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.
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.
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.
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.
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