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    • Introduction to Managerial Economics >
      • Basic Techniques
      • The firm: Stakeholders, Objectives and Decision Issues
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    • Demand and Revenue Analysis >
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    • Prodution and Cost Analysis >
      • Production Function
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      • Cost Concepts and Analysis I
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    • Pricing Decisions >
      • Pricing strategies
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  • Formulating an Optimization Problem
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  • Unstructured Data from Websites, APIs, and Other Important Sources
  • Structured Data from Delimited and Fixed-Width Sources
  • Preparing Tidy data sets

PRICING STRATEGIES


There are many ways to price a product. Let us have a look at some of them and try to understand the best
policy/strategy in various situations.

PREMIUM PRICING
Use a high price where there is uniqueness about the product or service. This approach is used where a
substantial competitive advantage exists. Such high prices are charge for luxuries such as Cunard Cruises,
Savoy Hotel rooms, and Concorde flights.

PENETRATION PRICING
The price charged for products and services is set artificially low in order to gain market share. Once this is
achieved, the price is increased. This approach was used by France Telecom in order to attract new
corporate clients.

ECONOMY PRICING
This is a no frills low price. The cost of marketing and manufacture are kept at a minimum. Supermarkets
often have economy brands for soups, spaghetti, etc.

PRICE SKIMMING
Charge a high price because you have a substantial competitive advantage. However, the advantage is not
sustainable. The high price tends to attract new competitors into the market, and the price inevitably falls
due to increased supply. Manufacturers of digital watches used a skimming approach in the 1970s. Once
other manufacturers were tempted into the market and the watches were produced at a lower unit cost,
other marketing strategies and pricing approaches are implemented.

Premium pricing, penetration pricing, economy pricing, and price skimming are the four main pricing
policies/strategies. They form the bases for the exercise. However, there are other important approaches to
pricing.

5. PSYCHOLOGICAL PRICING
This approach is used when the marketer wants the consumer to respond on an emotional, rather than
rational basis. For example 'price point perspective' 99 cents not one dollar.

6. PRODUCT LINE PRICING
Where there is a range of product or services, the pricing reflect the benefits of parts of the range. For
example car washes. Basic wash could be $2; wash and wax $4 and the whole package $6.

7. OPTIONAL PRODUCT PRICING
Companies will attempt to increase the amount customer spend once they start to buy. Optional 'extras'
increase the overall price of the product or service. For example, airlines will charge for optional extras such
as guaranteeing a window seat or reserving a row of seats next to each other.

8. CAPTIVE PRODUCT PRICING
Where products have complements, companies will charge a premium price where the consumer is
captured. For example, a razor manufacturer will charge a low price and recoup its margin (and more) from
the sale of the only design of blades, which fit the razor.

9. PRODUCT BUNDLE PRICING
Here sellers combine several products in the same package. This also serves to move old stock. Videos and
CDs are often sold using the bundle approach.

10. PROMOTIONAL PRICING
Pricing to promote a product is a very common application. There are many examples of promotional
pricing including approaches such as BOGOF (Buy One Get One Free).

11. GEOGRAPHICAL PRICING
Geographical pricing is evident where there are variations in price in different parts of the world. For
example rarity value, or where shipping costs increase price.

12. VALUE PRICING
This approach is used where external factors such as recession or increased competition force companies to
provide 'value' products and services to retain sales e.g. value meals at McDonalds.

RETAIL PRICING: EVERYDAY LOW PRICE (EDLP) AND HIGH LOW PRICING (HILO)

Conventional wisdom states that retailers follow either an EDLP or a HiLo pricing strategy at a store or
chain level, but – in reality -- retailer-pricing strategies are very different across brands, categories and
stores. Stores or chains use EDLP or (more frequently) HiLo pricing strategies as a positioning or signaling
option, but actual pricing decisions are made at the category, brand and store levels. In contrast with prior
research, this paper focuses on pricing at a brand - store level rather than at a store or chain level. Its
purpose is to empirically derive a taxonomy of retailers’ pricing strategies for an assortment of brands and
categories at different stores and markets. Our investigation is based on store level scanner data that
describe 1364 brand - store combinations from 17 chains, 212 stores and 6 categories of consumer package
goods in 5 U.S. markets, so we believe that the results are generalizable to many retailer-pricing situations.
The investigation has two stages. First, we develop a set of measures of retailer pricing decisions, and
conduct a principal components analysis to identify their underlying dimensions. Our results show that
retailer pricing can be characterized by a set of four stable underlying strategic dimensions, labeled: price
consistency, promotion intensity, price/promotion coordination, and brand price relative to reference price.
Second, we classify retailers' pricing strategies based on a cluster analysis of the pricing dimensions
practiced by retailers. Our analyses show that these strategies are different at the brand - store and the store
levels. At the brand - store level, retailers practice five fundamental pricing strategies, which we label as
exclusive, moderately promotional, competitive promotional, value, and aggressive pricing. Surprisingly,
the most prevalent pricing strategy is not a variation of a HiLo pricing strategy -- as researchers and
practitioners widely believe. It is a value pricing strategy characterized by high price consistency, medium
promotion intensity, medium price/promotion coordination, and average brand price. This strategy is
actually closer to an EDLP strategy than to a HiLo strategy. The findings provide some initial benchmarks
for retailers. They also suggest that retailers should closely monitor their competitor retail chain at the brand
store level to see what pricing strategies are being employed, and thereby develop appropriate competitive
strategies.
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  • Home
  • Applied Analytics
    • Analytics for Decision Making
    • Analytics for Marketing
    • Data Engine
    • Customer Insights
    • Analytics for Advance Marketing
  • Soft Skills
    • Adaptability
    • Confidence
    • Change Management
    • Unlearning and Learning
    • Collaboration and Teamwork
    • Cultural Sensitivity
  • Marketing
  • Finance
  • Economics
    • Introduction to Managerial Economics >
      • Basic Techniques
      • The firm: Stakeholders, Objectives and Decision Issues
      • Scope of Managerial Economics
    • Demand and Revenue Analysis >
      • Demand Estimation and Forecasting
      • Demand Elasticity
      • Demand Concepts and Analysis
    • Prodution and Cost Analysis >
      • Production Function
      • Estimation of Production and Cost Functions
      • Cost Concepts and Analysis I
      • Cost Concepts and Analysis II
    • Pricing Decisions >
      • Pricing strategies
      • Market structure and microbes barriers to entry
      • Pricing under pure competition and pure monopoly
      • Pricing under monopolistic and oligopolistic competition
    • Narendra Modi Development Model of Gujarat
  • JBDON Golf
  • Let's Talk
  • MBA Project Sharing
  • About Us
  • CET Knowledge Zone
    • Tips From JBIMS Students >
      • Prasad Sawant
      • Chandan Roy
      • Ram
      • Ashmant Tiwari
      • Rajesh Rikame
      • Ami Kothari
      • Ankeet Adani
      • Sonam Jain
      • Mitesh Thakker
      • Tresa Sankoorikal
    • Speed Techniques
    • CET Workshops
  • What is Cluster Analysis
  • Preparing Data and Measuring Dissimilarities
  • Data Reduction and Unsupervised Learning
  • Hierarchical and k-Means Clustering
  • Cluster analysis using excel and excel miner
  • Adding Uncertainty to a Spreadsheet Model
  • Defining Output Variables and Analyzing the Results
  • Using Historical Data to Model Uncertainty
  • Models with Correlated Uncertain Variables
  • Creating and Interpreting Charts
  • Using Average Values versus Simulation
  • Optimization and Decision Making
  • Formulating an Optimization Problem
  • Developing a Spreadsheet Model
  • Adding Optimization to a Spreadsheet Model
  • What-if Analysis and the Sensitivity Report
  • Evaluating Scenarios and Visualizing Results to Gain Practical Insights
  • Digital Marketing Application of Optimization
  • Advanced Models for Better Decisions
  • Business Problems with Yes/No Decisions
  • Formulation and Solution of Binary Optimization Problems
  • Metaheuristic Optimization
  • Chance Constraints and Value At Risk
  • Simulation Optimization
  • Marketing Analytics and Customer Satisfaction
  • Customer Satisfaction
  • Measurements and Scaling Techniques – Introduction
  • Primary Scales of Measurement
  • Comparative Scaling
  • Non-Comparative Scaling
  • Experiment Design: Controlling for Experimental Errors
  • A/B Testing: Introduction
  • A/B Testing: Types of Tests
  • ANOVA – Introduction
  • Example -Inspect Spray and Tooth Growth
  • Logit Model - Binary Outome and Forecastign linear regression
  • Social media Microscope
  • Text Summarization
  • N-Gram - Frequcy Count and phase mining
  • LDA Topic Modeling
  • Machine-Learned Classification and Semantic Topic Tagging
  • Visualisation and statistics (Political Advertising,Movie Theater and Data Assembly)
  • Excel Analysis of Motion Picture Industry Data
  • Displaying Conditional Distributions
  • Analyzing Qualitative Variables
  • Steps in Constructing Histograms
  • Common Descriptive Statistics for Quantitative Data
  • Regression-Based Modeling
  • Customer Analytics
  • Illustrating Customer Analytics in Excel
  • Customer Valuation Excel Demonstration
  • Understanding The Growth Of Data
  • Evaluating Methods Of Data Access
  • Communication journey
  • Data Journey
  • Finding Patterns In Data
  • New Page
  • Planning for data visualisation
  • Content Connection and Chart Legitibility
  • Preparation for efficient data analysis
  • Unstructured Data from Websites, APIs, and Other Important Sources
  • Structured Data from Delimited and Fixed-Width Sources
  • Preparing Tidy data sets