eQMS Spring Edition
Introduction
The Spring Calendar for the European Quant Marketing Seminar series is now available, Mar-May 20024
INTEREST CATEGORY: MARKETING RESEARCH
POSTING TYPE: Events
Posted by: Dominik Papies
It is our pleasure to announce the 9th season of the eQMS (European Quant Marketing Seminar – A European Marketing Research Online Seminar Supported by EMAC) starting on Thursday, March 7 and running through summer term until May.
Kicking off this spring edition is a talk jointly organized with EMAC’s retailing SIG. Join us on Thursday, March 7, 2024, at 14:00 CET (see for global times), as Paulo Albuquerque from INSEAD explores the question: Does fMRI Data Improve Predictions of Product Adoption by Store Managers and Sales to Consumers for Consumer-Packaged Goods?. You can find the abstract at the bottom of this e-mail, and you can access the seminar directly from our website.
Subsequent seminars will feature speakers such as Stephan Seiler from Imperial College London, Bryan Bollinger from NYU, and Anthony Dukes from USC, covering a diverse range of topics, methods, and career stages. You can find the complete lineup and schedule on our website:
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Our website also has a Google Calendar and an iCal link that you can use to import all seminar information into your calendar.
As before, seminars will usually take place on Thursdays at 2 p.m. CET (8 a.m. NYC, 1 p.m. London, 6.30 p.m. Delhi, 9 p.m. Shanghai & Hong Kong). After Europe switches back to daylight saving time on 31 March, all talks will be on CEST.
These seminars will be conducted via regular Zoom meetings, and registration is not required. We encourage you to share this invitation with colleagues who may be interested and mark your calendars to join us for the 2024 eQMS Spring edition.
We are very much looking forward to seeing you all on March 7!
Warm regards,
Bart Bronnenberg
Anja Lambrecht
Thomas Otter
Dominik Papies
Stephan Seiler
P.S.: If you have not received this e-mail directly, you can subscribe .
Abstract
This paper investigates the value of adding functional magnetic resonance imaging (fMRI) data to predict success of consumer-packaged goods, as captured in (1) store manager adoption and (2) store sales. Our approach combines fMRI data from a relatively small sample of individuals with in-house observable market data such as price and promotional level, customer attitudes based on a representative survey, and incentive-aligned purchasing behavior. Using a regression tree model, we show that fMRI data can significantly enhance predictions of consumer choice as captured in sales per store. We also show that the fMRI data is the most valuable to predict sales of more innovative products. However, more traditional measures such as representative surveys are more valuable in improving forecasts of product adoption rates by store managers. We quantified the benefits of the improved predictive power of each data type and compared it with its costs. Our findings help managers to evaluate the benefits of collecting fMRI data, beyond traditional data sources, to predict the future market success of their products.