What is the objective of Predicting Customer Behavior?

Predicting customer behaviors’ main objective is to know user intentionality towards a particular product or service or category, based on interactions or transactions within a website. But to keep tracking the customers’ searching patterns, online agents can have a better understanding of their client’s behaviors and intentions. Based on this information, online merchants automatically get an idea to improve their service or the quality of their products, etc.

Why do we need Predicting Customer Behavior?

An organization’s performance needs to improve continually, and its clients choose whether the organization is at standard with what they expect. So as to stay up with clients, it is basic for organizations to have the option to predict client behavior to such an extent that they can take into account the necessities of clients in the future too.

On account of clients and their needs, the greatest error an organization can make is utilizing mystery or guessing to understand them. Based on this, guessing companies can not provide what exactly the customer wants. Marketers comprehend that predicting customer behavior is currently a crucial piece of their jobs. While gathering current data, organizations could survey the current issues, pain points, enthusiastic reasons, and other such present information about customers. While this data can help somewhat to guarantee that an organization can come up with sustainable solutions, predicting customer behavior is critical.

Additionally, by using this technology, organizations can segment customers based on the value they add to the company and also offer highly personalized offerings and customized solutions.

How does an AI-based solution work in Predicting Customer Behavior?

AI-based models have a potential way to transform retailers or e-commerce interactions with customers. By using AI-Based models or solutions, retailers can know more insights about customer behavior, expectations, tastes, preferences, and wishes. Based on this information, AI improves companies’ ability to offer the right products in front of the right clients when the time is right.

So we can implement these models using popular models, including logistic regression (LR) and boosted decision trees. We can also advance algorithms like neural networks; it has more advantages than LR and other algorithms. Because Neural Networks have the ability to capture nonlinear relationships between the input features and their “deeper” architecture have inherently greater modeling strength.

Probabilistic generative models have more advantages than all before mentioned algorithms which are inspired by deep neural networks; these algorithms can mimic the process of customer’s purchase behavior and capture the important variables to explain the data.

Complete process

Data extraction

E-commerce websites have a huge amount of data. That data includes potential customers or users searching for product information before making purchasing decisions; this behavior depends on the customer purchase intentions. We can know different types of search patterns when users search for any item on a website, i.e., time spent per item, searching frequency of every item, etc. Datasets may contain userid, timestamp, and event type. Event type further divided as pageview of a product, basket view, buy, adclick and adview, etc.

Data annotation

For this problem statement, we are collecting a dataset from a specific e-commerce website database. That dataset may contain different features like user id, timestamp, and event type. Event type further divided as pageview of a product, basket view, buy, ad click and ad view, etc.

For that, we can find different other features like time spent per item, searching frequency of every purchased item, etc.

Data processing

After extracting users’ data from the websites, we should perform preprocessing techniques on it. In this step, we have to apply different types of preprocessing techniques for handling missing values, converting categorical data to numerical data, creating new features from already available features, etc. But this dataset is very high dimensional data, so we have to apply dimensionality reduction techniques to reduce it, such as Principal Component Analysis or Single Value Decomposition, etc.

We can create different features or attributes from the original dataset. For Example

Model development

While building a machine learning model for this problem statement, we will divide the whole problem into two stages.

  1. Finding whether in a session buying is happening or not
  2. If buying is happening, how many items are bought in that session?

For the first stage, we can use random forest decision tree algorithms to make decisions, i.e., in which sessions the activity of purchase is happening. Random forest tree algorithm creates an ensemble of decision trees using randomization. The random forest tree algorithm is one of the most useful algorithms to make decisions, such as whether a particular session purchase is made or not that. The final classifier is efficient and capable of dealing with large datasets, missing data, and outliers like our problem statement dataset.

For the second stage of building our model, we can use deep learning algorithms such as Deep Belief Networks (DBN), Auto-encoders, etc. because these algorithms are well performed with high dimensionality datasets like our problem statement dataset. Deep Belief Networks are one of the types of restricted Boltzmann machines(RBMs). The DBN algorithm’s main component is a greedy, layer-by-layer learning algorithm that is an advantage of this algorithm to optimize DBN weights.DBN is well performed on unbalanced datasets. The Autoencoders algorithm is an unsupervised algorithm. It is mostly used to learn good representations of the data transformation and reduce the problem’s dimensionality to facilitate the supervised learning stage.

Inference and deployment of the model

Now we can predict customers’ click decisions in specific contexts, and then the right products will be recommended by identifying the search patterns of customers by using trained models. Our trained model will be able to identify activities of search patterns of the customers, such as what type of category products will buy in particular sessions based on that model will display related products to individual customers. This process improves companies’ growth economically.

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