PhD student on the job market

Just wanted to put this out as an ad of sorts. My PhD student, Han Chen, is currently on the marketing academic job market. He does really interesting research on messaging effects using large scale lab and field experiments. His job market paper (currently r&r at JMR) is on the complementary effects of reminding users … Continue reading PhD student on the job market

AdStyleGAN

If you follow the news on deep learning, you may have encountered the applications of Nvidia’s StyleGAN that generates photo-realistic fake faces. Since the typical dataset that StyleGAN is trained on consist of amateur-produced portraits from Flickr, I was curious to see what StyleGAN would learn from social media posts. I crawled over 1M social … Continue reading AdStyleGAN

Social media receptivity of brands’ BLM support

Happy to share the pre-publication version of this paper just accepted at Marketing Science entitled “How support for Black Lives Matter impacts consumer responses on social media” coauthored with Marco Qin, Xueming Luo, and Eric (Yu) Kou (PhD student) In this paper, we show that online support for the BLM movement is associated with decreased follower … Continue reading Social media receptivity of brands’ BLM support

Matching notes

Here’s a set of notes on regression adjustments and propensity score and coarsened exact matching that I created for a training session at Amazon Ads. The code is in Python mostly for pedagogical reasons, should really just use the Matchit library in R. Continue reading Matching notes

(Code) Deep Learning for NLP: Predicting multi-dimensional hotel ratings

Working on making a repository of notebooks demonstrating applications of AI/ML to business problems with real datasets. This is the first installment of this series. In this notebook, I cover Neural network architecture Embedding layers LSTM Layers Attention layers Training details and model diagnostics The application is in predicting multi-dimensional hotel quality ratings (service, rooms, … Continue reading (Code) Deep Learning for NLP: Predicting multi-dimensional hotel ratings

Do consumers prefer AI or human to recommend products?

Many platforms employ a mix of algorithms and humans for recommending products. For example, Spotify features algorithm and editor / celebrity playlists. Amazon employs both algorithmic product recommendations and “staff picks.” Apple News features experienced editors in conjunction with algorithms when recommending products. This phenomenon begs the question, why do these big platforms, run by … Continue reading Do consumers prefer AI or human to recommend products?

5 principles for responding to customer reviews

First HBR now online! Much thanks to coauthors KT Manis and Alex Chaudhry for putting this concise summary of our previous JMR together. https://hbr.org/2020/05/5-principles-for-responding-to-customer-reviews The takeaways from the takeaways: Principle 1. Address a positive online review by providing a generic, short response. Principle 2. Delay responses for positive reviews. Principle 3. Respond to all negative … Continue reading 5 principles for responding to customer reviews