Why Some Platforms Thrive and Others Don't

Feng  Zhu; Marco   Iansiti

Article | Harvard Business Review | January–February 2019

Why Some Platforms Thrive and Others Don't

Abstract

In the digital economy, scale is no guarantee of continued success. After all, the same factors that help an online platform expand quickly—such as the low cost of adding new customers—work for challengers too. What, then, allows platforms to fight off rivals and grow profits? Their ability to manage five aspects of the networks they’re embedded in:

• network effects, in which users attract more users
• clustering, or fragmentation into many local markets
• the risk of disintermediation, wherein users bypass a hub and connect directly
• vulnerability to multi-homing, which happens when users form ties with two or more competing platforms
• network bridging, which allows platforms to leverage users and data from one network in another network

When entrepreneurs are evaluating a digital platform business, they should look at these dynamics—and the feasibility of improving them—to get a more realistic picture of its long-term prospects.

Keywords: Technology Platform; Competition; Network Effects; Competitive Strategy;

Format: Print

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Citation:

Zhu, Feng, and Marco Iansiti. "Why Some Platforms Thrive and Others Don't." Harvard Business Review 97, no. 1 (January–February 2019): 118–125.

In mid-December 2018, Peter Ma, Chairman and CEO of Ping An Insurance (Group) Company of China, Ltd. was considering whether the company should grow a fifth ecosystem of Smart City Services. Established in 1988, Ping An was one of the top 10 global financial institutions in terms of market capitalization with 342, 550 employees and nearly 1.4 million sales agents. Under Ma's leadership, Ping An was transforming from a traditional financial institution to a leading global technology-powered personal financial services group. In recent years it executed a strategy of "Finance+Ecosystem" by leveraging three frontier technologies-AI, blockchain, and cloud technology-to develop four ecosystems: Financial Services, Healthcare, Automobile Services and Real Estate Services. Smart City Services, with its business potential and social impact, had become an attractive new opportunity for Ping An to leverage its technology expertise and business experience while gaining new traffic for the other four ecosystems. Ma wondered: How important would this new ecosystem be strategically for the future of Ping An? If Ping An decided to go after this market, how should Ping An differentiate itself from competitors?

In summer 2020, Stephane Bancel, CEO of biotech firm Moderna, faces several challenges as his company races to develop a vaccine for COVID-19. The case explores how a company builds a digital organization, and leverages artificial intelligence and other digital resources to speed its operations, manage its processes and ensure quality across research, testing and manufacturing.
Built from the ground up as such a digital organization, Moderna was able to respond to the challenge of developing a vaccine as soon as the gene sequence for the virus was posted to the Web on January 11, 2020. As the vaccine enters Phase III clinical trials, Bancel considers several issues: How should Bancel and his team balance the demands of developing a vaccine for a virus creating a global pandemic alongside the other important vaccines and therapies in Moderna’s pipeline? How should Moderna communicate its goals and vision to investors in this unprecedented time? Should Moderna be concerned it will be pegged as “a COVID-19 company?”

Working Paper | HBS Working Paper Series | 2020

Time and the Value of Data

Ehsan Valavi, Joel Hestness, Newsha Ardalani and Marco Iansiti

This paper investigates the effectiveness of time-dependent data in improving the quality of AI-based products and services. Time-dependency means that data loses its relevance to problems over time. This loss causes deterioration in the algorithm's performance and, thereby, a decline in created business value. We model time-dependency as a shift in the probability distribution and derive several counter-intuitive results.
We, theoretically, prove that even an infinite amount of data collected over time may have limited substance for predicting the future, and an algorithm that is trained on a current dataset of bounded size can attain a similar performance. Moreover, we prove that increasing data volume by including older datasets may put a company in a disadvantageous position.
Having these results, we answer questions on how data volume creates a competitive advantage. We argue that time-dependency weakens the barrier to entry that data volume creates for a business. So much that competing firms equipped with a limited, but sufficient, amount of current data can attain better performance. This result, together with the fact that older datasets may deteriorate algorithms' performance, casts doubt on the significance of first-mover advantage in AI-based markets.
We complement our theoretical results with an experiment. In the experiment, we empirically measure the value loss in text data for the next word prediction task. The empirical measurements confirm the significance of time-dependency and value depreciation in AI-based businesses. For example, after seven years, 100MB of text data becomes as useful as 50MB of current data for the next word prediction task.

Keywords: economics of AI; Machine learning; non-stationarity; perishability; value depreciation;

Citation:

Valavi, Ehsan, Joel Hestness, Newsha Ardalani, and Marco Iansiti. "Time and the Value of Data." Harvard Business School Working Paper, No. 21-016, August 2020. View Details

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Why Some Platforms Thrive and Others Don't

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