These are some of my current projects (comments and suggestions are welcome)
Barak Libai, Eitan Muller and Verena Schoenmueller (2023), "The XaaS Life Cycle: Buzzers, Adopters, Users, Money,"
Much of the research on new product growth has become less relevant to modern markets. Historically, this research centered on first-purchase models tailored for durable goods, where adoption served as a strong proxy for profits. However, the rise of recurring consumption business models—often termed XaaS, or "everything as a service"—now characterizes many new product sectors. Within the XaaS framework, adoption merely signifies the beginning of a growth of a user base and a continuously evolving revenue stream. Managers, investors, and analysts in the XaaS realm, who focus on the evolution of revenues and profit over time, require innovative frameworks that go beyond mere adoption metrics. In fact, XaaS metrics like ARR (Annual Recurring Revenue), Net Dollar Retention, and Unit Economics (customer lifetime value divided by customer acquisition costs) have become fundamental in evaluating and managing new ventures and products. The shift towards XaaS thinking, however, remains underrepresented in the methods marketing researchers use to model and understand growth. Our objective is to introduce a comprehensive framework for examining XaaS growth. We propose that understanding XaaS growth demands an examination of a three-tiered sequence: Adopters, Users, and Money. We delve into key metrics and paradigms in this domain, offering insights on the trajectory of these tiers and their interconnections. We also outline the ramifications of redirecting new product growth research towards the burgeoning XaaS landscape.
Weiqing Zhang, Zekun Liu, Xiao Liu, and Eitan Muller (2022), "Doubling Revenues by Adopting Livestream Shopping:
A Synthetic DiD Approach," Read paper.
While livestream shopping has attracted enormous attention in the e-commerce world, whether and how it can help online sellers remains questionable. We study the effect of adopting the livestream shopping channel on seller performance. We analyze 2, 851 online sellers who adopted the livestream shopping channel from September 2019 to June 2020. To tackle
a series of identification challenges, we use three different estimators, two-way fixed effect DiD (TWFE), staggered DiD, and synthetic DiD (SynDiD). We find that adopting the livestream shopping channel increases sellers’ total revenue by 107%. Moreover, 47% of the total revenue increase is attributed to the online store channel, suggesting a positive cross-channel spillover effect from the livestream shopping channel to the online store channel. We further examine the mechanisms and find supporting evidence that livestream shopping can not only reduce consumer uncertainty about products via information provision, but also increase consumers’ awareness of sellers by offering sellers broader exposure to the public. In addition, although the average price for the same product is 7.6% lower in the livestream shopping channel, which may partially contribute to sellers’ increased overall revenue, we find that the salience of price promotions in the livestream shopping channel cannot explain the cross-channel spillover effect.
Zekun Liu, Weiqing Zhang, Xiao Liu, Eitan Muller, and Feiyu Xiong (2022), "Success and Survival in Livestream Shopping," Read paper.
The livestream shopping industry, in which consumers can purchase products directly from live video sessions, is expected to exceed $60 billion in China in 2021 and $25 billion in the US in 2023. Despite the popularity of livestream shopping, many sellers fail within just a few weeks. We investigate the lead indicators of the success and survival of livestream shopping sellers. We ask three questions: 1. Livestream viewers can make purchases directly within the session (the “within-channel direct selling effect") or can use the session to gain information that may inform purchases later on (the “cross-channel spillover effect”). Which of the two effects is more important for seller success? 2. Livestream shopping encompasses three industries: e-commerce, social networks, and entertainment. Which industry-specific key performance indicators (KPIs) predict success? 3. Some sellers use livestream shopping for new product introduction while others use it for mature product inventory liquidation. Which type of seller is more likely to survive? We use a unique dataset from Taobao Live to show that: 1. Sellers who rely more heavily on the within-channel direct selling effect (vs. the cross-channel spillover effect) are less likely to succeed. 2. The e-commerce KPI positively predicts success, while the entertainment KPI negatively predicts success. For the social network KPIs, reach positively predicts success, but engagement rate negatively predicts success, reinforcing the cross-channel spillover effect of livestream shopping. 3. Mature product sellers are more likely to succeed than new product sellers.