Puneet Manchanda | Searcharxiv

arXiv2021

From Live to Recording: Consumer Demand and Response to Price Across the Livestreaming Lifecycle

Livestreaming has evolved into a thriving industry where creators can directly monetize and engage with their audiences and followers. In practice, creators and platforms typically concentrate their marketing efforts on the period leading up to the livestream. However, livestreaming events naturally transition into recorded formats once the event concludes, creating potential "residual" opportunities for monetization. This study systematically examines consumer demand for live events throughout the entire livestream life-cycle, using data from a large livestreaming platform that allows consumers to purchase the recorded version of a paid live event after the livestream ends. We find that the demand is surprisingly more price-sensitive during the pre-livestream period compared to the post-period. This is partly driven by two mechanisms: consumer self-selection (infrequent consumers who may have missed the live events exhibit a higher willingness to pay for recorded versions) and quality uncertainty (consumers face higher uncertainty in event quality during the pre-period than in the post-period). Our findings generate implications for the pricing and targeting strategies in livestreaming markets.

stat.ML↗

arXiv2020

Unboxing Engagement in YouTube Influencer Videos: An Attention-Based Approach

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.

cs.LG↗