All Courses:

| Complete course list | List by Categoray

Exploration and Predictive Analytics Using SAS Text Analytics (HKG521)




Early Bird Program - We're offering up to 33% discount off for Early Bird registration. Learn more now!

Course duration: 1.5 days

Overview

Confronted with big data issues, many organizations struggle to get the best possible value from text data. Because of data ambiguity and complexity, it’s not easy to discern, quantify, analyze or exploit insights from text-based data. Analytics and Marketing executives struggle to combine text-based information with structured data to get a full, accurate view of the enterprise.

Customers use SAS to combine structured and unstructured text data into organizational assets - to assess, analyze, understand and act upon the insight buried in electronic text – including social media content, call center logs, product choices, customer applications and more. As a result, customers make effective, proactive business decisions, streamline priorities and achieve critical ROI in highly competitive markets.

Course objectives

This course is designed to introduce Big Data analysis methods and technologies to analytics/ marketing teams. Throughout the course, you will learn
  • the concepts around the foundation of SAS Text Analytics and SAS Natural Language Processing, entity extraction, categorization, taxonomy building, taxonomy accuracy tests
  • how to discover topics with Contextual Analysis – Text Parsing, Text Filtering, Text Topic discovery, automatically building categorization rules
  • how the global organizations are leveraging Big Data Analytics in practical business applications around the world.

Who should attend

Analytics and Marketing executives with the functions of:
  • Marketing Brand Strategy
  • Digital Analytics
  • Customer Intelligence Marketing
  • Strategy and Innovation
  • Global Marketing
  • Customer Relationship and Engagement
  • Brand Innovation
  • Marketing and Communications

Prerequisites

Some experience with SAS and SAS Enterprise Miner is useful, but it is not mandatory. No experience with text analysis is necessary.
This course addresses the topic of text analytics, with demonstrations and exercises with SAS Contextual Analysis and related SAS technologies.

Course Outline

  1. Introduction
  2. Global Business Trends in Unstructured Data Analytics
      Global case studies in customer analytics
    • Online/ digital markets
    • Consumer banking
    • Retail/ Marketing
  3. Understanding customer digital lifestyle from online behavior and preferences
      Understand technologies and methods behind:
    • Information retrieval of social data
    • Discovering insights from customer preferences of digital content
    • Organizing unstructured data
    • Visualizing text insights
  4. Leveraging both structured and unstructured data
    • Understand customer digital lifestyle by combining web surfing data with customer profile data
    • Analyzing Contact Center text and structured data to understand and predict acquisition, churn management
  5. Where will we go today?
    • How global organizations start small and grow big with Big Data Analytics
    • Growing the Big Data mindset in marketing/ analytical environments

Instructor


Jason
Jason Loh is a Product Manager for Information Management & Analytics of SAS North Asia regional team, with a focus on Text Analytics amongst other SAS solutions.
He graduated with a double degree in Business and IT from Monash University, Australia and been working in the field of analytics for 12 years – and the recent 5 years in SAS in advisory and technical roles, presenting annually in Text Analytics/ Analytics public conferences and customer knowledge sharing sessions, and conducted workshops for National University of Singapore.
Jason is involved in the design/ delivery of a range of successful analytics projects for customers in sectors including government/ manufacturing/ banking and communications across Asia.

BDTX

 

 
www.sas.com | support.sas.com
Terms of Use & Legal Information | Privacy Statement