With marketers are poised to be the largest users of data within the organization, there is a need to make sense of the variety of consumer data that the organization collects. Surveys, transaction histories and billing records can all provide insight into consumers’ future behavior, provided that they are interpreted correctly. In Introduction to Marketing Analytics, we introduce the tools that learners will need to convert raw data into marketing insights. The included exercises are conducted using Microsoft Excel, ensuring that learners will have the tools they need to extract information from the data available to them. The course provides learners with exposure to essential tools including exploratory data analysis, as well as regression methods that can be used to investigate the impact of marketing activity on aggregate data (e.g., sales) and on individual-level choice data (e.g., brand choices).
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Foundations of Marketing Analytics
von
Spezialisierung Foundations of Marketing AnalyticsEmory UniversityÜber diesen Kurs
Kompetenzen, die Sie erwerben
von
Emory University
Emory University, located in Atlanta, Georgia, is one of the world's leading research universities. Its mission is to create, preserve, teach and apply knowledge in the service of humanity.
Lehrplan - Was Sie in diesem Kurs lernen werden
Meet Dr. Schweidel & Course Overview
In this module, students will be introduced to the instructor, Dr. David Schweidel and get and overview of the course.
Exploring your Data with Visualization and Descriptive Statistics, Part 1
Modules 2 and 3 focus on identifying appropriate descriptive statistics (measures of central tendency and dispersion) for different types of data, as well as recoding data using reference commands to prepare it for analysis. Additionally, you will manipulate and summarize data using pivot tables in Excel, produce visualizations that are appropriate based on the type of data being analyzed, and interpret statistics and visualizations to draw conclusions to address relevant marketing questions.
Exploring your Data with Visualization and Descriptive Statistics, Part 2
Modules 2 and 3 focus on identifying appropriate descriptive statistics (measures of central tendency and dispersion) for different types of data, as well as recoding data using reference commands to prepare it for analysis. Additionally, you will manipulate and summarize data using pivot tables in Excel, produce visualizations that are appropriate based on the type of data being analyzed, and interpret statistics and visualizations to draw conclusions to address relevant marketing questions.
Regression Analysis for Marketing Data
In this module, you will be asked to determine the appropriate type of regression for different types of marketing data and will perform regression analysis to assess the impact of marketing actions on outcomes of interest, such as sales, traffic, and brand choices. You will also be asked to interpret regression output to understand overall model performance and importance of different predictors, as well as make predictions using the appropriate regression model.
Bewertungen
Top-Bewertungen von MEANINGFUL MARKETING INSIGHTS
Sometimes a bit heavy on the math but overall a very good analysis of how data and analytics are applied to real world marketing scenarios. One of the best structured courses I've taken on Coursera.
It was an amazing course. But i found some of the statistical models difficult to understand so needed to go back and review my statistics a bit to grapple with it. Overall, a brilliant course.
Very useful. I highly recommend it. Great Excel exercises, many ways to practice it and learn it. Professor Schweidel is remarkable and makes complicated topics clear and easy!
The last 2 video lectures: Customer Valuation Excel Demonstration and Inventory Management Excel Demonstrationm were very well explained and taught me something new and useful.
Über den Spezialisierung Foundations of Marketing Analytics
In this specialization you will learn how to:
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