Data report

Predicting Purchase Intent in Consumer Electronics Market

The table of content is going to be our guide for the presentation

Table of Content

Key Findings

Machine Learning Models

Methodology

Project Aims and Objectives

Problem Statement

Project Background

Introduction

Understanding Customer BehaviorWith increased competition, understanding and predicting consumer behavior has become crucial for companies to refine marketing strategies, enhance product offerings, and foster customer loyalty.

Impact of Technological Advancements.Technological innovations like AI, IoT, and 5G have dramatically transformed the landscape, introducing new product categories and reshaping customer expectations for features and performance.

Significance of the Consumer Electronics Industry,The consumer electronics market is one of the fastest-growing and most competitive sectors globally, with a wide range of products, from smartphones to smart home devices. It is essential for companies to understand market trends and customer preferences to maintain relevance.

We generally know that technoloy will keep evolving from one genetation to another . it is high time that business organization have to venture into the technology market.

Consumer Electronic Market

Introduction

Personalized marketing strategies based on data analysis enable businesses to tailor their products, services, and promotions to specific customer segments, improving engagement and conversion. rates

Need for Data-Driven Strategies

The consumer electronics market's rapid growth and the increasing complexity of customer needs necessitate data-driven solutions. Traditional broad-based marketing no longer captures the unique preferences and behavior of individual consumers, leading to a need for more sophisticated analysis.

Motivation Behind the Project

Project Background

Annual/quarterly report

By using predictive modeling and machine learning, companies can forecast customer purchase intent, predict future trends, and enhance customer satisfaction. These techniques provide actionable insights that lead to more effective decision-making and better business outcomes.

Role of Predictive Modeling and Machine Learning

Projects

Annual/quarterly report

Retaining customers and ensuring their satisfaction are essential for sustained business growth. The market’s fast pace requires businesses to constantly adapt to customer needs and expectations.

Identifying what drives customers to buy specific products remains a challenge in the consumer electronics marketFactors such as product features.

Customer Satisfaction and Retention

Problem Statement

Understanding Purchase Intent

Annual/quarterly report

Traditional methods often fail to integrate predictive modeling, customer satisfaction analysis, and segmentation into a single, cohesive framework

Gap in Existing Approaches

Main Aim:To develop a comprehensive analytical framework for understanding customer behavior, predicting purchase intent, and enhancing customer satisfaction in the consumer electronics market.

Feature Engineering for Enhanced Accuracy

Create and refine features that improve model accuracy.

Develop Predictive Models for Purchase Intent

Utilize machine learning algorithms like Naive Bayes,

Customer Segmentation

Use clustering techniques (K-Means) to segment customers for personalized marketing strategies

Identify Key Factors Influencing Purchase Intent & Satisfaction

Analyze product features, customer demographics, and behavior patterns.

Annual/quarterly report

4.Project Aims and Objectives

We generally grasp visual content better. Visual content is associated with cognitive and psychological mechanisms. We receive things through our eyes; the first image is what counts. We associate visual content with emotions.

Model Evaluation Used accuracy, AUC, and R² scores for performance assessment. Applied cross-validation and holdout testing for robust evaluation

Model Development Classification Models: Naive Bayes, Decision Tree, K-Nearest Neighbors, Gradient Boosting. Regression Models: Linear Regression, Support Vector Regressor. Clustering: K-Means for customer segmentation.

Feature Engineering Identified important features like CustomerAge, ProductPrice, AnnualIncome, PurchaseFrequency, etc. Derived new features to capture customer behavior patterns.

2. Data Preprocessing Handling missing values by replacing with column mean. Encoding categorical variables. Normalizing numerical data.

Data Collection Publicly available consumer electronics sales data. Includes features like product category, brand, price, customer demographics, satisfaction, and purchase behavior.

Methodology

Annual/quarterly report

These are Classification Models for Purchase Intent

Accuracy: 65% AUC Score: 0.739 Strengths: Reasonable performance for negative intents. Weakness: Struggles with positive purchase intents.

Accuracy: 75.8% AUC Score: 0.856 Strengths: Best at predicting negative purchase intent, high AUC score. Weakness: Lower recall for positive purchase intents.

Accuracy: 75.4%AUC Score: 0.814 Strengths: High recall for negative intents .Weakness: Slightly less balanced compared to Naive Bayes

Accuracy: 68.7% AUC Score: 0.686Strengths: Balanced prediction for both positive and negative purchase intents. Weakness: Lower overall performance compared to other models.

Gradient Boosting Classifier

Naive Bayes

K-Nearest Neighbors (K-NN)

Decision Tree

Machine Learning Models

Annual/quarterly report

R² Score: 1.0 Strengths: Ideal fit for product pricing prediction with minimal error.

Regression Models for Product Pricing and Clustering Model for Customer Segmentation

silhouette Score: 0.15 Weakness: Challenges in achieving distinct clusters due to imbalances in customer characteristics..

R² Score: 0.958 Strengths: Captures underlying data patterns well. Weakness: Slightly higher error compared to Linear Regression.

K-Means Clustering

Support Vector Regressor (SVR)

Linear Regression

Annual/quarterly report