Data about our browsing and buying patterns are everywhere. From credit card transactions and online shopping carts, to customer loyalty programs and user-generated ratings/reviews, there is a staggering amount of data that can be used to describe our past buying behaviors, predict future ones, and prescribe new ways to influence future purchasing decisions. In this course, four of Wharton’s top marketing professors will provide an overview of key areas of customer analytics: descriptive analytics, predictive analytics, prescriptive analytics, and their application to real-world business practices including Amazon, Google, and Starbucks to name a few. This course provides an overview of the field of analytics so that you can make informed business decisions. It is an introduction to the theory of customer analytics, and is not intended to prepare learners to perform customer analytics.
Course Learning Outcomes:
After completing the course learners will be able to...
Describe the major methods of customer data collection used by companies and understand how this data can inform business decisions
Describe the main tools used to predict customer behavior and identify the appropriate uses for each tool
Communicate key ideas about customer analytics and how the field informs business decisions
Communicate the history of customer analytics and latest best practices at top firms
제공자:
About this Course
4.5
5,979개의 평가
귀하가 습득할 기술
Predictive AnalyticsCustomer AnalyticsRegression AnalysisMarketing Performance Measurement And Management
강의 계획 - 이 강좌에서 배울 내용
주
1완료하는 데 4분 필요
Introduction to Customer Analytics
What is Customer Analytics? How is this course structured? What will I learn in this course? What will I learn in the Business Analytics Specialization? These short videos will give you an overview of this course and the specialization; the substantive lectures begin in Week 2.
2 videos (Total 4 min)
주
2완료하는 데 2시간 필요
Descriptive Analytics
In this module, you’ll learn what data can and can’t describe about customer behavior as well as the most effective methods for collecting data and deciding what it means. You’ll understand the critical difference between data which describes a causal relationship and data which describes a correlative one as you explore the synergy between data and decisions, including the principles for systematically collecting and interpreting data to make better business decisions. You’ll also learn how data is used to explore a problem or question, and how to use that data to create products, marketing campaigns, and other strategies. By the end of this module, you’ll have a solid understanding of effective data collection and interpretation so that you can use the right data to make the right decision for your company or business.
7 videos (Total 73 min), 2 readings, 1 quiz
7개의 동영상
Descriptive Data Collection: Survey Overview16m
Descriptive Data Collection: Net Promoter Score and Self-Reports11m
Descriptive Data Collection: Survey Design16m
Passive Data Collection4m
Media Planning8m
Causal Data Collection and Summary8m
2개의 읽기 자료
Descriptive Analytics Slides10m
Additional Readings: Descriptive Analytics10m
1개 연습문제
Descriptive Analytics Quiz20m
주
3완료하는 데 2시간 필요
Predictive Analytics
Once you’ve collected and interpreted data, what do you do with it? In this module, you’ll learn how to take the next step: how to use data about actions in the past to make to make predictions about actions in the future. You’ll examine the main tools used to predict behavior, and learn how to determine which tool is right for which decision purposes. Additionally, you’ll learn the language and the frameworks for making predictions of future behavior. At the end of this module, you’ll be able to determine what kinds of predictions you can make to create future strategies, understand the most powerful techniques for predictive models including regression analysis, and be prepared to take full advantage of analytics to create effective data-driven business decisions.
10 videos (Total 79 min), 4 readings, 1 quiz
10개의 동영상
Asking Predictive Questions3m
Regression Analysis, Part 1: The Demand Curve5m
Regression Analysis, Part 2: Making Predictions5m
Beyond Period 210m
Making Predictions Using a Data Set8m
Data Set Predictions: Mary, Sharmila, or Chris?10m
Probability Models10m
Implementation of the Model16m
Results and Predictions5m
4개의 읽기 자료
Reading: Customer Lifetime Value10m
Predictive Analytics and Regression Analysis Slides10m
Implementation of the Model Spreadsheet10m
Additional Readings: Predictive Analytics10m
1개 연습문제
Predictive Analytics Quiz20m
주
4완료하는 데 1시간 필요
Prescriptive Analytics
How do you turn data into action? In this module, you’ll learn how prescriptive analytics provide recommendations for actions you can take to achieve your business goals. First, you’ll explore how to ask the right questions, how to define your objectives, and how to optimize for success. You’ll also examine critical examples of prescriptive models, including how quantity is impacted by price, how to maximize revenue, how to maximize profits, and how to best use online advertising. By the end of this module, you’ll be able to define a problem, define a good objective, and explore models for optimization which take competition into account, so that you can write prescriptions for data-driven actions that create success for your company or business.
7 videos (Total 42 min), 1 reading, 1 quiz
7개의 동영상
What is Prescriptive Analytics?4m
Using the Data to Maximize Revenue6m
Parameters of the Model10m
Market Structure9m
Competition and Online Advertising Models5m
Conclusion(s)5m
1개의 읽기 자료
Prescriptive Analytics Slides10m
1개 연습문제
Prescriptive Analytics Quiz20m
4.5
1,283개의 리뷰32%
이 강좌를 수료한 후 새로운 경력 시작하기
30%
이 강좌를 통해 확실한 경력상 이점 얻기
최상위 리뷰
Though I have worked on Customer Analytics with my previous work experiences and also on Surveys etc at George Brown College Canada, this module was more than insightful. Lots of learning to learn eh!
Perfect Course for those who want to inquire insight and knowledge of how tons of data that we generate in our day to day life is being utilized by big organizations in optimizing their productivity.
강사
Eric Bradlow
Professor of Marketing, Statistics, and Education, Chairperson, Wharton Marketing Department, Vice Dean and Director, Wharton Doctoral Program, Co-Director, Wharton Customer Analytics InitiativeThe Wharton School
Peter Fader
Professor of Marketing and Co-Director of the Wharton Customer Analytics InitiativeThe Wharton School
Raghu Iyengar
Associate Professor of MarketingThe Wharton School
Ron Berman
Assistant Professor of MarketingThe Wharton School
펜실베이니아 대학교 정보
The University of Pennsylvania (commonly referred to as Penn) is a private university, located in Philadelphia, Pennsylvania, United States. A member of the Ivy League, Penn is the fourth-oldest institution of higher education in the United States, and considers itself to be the first university in the United States with both undergraduate and graduate studies.
비즈니스 분석 전문 분야 정보
This Specialization provides an introduction to big data analytics for all business professionals, including those with no prior analytics experience. You’ll learn how data analysts describe, predict, and inform business decisions in the specific areas of marketing, human resources, finance, and operations, and you’ll develop basic data literacy and an analytic mindset that will help you make strategic decisions based on data. In the final Capstone Project, you’ll apply your skills to interpret a real-world data set and make appropriate business strategy recommendations.
자주 묻는 질문
강의 및 과제를 언제 이용할 수 있게 되나요?
강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.
이 전문 분야를 구독하면 무엇을 이용할 수 있나요?
강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.
환불 규정은 어떻게 되나요?
재정 지원을 받을 수 있나요?
궁금한 점이 더 있으신가요? 학습자 도움말 센터를 방문해 보세요.
Coursera는 세계 최고의 교육에 대한 보편적인 접근 기회를 제공하며,
최고의 대학 및 기관들과 제휴하여 온라인 강좌를 제공합니다.
© 2019 Coursera Inc. All rights reserved.
