Bruce Hardie

Professor of Marketing

BCom MCom (Auckland) MA PhD (Pennsylvania)

Professor Bruce Hardie joined London Business School in 1994. His primary research and teaching interests focus on customer and marketing analytics.

His early research focused on the development of methods for new product sales forecasting and marketing mix analysis. Much of his current work focuses on the development of tools for customer analytics.

Professor Hardie holds BCom and MCom degrees from the University of Auckland (New Zealand), and MA and PhD degrees from the University of Pennsylvania.

  • Applied probability models
  • Customer base analysis
  • Customer analytics


Stochastic Models of Buyer Behavior

Hardie B G S; Sen S; Fader P

In: The History of Marketing Science. 2nd Ed. Winer R & Neslin S (Eds.), pp. 199-251.


Why You Aren’t Getting More from Your Marketing AI

Ascarza E; Ross M; Hardie B G S

Harvard Business Review 2021 Jul/Aug Vol 99:4 p 48-54


Exploring the equivalence of two common mixture models for duration data

Fader P S; Hardie B G S; McCarthy D; Vaidyanathan R

American Statistician 2019 Vol 73:3 p 288-295


In pursuit of enhanced customer retention management: review, key issues, and future directions

Ascarza E; Neslin SA; Netzer O; Anderson Z; Fader PS; Gupta S; Hardie BGS; Lemmens A; Libai A; Neal D; Provost F; Schrift R

Customer Needs and Solutions 2018 Vol 5:1-2 p 65-81

Some customers would rather leave without saying goodbye

Ascarza E; Netzer O; Hardie B G S

Marketing Science 2018 Vol 37:1 p 54-77


Marketing models for the customer-centric firm

Ascarza E; Fader P S; Hardie B G S

in Wierenga B & Van der Lans, R (eds.), Handbook of marketing decision models, 2nd ed Springer, 2017

Valuing subscription-based businesses using publicly disclosed customer data

McCarthy D M; Fader P S; Hardie B G S

Journal of Marketing 2017 Vol 81:1 p 17-35


Customer-base analysis using repeated cross-sectional summary (RCSS) data

Jerath K; Fader P S; Hardie B G S

European Journal of Operational Research 2016 Vol 249:1 p 340-350


Simple probability models for computing CLV and CE

Fader P S; Hardie B G S

in Kumar V, Denish S (eds), Handbook of research on customer equity in marketing, Edward Elgar Publishers, p 77-100


Stochastic models of buyer behaviour

Fader P S; Hardie B G S; Subrata S

in Winer R A & Neslin S A (eds), The History of Marketing Science, World Scientific Publishing, p 165-205, 2014


A joint model of usage and churn in contractual settings

Ascarza E; Hardie B G S

Marketing Science 2013 Vol 32:4 p 570-590


Consumer learning of new binary attribute importance accounting for priors, bias, and order effects

Chylinski M B; Roberts J H; Hardie B G S

Marketing Science 2012 Vol 31:4 p 549-566


New perspectives on customer ‘death’ using a generalization of the pareto/NBD model

Kinshuk J; Fader P S; Hardie B G S

Marketing Science 2011 February Vol 30:5 p 866-880


Analytics for customer engagement

Bijmolt T H A; Leeflang P S H; Block F; Eisenbeiss M; Hardie B G S; Lemmens A; Saffert P

Journal of Service Research 2010 August Vol 13:3 p 341-356

Customer-base analysis in a discrete-time noncontractual setting

Fader P S; Hardie B G S; Shang J

Marketing Science 2010 November-December Vol 29:6 p 1086-1108

Customer-base valuation in a contractual setting: The perils of ignoring heterogeneity

Fader P S; Hardie B G S

Marketing Science 2010 Vol 29:1 p 85-93


Probability models for customer-base analysis

Fader P S; Hardie B G S

Journal of Interactive Marketing 2009 January Vol 23:1 p 61-69


Estimating CLV using aggregated data: The Tuscan Lifestyles case revisited

Fader P S; Hardie B G S; Jerath K

Journal of Interactive Marketing 2007 Summer Vol 21 p 55-71

How to project customer retention

Fader P S; Hardie B G S

Journal of Interactive Marketing 2007 Winter Vol 21 p 76-90


Modeling customer lifetime value

Gupta S; Hanssens D; Hardie B G S; Kahn W; Kumar V; Lin N; Ravishankar N; Sriram S

Journal of Service Research 2006 November Vol 9 p 139-155


Bacon with your eggs?: applications of a new bivariate beta-binomial distribution

Danaher P J; Hardie B G S

American Statistician 2005 Nov Vol 59:4 p 282-286

'Counting your customers' the easy way: an alternative to the Pareto/NBD model

Fader P S; Hardie B G S; Lee K L

Marketing Science 2005 Spring Vol 24:2 p 275-286

RFM and CLV: using iso-value curves for customer-base analysis

Fader P S; Hardie B G S; Lee K L

Journal of Marketing Research 2005 Nov Vol 42:4 p 415-430

The value of simple models in new product forecasting and customer-base analysis

Fader P S; Hardie B G S

Applied Stochastic Models in Business and Industry 2005 Jul-Oct Vol 21:4/5 p 461-473


A dynamic changepoint model for new product sales forecasting

Fader P S; Hardie B G S; Huang C-Y

Marketing Science 2004 Winter Vol 23 p 50-65


Forecasting new product trial in a controlled test market environment

Fader P S; Hardie B G S; Zeithammer R

Journal of Forecasting 2003 Aug Vol 22:5 p 391-410


A note on an integrated model of customer buying behaviour

Fader P S; Hardie B G S

European Journal of Operational Research 2002 Vol 139:3 p 682-687

Bayesian inference for the negative binomial distribution via polynomial expansions

Bradlow E T; Hardie B G S; Fader P S

Journal of Computational and Graphical Statistics 2002 Vol 11:1 p 189-201


Forecasting repeat sales at CDNOW: a case study

Hardie B G S; Fader P S

Interfaces 2001 May/Jun Vol 31:3 Pt 2 p S94-S107

Marketing-mix variables and the diffusion of successive generations of a technological innovation

Danaher P J; Hardie B G S; Putsis W P

Journal of Marketing Research 2001 Nov Vol 38:4 p 501-514


A note on modeling underreported Poisson counts

Fader P S; Hardie B G S

Journal of Applied Statistics 2000 Vol 27:8 p 953-964


An empirical comparison of new product trial forecasting models

Hardie B G S; Fader P S; Wisniewski M

Journal of Forecasting 1998 Jun-Jul Vol 17 p 209-229


Modeling consumer choice among SKUs

Fader P S; Hardie B G S

Journal of Marketing Research 1996 Nov Vol 33:4 p 442-452

Technology adoption: amplifying vs simplifying innovations

Hardie B G S ; Robertson T S; Ross W T

Marketing Letters 1996 Oct Vol 7:4 p 355-369


Modeling loss aversion and reference dependence effects on brand choice

Hardie B G S; Johnson E J; Fader P S

Marketing Science 1993 Fall Vol 12:4 p 378-394


V(CLV): examining variance in models of customer lifetime value

McCarthy D M; Fader P S; Hardie B G S

Social Sciences Research Network

Teaching portfolio

Our teaching offering is updated annually. Faculty and programme material are subject to change.

  • Masters Degrees electives

    Optional courses providing a deep dive into specialist areas.

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    • Customer and Marketing Analytics
      Marketing professionals and consultants are charged with a wide variety of responsibilities that require them to have a good understanding of the workings of the market (at both the micro- and macro-level), evaluate the impact of (and therefore demonstrate the value of) past marketing activities, and use these insights in the development of new marketing programmes. The objective of this course is to familiarize you with some of the main descriptive, predictive, and prescriptive analytics methods that have now become fundamental to marketing decision making as well as to high-level marketing and strategy consulting engagements. The course guides you through the development and use of these tools without getting "bogged down" in the technical details. Fundamental to this course is the view that the way to truly appreciate the strengths and weaknesses of the various tools - so that you can be an "intelligent consumer" of them - is to gain first-hand experience as an end-user modeller.
    • The Data-Driven Enterprise
      We are currently seeing three important trends in business. First, an increasing number of firms are engaging with the concept of customer centricity with the implication that much of the growth dynamic of their business is framed in terms of customer acquisition, retention, and development. Second, the digitization of business has led to the potential for an extraordinary atomization of many decisions. Third, the development of machine-learning and AI methods means that firms are developing more decision automation systems. This course explores how firms should organize for data-driven decision making in today's customer-centric, digitally enabled world. We explore the basic enabling technologies, review the key analytical tools, identify the information and decision silos that prevent firms from taking a truly customer-centric approach to their activities, and explore the organizational and technological challenges of breaking down the barriers associated with these silos.
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