The Expert Guide to Retail Pricing: An Analytics-Based Approach to Maximise Margins

Description

Going under the hood of retail strategy, this book provides in-depth coverage of how retailers can leverage the latest in data analytics and technology to improve profitability and customer value through pricing. Retail pricing is not about dollars, pounds or euros, but the value a customer associates with a product, which can and does change over time. To maximize revenues and profits, pricing must be dynamic, strategic, and in today’s hyper-connected and -competitive world, scientific. Using technology to gather customer insights and create data-driven pricing approaches can also enhance the customer experience, improve vendor management, help monitor competitors, and ensure market efficiency – including the much-needed reduction of waste in the food sector. This book uses case studies from around the globe to illustrate the evolution of retailing and offers takeaways with each chapter to enable retailers to manage the future of pricing. Retail and pricing managers, retail sector consultants, and students of sales and marketing will welcome this book’s innovative solutions to one of bricks-and-mortar retailing’s most critical challenges. Cover Half Title Title Page Copyright Page Contents Foreword Preface Acknowledgements Introduction Chapter 1: Impact of Pricing 1.1 Introduction 1.2 Pricing Process Framework 1.2.1 Data Collection 1.2.1.1 POS Systems 1.2.1.2 Inventory and Logistics 1.2.1.3 IoT Devices 1.2.1.4 Market Data Sources 1.2.1.5 Competitive Data 1.2.2 Data Hosting 1.2.2.1 Data Lakes 1.2.2.2 Cloud vs On-Premise 1.2.2.3 ETL Processes 1.2.3 Analysis and Algorithms 1.2.3.1 Ordering Algorithms 1.2.3.2 Pricing and Merchandising Algorithms 1.2.4 Output Processes 1.2.4.1 Business Intelligence 1.2.4.2 KPI Monitoring 1.2.4.3 Promotion/Price Execution 1.2.4.4 Compliance Checking 1.2.4.5 Real-Time Execution 1.3 Strategic Pricing Framework 1.3.1 Profit/Bottom Line 1.3.2 Price Image/Customer Perception 1.3.3 Vendor Relationships 1.3.4 Private Label Products 1.3.5 Competitive Strategy 1.3.6 Market Share 1.4 Pricing Organisation Structure 1.4.1 Retail Strategy Consultant 1.4.2 Business/Pricing Analysts 1.4.3 Data Engineers 1.4.4 Algorithm Developers 1.4.5 Application Developers 1.4.6 Category Managers 1.5 Hardware Requirements 1.5.1 Smart Cameras 1.5.2 Traffic Counters 1.5.3 Inventory Sensors 1.5.4 Smart Refrigerators 1.5.5 IoT Devices/Sensors 1.5.6 Electronic Shelf Labels 1.5.6.1 Benefits of ESL 1.5.6.2 Disadvantages of ESL 1.5.6.3 Companies Providing ESL 1.5.7 Smart Digital Displays 1.5.8 Beacons/Proximity Sensors 1.5.8.1 Benefits of Beacons/Proximity Sensors 1.5.8.2 Limitations of Beacons/Proximity Sensors 1.5.8.3 Companies Providing Beacons 1.5.9 Smart Carts 1.5.10 Self-Checkouts 1.5.10.1 Benefits of Self-Checkouts 1.5.10.2 Disadvantages of Self-Checkout 1.6 Conclusion Chapter 2: Base Pricing 2.1 Introduction 2.2 Pricing Strategy Formulation 2.2.1 Retail Pricing Strategies 2.2.2 Initial Considerations 2.2.2.1 Outline the High-Level Pricing Strategy 2.2.2.2 Price Products to Meet Profit Goals 2.2.2.3 Examine Competitive Prices 2.2.2.4 Examine Competitive Pricing Actions and Reactions 2.2.2.5 Determine a Single Customer's Profitability 2.2.2.6 Understand Purchase Criteria 2.2.3 Pricing Goals 2.2.3.1 Increasing Market Share or Margins 2.2.3.2 Increasing Brand Awareness 2.2.3.3 Increasing Brand Loyalty 2.2.3.4 Increasing Customer Engagement 2.2.3.5 Creating a Multi-Channel Presence 2.2.3.6 Positioning 2.2.4 Strategic Components 2.2.4.1 Company Strategy 2.2.4.2 Competitive Strategy 2.2.4.3 Private Label Strategy 2.3 Science of Base Pricing 2.3.1 Price Elasticity and Cross Elasticity 2.3.1.1 Price Elasticity 2.3.1.2 Elasticity Formula 2.3.1.3 Cross Elasticity 2.3.1.4 Cross Elasticity Formula 2.3.2 Forecast Variables and Factors 2.3.2.1 Seasonality 2.3.2.2 Holidays 2.3.2.3 Events 2.3.2.4 Trends 2.3.2.5 Product Life Cycle 2.3.3 Customer Behaviour 2.3.3.1 Market Basket 2.3.3.2 Substitute Products 2.3.3.3 Complementary Products 2.3.4 Optimal Price 2.3.5 Product Relationships 2.3.5.1 Line Pricing 2.3.5.2 Size Relationships 2.3.5.3 Brand Relationships 2.3.5.4 Brand Hierarchy 2.3.6 Pricing Rules 2.4 Pricing Process 2.4.1 Data Collection 2.4.1.1 From Retailing 2.4.1.2 From External Sources 2.4.2 Data Preparation 2.4.3 Analytics and Modelling 2.4.3.1 Modelling 2.4.3.2 Working with Sparse Data 2.4.4 Strategy Formulation 2.4.4.1 Category Management 2.4.4.1.1 Pre Optimisation Preparation 2.4.4.2 Product Relationship Verification 2.4.4.3 Selecting Pricing Rules 2.4.4.4 Price Optimisation Plan 2.4.5 Price Optimisation 2.4.5.1 Price Optimisation Data Flow 2.4.5.2 Pricing Process in Retail 2.4.5.3 Optimisation Goals, Rules and Constraints 2.4.5.3.1 Optimisation Goals 2.4.5.3.2 Optimisation Rules 2.4.5.3.3 Optimisation Constraints 2.4.5.3.4 Good-Better-Best Strategy 2.4.5.3.5 Last Digit/Price Ending Rules 2.4.5.3.6 Scenario Selections 2.4.5.3.7 Test and Control Stores 2.4.6 Price Execution 2.4.7 Measurement and Maintenance 2.4.7.1 Sales Decomposition Analysis 2.4.7.2 Price Trackers 2.4.7.3 Course Corrections 2.4.7.4 Weekly Data Monitoring 2.4.7.5 Monitoring for Price Changes 2.4.7.6 Monitoring for Competitive Price Changes 2.4.8 Evolving the Company Strategy 2.4.9 Assortment-Wide and Store-Wide Rollout Plan 2.4.10 Roles and Responsibilities 2.4.10.1 Human Resources 2.4.11 Key Performance Indicators (KPIs) 2.4.12 Vendor Relationships 2.4.12.1 Vendor Management Process 2.4.13 Leveraging the Pricing Process for Promotions and Markdowns Chapter 3: Promotions 3.1 Introduction 3.2 Promotion Strategy Formulation 3.2.1 Promotion Objectives 3.2.2 Promotional Strategies 3.2.3 Types of Promotions 3.2.4 Promotion Considerations 3.2.5 Promotion Channels 3.3 Science-Based Promotions 3.3.1 The Promotional Lift 3.3.2 Factors Related to Retail Promotions 3.3.2.1 Cannibalisation Effect 3.3.2.2 Pantry Loading 3.3.2.3 Trade Funds 3.3.2.4 Promotion Constraints 3.3.3 Modelling Retail Promotions 3.4 Promotion Process 3.4.1 Promotions Data Infrastructure 3.4.2 Promotional Products/Market/Duration Identification 3.4.2.1 Product Identification 3.4.2.2 Market Identification 3.4.2.3 Promotional Period 3.4.3 Past Promotional Analysis 3.4.4 Promotion Optimisation 3.5.5 Trade Negotiations 3.4.6 Test vs Control Process 3.4.7 Promotions Execution 3.4.8 Results Measurement 3.5 Post-optimisation Processes and Regulations 3.5.1 Checking Competitive Reactions 3.5.2 Checking Customer Reactions 3.5.3 Promotion Regulations in Europe Chapter 4: Markdown 4.1 Introduction 4.2 Markdown Strategy Formulation 4.2.1 Markdown Strategy 4.2.2 Markdown Types 4.3 Science of Markdowns 4.3.1 Discount Response Modelling 4.3.2 Factors Involved in Markdowns 4.3.2.1 Cannibalisation Effect 4.3.2.2 Salvage Value 4.3.2.3 Sell-By Dates 4.3.2.4 Markdown Allowance 4.3.3 Customer Behaviour with Markdowns 4.4 Markdown Process 4.4.1 Markdown Data Infrastructure 4.4.2 Markdown Identification/Pareto/Space Allocation 4.4.3 Markdown Identification Process 4.4.4 Past Markdown Analysis 4.4.5 Negotiation Returns and Salvage 4.4.6 Markdown Modelling and Optimisations 4.4.6.1 Markdown Optimisation 4.4.6.2 Markdown Optimisation Process 4.4.6.3 Managing Markdown Optimisation 4.4.6.4 Networked Learning Models 4.4.6.5 Markdown with Advertising 4.5 Post-markdown Processes and Regulations 4.5.1 Execution and Weekly Revisions 4.5.2 Results Measurement 4.5.3 Markdown Regulations 4.5.4 Reducing Food Waste 4.5.5 Strategy and Image Protection 4.5.6 Avoiding Future Markdowns Chapter 5: Competitive Pricing 5.1 Introduction 5.2 Identifying the Competition 5.2.1 Who Is the Business Competing With? 5.2.1.1 Direct Competitors 5.2.1.2 Indirect Competitors 5.2.2 What Are They Competing With? 5.2.3 What Is the Degree of Competition? 5.2.3.1 The 5D Competitive Matrix 5.3 Competitive Pricing 5.3.1 Competitive Pricing Objectives 5.3.1.1 Gaining Market Share 5.3.1.2 Establishing the Desired Image 5.3.1.3 Competing Aggressively 5.3.2 Finding Competitive Prices 5.3.2.1 Competitive Price Shop Mechanisms 5.3.2.2 Web Scraping and Online Pricing 5.3.2.3 Spy Teams and Comp Shops 5.3.2.4 AI and Image Recognition 5.3.2.5 Pricing Business Intelligence and Alerting Mechanism 5.3.2.6 Real-Time Monitoring 5.3.2.7 Frequency of Competitive Shopping 5.3.2.8 Data Availability 5.3.3 Setting Your Price 5.3.4 Competitive Reactions 5.4 Post-markdown Processes and Regulations 5.4.1 Competitive Price Index (CPI) 5.4.2 Competitive Pricing Regulations 5.4.3 Vendor Negotiation 5.4.4 Avoiding the Downward Price Spiral 5.4.5 Collaborating vs Colluding 5.4.6 Finding Your Positioning Chapter 6: Business Intelligence 6.1 Introduction 6.2 BI Factors 6.2.1 Business Intelligence Strategy 6.2.2 Business Intelligence Process 6.2.3 Business Intelligence Techniques 6.2.3.1 Perpetual Licencing 6.2.3.2 Subscription Hosting Plans 6.2.4 Traditional vs Modern Business Intelligence 6.3 Tools of Business Intelligence 6.4 Business Intelligence for Pricing 6.4.1 Benefits of Business Intelligence for Pricing 6.5 Business Intelligence Future Trends Chapter 7: E-Commerce Pricing 7.1 Introduction 7.2 E-Commerce Pricing Considerations 7.2.1 Maintaining Transparency 7.2.2 Omnichannel Pricing 7.2.3 Customer Data 7.2.4 Customer Shopping Behaviour 7.2.5 Real-Time Store Monitoring 7.3 E-Commerce Pricing Strategy 7.3.1 E-Commerce Pricing Strategies 7.3.1.1 Combining Strategies 7.3.2 Optimisation of E-Commerce Pricing 7.4 Marketplace Pricing 7.4.1 Factors Affecting Marketplace Pricing 7.4.1.1 Marginal Cost 7.4.1.2 Competition 7.4.1.3 Network Effect 7.4.1.4 Provider Differentiation 7.4.1.5 Transaction Size and Volume 7.4.1.6 Quality vs Quantity 7.4.1.7 Who Pays the Bill? 7.4.2 Importance of a Marketplace 7.4.3 Marketplace Pricing Strategy 7.5 Impact of E-Commerce 7.6 Challenges in E-Commerce Pricing Chapter 8: Future of Pricing 8.1 Introduction 8.2 Factors Influencing the Future of Pricing 8.2.1 Low Brand Loyalty 8.2.2 Digital Currencies 8.2.3 Direct-to-Consumer Model 8.2.4 Multi-Channel Retailing 8.3 Changing Approaches to Optimisation 8.3.1 Data-Centric Optimisation 8.3.2 Product-Centric Optimisation 8.3.3 Customer-Centric Optimisation 8.4 Technology Innovations Changing Retailing 8.4.1 Artificial Intelligence in Retail 8.4.2 Internet Of Things (IoT) Devices 8.4.3 Blockchain Technology 8.4.4 GS1 Barcodes 8.5 Technology Innovations Changing Customer Behaviuor 8.5.1 Mobile Phone 8.5.2 Wearable Technology 8.5.3 Smart Homes/Office 8.5.4 Virtual Reality/Metaverse 8.6 Retail Price Automation 8.6.1 Availability of Data 8.6.2 Intelligent Algorithms 8.6.3 Instant Output 8.7 Retail Industry Evolution by Geography 8.7.1 Western Europe 8.7.2 United States of America 8.7.3 Latin America 8.7.4 Eastern Europe 8.7.5 The Indian Subcontinent 8.7.6 Japan and South Korea 8.7.7 Middle East Conclusion Index