| Autore |
Kerkhove Louis-Philippe
|
| Pubbl/distr/stampa |
Cham, Switzerland : , : Springer, , [2022]
|
| Descrizione fisica |
1 online resource (259 pages)
|
| Disciplina |
617.51
|
| Collana |
Management for Professionals
|
| Soggetto topico |
Retail trade - Data processing
|
| ISBN |
9783031129629
9783031129612
|
| Formato |
Materiale a stampa  |
| Livello bibliografico |
Monografia |
| Lingua di pubblicazione |
eng
|
| Nota di contenuto |
Intro -- Preface -- Acknowledgements -- Contents -- Part I Pricing -- 1 The Retailer's Pricing Challenge -- 1.1 The Potential of Data-Driven Pricing -- 1.2 Limitations of Traditional Economic Theory -- 1.3 The Shifting Objectives Behind Price -- 1.3.1 Price Strategy -- 1.3.1.1 Consistency in Pricing -- 1.3.1.2 Relative Price Position -- 1.3.1.3 Minimal Margin Rules -- 1.3.1.4 Competitive Price Position -- 1.3.1.5 Psychological Pricing Rules -- 1.3.1.6 Challenging Pricing Rules -- 1.3.2 Price Tactics During the Product Life Cycle -- 1.3.2.1 Product Introduction -- 1.3.2.2 Pricing During the Product Life Cycle -- 1.3.2.3 Shifting Objective over Time -- 1.3.2.4 End-of-Life Pricing -- 1.4 Escaping the Discount Trap -- 1.5 The Next Chapters -- References -- 2 Understanding Demand and Elasticity -- 2.1 Price-Response Curve, Not Demand Curve -- 2.2 Measures of Price Sensitivity -- 2.3 A Sensible Model of Demand -- 2.3.1 Linear Price-Response Model -- 2.3.2 Constant Elasticity Price-Response Model -- 2.3.3 Logit Price-Response Model -- 2.4 Fitting Demand Curves Using Data -- 2.4.1 Demand and Price Indices -- 2.4.2 Fitting Price-Response Curves to Historical Sales Observations -- 2.4.2.1 Grouping Products -- 2.4.2.2 Scaling Product Sales for Combination -- 2.5 Making Forecasts -- 2.6 Evaluating Performance -- 2.7 Conclusion -- References -- 3 Improving the List Price -- 3.1 Improving List Pricing -- 3.2 Market Conditions: Direct Versus Indirect Competition -- 3.3 Obtaining Competitor Price Information -- 3.3.1 Web Scraping -- 3.3.2 Transformation and Matching -- 3.3.3 Using Competitor Price Information -- 3.4 Dynamic Pricing -- 3.4.1 Preconditions for Dynamic Pricing -- 3.4.1.1 Data Availability and Quality -- 3.4.1.2 Variability of External Conditions -- 3.4.1.3 Dynamic Prices Are Socially Acceptable -- 3.4.1.4 Operational Feasibility.
3.4.2 Types of Dynamic Pricing -- 3.4.2.1 Fixed Rule Dynamic Pricing -- 3.4.2.2 Variable Rule Dynamic Pricing -- 3.4.2.3 Dynamic Pricing by Means of a Learning Agent -- 3.4.3 Dynamic Pricing and Price Wars -- 3.5 Differential Pricing -- 3.6 Optimizing Long-Term Value -- 3.7 Conclusion -- References -- 4 Optimizing Markdowns and Promotions -- 4.1 The Challenges of the Markdown Decision -- 4.2 The Traditional Markdown Process -- 4.3 Where the Markdown Process Fails -- 4.3.1 Not Making Use of Price Elasticity -- 4.3.2 Contaminating the Objective -- 4.3.3 No Anticipation of Changes in Demand Patterns -- 4.3.4 Time-Consuming and Error-Prone Process -- 4.3.5 Repeating Past Mistakes -- 4.4 Blueprint of an Improved Markdown Process -- 4.4.1 Objective -- 4.4.2 Portfolio Forecast and Price Selection Engine -- 4.4.3 Product-Level Forecast Model -- 4.5 Core Components of an Improved Markdown Process -- 4.5.1 Defining the Right Objective: Transaction Costs and Residual Value -- 4.5.1.1 Estimating Transaction Costs -- 4.5.1.2 Estimating Residual Value -- 4.5.2 Estimating Rotation Speed -- 4.5.3 Estimating Elasticity -- 4.5.4 Updating Elasticity -- 4.5.5 Satisfying Business Rules and Other Constraints -- 4.6 Complicating Factors -- 4.6.1 Operating in Multiple Markets -- 4.6.2 Demand Erosion -- 4.6.3 Combined Discount Types -- 4.6.4 Substitution and Cross-Price Elasticity -- 4.6.5 Virtual Stockouts and Low Inventory -- 4.7 Running Markdown Experiments -- 4.7.1 Single and Fixed Objective -- 4.7.2 A Good Split of Test and Control Groups -- 4.7.3 Avoid Contamination of the Control Group -- 4.7.4 Big Differences -- 4.7.5 Do Not Continue Testing Indefinitely -- 4.8 Promotional Discounts -- 4.8.1 The Purpose of Price Promotions -- 4.8.2 Estimating Promo Effects -- 4.8.3 Selecting Products for Promotional Discounts -- 4.9 Conclusion -- 4.10 Markdown Terms Glossary.
References -- Part II Inventory Management -- 5 Product (Re-)Distribution and Replenishment -- 5.1 Inventory Management as a Profit Driver -- 5.2 The Traditional Retailer's Perspective on InventoryManagement -- 5.3 Data-Driven Inventory Management Framework -- 5.4 Correcting Demand to Account for Lost Sales -- 5.4.1 Regular and High Sales Volumes -- 5.4.1.1 Traditional Time Series Models -- 5.4.1.2 Analyst in the Loop -- 5.4.1.3 Causally Related Time Series -- 5.4.2 Low Sales Volumes -- 5.5 Demand Forecasting Models -- 5.5.1 Forecasting Without Observed Sales -- 5.5.2 With Limited Historical Data -- 5.5.3 Improved Time Series Forecasting -- 5.6 Evaluating Forecast Accuracy -- 5.6.1 Basic Forecast Performance Measures -- 5.6.1.1 Use of Unseen Data -- 5.6.1.2 Evaluating a Point Estimate -- 5.6.1.3 Evaluating a Time Series Forecast -- 5.7 Optimizing Allocation -- 5.7.1 Initial Distribution of Inventory -- 5.7.2 Redistribution of Inventory -- 5.7.2.1 Identification of Sources and Destinations -- 5.7.2.2 Solving the Allocation Problem -- 5.7.2.3 Possible Extensions -- 5.7.3 Continuous Replenishment -- 5.8 Inventory Management When Selling on Third-PartyPlatforms -- 5.9 Conclusion -- References -- 6 Managing Product Returns -- 6.1 The Challenges Created by Returns -- 6.2 How to Measure the Impact of Returns -- 6.3 Investigating Patterns in Return Behavior -- 6.3.1 Estimating Return Likelihood Based on Product Properties -- 6.3.2 Estimating Return Likelihood Based on Product Performance -- 6.3.3 Estimating Return Likelihood Based on Customer Behavior -- 6.3.4 Estimating Return Likelihood Based on OrderProperties -- 6.4 Taking Action to Prevent or Reduce Returns -- 6.4.1 Product-Based Actions -- 6.4.1.1 Addressing Product-Specific Causes for Returns -- 6.4.1.2 Adjusting the Product Assortment -- 6.4.2 Transaction-Based Actions -- 6.4.3 Customer-Based Actions.
6.5 Conclusion -- References -- Part III Marketing -- 7 The Case for Algorithmic Marketing -- 7.1 What Is Algorithmic Marketing? -- 7.2 Why Algorithmic Marketing Systems Fail to Take Off -- 7.3 Precision Bombing, Not Carpet Bombing -- 7.4 Should You Focus on High-Value Customers? -- 7.5 Do Not Try to Beat Big Marketplaces at Their Own Game -- 7.6 The Low-Hanging Fruit: Get Started Without the Need for Complex Algorithms -- 7.7 Measuring and Experimenting -- 7.8 Conclusion -- References -- 8 Better Customer Segmentation -- 8.1 The Purpose of Segmentation -- 8.2 The Problem with Traditional Segmentation -- 8.2.1 Segments Based on Descriptive Properties -- 8.2.2 RFM Segmentation -- 8.3 What Makes a Segment Actionable? -- 8.4 Customer Value Done Right -- 8.4.1 The Traditional RFM Approach -- 8.4.2 CLV-Based Customer Segmentation -- 8.5 From Lifetime Value to Customer Segments -- 8.5.1 A Simple Approximation Using Customer Groups -- 8.5.2 Causal Model for Variable Selection -- 8.5.3 From Variables to Segments -- 8.5.3.1 Creating Segments Using Unsupervised Clustering Techniques -- 8.5.3.2 Creating Segments Using Supervised Prediction Models -- 8.6 You Have Your Segments, Now What? -- 8.6.1 Product-Specific Nudges -- 8.6.2 Measured Incentives -- 8.6.3 Creating Customer Journeys -- 8.7 Conclusion -- References -- 9 Anticipate What Customers Will Do -- 9.1 Propensity Modeling 101 -- 9.2 The Basic Principles of Scoring Models -- 9.3 Using the Outputs of Scoring Models to Experiment -- 9.4 Pitfall: Models That Are Too Generic to Perform Badly -- 9.5 What Can Be Predicted Using Propensity Models? -- 9.5.1 Will a Customer Buy Something? -- 9.5.2 The Bigger Picture: Actions During Life Cycle Stages -- 9.5.3 Will This Customer Act on This Promotion, Action, Event, etc. ? -- 9.6 Using Nudges to Influence Customers -- 9.7 What About Recommendation Engines?.
9.8 Conclusion -- References -- 10 Anticipate When CustomersWill Do Something -- 10.1 Getting the Timing Right -- 10.2 Survival Modeling Basics -- 10.3 Churn Prediction Using Survival Models -- 10.4 Find the Rhythm: Predicting Renewal Purchases -- 10.5 Putting Models to Work -- 10.6 Conclusion -- Part IV Conclusion -- 11 Conclusion -- 11.1 Where Is Retail Headed Next? -- 11.2 Three Big Forces -- 11.2.1 David and Goliath Will Keep Fighting -- 11.2.2 Environmental Impact Will Continue to Become More Important -- 11.2.3 Intelligent Models Will Learn to Cooperate -- 11.3 Being a Retailer -- A Experimenting the Right Way -- A.1 The Need for Experiments -- A.2 The Basics of a Good Experiment -- A.2.1 A Reasonable Path for Cause and Effect -- A.2.2 A Good Hypothesis -- A.2.3 Defining Success Measures -- A.2.4 Actionable Results -- A.3 Power: Estimate the Chance of a Successful Experiment -- A.4 Selecting an Audience -- A.4.1 Avoiding Experiment Contamination -- A.4.2 To Stratify or Not to Stratify? -- A.5 When Not to Experiment -- A.5.1 Volatile Environment -- A.5.2 When the Proof Has Already Been Delivered -- References.
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| Record Nr. | UNINA-9910616208203321 |
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Analytics for retail : a step-by-step guide to the statistics behind a successful retail business / / Rhoda Okunev
