16 SEPTEMBER, 2020: Dr K. Sudhir (James L. Frank ’32 Professor of Marketing, Private Enterprise & Management & Founder-Director of the Yale China India Insights Program Yale School of Management, Editor-in-Chief of Marketing Science) was the speaker for the third webinar organized by the IIMB Behavioural Sciences Lab. He is a leading scholar in quantitative marketing and his research has won many of the field’s most prestigious awards. He also leads the academic-industry research and consulting collaborations in quantitative marketing at the Yale Center for Customer Insights. He is currently a Visiting Fellow at Microsoft Research.
Dr Sudhir spoke on two major topics in this webinar. First, he provided an overview of the process of publication in the Marketing Science journal, specifically highlighting the three Cs essential for acceptance (contribution, correction, and clarity). He gave tips for young Indian academic scholars to increase their likelihood of getting published in the top-tiered journals, including meaningful collaboration with published researchers which could involve providing access to organization data in the Indian context. Improving training and infrastructure for academic scholars and building relationships with organizations and alumni to gain access to data for further research was also suggested. Researchers from China and Europe have followed this model of collaboration with published researchers for their own subsequent publications with a high degree of success in the past decade.
The second topic of this webinar was “Lookalike Targeting on Facebook: Seed Quality Versus Match Accuracy” based on Dr Sudhir’s own research. He began by describing the process of lookalike targeting, which is a model-based ad targeting approach that uses a seed database of customers to algorithmically identify matching “lookalikes” for targeted customer acquisition. The seed database is usually based on first party customer data from an advertiser and this is matched with the descriptors or behaviours of customers that is made available from a third party like Facebook or Google and used to algorithmically identify lookalikes (or people with a similar profile/behaviour) in the much larger third party database to get new customers. He discovered that there was almost no academic research in this field and decided to examine donor acquisition in collaboration with a non-profit organization (HelpAge India) by conducting advertising field experiments using Facebook Lookalike Audience Tool. Seed quality and match quality were compared to examine the impact on clicks and donations. Reducing seed quality did not impact the clicks and donations significantly, whereas reducing matching significantly reduced the donations and clicks. The paper also explored the trade-off between targeting effectiveness and privacy concerns. Besides the obvious advantage of new empirical research in this field, Dr Sudhir also demonstrated the importance of collaboration with a non-profit, where the latter reaped benefits from the findings for their future donor acquisition and received additional donations from Facebook as a part of the latter’s CSR initiative. This was in accordance with his earlier assertion about effective collaborations with organizations for productive research.
Besides Indian Institute of Management Bangalore (IIMB), participants from other academic and non-academic institutions from different parts of the world had registered for this webinar. The audience was engaged throughout this session and they posed questions and comments that lead to discussions about future directions of research in this relatively unexplored field.
16 SEPTEMBER, 2020: Dr K. Sudhir (James L. Frank ’32 Professor of Marketing, Private Enterprise & Management & Founder-Director of the Yale China India Insights Program Yale School of Management, Editor-in-Chief of Marketing Science) was the speaker for the third webinar organized by the IIMB Behavioural Sciences Lab. He is a leading scholar in quantitative marketing and his research has won many of the field’s most prestigious awards. He also leads the academic-industry research and consulting collaborations in quantitative marketing at the Yale Center for Customer Insights. He is currently a Visiting Fellow at Microsoft Research.
Dr Sudhir spoke on two major topics in this webinar. First, he provided an overview of the process of publication in the Marketing Science journal, specifically highlighting the three Cs essential for acceptance (contribution, correction, and clarity). He gave tips for young Indian academic scholars to increase their likelihood of getting published in the top-tiered journals, including meaningful collaboration with published researchers which could involve providing access to organization data in the Indian context. Improving training and infrastructure for academic scholars and building relationships with organizations and alumni to gain access to data for further research was also suggested. Researchers from China and Europe have followed this model of collaboration with published researchers for their own subsequent publications with a high degree of success in the past decade.
The second topic of this webinar was “Lookalike Targeting on Facebook: Seed Quality Versus Match Accuracy” based on Dr Sudhir’s own research. He began by describing the process of lookalike targeting, which is a model-based ad targeting approach that uses a seed database of customers to algorithmically identify matching “lookalikes” for targeted customer acquisition. The seed database is usually based on first party customer data from an advertiser and this is matched with the descriptors or behaviours of customers that is made available from a third party like Facebook or Google and used to algorithmically identify lookalikes (or people with a similar profile/behaviour) in the much larger third party database to get new customers. He discovered that there was almost no academic research in this field and decided to examine donor acquisition in collaboration with a non-profit organization (HelpAge India) by conducting advertising field experiments using Facebook Lookalike Audience Tool. Seed quality and match quality were compared to examine the impact on clicks and donations. Reducing seed quality did not impact the clicks and donations significantly, whereas reducing matching significantly reduced the donations and clicks. The paper also explored the trade-off between targeting effectiveness and privacy concerns. Besides the obvious advantage of new empirical research in this field, Dr Sudhir also demonstrated the importance of collaboration with a non-profit, where the latter reaped benefits from the findings for their future donor acquisition and received additional donations from Facebook as a part of the latter’s CSR initiative. This was in accordance with his earlier assertion about effective collaborations with organizations for productive research.
Besides Indian Institute of Management Bangalore (IIMB), participants from other academic and non-academic institutions from different parts of the world had registered for this webinar. The audience was engaged throughout this session and they posed questions and comments that lead to discussions about future directions of research in this relatively unexplored field.