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Data Science for Marketing Analytics

Data Science for Marketing Analytics

Free Practice Test

FREE
  • No. of Questions10
  • AccessImmediate
  • Access DurationLife Long Access
  • Exam DeliveryOnline
  • Test ModesPractice
  • TypeExam Format

Practice Exam

$12.99
  • No. of Questions100
  • AccessImmediate
  • Access DurationLife Long Access
  • Exam DeliveryOnline
  • Test ModesPractice, Exam
  • Last UpdatedOctober 2024

Online Course

$12.99
  • DeliveryOnline
  • AccessImmediate
  • Access DurationLife Long Access
  • No. of Videos45
  • No. of hours04+ hrs
  • Content TypeVideo

Data Science for Marketing Analytics

Marketers can acquire better insights into their customers' beliefs, opinions, and attitudes. They can also screen how customers respond to marketing campaigns and whether or not they're drawing in with their business.


Table of Contents

  • Data Preparation and Cleaning
  • Data Exploration and Visualization
  • Unsupervised Learning: Customer Segmentation
  • Choosing the Best Segmentation Approach
  • Predicting Customer Revenue Using Linear Regression
  • Other Regression Techniques and Tools for Evaluation
  • Supervised Learning - Predicting Customer Churn
  • Fine-Tuning Classification Algorithms
  • Modelling Customer Choice

Data Science for Marketing Analytics FAQs

The vital contrast between a Marketing Analyst and Data Scientist.  A marketing analyst is more focused on dissecting the marketing metrics. The data scientist usually works in a multidirectional and free form to remove better insights, while marketing analyst usually has a specific bearing to chip away at.

Marketing analytics staffing is skilled at recognizing past trends and showing you the results of your previous marketing campaigns. Then again, data science is more responsible for understanding how the data you've collected can have significant insights for your organization.

Marketing analytics is the study of data collected through marketing campaigns to determine patterns between such things as how a mission is added to conversions, consumer conduct, local preferences, imaginative preferences, and substantially more.

 

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