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Analytical Methods for Effective Data Analysis
Rating: 4.4 out of 5(331 ratings)
1,827 students

Analytical Methods for Effective Data Analysis

Master data analytics and explore marketing, social media, predictive, and prescriptive analytics.
Created bySimon Sez IT
Last updated 12/2025
English
English [Auto],

What you'll learn

  • Analyze customer data using segmentation models for targeted marketing campaigns.
  • Implement Recency Frequency Monetary (RFM) models to optimize customer engagement.
  • Evaluate the success of social media campaigns by measuring brand mentions and sentiment.
  • Apply predictive analytics techniques to make informed predictions about future events.
  • Utilize prescriptive analytics to develop strategies for achieving specific business goals.
  • Create revenue optimization models to optimize profit through data-driven decisions.
  • Demonstrate an understanding of dynamic pricing and its role in revenue management.
  • Develop practical skills in setting up and utilizing sentiment analysis for text data.

Course content

5 sections • 25 lectures • 3h 13m total length

  • Introduction6:22

    Explore how marketing analytics drive real-world decision making by examining descriptive, diagnostic, predictive, and prescriptive analytics, with practical models for customer lifecycle, segmentation, churn, attribution, and social media sentiment.

  • WATCH ME: Essential Information for a Successful Training Experience2:03

    Watch this video to access essential information for a successful training experience and learn how to download, unzip, and use exercise files with adjustable playback.

  • DOWNLOAD ME: Course Exercise File0:23
  • Downloadable Course Transcript0:19
  • Four Types of Analytics11:09

    Identify the four analytics types—descriptive, diagnostic, predictive, and prescriptive—guided by Gartner’s value escalator, and explore how analytics and business intelligence drive ROI across business operations.

  • Customer Life Cycle: Part 110:26

    Explore how analytics drive customer lifecycle management across discover to engage, using segmentation, lead scoring, lookalike models, and propensity models to tailor offers and grow lifetime value.

  • Customer Life Cycle: Part 29:21

    Map the customer life cycle through use and engage stages, using analytics to uncover usage patterns, sentiment, VOC, churn risk, next best actions, and lifetime value.

  • Marketing Model Types: Part 110:32

    Explore marketing model types in part 1, including customer segmentation, acquisition, RFM, market basket, lookalike, and propensity to respond models to guide data-driven marketing decisions.

  • Marketing Model Types: Part 28:50

    Explore how market basket analysis uncovers co-occurrence patterns and association rules from transactional data to optimize product placement and cross-selling, and leverage lookalike modeling to extend reach.

  • Marketing Model Types: Part 39:45

    Explore propensity to respond and other marketing models, including customer conversion and lifetime value analytics, detailing data collection, feature selection, model development, validation, scoring, and campaign optimization.

  • Marketing Model Types: Part 410:20

    Predict customer churn using models to identify at-risk customers and guide proactive retention campaigns. Emphasize data governance, KPI design, and evaluation metrics to optimize campaigns and outcomes.

  • Marketing Model Types: Part 56:54

    Explore last-click, first-click, linear, time-decay, and data-driven attribution alongside marketing mix models to optimize roi and budgets.

  • Marketing Model Types: Part 69:23

    Marketing optimization models use data-driven analysis to optimize budget, pricing, promotions, and product launches, while cross-sell, upsell, and lead scoring strategies maximize revenue and customer value.

  • Marketing Model Types: Part 710:57

    Explore next best action models in predictive analytics that propose relevant actions for customers, using historical data, preferences, and business rules with machine learning to boost satisfaction and revenue.

  • Section Quiz

Requirements

  • A basic understanding of data analytics is beneficial.

Description

**This course includes downloadable exercise files to work with**


Welcome to Analytical Methods for Effective Data Analysis. This course is designed to provide you with a comprehensive understanding of data analytics by breaking it down into four main components: marketing analytics, social media analytics, predictive analytics, and prescriptive analytics.


In this course, you will learn how these different types of analytics fit together seamlessly. We'll start by exploring the customer-centric world of marketing analytics, covering topics such as customer life cycles and various marketing models, including customer segmentation, acquisition, RFM, market basket analysis, and more. You'll discover how these models can help retain and engage customers effectively.


Moving on, we will dive into social media analytics, where you'll gain insights into measuring, collecting, and analyzing data from social media platforms. You'll also explore sentiment analysis, a crucial tool for understanding public opinions and sentiments expressed in textual content.


The course's third section focuses on predictive analytics, using statistics, machine learning, and data mining to predict future events. Additionally, we'll delve into prescriptive analytics, which guides decision-making by optimizing key metrics based on past performance and trends.


By the end of this course, you will possess the skills and knowledge needed to excel in the world of data analytics. Whether you're a marketing professional, business analyst, or anyone interested in harnessing the power of data, this course will help equip you with practical tools and insights to make informed decisions and drive success in your field. Don't miss this opportunity to master analytical methods for effective data analysis.


In this course, students will learn how to:

  • Analyze customer data using segmentation models for targeted marketing campaigns.

  • Implement Recency Frequency Monetary (RFM) models to optimize customer engagement.

  • Evaluate the success of social media campaigns by measuring brand mentions and sentiment.

  • Apply predictive analytics techniques to make informed predictions about future events.

  • Utilize prescriptive analytics to develop strategies for achieving specific business goals.

  • Create revenue optimization models to optimize profit through data-driven decisions.

  • Demonstrate an understanding of dynamic pricing and its role in revenue management.

  • Develop practical skills in setting up and utilizing sentiment analysis for text data.


This course includes:

  1. 3 hours of video tutorials

  2. 20 individual video lectures

  3. Exercise files to follow along

  4. Certificate of completion

Who this course is for:

  • People who want to learn data analytics and different analytical methods.
  • Those who want to understand marketing, social media, predictive, and prescriptive analytics.
  • Marketing professionals, business analysts, or anyone interested in harnessing the power of data.

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