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DESCRIPCION

 

Executive Program
CUSTOMER ANALYTICS

 

Cómo empresas exitosas, desde  Amazon, Google a Starbucks,
usan los datos para crear prácticas de marketing
innovadoras y centradas en el cliente.


En este curso, en el que se combinan clases presenciales y online, cuatro de los mejores profesores de marketing de The Wharton School – The University of Pennsylvania y consultores peruanos de reconocido prestigio expondrán sobre las áreas clave de análisis de clientes: análisis descriptivo, análisis predictivo, análisis prescriptivos y su aplicación a prácticas comerciales del mundo real, incluídas las mejores acciones y estrategias de empresas como Amazon, Google y Starbucks, por nombrar algunos de los casos que se expondrán. Aprenderá cómo las empresas exitosas usan los datos para crear prácticas de marketing innovadoras y centradas en el cliente.

• Duración: Cinco Semanas
• Idiomas: Inglés con traducción al Español y Español

• Inicio: primera semana de junio de 2018
• 5/6 horas online por semana desde la plataforma online de The Wharton School (Pennsylvania – Estados Unidos)
• 3 horas presenciales por semana en el Campus San Isidro de la ciudad de Lima

 

Con Certificado de



 

Sobre The Wharton School – Universidad de Pensilvania


Reconocida entre las mejores y más prestigiosas escuelas de negocios del mundo, The Wharton School – University of Pennsylvania (también conocida a nivel mundial como Penn) es una universidad privada, ubicada en Filadelfia, Pensilvania, Estados Unidos. Miembro de la Ivy League, Penn es la cuarta institución de educación superior más antigua de los Estados Unidos y se considera la primera universidad de los Estados Unidos con estudios de pregrado y posgrado.

About The Wharton School – University of Pennsylvania 

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.

The Wharton School en los rankings mundiales


Según el prestigioso ranking de Financial Times, estas son las ocho más prestigiosas escuelas de negocios que todo ejecutivo en carrera debe elegir. The Wharton School se ubica en la tercera posición por encima de escuelas como Harvard Business School, Cambridge Business School o London School of Economics.

 

Acerca de este curso:

Los datos sobre nuestros patrones de navegación y compra están en todas partes. Desde transacciones con tarjetas de crédito y carritos de compras en línea hasta programas de fidelización de clientes y clasificaciones / reseñas generadas por el usuario, existe una asombrosa cantidad de datos que pueden usarse para describir nuestros comportamientos de compra pasados, predecir futuros y prescribir nuevas formas de influencia futura en decisiones de compra. En este curso, cuatro de los mejores profesores de marketing de The Wharton School y expertos peruanos ofrecerán una descripción general de las áreas clave de análisis de clientes: análisis descriptivo, análisis predictivo, análisis prescriptivos y su aplicación a prácticas comerciales del mundo real, incluidas las mejores acciones y estrategias de Amazon, Google y Starbucks, por nombrar algunas. Este curso proporciona una visión general del campo de análisis para que se puedan tomar decisiones comerciales informadas.

Resultados del aprendizaje del curso: 
Después de completar el curso, los alumnos podrán…

• Describir los principales métodos de recopilación de datos de clientes utilizados por las empresas y comprender cómo estos datos pueden influir en las decisiones comerciales.
• Describir las principales herramientas utilizadas para predecir el comportamiento del cliente e identificar los usos apropiados para cada herramienta
• Comunicar ideas clave sobre análisis de clientes y cómo el campo informa las decisiones comerciales
• Comunicar el historial de análisis de clientes y las mejores prácticas más recientes en prestigiosas y reconocidas empresas

About this course:


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.

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

  SEMANA 1
Clase online durante la semana del 04 al 08 de junio
Clase presencial: Campus Lima – San Isidro – Sábado 09 de junio de 10.00 a 13.00 horas

Introducción al análisis de clientes 
¿Qué es Customer Analytics? ¿Cómo está estructurado este curso? ¿Qué aprenderé en este curso? ¿Qué aprenderé en la especialización Business Analytics? Estas clases primeras clases le darán una descripción general de este curso y la especialización; las conferencias sustantivas comienzan en la Semana 2.

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 videos will give you an overview of this course and the specialization; the substantive lectures begin in Week 2.

  SEMANA 2
Clase online durante la semana del 11 al 15 de junio
Clase presencial: Campus Lima – Sede San Isidro – Sábado 16 de junio de 10.00 a 13.00 horas

Análisis descriptivo 
En este módulo, aprenderá qué datos pueden y qué no pueden describir el comportamiento del cliente, así como los métodos más efectivos para recopilar datos y decidir qué significa. Comprenderá la diferencia crítica entre los datos que describe una relación causal y los datos que describen uno correlativo a medida que explora la sinergia entre datos y decisiones, incluidos los principios para recopilar e interpretar sistemáticamente datos para tomar mejores decisiones comerciales. También aprenderá cómo se utilizan los datos para explorar un problema o pregunta, y cómo usar esa información para crear productos, campañas de marketing y otras estrategias. Al final de este módulo, tendrá una sólida comprensión de la recopilación e interpretación efectiva de datos para que pueda utilizar esos datos correctamente  para tomar las mejores decisiones para su empresa o negocio.

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.

SEMANA 3
Clase online durante la semana del 18 al 22 de junio
Clase presencial: Campus Lima – Sede San Isidro – Sábado 23 de junio de 10.00 a 13.00 horas

Análisis Predictivo 
Una vez que haya recopilado e interpretado los datos, ¿qué hacer con ellos? En este módulo, aprenderá cómo dar el siguiente paso: cómo usar los datos sobre las acciones del pasado para realizar predicciones sobre acciones en el futuro. Examinará las principales herramientas utilizadas para predecir el comportamiento y aprenderá a determinar qué herramienta es la adecuada para los fines de la toma de decisiones. Además, aprenderá el lenguaje y los marcos para hacer predicciones de comportamiento futuro. Al final de este módulo, podrá determinar qué tipo de predicciones puede hacer para crear estrategias futuras, comprender las técnicas más potentes para los modelos predictivos, incluido el análisis de regresión, y estar preparado para aprovechar al máximo las estadísticas para crear estrategias efectivas-decisiones comerciales basadas en datos.

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.

SEMANA 4
Clase online durante la semana del 25 al 29 de junio
Clase presencial: Campus Lima – Sede San Isidro – Sábado 30 de junio de 10.00 a 13.00 horas

Análisis prescriptivo
¿Cómo conviertes datos en acción? En este módulo, aprenderá cómo el análisis prescriptivo brinda recomendaciones sobre las acciones que puede realizar para alcanzar sus objetivos comerciales. Primero, explorará cómo hacer las preguntas correctas, cómo definir sus objetivos y cómo optimizar las acciones para tener éxito. También examinará ejemplos críticos de modelos prescriptivos, que incluyen cómo el precio impacta en la cantidad, cómo maximizar los ingresos, cómo maximizar las ganancias y cómo usar mejor la publicidad en línea. Al final de este módulo, podrá definir un problema, definir un buen objetivo y explorar modelos de optimización que tengan en cuenta la competencia, de modo que pueda escribir recetas para acciones basadas en datos que creen éxito para su empresa o negocio.

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.

SEMANA 5
Clase online durante la semana del 02 al 06 de julio
Clase presencial: Campus Lima – Sede San Isidro – Sábado 07 de julio de 10.00 a 13.00 horas

Aplicación / Casos de estudio 
¿Cómo las principales firmas ponen los datos a trabajar? En este módulo, aprenderá cómo las empresas exitosas usan los datos para crear prácticas de marketing innovadoras y centradas en el cliente. Explorará ejemplos reales del enfoque de cinco puntas para aplicar el análisis de clientes al marketing, comenzando con la recopilación de datos y la exploración de datos, avanzando hacia la creación de modelos predictivos y la optimización, continuando hasta llegar a decisiones basadas en datos. Al final de este módulo, sabrá cuál es la mejor manera de poner datos a trabajar en su propia empresa o negocio, basándose en las prácticas más innovadoras y efectivas basadas en datos de las mejores empresas actuales.

Application/Case Studies
How do top firms put data to work? In this module, you’ll learn how successful businesses use data to create cutting-edge, customer-focused marketing practices. You’ll explore real-world examples of the five-pronged attack to apply customer analytics to marketing, starting with data collection and data exploration, moving toward building predictive models and optimization, and continuing all the way to data-driven decisions. At the end of this module, you’ll know the best way to put data to work in your own company or business, based on the most innovative and effective data-driven practices of today’s top firms.

 

CLAUSTRO DE PROFESORES DE THE WHARTON SCHOOL Y EXPERTOS DE PERU 


 

  ERIC BRADLOW:
Professor of Marketing, Statistics, and Education, Chairperson, Wharton Marketing Department, Vice Dean and Director, Wharton Doctoral Program, Co-Director, Wharton Customer Analytics Initiative
The Wharton School.

Professor Eric T. Bradlow is the K.P. Chao Professor, Professor of Marketing, Statistics, Education and Economics and Faculty Director of the Wharton Customer Analytics Initiative. An applied statistician, Professor Bradlow uses high-powered statistical models to solve problems on everything from Internet search engines to product assortment issues. Specifically, his research interests include Bayesian modeling, statistical computing, and developing new methodology for unique data structures with application to business problems.
Eric was recently named a fellow of the American Statistical Association, American Educational Research Association, is past chair of the American Statistical Association Section on Statistics in Marketing, past Editor-in-Chief of Marketing Science, is a past statistical fellow of Bell Labs, and worked at DuPont Corporation’s Corporate Marketing and Business Research Division and the Educational Testing Service.
A prolific scholar, Professor Bradlow’s research has been published in top-tier academic journals such as the Journal of the American Statistical Association, Psychometrika, Statistica Sinica, Chance, Marketing Science, Management Science, and Journal of Marketing Research. He also serves as Associate Editor for the Journal of the American Statistical Association and the Journal of Marketing Research, and is on the Editorial Boards of Marketing Letters, Marketing Science, Journal of Marketing Research, Quantitative Marketing and Economics, and the Quarterly Journal of Electronic Commerce.
Professor Bradlow has won numerous teaching awards at Wharton, including the Anvil Award, MBA Core Curriculum teaching award, the Miller-Sherrerd MBA Core Teaching award and the Excellence in Teaching Award. His teaching interests include courses in Statistics, Marketing Research, Marketing Management and PhD Data Analysis, as well as any material related to customer analytics.
Professor Bradlow earned his PhD and Master’s degrees in Mathematical Statistics from Harvard University and his BS in Economics from the University of Pennsylvania.

  PETER FADER:
Professor of Marketing and Co-Director of the Wharton Customer Analytics Initiative
The Wharton School

Peter S. Fader is the Frances and Pei-Yuan Chia Professor of Marketing at the Wharton School of the University of Pennsylvania. His expertise centers around the analysis of behavioral data to understand and forecast customer shopping/purchasing activities. He works with firms from a wide range of industries, such as telecommunications, financial services, gaming/entertainment, retailing, and pharmaceuticals. Managerial applications focus on topics such as customer relationship management, lifetime value of the customer, and sales forecasting for new products. Much of his research highlights the consistent (but often surprising) behavioral patterns that exist across these industries and other seemingly different domains.  These insights are reflected in his book, “Customer Centricity: Focus on the Right Customers for Strategic Advantage.
”Professor Fader believes that marketing should not be viewed as a “soft” discipline, and he frequently works with different companies and industry associations to improve managerial perspectives in this regard. His work has been published in (and he serves on the editorial boards of) a number of leading journals in marketing, statistics, and the management sciences. He has won many awards for his teaching and research accomplishments.
In addition to his various roles and responsibilities at Wharton, Professor Fader is also co-founder of Zodiac, a predictive analytics firm that aims to make top-notch customer valuation models and insights easily accessible to a broad array of data-driven organizations.

RAGHU IYENGAR:
Associate Professor of Marketing
The Wharton School

Professor Raghu Iyengar’s research interests fall in two domains: pricing and social influence. In the area of pricing, his work focuses on the impact of multi-part pricing schemes on consumer response. The success of such pricing mechanisms to extract consumer surplus depends on how consumers respond to different components. Methodologically, Iyengar has developed novel consumer demand models that capture the effect of multi-part pricing tariffs in a theoretically meaningful way and include contextual factors such as consumers’ uncertainty about usage. Substantively, he has shown that accounting for consumers’ uncertainty is important for firm profits especially when multi-part prices are employed. In the area of social networks, Iyengar has done work that has investigated how and why such influence may be at work. Across several studies, Iyengar has identified the underlying mechanism(s) such as awareness, social learning or social normative pressure that may be at work in different contexts. Understanding the mechanism(s) is important not only theoretically but also managerially, because which customers to target and which ties to activate using what message depends on what mechanism is at work.
Professor Iyengar’s other current research projects focus on the impact of referral coupons on consumer behavior and how changes in loyalty program requirements may change future customer behavior. His research has been published or forthcoming in Journal of Marketing Research, Marketing Science, Psychometrika, Quantitative and Marketing Economics and Experimental Economics. He serves on the Editorial Boards of Journal of Marketing Research, Marketing Science and the International Journal of Research in Marketing.
Professor Iyengar’s teaching interests are in the area of Marketing Analytics. He earned his PhD and MPhil from Columbia University and his B. Tech. from IIT Kanpur, India.

RON BERMAN:
Assistant Professor of Marketing
The Wharton School

Ron Berman is an assistant professor of marketing at the Wharton School. He focuses his research on online marketing, marketing analytics and the marketing actions of startup firms. His recent research looks at how advertisers incorrectly attribute sales to online advertising which results in suboptimal campaigns, and how search engine optimization (SEO) may improve search engine results contrary to common belief.Ron’s previous experience includes working on Internet and Media investments as a venture capitalist at Carmel Ventures, and developing software for the IDF. Currently Ron mentors startups at the UpWest Labs accelerator and spends time meeting and advising young entrepreneurs.Ron holds a PhD and MSc in Business Administration (Marketing) from the University of California, Berkeley, an MBA and MSc in Computer Science from Tel-Aviv University, and a BSc in Computer Science, Physics and Mathematics from the Hebrew University in Jerusalem.
More information is available at Ron’s personal page: www.ron-berman.com

 

Coordinador General en Lima, Perú

 

JONATHAN RAMIREZ CASTILLO
www.linkedin.com/in/jonathanramirezcastillo/

Director de Planeamiento y Estrategias Digitales en Medialab LA, a cargo de las estrategias de marketing online de los clientes de la consultora, realizando la planificación, seguimiento y obtención de resultados.
Coordinador General del Programa Avanzado de “Marketing Automation & Funnels” dictado por MANAGENENT SOCIETY Business Education y el IEBS de España, como Centro Colaborador de la Universidad Rey Juan Carlos de España y Medialab.Posee once años de experiencia en el entorno digital. Ingeniero de Sistemas con estudios de postgrado en Business Intelligence y Minería de Datos en la UPC. Experiencia en el manejo de estrategias de Marketing Digital y Comercio Electrónico para marcas en Perú, Chile y Costa Rica. Especialista certificado en Google Analytics, Hubspot e IBM.
Es consultor de negocios para transformarse digitalmente, enfocándolos en la necesidad y comportamiento de sus usuarios, aplicando metodologías y herramientas para la captura, análisis, medición y optimización de sus estrategias.Sus especialidades son: – Marketing Automation, certificado por IBM; Customer Experience (CX); Business Intelligence, Minería de Datos, CRM; Marketing Digital; Inbound Marketing, certificado por Hubspot; Posicionamiento en buscadores (SEO); Comercio Electrónico;  Embudos de Conversión y Analítica Digital, Certificado por Google.


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