Puneet Manchanda

Ziwei Cong, Jia Liu, Puneet Manchanda

A common belief about the growing medium of livestreaming is that its value lies in its "live" component. We examine this belief by comparing how the price elasticity of demand for live events varies before, on the day of, and after livestream. We do this using unique and rich data from a large livestreaming platform that allows consumers to purchase the recorded version of livestream after the stream is over. A challenge in our context is that there exist high-dimensional confounders whose relationships with treatment policy (i.e., price) and outcome of interest (i.e., demand) are complex and only partially known. We address this challenge via the use of a generalized Orthogonal Random Forest framework for heterogeneous treatment effect estimation. We find significant temporal dynamics in the price elasticity of demand over the entire event life-cycle. Specifically, demand becomes less price sensitive over time to the livestreaming day, turning to inelastic on that day. Over the post-livestream period, the demand for the recorded version is still sensitive to price, but much less than in the pre-livestream period. We further show that this temporal variation in price elasticity is driven by the quality uncertainty inherent in such events and the opportunity of real-time interaction with content creators during the livestream.

Prashant Rajaram, Puneet Manchanda

Influencer marketing has become a widely used strategy for reaching customers. Despite growing interest among influencers and brand partners in predicting engagement with influencer videos, there has been little research on the relative importance of different video data modalities in predicting engagement. We analyze unstructured data from long-form YouTube influencer videos - spanning text, audio, and video images - using an interpretable deep learning framework that leverages model attention to video elements. This framework enables strong out-of-sample prediction, followed by ex-post interpretation using a novel approach that prunes spurious associations. Our prediction-based results reveal that "what is said" through words (text) is more important than "how it is said" through imagery (video images) or acoustics (audio) in predicting video engagement. Interpretation-based findings show that during the critical onset period of a video (first 30 seconds), auditory stimuli (e.g., brand mentions and music) are associated with sentiment expressed in verbal engagement (comments), while visual stimuli (e.g., video images of humans and packaged goods) are linked with sentiment expressed through non-verbal engagement (the thumbs-up/down ratio). We validate our approach through multiple methods, connect our findings to relevant theory, and discuss implications for influencers, brands and agencies.