Steam: Maximizing Profit on Digital Goods

Posted on Oct 29, 2017

The skills the authors demonstrated here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

OK, pop quiz: can you tell me what this is?

Why yes, it is a data visual representation of how a price setting firm finds the profit maximizing price for their goods! You're very smart. As you remember from college, economic profit is maximized at the point where marginal cost equals marginal revenue.  The basic idea is that as you scale in size and produce more goods, the cost to produce one more good goes up and up until you reach the point where it'll cost as much to produce that good as you would make in revenue from selling it and that is a great time to stop making any more.

But what if you work in an industry where marginal cost just doesn't go up? Or rather, an industry where marginal cost is practically irrelevant?

Welcome to the digital age.

Digital goods can have high development costs, but after you make the original good, making any additional copies costs you practically nothing.  You don't have to hire factory workers to produce more.  You don't have to pay shipping to get them into retail stores.

So how do you maximize your profit when selling digital goods?

this is the question I sought to answer when I started scraping the Steam store.  In the end, I can't say exactly but I did observe the following strategies:

◦Low pricing per unit

◦freemium pricing


◦Step down pricing

 What is Steam?

◦The “app store” for PC games

◦ There are 67 million monthly active players.

◦Steam is averaging 14 million concurrent users per day

◦Valve reported 125 million total active lifetime users last year. (as of April 2016)

They provide over 30 thousand products with over 10 thousand developers and 7 thousand publishers.  They are kind of the go- to company for PC gaming.

General Price info

To start out my look into steam and their pricing, I looked at the average price points for their games.  As you can clearly see from this graph, Steam games don't follow a normal distribution. At all.

Steam games tend to fall into certain prices: Free, 99 cents, $1.99, $4.99, $9.99, then mostly continuing in 5 dollar increments after that. if we adjust the graph to include games up to $60 we get the following:

This really drives home how few games are offered at the $40 - 60 price point that most big developers launch their games at. We see that a large portion of the games are available for cheap- the low pricing per unit that I mentioned earlier.  I believe smaller developers do this so that people will be more willing to give their game a try.

Release Date Vs Price

I had expected to see step down pricing on the games- basically that as a game got older, the price of the game would gradually fall; however, here we see that we have quite a few outliers on both ends of the graph.  A little research revealed that most of the points in the 1970's through 90's were movies, including the gem that is Arnold Schwarzenegger's Hercules in New York.

The most expensive goods tended to be software, although that one at the $850 price point is actually a flight simulator.  disappointingly, while we do see prices go down as the release date goes back, we also see that there are points all the way under the curve.   To figure out what's going on, I took a closer look at two major developer's releases and two more major  indie developer releases.



Release date vs price follows my expected distribution except for a couple outliers on valve( the 3 at zero dollars) and a couple points on SEGA(the 2013 at about 3 dollars). As it turns out, the SEGA games are all (with the exception of the 2015 game) just re-releases of old nostalgic filled games, like Streets of Rage, Sonic the Hedgehog, and many many others, which is why the price point is so low- they already made back their money when they released it on console, and now they're just getting a little bit more off the nostalgia of that generation.

Valve's outliers are actually free to play games that rely heavily on multiplayer aspects to continue to be playable- Team Fortress 2 and Alien Swarm. outside of that though the step down pricing holds true, with older games tending to be available at lower prices.

Klei entertainment

Telltale Games

Klei Entertainment and Telltale Games also have the pattern of older games being cheaper, but they have outliers for a different reason:  Klei's outliers are soundtracks for their games and expansion content, whereas Telltale Game's outliers are 3 versions of poker and expansion content.  It would seem that for the most part, games become cheaper as they grow older.

So why do these companies reduce their game's price as time goes on?

They make more money.  As they reduce their price, more people who were unwilling to pay the higher price are more likely to buy it at the new price.  Since the marginal cost of that extra unit is so low, there's really no reason for them not to keep driving the price lower to keep selling more copies.  And Steam has this gift for making products seem so cheap that you'd feel like you'd be fiscally irresponsible to NOT buy them. In the immortal words of the internet:

About Author

Ben Brunson

Ben Brunson is a man whose curiosity has led him to work in many industries. He handled day to day operations and special projects for AuST Development, a medical devices development company. He Managed paid search campaigns for...
View all posts by Ben Brunson >

Related Articles

Leave a Comment

No comments found.

View Posts by Categories

Our Recent Popular Posts

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI