Using machine learning to personalize user experience

Using machine learning to personalize user experience

E-commerce websites, such as shops and platforms with many users, are designed to meet the needs of customers. Usually, a website behaves the same for each customer.

E-commerce websites, such as shops and platforms with many users, are designed to meet the needs of customers. Usually, a website behaves the same for each customer. However, this “one-size-fits-all” approach does not always meet the needs for all situations. Understanding the customers’ intentions can help to improve the journey, e.g. by taking shortcuts or giving recommendations, and make it a better experience overall. This article shows how to use existing data on customer behavior to create a machine learning model that is capable of predicting intent.

Data Privacy

I personally don’t like it when advertising technology companies like Google and Facebook follow online activities extensively. Nevertheless, I think that individual websites can use personalization techniques without violating privacy as long as the data is not shared or linked to external services. It makes a difference whether the data is used to improve the customer experience or whether all activities are tracked over the Internet to generate profits from advertising. Furthermore, any personalization should be an opt-out.

Customer journey in data

Typically, a user’s intention on a Web site can be understood by looking at their past interactions. In concrete terms, this means that a user leaves a sequence of events about the history of his page views and interactions. An event can be that a user makes a search query, calls up an article page or receives an e-mail. This data forms the basis for working with the following techniques. Therefore the first step is to collect or extract this data. Usually the raw data is already stored on web servers or in databases, which then need to be refined to be usable.

Example:

Image for post

Three different user event streams

This image represents three different user journeys at the point, where he arrives on the website. In this case it’s a simple webshop and for this examples it’s a very simple journey. User 1 might be looking for a specific Product, while User 2 might be just browsing through the page and User 3 just bought something. To start with a simple intent, we want to predict if a user makes a purchase.

tensorflow machine-learning neural-networks data-science

Bootstrap 5 Complete Course with Examples

Bootstrap 5 Tutorial - Bootstrap 5 Crash Course for Beginners

Nest.JS Tutorial for Beginners

Hello Vue 3: A First Look at Vue 3 and the Composition API

Building a simple Applications with Vue 3

Deno Crash Course: Explore Deno and Create a full REST API with Deno

How to Build a Real-time Chat App with Deno and WebSockets

Convert HTML to Markdown Online

HTML entity encoder decoder Online

Most popular Data Science and Machine Learning courses — July 2020

Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant

Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data

Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data

15 Machine Learning and Data Science Project Ideas with Datasets

Learning is a new fun in the field of Machine Learning and Data Science. In this article, we’ll be discussing 15 machine learning and data science projects.

Learn Machine Learning with Python (Part 3) | Machine Learning with Neural Networks

Learn Machine Learning with Python using neural networks with this machine learning beginners course. In this tutorial we will look at taking an existing sol...

Fundamentals of Neural Network in Machine Learning

Fundamentals of Neural Network in Machine Learning. What is a Neuron? What is the Activation Function? How do Neural Network Works? How do Neural Networks Learn?