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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.
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:
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.
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.
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.
The above chart shows the relation of customer’s reaction to the product with the help of metrics such as affinity towards the product i.e., customer_affinity_score, no. of times customer ordered the product and their relation with customer_product_variation_score. Such instructive charts help understand and analyze how changes to products affect the customers’ behavior.
Within ANAI, starting from the data ingestion to data engineering, to feature selection and model selection, to building and training the best model, has been completely automated with a no code approach-based UI. This becomes helpful to users or business owners who don’t have teams set up to manage their data science workloads with little requirement of prior knowledge for machine learning or even coding.
For the customer data set selected, we ran it through the ANAI ML pipeline to train a bunch of models that perform the best at categorizing customers with various features given as the input. The chart below is generated when the training is done. It contains the name of the models used, their normal accuracies, and their cross-validated accuracies.
It can be seen that CatBoost Classifier model has shown the best cross-validated accuracy overall (at around 97.61%) and hence perform the best out of all the models on this particular customer data set.
Predictions were also improved by ANAI conducting optimal data engineering, and feature engineering techniques before passing the data to the model. This means that ANAI shows quite a prominence in the entire machine learning workflow when it automatically preprocesses the data, conducts feature engineering, helps with generating insights through charts, and even trains necessary models with the right set of methods.
With ANAI, organizations can leverage the customer data that they have, to build insights into the customer mindsets and provide them with what they actually need, to drive growth and customer satisfaction. ANAI provides an all-in-one solution for solving all the retail and e-commerce related problems such as churn prediction, seasonal forecasting, demand prediction, supply chain optimization, inventory management, and a lot more, by truly democratizing AI for everyone.
A 2019 Gartner study revealed that the number of enterprises implementing AI grew by 270% since 2015, with even more companies adopting it today and it’s no secret that personalization is king when it comes to content distribution. Marketers have found personalized outreaches to be 14 percent more effective in engaging with the customers.
While mass marketing strategies may still be effective, believing that everyone would want to purchase what you’re offering is a time-consuming, inefficient, and expensive strategy. Customer segmentation divides your customer data into groups with similar properties or behavioral characteristics, allowing you to use dynamic content and personalization tactics to send out marketing communications that are more timely, relevant, and effective than a “one-size-fits-all” approach.
The results of ANAI will aid in classifying/clustering customers according to the organization’s segmentation plan, such as product type, customer behavior, demographics, and so on. This will allow businesses to find trends in how clients were recruited and how various groups interacted with the organization.
Also, in a study by Demand Metric, it was found that 80 percent of marketers agreed that personalized content is more effective than ‘impersonal’ content. Sending personalized content to a customer not only increases the probability of purchase but also ensures customers feel appreciated and valued and reinforces brand loyalty.
With the help of ANAI’s AI engine, marketers can optimize campaigns, select desired segments, and send the appropriate features, at the right cadence, across all channels. This helps us to understand more about what types of prospects engage with throughout the marketing and sales process, as well as what forms of communication they will like during the service delivery. It also aids in capturing a target audience by designing a market strategy aimed at attracting only excellent fit leads.
Understanding the customer segmentation using ANAI will help organizations within the Retail sector understand the important features and variables that affect the customer segmentation. ANAI simplifies the analysis and the ML part for training useful models, so that businesses can cater to their clients and customers the best they can without worrying so much about the technology involved.
Automated processes ranging from ingesting the data to ANAI making analysis and predictions provide such entities an upper hand by giving them the necessary details related to customers and hence helping them grow their customer base and making good product-related decisions. Using graphs to examine feature relations and the effect of certain product decisions will help understand customer behavior. Also, classifying customers into various categories makes it possible to identify risk and profit factors that affect the business.
To implement such solutions or to get a personalized solution for your niche use case, contact us at email@example.com or visit www.anai.io/contact/