Tomatoes rating & 5.5/10 on IMDB.

Challenge

TV Content Strategy

SVP TV Content Strategy

At leading Television Studio

WHY?

TV executives constantly ponder what shows/content to make next. Content, Audience, Budget, Cast, Will it have multiple seasons, Will it be Top 10?

WHAT?

Solving the Content issue is key to surviving the streaming race.

HOW?

Data Science was used to help answer some of the questions related to new content ideas.

Using data for existing shows: Audience reactions & engagement metrics.

Textual information like genres, keywords, synopses were converted into embeddings using Language Models.

Models were built to predict how the combination of genres, keywords, synopses, cast, producers, and writers perform together.

These models predict the probability of success for that combination.

Users Were Able To Perform What-if Scenarios By Changing Inputs & Getting A Sense For
How Well Their ‘New Show’ Would Fare In Comparison To Existing Content.

Challenge

Bringing Big Data To
Market Research

SVP Market Research

At leading Movie Studio

WHY?

Keeping abreast of changing audience preferences to ensure your content is still relevant by the time it hits the market.

WHAT?

Market Research Teams at the Studios generate large amounts of research data. They also procure several data feeds. Research teams struggle with aggregating data from multiple sources.

HOW?

The Data Science team developed interactive apps & automation dashboards.

Self-serve tools enabled the research team to do lot more with their time.

Aggregate large amounts of respondent-level data.

Provide standardized insights/ visualizations very quickly.

Provide insights across franchises/ segments.

Market Research Teams Became More Efficient Using Standardized Solutions Provided By Our Team.

Challenge

Home Entertainment:
Right Pricing

SVP Digital Distribution

At leading Movie Studio, Home Entertainment Division

WHY?

Home Entertainment executives were pushed to a corner – DTC was cannibalizing their revenues.

WHAT?

Their experienced pricing teams had no solution other than rampant discounting.

HOW?

Our team was called in to provide a solution.

With access to what catalog title is available on which DTC & when, we quickly realized that people bought certain movies even when they were available for ‘free’ on DTC.
We built a model for dynamic pricing for catalog titles, that accounted for seasonality & preference for certain type of content. We were no longer leaving money on the table.
While Deployed, Dynamic Pricing Contributed To Additional Revenues Of More Than Half A Million Each Week.

Is there latent demand for certain content at the right price?

Is the demand seasonal, and what content attracts which consumer?

Challenge

Mobile Games: Improve ROAS

VP Marketing

At leading Video Game Company, Mobile Games Division

WHY?

Current ad spends based on heuristics and standard segments were not providing sufficient returns. Personalized marketing campaigns have dramatically higher engagement resulting in better returns

WHAT?

Marketing executives tasked us with optimizing their advertisement spends.

HOW?

We built audience segments in real-time based on their in-game activity & delivered personalized ads that increased re-engagement.

Built models to identify churn propensity, spend propensity, cart abandoners.

Everyday, our models generated lists of users that we reached out through in-app engagement.

An attribution pipeline was built to monitor campaign performance.

Launched multiple re-engagement campaigns for Top-5 mobile games of the company.
Many Campaigns Achieved ROAS Higher Than 500% Campaigns that did not meet their goals were promptly discontinued.