Papers by Simon Bonaventure
This study investigates the relationship between touching products and consumers’ willingness to ... more This study investigates the relationship between touching products and consumers’ willingness to pay (WTP) for a related extended warranty, whose relation is hypothesized to be mediated by psychological ownership (PO) and moderated by both the hedonic versus utilitarian nature of the products and the financial risk involved in the products. A 2 (hedonic versus utilitarian products) × 2 (touching–no touching the products) factorial design experiment was conducted to test the hypotheses (N = 133). PO mediates the relation between touch and WTP. PO increases WTP only if the financial risk is low. Touching products increases WTP only for utilitarian products. This study contributes to understanding how haptic information generates a sense of ownership when consumers buy warranties. Managerial implications for retailers are proposed.
This study investigates the relationship between touching products and consumers' willingness to ... more This study investigates the relationship between touching products and consumers' willingness to pay (WTP) for a related extended warranty, whose relation is hypothesized to be mediated by psychological ownership (PO) and moderated by both the hedonic versus utilitarian nature of the products and the financial risk involved in the products. A 2 (hedonic versus utilitarian products) × 2 (touching–no touching the products) factorial design experiment was conducted to test the hypotheses (N = 133). PO mediates the relation between touch and WTP. PO increases WTP only if the financial risk is low. Touching products increases WTP only for utilitarian products. This study contributes to understanding how haptic information generates a sense of ownership when consumers buy warranties. Managerial implications for retailers are proposed.
In database marketing, data mining has been used extensively to
find the optimal customer targets... more In database marketing, data mining has been used extensively to
find the optimal customer targets so as to maximize return on
investment. In particular, using marketing campaign data, models
are typically developed to identify characteristics of customers
who are most likely to respond. While these models are helpful in
identifying the likely responders, they may be targeting customers
who have decided to take the desirable action or not regardless of
whether they receive the campaign contact (e.g. mail, call). Based
on many years of business experience, we identify the appropriate
business objective and its associated mathematical objective
function. We point out that the current approach is not directly
designed to solve the appropriate business objective. We then
propose a new methodology to identify the customers whose
decisions will be positively influenced by campaigns. The
proposed methodology is easy to implement and can be used in
conjunction with most commonly used supervised learning
algorithms. An example using simulated data is used to illustrate
the proposed methodology. This paper may provide the database
marketing industry with a simple but significant methodological
improvement and open a new area for further research and
development.
This paper seeks to document the current state of the art in ‘uplift modelling’—the practice of m... more This paper seeks to document the current state of the art in ‘uplift modelling’—the practice of modelling the change in behaviour that results directly from a specified treatment such as a marketing intervention. We include details of the Significance- Based Uplift Trees that have formed the core of the only packaged uplift modelling software currently available. The paper includes a summary of some of
the results that have been delivered using uplift modelling in practice, with examples drawn from demand-stimulation and customer- 日etention applications. It also surveys and discusses approaches to each of the major stages involved in uplift modelling—variable selection, model construction, quality measures and postcampaign
evaluation—all of which require different approaches from traditional
response modelling.
There is a subtle but important difference between targeting people who are likely to buy if they... more There is a subtle but important difference between targeting people who are likely to buy if they are included in a campaign and targeting people who are only likely to buy if they are included in a campaign.
It transpires that this single-word distinction is often the difference between a strongly profitable and a severely loss-making campaign. We have seen many cases in which moving to targeting on the second basis (for incremental sales) has more than doubled the extra sales
generated by a campaign. Conventional “response” models—despite their name—target on the former basis, and have a marked tendency to concentrate on people who would have bought anyway, thus misallocating marketing resources by increasing costs and failing to
maximize sales. This paper discusses the use of a radical new type of predictive modelling— uplift modelling—that allows campaigns to be targeted on the second basis, i.e. so as to maximize incremental sales from cross-sell, up-sell and other sales-generation campaigns.
Display ads proliferate on the web, but are they effective?
Or are they irrelevant in light of al... more Display ads proliferate on the web, but are they effective?
Or are they irrelevant in light of all the other advertising
that people see? We describe a way to answer these ques-
tions, quickly and accurately, without randomized experi-
ments, surveys, focus groups or expert data analysts. Dou-
bly robust estimation protects against the selection bias that
is inherent in observational data, and a nonparametric test
that is based on irrelevant outcomes provides further de-
fense. Simulations based on realistic scenarios show that
the resulting estimates are more robust to selection bias
than traditional alternatives, such as regression modeling or
propensity scoring. Moreover, computations are fast enough
that all processing, from data retrieval through estimation,
testing, validation and report generation, proceeds in an au-
tomated pipeline, without anyone needing to see the raw
data.
Abstract: An important problem in econometrics and marketing is to infer the causal impact that a... more Abstract: An important problem in econometrics and marketing is to infer the causal impact that a designed market intervention has exerted on an outcome metric over time. In order to allocate a given budget optimally, for example, an advertiser must assess to what extent different campaigns have contributed to an incremental lift in web searches, product installs, or sales. This paper proposes to infer causal impact on the basis of a diffusion-regression state-space model that predicts the counterfactual market response that would have occurred had no intervention taken place. In contrast to classical difference-in-differences schemes, state-space models make it possible to (i) infer the temporal evolution of attributable impact, (ii) incorporate empirical priors on the parameters in a fully Bayesian treatment, and (iii) flexibly accommodate multiple sources of variation, including the time-varying influence of contemporaneous covariates, i.e., synthetic controls. Using a Markov chain Monte Carlo algorithm for model inversion, we illustrate the statistical properties of our approach on synthetic data. We then demonstrate its practical utility by evaluating the effect of an online advertising campaign on search-related site visits. We discuss the strengths and limitations of our approach in improving the accuracy of causal attribution, power analyses, and principled budget allocation.
Evolutionary psychology is an emerging paradigm in psychological science. The current article int... more Evolutionary psychology is an emerging paradigm in psychological science. The current article introduces this framework to marketing scholars and presents evidence for its increasing acceptance within the social science community. As a result, a case is made for the application of evolutionary psychology to marketing, and especially consumer behavior. Application of the evolutionary framework in studying gender-related consumption behavior is illustrated by comparing the evolutionary predictions with results obtained from previous studies, by supporting these predictions with market-level consumption data, and by proposing new hypotheses based on this framework. Also discussed are the potential applications of evolutionary psychology to other consumption-related phenomena like evaluation of endorser attractiveness in advertising, biologically driven consumption choices among women, consumer-experienced emotions in service encounters, and consumption choices as inclusive fitness maximization rather than utility maximization.
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Papers by Simon Bonaventure
find the optimal customer targets so as to maximize return on
investment. In particular, using marketing campaign data, models
are typically developed to identify characteristics of customers
who are most likely to respond. While these models are helpful in
identifying the likely responders, they may be targeting customers
who have decided to take the desirable action or not regardless of
whether they receive the campaign contact (e.g. mail, call). Based
on many years of business experience, we identify the appropriate
business objective and its associated mathematical objective
function. We point out that the current approach is not directly
designed to solve the appropriate business objective. We then
propose a new methodology to identify the customers whose
decisions will be positively influenced by campaigns. The
proposed methodology is easy to implement and can be used in
conjunction with most commonly used supervised learning
algorithms. An example using simulated data is used to illustrate
the proposed methodology. This paper may provide the database
marketing industry with a simple but significant methodological
improvement and open a new area for further research and
development.
the results that have been delivered using uplift modelling in practice, with examples drawn from demand-stimulation and customer- 日etention applications. It also surveys and discusses approaches to each of the major stages involved in uplift modelling—variable selection, model construction, quality measures and postcampaign
evaluation—all of which require different approaches from traditional
response modelling.
It transpires that this single-word distinction is often the difference between a strongly profitable and a severely loss-making campaign. We have seen many cases in which moving to targeting on the second basis (for incremental sales) has more than doubled the extra sales
generated by a campaign. Conventional “response” models—despite their name—target on the former basis, and have a marked tendency to concentrate on people who would have bought anyway, thus misallocating marketing resources by increasing costs and failing to
maximize sales. This paper discusses the use of a radical new type of predictive modelling— uplift modelling—that allows campaigns to be targeted on the second basis, i.e. so as to maximize incremental sales from cross-sell, up-sell and other sales-generation campaigns.
Or are they irrelevant in light of all the other advertising
that people see? We describe a way to answer these ques-
tions, quickly and accurately, without randomized experi-
ments, surveys, focus groups or expert data analysts. Dou-
bly robust estimation protects against the selection bias that
is inherent in observational data, and a nonparametric test
that is based on irrelevant outcomes provides further de-
fense. Simulations based on realistic scenarios show that
the resulting estimates are more robust to selection bias
than traditional alternatives, such as regression modeling or
propensity scoring. Moreover, computations are fast enough
that all processing, from data retrieval through estimation,
testing, validation and report generation, proceeds in an au-
tomated pipeline, without anyone needing to see the raw
data.
find the optimal customer targets so as to maximize return on
investment. In particular, using marketing campaign data, models
are typically developed to identify characteristics of customers
who are most likely to respond. While these models are helpful in
identifying the likely responders, they may be targeting customers
who have decided to take the desirable action or not regardless of
whether they receive the campaign contact (e.g. mail, call). Based
on many years of business experience, we identify the appropriate
business objective and its associated mathematical objective
function. We point out that the current approach is not directly
designed to solve the appropriate business objective. We then
propose a new methodology to identify the customers whose
decisions will be positively influenced by campaigns. The
proposed methodology is easy to implement and can be used in
conjunction with most commonly used supervised learning
algorithms. An example using simulated data is used to illustrate
the proposed methodology. This paper may provide the database
marketing industry with a simple but significant methodological
improvement and open a new area for further research and
development.
the results that have been delivered using uplift modelling in practice, with examples drawn from demand-stimulation and customer- 日etention applications. It also surveys and discusses approaches to each of the major stages involved in uplift modelling—variable selection, model construction, quality measures and postcampaign
evaluation—all of which require different approaches from traditional
response modelling.
It transpires that this single-word distinction is often the difference between a strongly profitable and a severely loss-making campaign. We have seen many cases in which moving to targeting on the second basis (for incremental sales) has more than doubled the extra sales
generated by a campaign. Conventional “response” models—despite their name—target on the former basis, and have a marked tendency to concentrate on people who would have bought anyway, thus misallocating marketing resources by increasing costs and failing to
maximize sales. This paper discusses the use of a radical new type of predictive modelling— uplift modelling—that allows campaigns to be targeted on the second basis, i.e. so as to maximize incremental sales from cross-sell, up-sell and other sales-generation campaigns.
Or are they irrelevant in light of all the other advertising
that people see? We describe a way to answer these ques-
tions, quickly and accurately, without randomized experi-
ments, surveys, focus groups or expert data analysts. Dou-
bly robust estimation protects against the selection bias that
is inherent in observational data, and a nonparametric test
that is based on irrelevant outcomes provides further de-
fense. Simulations based on realistic scenarios show that
the resulting estimates are more robust to selection bias
than traditional alternatives, such as regression modeling or
propensity scoring. Moreover, computations are fast enough
that all processing, from data retrieval through estimation,
testing, validation and report generation, proceeds in an au-
tomated pipeline, without anyone needing to see the raw
data.