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Use Case

Predicting Customer Purchase Behavior in Online Marketplaces

Analyzing customer purchase behavior trends to optimize sales strategies in online marketplaces.

1Overview & Strategic Importance

Predicting Customer Purchase Behavior in Online Marketplaces
Classification Solution Retail Data

Problem Statement

Understanding customer purchase behavior is critical for retail and e-commerce businesses to enhance sales, improve customer retention, and optimize marketing strategies. The factors influencing purchase decisions include customer demographics, purchasing habits, and engagement with loyalty programs or discounts. Businesses need predictive tools to target the right audience, reduce churn, and improve conversion rates. This problem is crucial for refining customer experiences, boosting sales, and maintaining a competitive edge in the retail and e-commerce sector.

Required Solutions

  • Analyzing relationships between customer demographics, purchasing habits, and purchase likelihood.
  • Evaluating the impact of loyalty programs and discounts on purchase decisions.
  • Providing actionable insights to tailor marketing campaigns and promotional strategies.

Solution Objectives

  • Perform exploratory data analysis to uncover patterns influencing purchase behavior.
  • Build a classification model to predict the likelihood of purchase (PurchaseStatus).
  • Conduct scenario analysis to understand the impact of discounts and loyalty programs.
  • Optimize marketing and engagement strategies based on predictive insights.

Understanding the Problem

Customer purchase behavior is driven by various interconnected factors such as age, gender, income, and purchasing habits.

Predictive models can provide valuable insights into these dynamics, helping businesses enhance targeting strategies and improve decision-making to drive higher sales and profitability.

2About the Data

Data Collection

The dataset was aggregated from e-commerce platforms, capturing customer interactions, purchase histories, and engagement metrics. The data may come from transaction records, website analytics, and customer surveys.

Dataset Source

Major Parameters Description

Download Training Data
Age

Customer's age.

Gender

Customer's gender (0 = Male, 1 = Female).

Annual Income

Customer's annual income in dollars.

Number of Purchases

Total purchases made by the customer.

Product Category

Category of purchased products (0 = Electronics, 1 = Clothing, 2 = Home Goods, 3 = Beauty, 4 = Sports).

Time Spent on Website

Average time spent on the website in minutes.

Loyalty Program

Membership status in a loyalty program (0 = No, 1 = Yes).

Discounts Availed

Number of discounts availed by the customer (0 to 5).

PurchaseStatus

Target variable indicating purchase likelihood (0 = No, 1 = Yes).

3Using iDareAI

Guided Mode Initialization

AUploading Dataset

Click on the **'Upload CSV or Excel Data'** button → Select a source for the dataset → Upload `purchase_behavior_train.csv`. The system automatically analyzes the file, extracts column descriptions, and identifies the top value-adding targets for prediction.

Upload UI

BChoosing Analysis Mode

Choose between autonomous machine learning or manual building. In autonomous mode, simply ask a question like:
  • What changes can improve customer purchase status based on the patterns in this data?
  • Which factors in the data have the biggest impact on customer purchase status?

Operation Using Autonomous Guided Mode

AQuery Response

'TimeSpentOnWebsite', 'LoyaltyProgram', and 'DiscountsAvailed' emerged as the most significant predictors. The Random Forest model achieved a test accuracy of 96%, indicating strong predictive capability for purchase status.

Auto Analysis

BAI Application

The generated AI application allows users to choose between models and use sliders to test different scenarios, seeing how changes in engagement metrics impact purchase probability.

Auto Application

Model Fine-Tuning/Manual Model Building

ASelecting Prediction Target

'PurchaseStatus' column was selected as the target.

Target Selection

BSelecting Analysis Type

The analysis target is a categorical column. Hence, the 'Classification' analysis type is selected.

Analysis Type

CSelecting Model Group/Item

Model Group

DSelecting Features

Select the most relevant demographic and engagement features.

Feature Selection

ESelecting Training Level

The "Slow" training level with "High Performance" configuration was selected for thorough analysis.

Training Level

AI Modeling Details

Among the four algorithms, Random Forest is considered the best ML for having the highest accuracy and lowest possible error percentages.

Modeling Details

Training Analysis Details

APredicted Target

Predicted Target

BROC AUC

ROC AUC

CError Trend

Error Trend

DFeature Importance

Feature Importance

Finalize Models

Once satisfied with performance, click 'Deploy'. The system saves and deploys models for future demand analysis or production environment.

Finalize Models

4AI APPLICATION

Manual Model Building

In Manual Training Mode, users can modify sliders for variables like TimeSpentOnWebsite, NumberOfPurchases, DiscountsAvailed, and LoyaltyProgram. Clicking ‘Get Response’ triggers a tailored output that directly corresponds to the selected feature values.

Manual App

AI Application Demo

Variables like TimeSpentOnWebsite, NumberOfPurchases, DiscountsAvailed, and LoyaltyProgram affect the result significantly. High values for these features yield positive purchase status predictions.

Saving the Project

Save your project by clicking the icon at the bottom left corner of the textbox.

Saving

Sharing the Project

Share the application for single on-demand predictions once the analysis is saved.

Sharing

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