A few weeks ago, I posed a question on Quora about the differences between machine learning and predictive analytics. I was surprised at the number of people who started following the question and actually liked it (although it sounded rhetorical, I was hoping to understand machine learning better). A recent article in Fast Company about The New York Times, of all companies, did a great job of explaining the fundamental value of machine learning.
My interest in machine learning was ignited recently as it has become the hot buzzword in the Bay Area; some have argued if you add machine learning to your PowerPoint you can add a zero to the end your company’s valuation. While that claim is obviously an exaggeration, investors are among the savviest businessmen, so their interest in machine learning shows it is a crucial emerging space.
The article discusses Chris Wiggins, a biologist The New York Times just hired as its Chief Data Scientist. Wiggins’ mandate is to build and lead the Times’ machine learning team. In Fast Company’s interview with Wiggins, it became clear exactly what machine learning is, how it is different than predictive analytics and why it is important.
What is machine learning
According to Wiggins, “Machine learning sits at the intersection of data engineering and mathematical modeling. The thing that makes it different from statistics traditionally, is far more focus on building algorithms.”
Also, while statistics is traditionally focused on explaining data, machine learning is geared to building predictive models. When Netflix or Amazon make product recommendations to you, they are using machine learning to predict what you would be interesting in experiencing.
How you can use machine learning
What is exciting about machine learning is that its applications are virtually endless. Uber can use machine learning to understand the patterns of its customers and pre-position cars when and where people will need them to reduce wait times and increase revenue for drivers. American Express can use it to understand when people are nearing a purchase decisions and work with their retailers to present special offers to these customers automatically. McDonalds can use it to change its menu options based on time of day, day of week, weather conditions and other variables to present those most likely to optimize the total purchase of customers at any given time.
As the article points out, The New York Times is planning to use it both on the business and editorial fronts. On the business side, the Times is trying to understand what are the behaviors that seem to be correlated with loyalty, what are the behaviors that seem to be correlated with dissatisfaction, particularly among subscribers—the way a long-term relationship manifests itself at the Times is via subscribers. On the editorial front, it is using machine learning to understand how people engage with its website, gaining insights about what kind of content engages users.
In the game space, there are many applications for machine learning. It can be used to understand each player’s skill level and deliver the holy grail of gaming: An experience that is easy to play but impossible to master. In a free-to-play title, machine learning can determine when a player is most likely to monetize. It can be used to predict when a player is likely to leave the game forever so you can give them a better experience.
Why machine learning is important
The short answer to why machine learning is important is that it makes your company more valuable. If you go back to the two early examples of machine learning that I used, Amazon and Netflix, machine learning has been central in creating billions of dollars of value. For a game company, it can directly improve all the elements of lifetime value:
- Monetization. You can use machine learning to improve monetization of your game. It can identify when a person is most likely to respond positively to a purchase prompt. It can help you tailor your offering to what a player is most likely to purchase. You can use machine learning to set optimal price points for each players.
- Retention. Machine learning can help you understand and target players when they normally would not come back to the game. It can be used to help tailor the in-game experience to get the player to return later. It can be used to determine what type of offer to email a lapsed player to get them back into the game. Also, by using machine learning to tailor the game to each player, they will have a better experience in your game than those of your competitors.
- Virality. You can apply machine learning to asking players to invite friends at the time that specific player is most likely to respond positively. You can also apply machine learning to recommend those friends most likely to come to your game.
Integrate machine learning with your analytics program
I hope the examples above get you thinking about some of the ways you can apply machine learning to your product or game. Those companies that apply machine learning will give their players or users a better product and optimize their customers’ value.