Marketing Analytics with Python: From Data to Insights
Marketing Analytics with Python: From Data to Insights, available at Free, has an average rating of 4.4, with 25 lectures, based on 18 reviews, and has 1282 subscribers.
You will learn about Clustering and Segmentation Similarity Analysis Unsupervised Machine Learning Linear Regression Lag Analysis This course is ideal for individuals who are Beginner Python, Marketing Professionals, and Data Analysis It is particularly useful for Beginner Python, Marketing Professionals, and Data Analysis.
Enroll now: Marketing Analytics with Python: From Data to Insights
Summary
Title: Marketing Analytics with Python: From Data to Insights
Price: Free
Average Rating: 4.4
Number of Lectures: 25
Number of Published Lectures: 25
Number of Curriculum Items: 25
Number of Published Curriculum Objects: 25
Original Price: Free
Quality Status: approved
Status: Live
What You Will Learn
- Clustering and Segmentation
- Similarity Analysis
- Unsupervised Machine Learning
- Linear Regression
- Lag Analysis
Who Should Attend
- Beginner Python, Marketing Professionals, and Data Analysis
Target Audiences
- Beginner Python, Marketing Professionals, and Data Analysis
Dive deep into the captivating world of marketing analytics with our comprehensive course! Unlock the mysteries of customer behavior with cutting-edge clustering and segmentation, craft the perfect persona using similarity analysis, and journey through time with cohort insights. Harness the power of linear regression to master marketing strategies and decode the nuances of ad impact with our segments on lag and decay. This is more than just a course; it’s your gateway to becoming a marketing maestro. Are you ready to transform data into actionable insights? Join us and revolutionize your marketing game!
Clustering and Segmentation
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Dive into customer segmentation, learning the art of grouping individuals based on shared attributes.
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Explore unsupervised segmentation techniques, primarily using the K-Means clustering algorithm.
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Visualize customer clusters and reduce data complexity through dimension-reduction techniques, including PCA plots.
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Determine the optimal number of clusters using the Elbow Method and Silhouette Analysis.
Similarity Analysis (Persona Building)
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Grasp the concept of building the ideal customer profile using similarity analysis.
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Preprocess and scale dataset features for uniformity.
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Focus on “seeds” as representations of ideal customers and learn how to propagate these seeds to identify similar prospects.
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Employ Euclidean distance to measure similarity and pinpoint top prospects for targeted outreach.
Cohort Analysis
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Delve into cohort analysis, understanding its significance in marketing.
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Craft columns to represent invoice dates and cohorts, aiding in customer grouping based on their first purchase month.
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Construct pivot tables to showcase customer retention across different cohorts over time.
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Visualize cohort retention rates using heatmaps for a clearer depiction of customer behavior.
Marketing Strategy (Linear Regression)
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Begin with an introduction to marketing strategy analysis.
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Load and inspect datasets using pandas, understanding their structure.
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Emphasize feature engineering, creating new data attributes.
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Engage in Exploratory Data Analysis (EDA), visualizing data distributions and the impact of marketing strategies on sales using seaborn.
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Delve into correlation and regression analyses with the statsmodels library.
Marketing Lag and Decay
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Dive into the “Erosion Effect” in marketing, understanding how advertising impact decays over time.
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Learn to account for this effect by introducing lags to the data, and observing changing correlations.
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Explore the synergy impact analysis, discerning how marketing channels interact and enhance each other’s effects.
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Determine marketing thresholds and budget optimizations using decision tree regressors and Scipy.
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction to the Course
Chapter 2: Segmentation and Clustering
Lecture 1: Intro to Segmentation and Clustering
Lecture 2: Exploration of Data for Segmentation and Clustering
Lecture 3: Rule Based Segmentation
Lecture 4: Analyzing Segments
Lecture 5: Clustering Kmeans Algorithm
Lecture 6: Visualizing Clusters and PCA
Lecture 7: Clustering Insights and Personas
Lecture 8: Elbow and Silhouette Method for Clustering
Chapter 3: Ideal Customer Profile
Lecture 1: Intro to Similarity Analysis
Lecture 2: ICP Dataset
Lecture 3: Scaling Data
Lecture 4: Customer Seed and Prospecting
Lecture 5: Prospecting and Manual Seed Creation
Chapter 4: Cohort Analysis
Lecture 1: Cohort Analysis Part 1
Lecture 2: Cohort Analysis Part 2
Chapter 5: Marketing Strategy with Linear Regression
Lecture 1: Marketing Strategy Intro
Lecture 2: Exploratory Data Analysis
Lecture 3: Correlation of Features
Lecture 4: Linear Regression
Lecture 5: Channels ROI
Chapter 6: Marketing Lag Analysis and Erosion
Lecture 1: Exploring the Data
Lecture 2: Creating Lag Variables
Lecture 3: Measuring Advertising Decay
Lecture 4: Marketing Channel Synergy
Instructors
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Gaelim Holland
Senior Data Professional
Rating Distribution
- 1 stars: 0 votes
- 2 stars: 0 votes
- 3 stars: 4 votes
- 4 stars: 8 votes
- 5 stars: 6 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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