A SCALING FRAMEWORK FOR NETWORK EFFECT PLATFORMS A guide to scaling value and volume on platforms.

Sangeet Paul Choudary

ABOUT THE AUTHOR SANGEET PAUL CHOUDARY is the founder of Platformation Labs and the best-selling author of the books Platform Scale and Platform Revolution. He has been ranked as a leading global thinker for two consecutive years by Thinkers50, ranking among the top 30 emerging thinkers globally in 2016 (Thinkers50 Radar) and ranking among the top 50 thinkers of Indian origin in 2015 (Thinkers50 India). He is the co-chair of the MIT Platform Strategy Summit at the MIT Media Labs and an Entrepreneur-in-residence at INSEAD Business School. He is also an empaneled expert on the global advisory council for the World Economic Forum’s initiative on the Digital Transformation of Industries. His work has been featured as the Spotlight article on Harvard Business Review (April 2016 edition) and the themed Business Report of the MIT Technology Review (September 2015). As the founder of Platformation Labs, Sangeet is an advisor to leading executives globally. He is also an empaneled executive educator with Harvard Business School Publishing, and has advised the leadership of Fortune 500 firms, family-owned conglomerates, and key government bodies. He is frequently quoted and published in leading journals and media including the Harvard Business Review, MIT Technology Review, MIT Sloan Management Review.The Economist, The Wall Street Journal, WIRED Magazine, Forbes, Fortune, and others. Sangeet is a frequently sought after advisor to CXOs globally on the topic of digital transformation and also serves as a fellow at the Centre for Global Enterprise in New York. He is a frequent keynote speaker and has been invited to speak at leading global forums including the World Economic Forum’s Annual Meeting of the New Champions (Summer Davos), the WEF ASEAN Summit, and the G20 Summit 2014 events. Sangeet has a bachelors in computer science from IIT Kanpur and a masters in management from IIM Bangalore.



Marshall Van Alstyne and Geoffrey Parker are contributing authors to the research published by Platformation Labs, including the books Platform Revolution (coauthors) and Platform Scale.

Platformation Labs is C-level executive advisory firm and think-tank, focused on the analysis and implementation of platform business models and network effects towards the digital transformation of industries. Platformation Labs has advised governments, Fortune 100 firms and high growth startups in 40+ countries across the Americas, Europe, Africa and Asia-Pacific. Our thought leadership and intellectual capital are commissioned and licensed by leading consulting firms globally and have been featured in leading global forums.

One of the most common misconceptions about running a platform business is that it’s all straight and easy once the initial chicken and egg problem of getting both producers and consumers is solved. Ironically, in an age where kicking off new platforms isn’t quite as difficult as it used to be, the most important platform management issues really come up once the platform starts working and delivering value. But let’s start this at the beginning:

The original platform hassle Everyone loves a good network effect. The problem is often that it is quite difficult achieving it. But without network effects, there is little or no value on the platform (unless the platform has some alternate form of standalone value). As we so often discuss on this blog, the goal of the platform is to enable interactions between producers and consumers. Think of Dribbble, TaskRabbit or Salesforce, you see this theme repeatedly. As the platform manager, you want producers to create value and consumers to consume value for the platform to fulfill its purpose. Additionally, the platform needs some mechanism for curation; that ensures quality. Network effects are realized when there are enough producers and consumers with overlapping intent for interactions to spark off between them. In such a scenario, the platform starts fulfilling its role of enabling interactions. From here on, the goal of the platform is to scale its ability to enable more and better interactions.

A scaling manifesto for platforms If we were to condense it in one line: A platform’s goal is to scale the quantity and the quality of interactions that it enables. The first part is obvious but involves several nuances. The second part is less obvious and is often ignored at the platform’s peril. Let’s peel this further.

Scaling interactions To start with, it goes without saying that the platform wants its core interaction to be repeated as often as possible. More rides on Uber and more tasks booked on TaskRabbit is good news. The Core Interaction consists of three actions: Creation, Curation and Consumption. Depending on the type of the platform and the stage of evolution it is in, scaling interactions may involve scaling one or more of these actions.

Scaling Quantity: Creation Some platforms scale by focusing solely on scaling creation of new value. A true blue classifieds platform like Craigslist largely cares about the depth and breadth of its listings. A platform can stall despite having strong network effects if producers stop creating value on the platform. Thankfully, this is one of the most obvious goals and metrics to watch out for and platforms rarely fail because of an inability to focus here. There are several ways platforms encourage a constant stream of value creation, a framework of which is discussed in detail in the article here.

Scaling Quantity: Consumption Scaling creation and consumption often go hand in hand. This is because producers are likely to participate on a platform only when there is active participation from consumers as well. No one wants to speak to an empty room. Hence, efforts to scale creation of new value are not going to be sustainable unless complemented by efforts to scale consumption as well. For demand-driven platforms like Upwork and 99designs, where the value is created in response to a request, scaling consumption is a critical first step to scaling interactions. But even for a creation-centric platform like YouTube, scaling consumption is very important, especially when producers participate with a need to selfexpress or self-promote. Of course, to scale consumption, the platform needs to capture better data about the consumer and use that to make more relevant recommendations. No amount of marketing and alerts/notifications can substitute relevance for consumers. This is also largely why we see the role of data scientist rapidly emerging in importance, not only in the tech industry but also outside it.

A consequence of this is the fact that platforms need to start acquiring data about users the moment they sign up. This translates itself into simple tweaks in the onboarding flow. e.g. Pinterest will ask you to like a few boards and a few topics as part of its onboarding flow. It is data that helps it understand what to serve you next so that you continue to consume. Increasingly, marketplaces aren’t focused on transactions alone. Additionally, they encourage users to follow topics and listings, in the hope that such data on users’ interests can help create opportunities for transactions in the future without the user explicitly initiating one.

Scaling Quality: Curation Finally, as more value gets created and consumed on the platform, the platform also needs to get better at its ability to curate and differentiate high-quality from low-quality. To ensure value and a desirable experience to its users, the platform needs to ensure that it encourages actions that result in high-quality creations and discourages actions that result in low-quality contributions. In its early days, YouTube ran competitions where the most upvoted videos were rewarded. Wikipedia blocks IPs and accounts that generate a lot of suspicious activity. Sittercity and TaskRabbit do intensive background checks on service providers on their platform. Whether social feedback or editorial prescreening, curation is a necessary part of running a platform. Curation is typically done in one of three forms: 1. Editorial: An editor, admin or community manager approves and disapproves contributions to the platform. 2. Algorithmic: Algorithms take decisions on what’s desirable and what’s not based on certain parameters. 3. Social: The community curates through signals about quality, like rating, voting, etc.

Scaling curation may mean different things depending on which of these aspects are being scaled. Scaling editorial curation: The brute force method to scale editorial curation, traditionally, has been to get more editors on board. That never works well for a network effects platform. Editorial actions scale only when they are gradually moved out to the community over time. The editors do not become redundant; they simply take on more abstracted roles. This may involve educating the community on how to curate and ensuring that the tools of curation (e.g. rating, review, reporting, etc.) are being used correctly and often enough. In the case of platforms like Viki and Wikipedia, it may also mean creating a hierarchy of sorts in the community to differentiate highly reputed users from less reputed ones to phase out actions gradually, from internal editors to highly reputed users, and so on.

Scaling algorithmic curation: Very briefly, algorithmic curation may scale by improving the algorithms themselves or improving the inputs to the algorithms. The inputs to the algorithms are provided by editors or by the community and hence scaling algorithmic curation works very closely with scaling the other two forms of curation. Scaling social curation: Social curation scales either by permeating a reputation model through the community or by merely relying on the strength of numbers. In the former, the opinions of experts are given more weight than that of novices. In the latter, all opinions count towards the same. This is, of course, a continuum rather than a duality and most platforms lie somewhere along the continuum. The more the expertise required to make a judgment on curation, the more likely is it to rely on a reputation model. Users who have curated well in the past have a greater say in future curation.

Scaling Quality – Overall Governance Finally, this specifically involves looking for corner cases and situations where the platform is used/abused in ways that the platform creator hadn’t planned for. Specifically, this involves identifying undesirable interactions and ensuring that those are not repeated. A murderer using a dating site to find his next victim is an undesirable interaction as is a contributor defacing the Wikipedia profile of a public figure. In some cases, scaling quality through governance may have limits. No matter how much you govern, you may still have the odd traveler trashing an Airbnb apartment. In such cases, creating centralized trust mechanisms becomes critical to scaling the platform and ensuring widespread adoption among mainstream users. This brings us to our final point on scaling platform activity.

Scaling Quality – Mitigating Interaction Risk The final element of scaling quality of interactions has to do with the risk inherent to either side in participating in an interaction. In an age where people actually die when meeting people through Craigslist, mitigating risk becomes an important aspect of driving platform adoption. Not all interactions are created equal. Some are riskier than others. Participating on Twitter doesn’t involve many risk for either side but participating on a platform for discovering home cooked food may have higher risks associated. Depending on the degree of risk involved, the platform may have to invest heavily in offering centralized guarantees and insurance. Most sharing economy platforms invest in creating insurance and trust mechanisms to ensure that users aren’t discouraged from participating.


C-level Executive Education

Platform Architecture and Strategy

C-level and business leadership-level exec ed towards a platform implementation at a client organization. It may also include workshops for execution and implementation teams. For larger teams, this may be done as webinars remotely.

Engagement on a specific platform strategy and implementation. Includes: platform business design, layout of feedback loops and network effects, monetization scenarios, management of curation and governance of the ecosystem, data strategy, roadmap creation and metrics definition, among other things. This may be done remotely or in-person or through a combination.

Commissioned Research

Retained Advisory

In-depth research, commissioned by the client, to create thought leadership material, layout future industry scenarios or study business model transformation.

Retained advisory relationships with a specific project (or multiple projects) at a company, or advisory boards, typically structured as 6-12 month retainers.

Corporate Speaking Keynote speaking at sales events, executive briefings for C-level execs, and speaking and briefings at executive planning sessions and offsites.

To engage further, please write in at the following: [email protected] [email protected]


Page 2 of 7. ABOUT THE AUTHOR. SANGEET PAUL CHOUDARY. is the founder of Platformation Labs and the best-selling author of the books Platform Scale and Platform Revolution. He has been ranked. as a leading global thinker for two consecutive years by Thinkers50, ranking among the top 30 emerging thinkers ...

213KB Sizes 0 Downloads 123 Views

Recommend Documents

Capacity Scaling in Mobile Wireless Ad Hoc Network with ...
... tends to infinity. This is the best perfor- ...... The next theorem reveals a good property of uniformly ..... 005); National High tech grant of China (2009AA01Z248,.

Capacity Scaling in Mobile Wireless Ad Hoc Network with ...
less ad hoc networks with infrastructure support. Mobility and ..... different complete proof of the upper bound is also available in [3]. But our approach is simpler.

Capacity Scaling in Mobile Wireless Ad Hoc Network ...
Jun 24, 2010 - Uniformly Dense Networks. Non-uniformly Dense Networks. Capacity Scaling in Mobile Wireless Ad Hoc. Network with Infrastructure Support.

Capacity Scaling in Mobile Wireless Ad Hoc Network ...
Keywords-Ad hoc wireless networks; hybrid wireless net- work; mobility; capacity .... A smaller m represents a more severe degree of clustering and vice versa.

Developing a Framework for Decomposing ...
Nov 2, 2012 - with higher prevalence and increases in medical care service prices being the key drivers of ... ket, which is an economically important segmento accounting for more enrollees than ..... that developed the grouper software.

A framework for consciousness
needed to express one aspect of one per- cept or another. .... to layer 1. Drawing from de Lima, A.D., Voigt, ... permission of Wiley-Liss, Inc., a subsidiary of.

procedure to obtain natural dualities for classes of algebras that fit into the general ...... So, a v-involution (where v P tt,f,iu) is an involutory operation on a trilattice that ...... G.E. Abstract and Concrete Categories: The Joy of Cats (onlin

a novel coherence measure for discovering scaling ...
Discovering Scaling Biclusters from Gene Expression Data 855 ... There are different types of biclusters which are defined as follows. 12 ...... and data mining.

AGILE: elastic distributed resource scaling for Infrastructure-as-a-Service
Elastic resource provisioning is one of the most attractive ... provide the same replicated service. ..... VM, causing duplicate network packets and application.