Understand Customer Behavior with analytics and AI using ANAI

Customer segmentation refers to the process of dividing customers into several groups on the basis of certain behavior they have toward a range of products. Separating individual customers based on their demands and meeting the requirements is a tedious and complicated process. One such method is dividing customers into segments depending on their market behavior such as their rate of buying products, visiting various pages, click-through rates, etc.

This has widespread application across various industries, one of the most prominent being the e-Commerce one. Here, the customer base has always been huge and every day a vast amount of data is collected. Modern advancements using ML-backed approaches are being used to gain deeper insights and analyses into the customer base, which organizations can put into use and come up with strategies to market their products to their targeted audience.

Why use ANAI?

Feature Transformation

ANAI helps process and transform customer data to make it ready for model building and other analytical processes. The goal is to transform the data into such a form that can easily be interpreted further down the line and hence making it easier to generate predictions from it. For data to be ML-ready it needs to satisfy certain conditions and meet requirements, hence the data undergoes a few pre-processing steps like data analysis, wrangling, transformation, encoding, etc.

ANAI’s Automated Data Engineering automates the data pre-processing pipeline, wherein it implements Auto-Data Wrangling which covers various aspects of pre-processing such as:     

• Handling Missing Data
• Handling Imbalanced data
• Rectifying Bias/Skewness
• Categorical Encoding

Feature selection, one of the key processes while feature engineering, can be completely automatized using ANAI. ANAI selects the best possible features correlating with the target variable that gives out the most information. Customer segmentation data contains various features or attributes which depict the behaviors of customers. These features are essential in determining and dividing customers into clusters for further analysis. Selecting the optimal features is the key to clustering customers accurately, hence ANAI selects and feeds the features that are highly correlated further to the ML pipeline to get the most accurate predictions.

ANAI deploys AutoML to analyze customer data and identify recurring patterns across a variety of features. Predictions from ANAI can often assist marketing analysts in identifying customer segments that would be difficult to identify using intuition and manual data analysis. Also, the results can assist product teams in key decision-making to launch products or product features that actually matter.     

Advantages of customer segmentation using ANAI:

With ANAI, businesses can generate charts and perform instant analysis directly on the ANAI platform using the data that they have without any need to look at the issues within the data and solve them wasting precious time that can be used to solve the real problems. For this case study, customer segmentation data was used to help interested businesses understand how ANAI could be deployed to perform market analysis and train models on customer data sets without the hassle of ML expertise and the coding experience needed.

• Refined Marketing: With projections made using ANAI, marketing teams can get help in identifying patterns in customers’ behavior and come with prospects during the marketing and sales process.
• Focusing on the target audience: Identifying frequent and important customers, and analyzing their behavior can help scale business to great heights with customer satisfaction in mind.

The chart below generated on ANAI, helps us understand the relationship between customer frequency and the visit score given to each customer, depicting how frequently a customer visits the product, using a dispersion plot and also the segment in which the customer lies. Such informative visuals will help enterprises identify characteristics of frequent customers and target the right audience correctly.