Driving a More Prosperous Future
Transforming Products into Platforms: Unearthing New Avenues for Business Innovation
NIM Marketing Intelligence Review, October 2024
It is impossible for brands to ignore digital platform opportunities. Network effects are one of the strongest sources of power and defensibility ever invented and underlie some of the most valuable businesses in the world. Managers and entrepreneurs can leverage the power of platforms by adding some platform elements to their existing products or services, by distributing their brands via existing platforms or by developing their own new platforms. By using one’s own brands as platforms requires creativity but can help businesses unlock new value and build resilient ecosystems around their products. There are three key methods. The first is to invite third-party sellers to enhance existing products. Examples include selling advertising space around products or creating app stores to extend offers. The second is to connect one’s customers by enabling interactions among users to add value. Third, brands might reach out to customers’ customers by enhancing the end-user experience in a way that benefits both themselves and their direct customers. If thoughtfully implemented, any platform strategy will create self-reinforcing feedback loops sparking growth and keeping competitors at bay.
Andrei Hagiu, Associate Professor of Information System, Boston University; Bobby Zhou, Associate Professor of Marketing, University of Maryland
How to Talk When a Machine Is Listening: Corporate Disclosure in the Age of AI
The Review of Financial Studies, March 2023
Growing AI readership (proxied for by machine downloads and ownership by AI-equipped investors) motivates firms to prepare filings friendlier to machine processing and to mitigate linguistic tones that are unfavorably perceived by algorithms. Loughran and McDonald (2011) and BERT available since 2018 serve as event studies supporting attribution of the decrease in the measured negative sentiment to increased machine readership. This relationship is stronger among firms with higher benefits to (e.g., external financing needs) or lower cost (e.g., litigation risk) of sentiment management. This is the first study exploring the feedback effect on corporate disclosure in response to technology.
Sean Cao, Associate Professor (with tenure), Robert H. Smith School of Business, University of Maryland, United States of America
Bayesian Ensembles of Exponentially Smoothed Life-Cycle Forecasts
Manufacturing and Servoce Operations Management
We study the problem of forecasting an entire demand distribution for a new product before and after its launch. Firms need accurate distributional forecasts of demand to make operational decisions about capacity, inventory and marketing expenditures. We introduce a unified, robust, and interpretable approach to producing these pre- and post-launch distributional forecasts. Our approach is inspired by Bayesian model averaging. Each candidate model in our ensemble is a life-cycle model fitted to the completed life cycle of a comparable product. A pre-launch forecast is an ensemble with equal weights on the candidate models’ forecasts, while a post-launch forecast is an ensemble with weights that evolve according to Bayesian updating. Our approach is part frequentist and part Bayesian, resulting in a novel form of regularization tailored to the demand forecasting challenge. We also introduce a new type of life-cycle or product diffusion model with states that can be updated using exponential smoothing. The trend in this model follows the density of an exponentially tilted Gompertz random variable. For post-launch forecasting, this model is attractive because it can adapt itself to the most recent changes in a product’s life cycle. We provide closed-form distributional forecasts from our model. In two empirical studies, we show that when the ensemble’s candidate models are all in our new type of exponential smoothing model, this version of the ensemble outperforms several leading approaches in both point and quantile forecasting. In a data-driven operations environment, our model can produce accurate fore- casts frequently and at scale. When quantile forecasts are needed, our model has the potential to provide meaningful economic benefits. In addition, our model’s interpretability should be attractive to managers who already use exponential smoothing and ensemble methods for other forecasting purposes.
Xiaojia Guo (Assistant professor, Robert H. Smith School of Business, UMD), Casey Lichtendahl (Google), Yael Grushka-Cockayne (Professor, Darden school of business, University of Virginia)