Logo

The Data Daily

What Should Machine Learning Actually Learn About Your Customers?

Last updated: 02-23-2018

Read original article here

What Should Machine Learning Actually Learn About Your Customers?

The buzzwords for the future are undoubtedly “machine learning” and “automation”. But talking about technology and actually using it to drive your business and reach new customers are two different things. In many cases it comes down to personalization and having the data to create strong customer experiences.

What actually is machine learning? Essentially, it’s the part of artificial intelligence that allows computer models to recognize patterns in existing data so that it can learn what to do or predict what will happen in the future. Just like how humans can absorb information and use it to formulate their observations or predictions, machine learning can do it without bias and by quickly consuming much more data than any human could ever sort through.

You Knew Netflix Used Machine Learning To Tailor Preferences, Did You Know It Earned $1Billion A Year

In the customer world, machine learning helps absorb all of the existing data about customers and shrink it into small nuggets that companies can use to create accurate and personalized experiences. For example, much of Netflix’s algorithm is based on machine learning—with each show a customer watches or rates, the system can learn their preferences and make more accurate recommendations in the future. Part of the reason customers love Netflix is because they can find shows they might not know about otherwise. It’s a nuanced system to find connections and patterns in a user’s preferences, but when done right it can spit out recommendations of shows customers love. And it’s working—Netflix’s machine learning recommendations brings in an estimated $1 billion a year alone.

There are a lot of things machine learning can find out--many directions machine learning can take the company. But what should machine learning actually learn about your customers and where should it focus its efforts? Consider these three areas:

Purchasing Patterns Through Machine Learning At Target and Amazon

Perhaps one of the easiest things for machine learning to track is what customers purchase and when they purchase it. Machine learning can easily track if a customer buys laundry detergent every three months or shops for swimsuits at the beginning of every summer. Taking it a step further, machine learning can then predict what a customer will buy next and make recommendations. Target’s machine learning program can recognize when a female customer starts purchasing things like hand sanitizer, vitamins, and unscented lotion that she is likely pregnant. From there, the store can make subtle recommendations for pregnancy and baby-related products.

The buzzwords for the future are undoubtedly “machine learning” and “automation”. But talking about technology and actually using it to drive your business and reach new customers are two different things. In many cases it comes down to personalization and having the data to create strong customer experiences.

What actually is machine learning? Essentially, it’s the part of artificial intelligence that allows computer models to recognize patterns in existing data so that it can learn what to do or predict what will happen in the future. Just like how humans can absorb information and use it to formulate their observations or predictions, machine learning can do it without bias and by quickly consuming much more data than any human could ever sort through.

You Knew Netflix Used Machine Learning To Tailor Preferences, Did You Know It Earned $1Billion A Year

In the customer world, machine learning helps absorb all of the existing data about customers and shrink it into small nuggets that companies can use to create accurate and personalized experiences. For example, much of Netflix’s algorithm is based on machine learning—with each show a customer watches or rates, the system can learn their preferences and make more accurate recommendations in the future. Part of the reason customers love Netflix is because they can find shows they might not know about otherwise. It’s a nuanced system to find connections and patterns in a user’s preferences, but when done right it can spit out recommendations of shows customers love. And it’s working—Netflix’s machine learning recommendations brings in an estimated $1 billion a year alone.

There are a lot of things machine learning can find out--many directions machine learning can take the company. But what should machine learning actually learn about your customers and where should it focus its efforts? Consider these three areas:

Purchasing Patterns Through Machine Learning At Target and Amazon

Perhaps one of the easiest things for machine learning to track is what customers purchase and when they purchase it. Machine learning can easily track if a customer buys laundry detergent every three months or shops for swimsuits at the beginning of every summer. Taking it a step further, machine learning can then predict what a customer will buy next and make recommendations. Target’s machine learning program can recognize when a female customer starts purchasing things like hand sanitizer, vitamins, and unscented lotion that she is likely pregnant. From there, the store can make subtle recommendations for pregnancy and baby-related products.


Read the rest of this article here