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Show Results For
- All HBS Web
(1,235)
- News (259)
- Research (901)
- Events (4)
- Multimedia (1)
- Faculty Publications (364)
- January 2003 (Revised September 2007)
- Background Note
A Note on Racing to Acquire Customers
Examines factors that motivate a firm's race to acquire customers in newly emerging markets and explores conditions under which racing strategies are likely to yield attractive returns. Provides a definition of racing behavior, introduces the notion of an optimal level... View Details
Keywords: Customers; Price Bubble; Network Effects; Emerging Markets; Market Entry and Exit; Behavior; Competition
Eisenmann, Thomas R. "A Note on Racing to Acquire Customers." Harvard Business School Background Note 803-103, January 2003. (Revised September 2007.)
- 24 Feb 2014
- Research & Ideas
Busting Six Myths About Customer Loyalty Programs
percent price advantage. For Asda, short-term profits don't seem to top its list of objectives. Loyalty Systems And Infrequently Purchased Goods Another myth is that loyalty programs do not work in categories that are purchased infrequently or do not affect the View Details
- 21 Mar 2016
- HBS Case
Can Customer Reviews Be 'Managed?'
Brian Kenny: What motivated you to write the case? Why were you interested in it? Thales Teixeira: Some of my research is on the economics of attention and online reviews have amassed a vast amount of attention nowadays. People have changed their View Details
- 16 May 2011
- Research & Ideas
What Loyalty? High-End Customers are First to Flee
Businesses that offer their customers the highest levels of service might like to believe that all their efforts to pamper and please will pay off with an extremely loyal following. “Customers you might expect to be the most 'stuck' are... View Details
Keywords: by Julia Hanna
- March 2011
- Background Note
Customer Loyalty Schemes in the Retail Sector
By: Jose B. Alvarez and Aldo Sesia
Customer loyalty schemes (or programs) are explicit efforts by retailers to gain long-term patronage from customers. Loyalty schemes are developed for a variety of reasons: to reward loyal customers, to generate more robust information about customer behavior, to... View Details
Keywords: Customer Relationship Management; Consumer Behavior; Business Strategy; Retail Industry; United Kingdom; United States
Alvarez, Jose B., and Aldo Sesia. "Customer Loyalty Schemes in the Retail Sector." Harvard Business School Background Note 511-077, March 2011.
- 2025
- Working Paper
Dynamic Personalization with Multiple Customer Signals: Multi-Response State Representation in Reinforcement Learning
Reinforcement learning (RL) offers potential for optimizing sequences of customer interactions by modeling the relationships
between customer states, company actions, and long-term value. However, its practical implementation often faces significant
challenges.... View Details
Keywords: Dynamic Policy; Deep Reinforcement Learning; Representation Learning; Dynamic Difficulty Adjustment; Latent Variable Models; Customer Relationship Management; Customer Value and Value Chain; Foreign Direct Investment; Analytics and Data Science
Ma, Liangzong, Ta-Wei Huang, Eva Ascarza, and Ayelet Israeli. "Dynamic Personalization with Multiple Customer Signals: Multi-Response State Representation in Reinforcement Learning." Harvard Business School Working Paper, No. 25-037, February 2025.
- 2016
- Book
Competing Against Luck: The Story of Innovation and Customer Choice
By: Clayton M. Christensen, Taddy Hall, Karen Dillon and David S. Duncan
The foremost authority on innovation and growth presents a path-breaking book every company needs to transform innovation from a game of chance to one in which they develop products and services that customers want to buy and are willing to purchase at a premium price.... View Details
Christensen, Clayton M., Taddy Hall, Karen Dillon, and David S. Duncan. Competing Against Luck: The Story of Innovation and Customer Choice. New York: Harper Business, 2016.
- 13 Mar 2017
- Research & Ideas
Hiding Products From Customers May Ultimately Boost Sales
portion of your full product catalog—and leaving customers to wonder what you’ll offer next? Or is it better to display all your wares at once, in hopes of encouraging a shopping spree? For example, is a View Details
- December 2008
- Article
Behavioral Frontiers in Choice Modeling
We review the discussion at a workshop whose goal was to achieve a better integration among behavioral, economic, and statistical approaches to choice modeling. The workshop explored how current approaches to the specification, estimation, and application of choice... View Details
Keywords: Mathematical Methods; Integration; Goals and Objectives; Decision Choices and Conditions; Problems and Challenges; Business Processes; Customers; Behavior; Economics
Adamowicz, Wiktor, David Bunch, Trudy Ann Cameron, Benedict G.C. Dellaert, Michael Hanneman, Michael Keane, Jordan Louviere, Robert Meyer, Thomas J. Steenburgh, and Joffre Swait. "Behavioral Frontiers in Choice Modeling." Marketing Letters 19, nos. 3/4 (December 2008): 215–219.
- March 31, 2023
- Article
What Is the Optimal Pattern of a Customer Journey?
Even though customer experience (CX) leaders are becoming increasingly focused on optimizing their firms’ customer journeys, they face a clear challenge: Which touchpoints along the journey should they invest in? That is, which moments when the customer interacts with... View Details
De Freitas, Julian. "What Is the Optimal Pattern of a Customer Journey?" Harvard Business Review (website) (March 31, 2023).
- 22 Feb 2012
- News
Disclosures Are Found to Change Financial Behavior
- 06 May 2002
- Research & Ideas
A Toolkit for Customer Innovation
"Listen carefully to what your customers want and then respond with new products that meet or exceed their needs." That mantra has dominated many a business, and it has undoubtedly led to great products and has even shaped... View Details
Keywords: by Stefan Thomke & Eric Von Hippel
- 16 Jun 2020
- Research & Ideas
Your Customers Have Changed. Here's How to Engage Them Again.
engage them? How should firms adjust? What is clear in the COVID-deaccession is that this change in customer behavior is pushing firms into a new “directional reality.” Firms need to adapt to shifting View Details
- 2009
- Chapter
Creating Superior Customer Value in a Connected World
By: Ranjay Gulati
"In the early twenty-first century, customers are more demanding than ever, and difficult economic times make them all the more so. As customers tighten their wallets and increase their demands, firms face greater pressure to provide superior customer value. Reducing... View Details
- 10 Sep 2021
- News
Human or Computer? Who’s Really Helping You With Customer Service?
- 09 Apr 2001
- Research & Ideas
The Manager’s Guide to Communicating with Customers Collection
Are you reaching your customers? The key is knowing who they are and what they want To appeal to retail customers you need to understand what makes them tick. What better way to do that than by studying actual consumer behavior? A great... View Details
- Web
Research - Behavioral Finance & Financial Stability
productivities at collecting deposits and making loans. They find that deposit productivity is responsible for two thirds of the value of the median bank and most variation in value across banks. Variation in productivity is driven by differences across banks in... View Details
- 17 Jun 2016
- Op-Ed
Companies Need to Start Marketing Security to Customers
behavior would not sit well at the annual industry association meeting and your holier-than-thou superiority claim might tempt fate and attract a terrorist attack. If anything, safety is downplayed as pre-flight instructions to airline... View Details
- 08 Apr 2013
- Video
Booya Fitness, Inc. - Episode 2- Shifting Behavior
- August 2018 (Revised September 2018)
- Supplement
Predicting Purchasing Behavior at PriceMart (B)
By: Srikant M. Datar and Caitlin N. Bowler
Supplements the (A) case. In this case, Wehunt and Morse are concerned about the logistic regression model overfitting to the training data, so they explore two methods for reducing the sensitivity of the model to the data by regularizing the coefficients of the... View Details
Keywords: Data Science; Analytics and Data Science; Analysis; Customers; Household; Forecasting and Prediction
Datar, Srikant M., and Caitlin N. Bowler. "Predicting Purchasing Behavior at PriceMart (B)." Harvard Business School Supplement 119-026, August 2018. (Revised September 2018.)