Tomomichi Amano is an Assistant Professor of Business Administration in the Marketing Unit at HBS. He teaches the Marketing course in the MBA required curriculum.
Tomomichi Amano is an Assistant Professor of Business Administration in the Marketing Unit at HBS. He teaches the Marketing course in the MBA required curriculum.
Professor Amano's research agenda is twofold. First, he is interested in uncovering ways to merge economics and marketing with rich datasets in order to deepen the understanding of consumer behavior in online markets. The second strand of his research focuses on understanding product line decisions and the diffusion of innovation, particularly in the contexts of markets or products that have substantial environmental consequences.
Professor Amano received a B.A. in economics from Harvard College, as well as a M.A. in economics and Ph.D. in business administration from Stanford University. He has previously taught at Columbia Business School.
My research
agenda
is twofold
.
First,
I am interested in
uncovering
ways
to
merge economics and
marketing
with “big data” and
new methods
,
in order
to deepen
our
understanding of consumer behavior
and marketing.
The second strand
of my research is driven by the question of how marketing influence
Davide Proserpio, John R. Hauser, Xiao Liu, Tomomichi Amano, Alex Burnap, Tong Guo, Dokyun Lee, Randall Lewis, Kanishka Misra, Eric Schwarz, Artem Timoshenko, Lilei Xu and Hema Yoganarasimhan
Machine learning is bringing us self-driving cars, improved medical diagnostics, and machine translation, but can it improve marketing decisions? It can. Machine learning models predict extremely well, are scalable to “big data,” and are a natural fit to rich media such as text, images, audio, and video. Examples include identification of customer needs from online data, accurate prediction of consumer response to advertising, personalized pricing, and product recommendations. But without a soul, the applications of machine learning are limited. Consumer behavior and competitive strategies are nuanced and richly described by formal theory. To learn across applications, to be accurate for “what-if” and “but-for” applications, and to advance knowledge, machine learning needs theory and a soul. The brightest future is based on the synergy of what the machine can do well and what humans do well. We provide examples and predictions for the future.
Proserpio, Davide, John R. Hauser, Xiao Liu, Tomomichi Amano, Alex Burnap, Tong Guo, Dokyun Lee, Randall Lewis, Kanishka Misra, Eric Schwarz, Artem Timoshenko, Lilei Xu, and Hema Yoganarasimhan. "Soul and Machine (Learning)." Harvard Business School Working Paper, No. 20-036, September 2019.
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We document that recent quasi-experimental strategies for identifying advertising effects can be derived from a model in which ad decisions are made at a more aggregate level than conversion is measured. Next, we show that the identifying variation in one of these strategies, the Border Approach, is conceptually similar to what are commonly known as Waldfogel IVs. We compare these, as well as supply-side instruments and fixed effects, in a data set on advertising in US presidential elections. Both border approaches and IVs are known to sacrifice statistical power and they do, but not by enough to affect statistical significance, in this application. The Waldfogel IVs are much more powerful than the supply-side IVs and, when combined, the standard errors are substantially reduced. Each IV estimator has the potential to produce a local average treatment effect that weights aggregate markets differently. Estimates suggest differences may exist, but they are not significant. When both IVs are combined, the point estimate is identical to a fixed effect estimate that is likely to be unbiased. The Border Approach can produce local effects at the disaggregate level when border and non-border regions differ. We find evidence of a statistically signficant difference when analysis is restricted to those counties where identifying assumptions are more plausible. The point estimate drops to nearly zero and becomes insignificant despite a standard error that is as small as the lowest IV standard error. We suspect local estimate concerns are greater for the Border Approach because it identifies advertising effects that exclude the high population counties in all markets, whereas IVs may weight each market differently but include counties of all types within each market.
In differentiated goods markets with societal implications, quality standards are commonly implemented to avoid the under-provision of innovation. Firms have clear incentives to engage in strategic behavior because policymakers use market outcomes as a benchmark in designing regulation. This study examines a unique energy efficiency standard for television sets, under which future minimum efficiency standards are explicitly a function of current product offerings. The setting illustrates firms' dual incentives at work: A firm better differentiates products under a looser standard but may want to induce a tighter standard if it can benefit from raising rivals' costs. These incentives drive firms to ratchet quality. We develop a structural model of product entry that illustrates how the regulator's standard setting rule affects a firm's product quality decision. Counterfactual simulations illustrate that ratcheting down was prevalent in this market and that incentives to ratchet up did not exist. The results suggest that in many commonly regulated markets in which firms share similar cost structures, firms are likely to experience incentives to ratchet down and delay the introduction of innovative products. The study highlights the importance of understanding supply side incentives, such as ratcheting, in designing and assessing policy.
In many online markets, traditional methods of demand estimation are difficult to implement because assortments are very large and individual products are sold infrequently. At the same time, data on consumer search (i.e., browsing) behavior are often available and are much more abundant than purchase data. We propose a demand model that caters to this type of setting. Our approach is computationally light and allows for flexible cross-price elasticities that are informed by search patterns. We apply the model to a data set containing search and purchase information from a retailer stocking almost 600 products, recover the elasticity matrix, and solve for optimal prices for the entire assortment.
Amano, Tomomichi, Andrew Rhodes, and Stephan Seiler. "Large-Scale Demand Estimation with Search Data." Harvard Business School Working Paper, No. 19-022, September 2018. (Revised June 2019. Stanford University Research Paper, No. 18-36, 8-20 2018.)
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Launched in 1981 as an “all occasion” sparkling water brand, LaCroix Sparkling Water has had a number of ups and downs as a brand. After being purchased by National Beverage in 1996, the brand was repositioned as a new, colorful, fun alternative to the other sparkling water players at the time. As time passed; however, the brand was faced with internal turmoil as well as shifting customer expectations. How did LaCroix react to change both within the company and within the industry?
Launched in 1981 as an “all occasion” sparkling water brand, LaCroix Sparkling Water has had a number of ups and downs as a brand. After being purchased by National Beverage in 1996, the brand was re-positioned as a new, colorful, fun alternative to the other sparkling water players at the time. As time passed; however, the brand was faced with internal turmoil as well as shifting customer expectations. How did LaCroix react to change both within the company and within the industry?
Othellonia surprised the Japan’s mobile game market by supporting the launch of the game with an unusual approach: Offline community management. The management team favored a slow and steady launch entirely focused on bringing very loyal users instead of attracting too many users at once. Doing so had positioned Othellonia among the mobile games with the most loyal customer base and with monetization rates above the levels usually observed in the industry. However, after a few years of steady growth, strong loyalty from its users and healthy monetization, recent acquisition, engagement and retention metrics were worrisome for Yu Sasaki and Taku Kojo (KJ). Should the team invest in the promotional activities that drove acquisition in the past? Should they rely on the loyal base of players to keep growing the game? How could they increase user engagement without hurting monetization?
