General

Why Mobile Marketers Need to Learn Machine Learning Basics

Ever wonder how Gmail or even Outlook knows when an email is spam? How does it automatically categorize messages as worth trashing?

Or how does Amazon know to recommend buying socks and foot powder to someone browsing shoes?

The answer in both cases is machine learning. And it is the key to growing your app and marketing it at scale.

Introduction to Machine Learning

Machine learning (ML) is the use of artificial intelligence (AI) to give a computer system the ability to learn by itself. The process makes machines more intelligent over time. And it allows computers to adapt to human behavior.

The computer system learns from past data that we feed it. Some of that may occur at the start, with a person pointing out which data is useful. Some of it may happen when people give the system data points that help it make decisions later on.

The outcome: the computer system can make predictions and decisions on its own thanks to the data.

Machine Learning Examples in Everyday Life

You may have been using machine learning already without realizing it.

One of the best machine learning examples is Facebook’s facial recognition feature. Tag a friend in a photo once and you may never need to tag her again. And if the system isn’t sure, it asks. Facebook learns from every person tagged which name fits which face.

Then there is Netflix’s recommendation engine. The more you watch and rate movies, the more data it has to recommend movies that you might also like. Every time you rate a movie, you feed data to Netflix’s system. Every time you start a movie or show then abandon it, you provide another data point. The result: Netflix knows what you do or don’t like watching, and what you enjoy bingeing! Thanks to machine learning.

Paypal has a feature that allows it to detect whether a transaction is real or fraudulent. Their system can identify when you are using the account and when it looks like someone else has used it. Yet another of the many machine learning examples we rely on every day.


Machine learning basics - ML can help segment your millions of users

The Different Types of Machine Learning

There are three major types of machine learning:

  • Supervised learning is about making predictions. This is where the system, after enough training, has the ability to predict outcomes. Even when it comes across new data, it can work the problem out. It will incorporate the new data into the system using the rules it started with.
  • Unsupervised learning is about finding patterns. Here, the system uses algorithms when it meets new data. It has to draw inferences and find hidden patterns, similarities, or anomalies in the data. Then it can find relationships between this new data and existing data.
  • Reinforcement learning is about iterative learning based on past results. Here the system learns as it cycles through decisions. Is the decision good or bad? It can use the result of a current decision to inform the next one. We use this type of machine learning to increase the efficiency of a computer program.

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The Benefits of Machine Learning

In general terms, ML gives us five benefits:

  1. The power to process complex data, and
  2. The ability to do so faster, which leads to:
  3. Better decision making and more accurate predictions.
  4. ML also gives us a more affordable way to manage big data, since
  5. ML is less expensive than manual methods of coding

Every time you think of “big data” you get an image of a river of user data flowing through your systems. ML gives us the power to process large volumes of complex information. And to do it faster and more inexpensively than any manual method.

End result: the more data you feed the system, the more accurate the predictions. And the better the decisions the system makes on its own.

These benefits give marketers insight into what actions they need to take next.


Machine learning basics - Some form of human interaction is needed.

Three Ways Machine Learning Helps Mobile Marketers

Machine learning is an essential tool in the work of the mobile marketer. There are three specific ways it helps:

1. Machine learning can predict the possibility of a user converting or churning.

In mobile marketing, the machine learning process involves algorithms that lead to predictions. Marketers feed data to the system so that the computer can predict outcomes.

For example: your algorithm can analyze all the user actions leading to churn. It can then warn you when another user is going down that same path. This gives you a greater opportunity to win these endangered users back. To engage them before they’re lost forever.

It can also do the same to predict which users are likely to convert. This allows you to focus your marketing efforts on the segments most likely to buy.

2. Machine learning lets you segment users automatically.

And all based on their behavior, demographic, and psychographic data.

CleverTap has this ability to automate segmentation via recency, frequency, and monetary (RFM) analysis. The algorithm examines user activity to see how recently or frequently they performed an action such as purchasing a product or booking a trip. Finally, it divides your audience into segments. You get champions, at-risk, inactive customers, and so on.

3. Machine learning allows you to tailor the offers or promotions you send.

The system knows your users’ likes, actions, behaviors, values, and even interests. With all that data at hand, the system can then pick an offer to match the user data. End result: a more compelling offer that is more likely to engage your customer.


Machine learning basics - ML can decide which is the best offer to give your users.

Use a Mobile Marketing Platform With Machine Learning

Artificial intelligence and machine learning are more than just buzzwords. Today, they’re real tools that help you analyze all the data you possess. They allow you to scale your mobile marketing efforts and personalize outreach campaigns to millions of users.

Hopefully, this introduction to machine learning convinces you that so much more can be done to improve your campaigns with the right tools.

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