Innovation Strategy and Entry Deterrence

Ozge  Turut; Elie  Ofek

Article | Journal of Economics & Management Strategy | Fall 2012

Innovation Strategy and Entry Deterrence

by Ozge Turut and Elie Ofek

Abstract

We model an incumbent's decision to pursue radical or incremental innovation when facing a rival entrant. The radical innovation may yield lucrative financial returns but entails significant technological and market-related uncertainties. It is also particularly attractive to the rival entrant: if the market for it pans out, such an innovation obsoletes the existing technology and any incremental improvements to it. Each firm has its own assessment of the market potential for the radical innovation, and the reliability of these market forecasts can differ. We show that when the entrant's market-assessment capability is weak, the incumbent will pursue incremental innovation and postpone its plans to develop radical innovation even when it thinks highly of the market potential for the radical innovation. The incumbent does so to avoid validating the high market potential to the entrant, who may otherwise be encouraged to invest aggressively. The incumbent thus prefers to look 'soft' with respect to its innovation strategy in order to discourage entry. Even if its innovation strategy is not observable, we show that an incumbent that assesses the commercial potential for a radical innovation favorably may pursue an incremental path and communicate its plans publicly; this strategy serves to reduce entry by affecting the entrant's beliefs about the market potential of the innovation. Finally, we extend the model to investigate the entrant's decision to communicate its innovation intentions. We find that the entrant communicates its plans to aggressively pursue radical innovation only if the incumbent's market-assessment capabilities are strong. In doing so, the entrant acts preemptively to discourage the incumbent from pursuing the radical innovation and is less concerned with validating market potential.

Keywords: Competition; Innovation Strategy; Risk and Uncertainty; Markets; Mathematical Methods;

Format: Print

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Citation:

Turut, Ozge, and Elie Ofek. "Innovation Strategy and Entry Deterrence." Journal of Economics & Management Strategy 12, no. 3 (Fall 2012).

Since its launch in 1998 as “the Amazon.com of DVDs,” Netflix had evolved from a DVD rental company to a video streaming platform and producer of original films and television shows. As the company matured, it regularly increased prices and adjusted its product offerings while continuing to add new subscribers. However, between late 2019 and mid-2020, competition within the streaming industry intensified with the launch of new entrants such as Disney+, Apple TV+, and HBO Max, jeopardizing Netflix’s position as the industry leader. In spite of the heightened competition in the streaming industry, some analysts and customer willingness-to-pay surveys suggested that Netflix had the opportunity to implement another rate hike in the near future. By May 2020, Netflix must decide whether to increase prices again, or whether it should consider a different pricing model altogether.

Case | HBS Case Collection | July 2020

Pattern Brands

Sunil Gupta, Elie Ofek and Julia Kelley

In March 2020, direct-to-consumer (DTC) company Pattern Brands needed to decide how to allocate resources across its different brands. Pattern Co-Founders Nick Ling and Emmett Shine hoped to avoid the pitfalls faced by some DTC companies—such as inability to scale and lack of competitive differentiation—by relying on their previous experience running a successful branding and marketing agency. Rather than focusing on a single brand, they conceived Pattern as a “DTC 2.0” model—a portfolio of 5 to 10 brands that shared a common theme of activities related to the home and a common mission of helping millennials enjoy daily life. Since its founding in August 2019, Pattern had launched cookware brand Equal Parts and home organization brand Open Spaces. Although Pattern’s team still believed strongly in the benefits of a multi-brand strategy, Open Spaces’ sales and unit economics had significantly outperformed Equal Parts’ to date. Pattern’s leadership wanted to build on Open Spaces’ early momentum with new product colors and product lines, but they debated whether to act on the results of a recent customer survey indicating a positive response to an Equal Parts product redesign. Ling and Shine now had several decisions to consider. How should they allocate resources between repositioning Equal Parts and continuing to grow Open Spaces? How and when should they continue building Pattern’s portfolio by developing a third brand? How much should they invest in marketing parent brand Pattern? More broadly, did Pattern’s model represent the future of DTC brands?

Keywords: Brands and Branding; Marketing Channels; Marketing Strategy; Product Marketing; Product Launch; Product Positioning; Business Model; Business Startups; Growth and Development Strategy; Demand and Consumers; Business Strategy; Diversification; Competitive Advantage; Consumer Products Industry; Retail Industry; North and Central America; United States; New York (city, NY); New York (state, US);

Citation:

Gupta, Sunil, Elie Ofek, and Julia Kelley. "Pattern Brands." Harvard Business School Case 521-009, July 2020. View Details

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The digital revolution has allowed companies to amass considerable amounts of data on their customers. Using this information to generate actionable insights is fast becoming a critical skill that firms must master if they wish to effectively compete and win in today’s data-driven marketplace. This note explains the value of examining customer metrics, such as retention rate, revenues, and social influence (k-factor), on an ongoing basis by conducting a cohort analysis. Such an analysis segments customers using one or more criteria, and tracks the behavior and performance of each of these segments over time. Using several instructive examples, the presentation highlights the benefits of running a cohort analysis for deriving a deep understanding of customer trends. Moreover, the examples expose the pitfalls that arise from not accounting for segment characteristics (such as when a customer joined, from what media vehicle they were acquired, how heavily they use the product, etc.) and not paying attention to the evolution of customer metrics over time. The implications of conducting a cohort analysis for firm strategy and for determining customer lifetime value (CLV) are discussed.

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