Machine Learning Guide

Overview

InboxSuite allows you to reach out to more customers than you could using manual marketing engagement processes. 

By automating your repetitive tasks, you can free up your time to concentrate on developing content that speaks directly to your target audience. Our constantly improving tools also give you the chance to evaluate the performance of your campaigns using precise metrics, which in turn gives you pointers on your next actions based on real data rather than pure intuition.

It is no secret that paying attention to small details like customers’ habits and interests can lead to higher interaction from their part but it can be challenging, if not impossible, without the help of AI and Machine Learning

Getting started

What is Machine Learning and why is it important?

In essence, Machine Learning is a form of Artificial Intelligence that helps software applications predict outcomes more accurately without needing to be specifically programmed to do so. Machine Learning algorithms use past data to make predictions about future outcomes. To rephrase it, it is a process by which software applications can learn how to do things on their own, by analyzing data. 

The goal? To consistently enhance proficiency in a specific task.

With Machine Learning, you can react more quickly to changes in customers’ behavior. This allows you to spend more time building strategies instead of doing routine actions. 

Needless to say, Machine Learning systems can process huge amounts of data in no time, organize it for you and provide the results along with guidance on your future actions.

Machine Learning and Email Marketing

1. Email Timing: When?

Send emails to your subscribers too often, and they might unsubscribe. Send too little, and your brand faces the risk of falling behind rivals.

Machine Learning algorithms use past behavior and other factors such as time zone and habits to predict the best time and day a subscriber is most likely to interact with your emails.

2. Content: What?

Recommendation systems

• SVD (Singular Value Decomposition) is a widely used unsupervised Machine Learning algorithms. The aim for its implementation is to provide content recommendation from the latent features of content-subscriber matrices. It establishes the relatedness of subscribers’ interests based on a set of factors.
• CTDR (Click-Through Delivery Rate) is a metric that helps you evaluate the performance of your email contents and determine the best content by measuring how many people clicked on at least one link after receiving an email.
  
  The CTDR formula is as follows:
  
  CTDR = Number of people who clicked the link / Number of Emails delivered successfully x 100
  Let’s say you sent an email to 120 subscribers and 100 were delivered successfully to their intended recipients and 5 of those recipients clicked on your CTA and were sent to a new page.
  Using this data, your CTDR equates to 5%.
  Inside our InboxSuite service we have an algorithm that uses the actual value of this metric to adjust the recommendations for the Best content to boost your CTDR.

3. Delivery: To whom?

Clustering: Clustering is considered among the most widely-used, unsupervised machine learning techniques that help you speed up the whole segmentation process by quickly identifying particular subscriber behaviors and activity levels. The Clustering Model divides your email subscribers’ database into smaller, more targeted segments, providing a more personalized experience for your subscribers.

Scoring: Scoring shows how engaged a subscriber is with a particular website. The Scoring of a subscriber is calculated when the first subscriber trigger is sent and then updated hourly, considering all subscriber’s actions performed during this time period. This allows you to select profiles with the highest ratings.

Summary

AI and Machine Learning take your Email Marketing to the next level and help you determine the best timing and content for an individual subscriber.

The human mind simply can’t process the vast amounts of data that are collected every minute by analytical systems, let alone analyze them. Machine Learning systems can process big data, organize them, and provide results within minutes.

Concisely, Machine Learning improves the quality of data analysis and automates marketing processes which can lead to faster and more accurate decisions. It also helps you analyze data quickly and adapt to changes. 

With InboxSuite, you can avoid routine work and spend more time on building effective strategies.