They’re using machine learning to parse through the email’s subject line and categorize it accordingly.
Take Gmail for example.
The machine learning algorithm Google uses has been trained on millions of emails so it can work seamlessly for the end-user (us).
While Gmail allows us to customize labels, the service offers default labels: Primary Social Promotions The machine learning algorithms immediately categorize the email into one of these three labels as soon as you receive an email.
We get an instant alert if Gmail deems it a ‘Primary’ email.
Of course, Gmail also uses machine learning to figure out if the email is spam or not.
A feature we are all truly grateful for.
Google’s algorithm has become a lot smarter over the years in deciding if an email is spam or not.
This is where getting more data for a machine learning algorithm is so helpful – something Google has in abundance.
Google Search The most popular machine learning use case in this (or any) list.
Everyone has used Google Search and most of us use it multiple times on a daily basis.
I would venture to say we take it for granted that Google will serve us the best results up front.
But how does Google Search work?.Google Search has become an impenetrable behemoth that mortals cannot crack.
How it works underneath is something only those folks who have designed Google Search know.
One thing we can say for certain – Google uses machine learning to power its Search engine.
The amount of data Google has to constantly train and refine its algorithms is a number we cannot fathom.
No calculator in the world will tell us the number of queries Google has processed in the last two decades.
It is a trasure trove for data scientists!.Now – imagine you were asked to build your own Google search.
What rules would you use?.What kind of content would you include?.How would you rank sites?.Here’s an article that will get you started: Google PageRank explained in simple terms!. Google Translate I’m fluent in Google Translate.
I’ve picked up bits and pieces of foreign languages like German, Spanish, and Italian thanks to this wonderful service by Google.
Anytime I come across a bit of text in a foreign language, Google Translate immediately offers me the answer.
It won’t surprise you to know that Google uses machine learning to understand the sentence(s) sent by the user, convert them to the requested language, and show the output.
Machine learning is deeply embedded in Google’s ecosystem and we are all benefitting from that.
Fortunately, we have a sense of how Google uses machine learning to power it’s Translate engine.
This article will help you understand and get started with the topic: A Must-Read NLP Tutorial on Neural Machine Translation – The Technique Powering Google Translate LinkedIn and Facebook recommendations and ads Social media platforms are classic use cases of machine learning.
Like Google, these platforms have integrated machine learning into their very fabric.
From your home feed to the kind of ads you see, all of these features work thanks to machine learning.
A feature which we regularly see if ‘People you may know’.
This is a common feature across all social media platforms, Twitter, Facebook, LinkedIn, etc.
These companies use machine learning algorithms to look at your profile, your interests, your current friends, their friends, and a whole host of other variables.
The algorithm then generates a list of people that match a certain pattern.
These people are then recommended to you with the expectation that you might know them (or at least have profiles very similar to yours).
I have personally connected with a lot of my professional colleagues and college friends thanks to LinkedIn’s system.
It’s a use case of machine learning benefitting everyone involved in the process.
The ads that we see work in a similar fashion.
They are tailored to your tastes, interests and especially your recent browsing or purchase history.
If you are a part of a lot of data science groups, Facebook or LinkedIn’s machine learning algorithm might suggest machine learning courses.
Pay attention to this next time you’re using social media.
It’s all machine learning behind the curtains!. Machine Learning Use Cases in Sales and Marketing Top companies in the world are using machine learning to transform their strategies from top to bottom.
The two most impacted functions?.Marketing and Sales!.These days if you’re working in the marketing an sales field, you need to know at least one Business Intelligence tool (like Tableau or Power BI).
Additionally, marketers are expected to know how to leverage machine learning in their day-to-day role to increase brand awareness, improve the bottomline, etc.
So, here are three popular use cases in marketing and sales where machine learning is changing the way things work.
Recommendation Engines We briefly spoke about recommendation engines earlier.
I mentioned that these systems are ubiquitous.
But where are they used in the marketing and sales field?.And how?.Let’s take a simple example to understand this.
Before the advent of IMDb (and Netflix), we all used to go to DVD stores or rely on Google to search for movies to watch.
The store clerk would offer suggestions on what to watch and we took a hail mary pass by picking up movies we had no idea about.
That world is almost completely in the past now thanks to recommendation engines.
We can log on to a site and it recommends products and services to me based on my taste and previous browsing history.
Some popular examples of recommendation engines: E-commerce sites like Amazon and Flipkart Book sites like Goodreads Movie services like IMDb and Netflix Hospitality sites like MakeMyTrip, Booking.
Retail services like StitchFix Food aggregators like Zomato and Uber Eats The list is long.
Recommendation engines are everywhere around us and marketing and Sales departers are leaning on them more than ever before to attract (and retain) new customers.
I encourage you to read this beginner-friendly tutorial on how to build your own recommendation engine: Comprehensive Guide to building a Recommendation Engine from scratch (in Python) Personalized Marketing Recommendation engines are part of an overall umbrella concept called personalized marketing.
The meaning of this concept is in the name itself – it is a type of marketing technique tailored to an individual’s need.
Think about this.
How many calls do you get from credit card or loan companies offering their services “for free”?. More details