Cameo shoutouts? Optimizing Cameo as a Revenue Stream

Posted on Mar 16, 2020
The skills I demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

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Cameo is an online platform where users can request personalized video shout outs from a range of celebrities. Social media like Twitter and Instagram has allowed celebrities to connect with their fans personally. Cameo monetizes the excitement of communicating with a famous person directly, and has become a nontrivial revenue stream for many celebrities. 

There is no publicly available Cameo data, but scraping their website provides a snapshot of who is available for hire and how much they charge, as well as information about how long it takes them to respond to a request and ratings (on a scale of five stars) by customers. There is no available information about how celebrities have adjusted their prices over time or how many requests they recieve, but we are able to gather how many reviews have been written, which can be used as a rough estimate of their success on the website. 

Although scraping this website captured the information at a single moment in time, interesting pricing trends emerged, and two different business models are being used successfully by talent on the site. Scraping Cameo periodically will provide further insight into customer engagement and how celebrities can price themselves appropriately to maximize their profit. 

Data Collection

I scraped data from using Selenium. Each celebrity has a dedicated Cameo page, which Selenium crawled through. From each celebrity’s page, the scraper gathered their:

  • Name
  • Cost per video
  • Celebrity category
  • Average response time
  • Number of reviews
  • Average star rating
  • List of talent categories the celebrity appears in on Cameo

Data Analysis

My analysis sought to answer if there was an optimal price a celebrity could charge per video depending on that celebrity’s category and fan engagement. There are over 70 categories of celebrity on Cameo ranging in granularity. I focused on the top ten most popular categories, and created an eleventh called ‘other’ to capture all the others. These top ten categories tended to be the coarsest: 


Historic Cameo data is not available, so I used the number of reviews to estimate how often a celebrity is requested for a personal video, and I multiplied the price per video by the number of reviews to generate an estimate of each celebrity’s earnings. The chart below visualizes the price distributions by category, with some outliers removed (Chris D’Elia at $50,000 per video; Caitlyn Jenner at $2,500 per video). 


Most talent charges between 0 and $25 per video, with spikes at common values like $50, $99, $100, and $150 per video. 

Upon closer examination, we see slight variations in pricing distributions. If we focus on four categories, Actors, Reality, Youtubers, and Influencers, we see that actors and reality stars have similar pricing patterns, and influencers and youtubers have similar pricing patterns, but that these two pairs differ from each other.

Reveals Difference in Talents

This perhaps reveals a difference in the talents’ platform of celebrity and their perceived accessibility: Actors and reality stars are present on our movie and tv screens, and thus our interaction with them is less personal. We see that these stars charge more on average than youtubers and influencers. Youtubers and influencers are famous to their fans through social media, therefore their content is more personal and available.

Interestingly, we see a greater proportion of reality stars that charge $150 per video than actors, and reality stars can charge up to $250 per video before seeing a significant drop off in customer engagement in terms of number of reviews. Because their price distributions are otherwise nearly identical, it would appear that actors can charge more on average than they currently do without sacrificing customer engagement, thereby maximizing their earnings. 

Highest earner on Cameo


Two of the highest earners on Cameo (price per video * number of reviews) are Perez Hilton of celebrity gossip fame and Kevin O Leary of Shark Tank. Perez Hilton is highly active on Cameo: he responds to requests very quickly and has filmed at least 1638 personal videos to date, currently charging $90 for each. Most of the top earners on Cameo follow this business model - highly active charging moderate rates. 

Kevin O’Leary’s page reveals a very different business model. Cameo is also a platform for hiring celebrity product endorsements, though currently it is less commonly used for this purpose. A celebrity may record a product endorsement, and that business is allowed to post that video on their social media platforms for up to three months (with the possibility of renegotiating for a longer period of time). While some celebrities explicitly indicate in their bios that they will not fulfill these kinds of requests, Kevin O’Leary has embraced them.

He also is often hired to record messages which are displayed at company-wide events and team-building retreats. He charges $1,200 per video, and though he has a much lower engagement rate than Perez Hilton at 139 reviews, he is still earning more money than Hilton is. 

Scraping Data

Scraping Cameo data provided insight into pricing trends and identified opportunities for celebrities to increase their gains. Nevertheless, it is important to keep in mind that the analysis is based only on what was available on the Cameo website, and ballpark estimations are just that. The data was out of date almost immediately after being captured - celebrities adjusted their prices and the number of reviews continues to increase. Furthermore, it is unclear if Cameo advises any of the talent on pricing, and what data they might use if so.

The goal of this project is to continue to scrape Cameo periodically. This will reveal changes over time to prices and celebrity engagement and will allow for better pricing adjustment recommendations. Even more effective analysis can be made possible by pulling follower information from other platforms such as Instagram, Twitter, Youtube, TikTok, and others. These are promising and exciting avenues of future research. Stay tuned! 

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