Direct Marketing Analytics Using SPSS

- Direct Marketing-এর বিভিন্ন Channel এবং Marketing Analytics-এ SPSS-এর ব্যবহার সম্পর্কে জানতে পারবেন।
- SPSS ব্যবহার করে Customer Database তৈরি ও Data Preparation করতে পারবেন।
- Customer-এর Purchasing Behavior ও Marketing Patterns বিশ্লেষণ করতে পারবেন।
- RFM Analysis-এর মাধ্যমে High-value, Loyal ও At-risk Customers শনাক্ত করতে পারবেন।
- Cluster Analysis ব্যবহার করে Customer Segmentation করতে পারবেন।
- বিভিন্ন Marketing Campaign-এর Performance তুলনা ও বিশ্লেষণ করতে পারবেন।
- Logistic Regression-এর মাধ্যমে Customer Response Predict করতে পারবেন।
- Campaign-এর খরচ, আয় ও Profitability মূল্যায়ন করতে পারবেন।
- একটি Practical SPSS Project-এর মাধ্যমে Data Analysis করে কার্যকর Marketing Recommendations তৈরি করতে পারবেন।

Course Content

Orientation

Session 1: Introduction to Direct Marketing and SPSS
Introduces direct marketing, its major channels, customer databases, and the role of SPSS in analyzing customer behavior and campaign performance.

Session 2: Designing a Direct Marketing Database
Explains how to create an SPSS database containing customer demographics, purchase history, campaign contacts, responses, revenue, and marketing channels.

Session 3: Data Cleaning and Preparation in SPSS
Covers missing values, duplicate cases, incorrect entries, outliers, variable transformation, recording, computing new variables, and selecting target cases.

Session 4: Customer Profiling with Descriptive Statistics
Uses frequencies, percentages, averages, cross-tabulations, tables, and charts to describe customers and compare their purchasing and response patterns.

Session 5: RFM Customer-Value Analysis
Apply Recency, Frequency, and Monetary analysis to classify customers according to their purchasing value and identify loyal, high-value, inactive, and at-risk customers.

Session 6: Customer Segmentation Using Cluster Analysis
Demonstrates hierarchical and K-means cluster analysis to divide customers into meaningful groups based on demographics, purchasing behavior, and customer value.

Session 7: Testing Differences in Campaign Performance
Uses chi-square tests, independent samples t-tests, and ANOVA to examine whether campaign responses and customer spending differ across groups and marketing channels.

Session 8: Predicting Customer Response with Logistic Regression
Develops a binary logistic regression model to predict whether a customer will respond to a campaign using demographic and behavioral characteristics.

Session 9: Evaluating Campaign Effectiveness and Profitability
Calculates response rate, conversion rate, cost per acquisition, campaign revenue, return on marketing investment, and other measures of campaign success.

Session 10: Direct Marketing Project and Management Reporting
Integrates data preparation, customer profiling, RFM analysis, segmentation, prediction, and profitability evaluation into a complete SPSS project with practical marketing recommendations.

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What Will You Learn?

Course Content

Orientation

Session 1: Introduction to Direct Marketing and SPSS
Introduces direct marketing, its major channels, customer databases, and the role of SPSS in analyzing customer behavior and campaign performance.

Session 2: Designing a Direct Marketing Database
Explains how to create an SPSS database containing customer demographics, purchase history, campaign contacts, responses, revenue, and marketing channels.

Session 3: Data Cleaning and Preparation in SPSS
Covers missing values, duplicate cases, incorrect entries, outliers, variable transformation, recording, computing new variables, and selecting target cases.

Session 4: Customer Profiling with Descriptive Statistics
Uses frequencies, percentages, averages, cross-tabulations, tables, and charts to describe customers and compare their purchasing and response patterns.

Session 5: RFM Customer-Value Analysis
Apply Recency, Frequency, and Monetary analysis to classify customers according to their purchasing value and identify loyal, high-value, inactive, and at-risk customers.

Session 6: Customer Segmentation Using Cluster Analysis
Demonstrates hierarchical and K-means cluster analysis to divide customers into meaningful groups based on demographics, purchasing behavior, and customer value.

Session 7: Testing Differences in Campaign Performance
Uses chi-square tests, independent samples t-tests, and ANOVA to examine whether campaign responses and customer spending differ across groups and marketing channels.

Session 8: Predicting Customer Response with Logistic Regression
Develops a binary logistic regression model to predict whether a customer will respond to a campaign using demographic and behavioral characteristics.

Session 9: Evaluating Campaign Effectiveness and Profitability
Calculates response rate, conversion rate, cost per acquisition, campaign revenue, return on marketing investment, and other measures of campaign success.

Session 10: Direct Marketing Project and Management Reporting
Integrates data preparation, customer profiling, RFM analysis, segmentation, prediction, and profitability evaluation into a complete SPSS project with practical marketing recommendations.

Contact:

+880 1911 814 842

contact@aoledu.org

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What Will You Learn?

Course Content

Orientation

Session 1: Introduction to Direct Marketing and SPSS Introduces direct marketing, its major channels, customer databases, and the role of SPSS in analyzing customer behavior and campaign performance.

Session 2: Designing a Direct Marketing Database Explains how to create an SPSS database containing customer demographics, purchase history, campaign contacts, responses, revenue, and marketing channels.

Session 3: Data Cleaning and Preparation in SPSS Covers missing values, duplicate cases, incorrect entries, outliers, variable transformation, recording, computing new variables, and selecting target cases.

Session 4: Customer Profiling with Descriptive Statistics Uses frequencies, percentages, averages, cross-tabulations, tables, and charts to describe customers and compare their purchasing and response patterns.

Session 5: RFM Customer-Value Analysis Apply Recency, Frequency, and Monetary analysis to classify customers according to their purchasing value and identify loyal, high-value, inactive, and at-risk customers.

Session 6: Customer Segmentation Using Cluster Analysis Demonstrates hierarchical and K-means cluster analysis to divide customers into meaningful groups based on demographics, purchasing behavior, and customer value.

Session 7: Testing Differences in Campaign Performance Uses chi-square tests, independent samples t-tests, and ANOVA to examine whether campaign responses and customer spending differ across groups and marketing channels.

Session 8: Predicting Customer Response with Logistic Regression Develops a binary logistic regression model to predict whether a customer will respond to a campaign using demographic and behavioral characteristics.

Session 9: Evaluating Campaign Effectiveness and Profitability Calculates response rate, conversion rate, cost per acquisition, campaign revenue, return on marketing investment, and other measures of campaign success.

Session 10: Direct Marketing Project and Management Reporting Integrates data preparation, customer profiling, RFM analysis, segmentation, prediction, and profitability evaluation into a complete SPSS project with practical marketing recommendations.

Student Ratings & Reviews

Contact:

+880 1911 814 842

contact@aoledu.org

Quick Links

Subscribe us

We Accept:

Session 1: Introduction to Direct Marketing and SPSS
Introduces direct marketing, its major channels, customer databases, and the role of SPSS in analyzing customer behavior and campaign performance.

Session 2: Designing a Direct Marketing Database
Explains how to create an SPSS database containing customer demographics, purchase history, campaign contacts, responses, revenue, and marketing channels.

Session 3: Data Cleaning and Preparation in SPSS
Covers missing values, duplicate cases, incorrect entries, outliers, variable transformation, recording, computing new variables, and selecting target cases.

Session 4: Customer Profiling with Descriptive Statistics
Uses frequencies, percentages, averages, cross-tabulations, tables, and charts to describe customers and compare their purchasing and response patterns.

Session 5: RFM Customer-Value Analysis
Apply Recency, Frequency, and Monetary analysis to classify customers according to their purchasing value and identify loyal, high-value, inactive, and at-risk customers.

Session 6: Customer Segmentation Using Cluster Analysis
Demonstrates hierarchical and K-means cluster analysis to divide customers into meaningful groups based on demographics, purchasing behavior, and customer value.

Session 7: Testing Differences in Campaign Performance
Uses chi-square tests, independent samples t-tests, and ANOVA to examine whether campaign responses and customer spending differ across groups and marketing channels.

Session 8: Predicting Customer Response with Logistic Regression
Develops a binary logistic regression model to predict whether a customer will respond to a campaign using demographic and behavioral characteristics.

Session 9: Evaluating Campaign Effectiveness and Profitability
Calculates response rate, conversion rate, cost per acquisition, campaign revenue, return on marketing investment, and other measures of campaign success.

Session 10: Direct Marketing Project and Management Reporting
Integrates data preparation, customer profiling, RFM analysis, segmentation, prediction, and profitability evaluation into a complete SPSS project with practical marketing recommendations.