Pricing Analytics with R and Tableau

Name: Pricing Analytics with R and Tableau
Rating: 5.0 (3 reviews)

Master data-driven pricing strategies with R and Tableau to optimize business profitability and decision-making.

Created byEDUCBA Bridging the Gap

Last updated 12/2024

English

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What you'll learn

The fundamentals of pricing analytics and its significance.
Different pricing strategies and how they influence business outcomes.
The role of cost, elasticity, and regression models in pricing decisions.
Descriptive, predictive, and prescriptive analytics for pricing optimization.
Visualizing and analyzing data with Tableau and R.

Course content

3 sections • 15 lectures • 2h 1m total length

Introduction to Pricing Analytics Using R and Tableau7:36
Explore pricing analytics principles, theories, and strategies to set prices that align with costs, elasticity, and business objectives. Apply descriptive, predictive, and prescriptive analytics with Excel and R to model pricing decisions and study consumer and social behavior through toll booths and taxi fleets case studies.
What is Pricing Analytics8:16
Balance pricing analytics by aligning price, volume, and profit to optimize profitability and market share. Guide pricing strategy by analyzing demand and price trade-offs.

Pricing Strategy in Business10:50
Analyze pricing strategies that maximize profit by balancing production and distribution costs, reference prices, and competitor offerings, while aligning with positioning, target customers, and survival, profit, and sales objectives.
Different Pricing Strategies10:14
Explore premium, market penetration, economy pricing, price skimming, product psychology, and bundle pricing, and how early days of a product's life cycle and value perception shape demand.
Understanding Cost6:32
Analyze how costs influence pricing by distinguishing fixed and variable costs, calculating total cost, margins, break-even volume, and target profit to set pricing strategy.
Elasticity6:19
Explore elasticity as a measure of demand sensitivity to price changes and learn to calculate changes in quantity and price, including slope interpretation and elastic, unit elastic, or inelastic classifications.
Calculating Elasticity from Regression Model8:07
Explore calculating elasticity from regression models, using linear, semi-log, and log-log approaches to link sales to price; learn elasticity from coefficients and the impact of including promotions.
Pricing Strategy of Ratio Taxis and Toll Booths9:59
Explore how radio taxi pricing uses dynamic and static pricing, demand elasticity, and competitive analytics, while US tollbooths apply congestion pricing and varied tolling strategies to manage demand.
Snapshot of Dataset10:02
Explore elasticity analysis of toll booth pricing using regression models to link price and volume across gates, uncovering the impact of year and the need to control it.
Toll Gate 1 Results3:39
This lecture compares toll gate results using simple regression with and without the year variable, showing higher accuracy and a negative elasticity as price increases reduce demand and congestion.

Descriptive Analytics4:43
Explore descriptive analytics to understand historical pricing data, identify correlated variables, and guide data collection for pricing decisions; apply predictive and prescriptive analytics to forecast demand and optimize next actions.
Predictive Analytics8:21
Describe descriptive analytics of pricing using a beer sales dataset to explain and predict weekly demand as price changes across 12, 18, and 30 pack sizes.
Descriptive Analytics - Graph6:06
Analyze historical beer sales data to reveal how price, carton size, and week trends drive demand, using descriptive analytics visuals and correlations to guide pricing and promotions.
Predictive Analytics - Understanding the Future9:49
Explore predictive analytics to estimate future demand from price using regression models, elasticity concepts, and probability of outcomes, with data preparation and log transformations to improve accuracy.
Prescriptive Analytics - Advice on Possible Outcomes10:29
Prescriptive analytics builds on descriptive and predictive analytics to advise possible outcomes via what-if analyses, using regression and the log of price and log of demand for pricing decisions.

Requirements

Basic knowledge of R and Tableau. Familiarity with fundamental statistics and regression analysis. Interest in business strategy and data-driven decision-making.

Description

Course Introduction

Pricing analytics is at the core of strategic business decisions. This course introduces you to the art and science of pricing analytics using R and Tableau. Learn to analyze pricing strategies, understand market elasticity, and leverage analytics to predict and prescribe optimal pricing. With real-world examples, this course equips you with skills to make informed pricing decisions.

Section-wise Writeup

Section 1: Introduction

The course begins with an overview of pricing analytics, defining its role and importance in business. You will explore what pricing analytics entails and understand its application in various industries to make data-informed decisions.

Section 2: Getting Started

This section dives into the fundamentals of pricing strategies in business. Learn about different pricing strategies, the role of cost and elasticity in pricing decisions, and methods to calculate elasticity using regression models. Real-world examples, like toll booths and taxi services, illustrate these concepts. You'll also get familiar with a sample dataset and analyze toll gate results.

Section 3: Analytics

Explore the three pillars of analytics: descriptive, predictive, and prescriptive. You’ll visualize trends with graphs, predict future pricing scenarios, and receive actionable insights for pricing strategies. This section teaches how to use data to provide comprehensive advice for optimal pricing outcomes.

Conclusion

This course provides you with a holistic understanding of pricing analytics and its practical applications using R and Tableau. By the end of the course, you'll have the tools and expertise to design and evaluate pricing strategies that align with business objectives.

Who this course is for:

Business professionals seeking to enhance their pricing strategies.
Data analysts and aspiring data scientists interested in analytics-driven decision-making.
Students and researchers focusing on business intelligence and analytics.

Pricing Analytics with R and Tableau

What you'll learn

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Course content

Introduction2 lectures • 16min

Getting Started8 lectures • 1hr 6min

Analytics5 lectures • 39min

Requirements

Description

Who this course is for: