Machine Learning for Digital Customer Insights: How to Perform Predictive Analytics and Deploy to Interactive Dashboards
- Analytics & Tech
- Artificial Intelligence
This programme is conducted online.
3 days
Weekdays (9am - 6pm)
Who Should Attend
- Professionals, managers and executives of service quality and customer-centric initiatives
- Professionals with work exposure to customer experience, marketing, quality assurance, operations, collaborative partnership or customer analytics
Overview
This course is designed for working professionals to understand how machine learning can be used to gain customer insights. Participants will learn how to draw insights from the results of predictive analytics on customer attrition (supervised machine learning) and segmentation (unsupervised machine learning).
With a practice-oriented approach, the course provides participants with step-by-step guidance on how to employ machine learning using computational programming in R, and deploy the analytic results into an interactive dashboard, using Shiny. Ultimately, the course will enable participants to make data-driven and data-informed strategic decisions on customers.
This module is available on a standalone basis.
Learning Objectives
- Gain a basic understanding of supervised machine learning predictive analytics which encompasses core concepts, application examples, and demonstrations
- Understand the setup, features and basic functions of computational programming software R and R Studio
- Demonstrate and practice descriptive analytics by employing data exploration (using R) and ggplot2 visualisation on customer attrition data
- Demonstrate and practice predictive analytics by employing supervised machine learning using R on customer attrition data
- Gain a basic understanding of how unsupervised machine learning can be used to perform customer clustering and market segmentation analysis
- Learn how to create an interactive dashboard with Shiny
- Demonstrate and perform customer segmentation analysis using R and Shiny
Topic/Structure
- Introduction to Predictive Analytics: What Supervised Machine Learning Can do (and cannot do)
- Introduction to R and R Studio
- Running “Descriptive” Analytics for Customer Attrition Data: Data Exploration using R
- Running “Descriptive” Analytics for Customer Attrition Data with ggplot2 Visualization
- Running “Predictive” Analytics for Customer Attrition Data: Supervised Machine Learning using R
- Drills & Exercises on Predictive Analytics for Customer Attrition Data
- Introduction to Shiny and Interactive Dashboarding
- Drills & Exercises on Shiny and Interactive Dashboarding
Assessment
As part of the requirement for SkillsFuture Singapore, there will be an assessment conducted at the end of the course. The mode of assessment, which is up to the trainer’s discretion, may be an online quiz, a group/ individual presentation or based on classroom exercises.
Participants are required to attain a minimum of 75% attendance and pass the associated assessment in order to receive a digital Certificate of Completion issued by Singapore Management University.
Calculate Programme Fee
Fee Table
COMPANY-SPONSORED | |||
PARTICIPANT PROFILE |
SELF-SPONSORED |
SME |
NON-SME |
Singapore Citizen < 40 years old Permanent Resident LTVP+
|
$981 (After SSG Funding 70%) |
$381 (After SSG Funding 70% |
$981 (After SSG Funding 70%) |
Singapore Citizen ≥ 40 years old |
$381 (After SSG Funding 70% |
$381 (After SSG Funding 70% |
$381 (After SSG Funding 70% |
International Participant |
$3,270 (No Funding) |
$3,270 (No Funding) |
$3,270 (No Funding) |
All prices include 9% GST
Post Secondary Education Account (PSEA)
PSEA can be utilised for subsidised programmes eligible for SkillsFuture Credit support. Click here to find out more.
Self Sponsored
- SkillsFuture Credit
-
Singapore Citizens aged 25 and above may use their SkillsFuture Credits to pay for the course fees. The credits may be used on top of existing course fee funding.
This is only applicable to self-sponsored participants. Application to utilise SkillsFuture Credits can be submitted when making payment for the course via the SMU Academy TMS Portal, and can only be made within 60 days of course start date.
Please click here for more information on the SkillsFuture Credit. For help in submitting an SFC claim, you may wish to refer to our step-by-step guide on claiming SkillsFuture Credits (Individual). - Workfare Skills Support Scheme
-
From 1 July 2023, the Workfare Skills Support (WSS) scheme has been enhanced. Please click here for more details.
Company Sponsored
Enhanced Training Support for SMEs (ETSS)
- Organisation must be registered or incorporated in Singapore
- Employment size of not more than 200 or with annual sales turnover of not more than $100 million
- Trainees must be hired in accordance with the Employment Act and fully sponsored by their employers for the course
- Trainees must be Singapore Citizens or Singapore Permanent Residents
- Trainees must not be a full-time national serviceman
- Trainees will be able to enjoy ETSS funding only if the company's SME's status has been approved. To verify your SME's status, please click here.
Please click here for more information on ETSS.
Absentee Payroll
Companies who sponsor their employees for the course may apply for Absentee Payroll here. For more information, please refer to:
AP Guide (Non-SME Companies)
Declaration Guide (SME Companies)
Intake Information
Next Intake: To be advised