16 Sep 2020 20:31 IST

Yale professor discusses Facebook’s advertising strategy at IIMB

Dr Sudhir talked about the trade-off between targeting effectiveness and privacy concerns in Facebook

Yale professor Dr K Sudhir discussed the nitty-gritty of getting published in academic journals, and his research topic about Facebook’s ad strategy that identifies matching lookalikes for a targeted customer to reach more people, at IIM Bangalore.

 

 

 

 

 

IIMB’s behavioural sciences lab webinar by Dr K Sudhir, James L Frank Professor of Marketing, Private Enterprise and Management, Yale School of Management, and Editor-in-Chief of Marketing Science, garnered participation from academic and non-academic institutions from different parts of the world.

Ways to get published

Dr Sudhir provided an overview of the publication process in journals such as Marketing Science, specifically highlighting the three Cs essential for acceptance — contribution, correction, and clarity. To increase the likelihood of getting published in the top-tiered journals, meaningful collaboration with published researchers provide access to organisation data for young scholars.

He also suggested ways to improve training and infrastructure for academic scholars and building relationships with organisations and alumni to gain access to data. Researchers from China and Europe adopted the same method and experienced a great degree of success in the past decade, he added.

What is lookalike targeting on Facebook

Drawing from his own research, “Lookalike Targeting on Facebook: Seed Quality Versus Match Accuracy,” he began by describing the process of lookalike targeting. It is a model-based ad targeting approach that uses a seed database of customers to algorithmically identify matching lookalikes for customer acquisition for companies and business.

The seed database is usually the first-party customer data from an advertiser. This is matched with the behaviours of customers that is made available by a third-party like Facebook or Google and used to algorithmically identify lookalikes, or people with a similar profile or behaviour, to get new customers.

Experimental research

He discovered that there was almost no academic research in this field and decided to examine donor acquisition in collaboration with a non-profit organisation 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 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 NGO reaped benefits from the findings for their future donor acquisition and received additional donations from Facebook, as a part of the CSR initiative. This was in accordance with his earlier assertion about effective collaborations with organisations for productive research.

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