Network effects. It’s one of the most important concepts for business in general and especially for tech businesses, as it’s the key dynamic behind many successful software-based companies. Understanding network effects not only helps build better products, but it helps build moats and protect software companies against competitors’ eating away at their margins.
Yet what IS a network effect? How do we untangle the nuances of 'network effects' with 'marketplaces' and 'platforms'? What’s the difference between network effects, virality, supply-side economies of scale? And how do we know a company has network effects?
Most importantly, what questions can entrepreneurs and product managers ask to counter the wishful thinking and sometimes faulty assumption behind the belief that “if we build it, they will come” … and instead go about more deterministically creating network effects in their business? Because it's not a winner-take-all market by accident.
This lecture describes the Platform model or Two-sided Markets. Platforms serve multiple customer groups and benefit from network effects that take place with and between those groups. Businesses based on Platforms are able to adopt innovative pricing structures in which one side subsidizes another. When the marginal costs are near zero it can be practical to drop the subsidized price all the way to zero.
The 10 steps to product/market fit are:
1) Document your initial business plan or "Plan A".
2) Identify and tackle the riskiest parts of your business model first.
3) Understand that startups go through three stages - problem/solution fit, product/market fit, and scaling. Focus on validated learning through experiments and pivots before trying to optimize or scale.
4) Focus on the right key metrics like acquisition, activation, retention, revenue, and referrals that provide valuable insights before achieving product/market fit.
5) Formulate specific and testable hypotheses about what will drive customer acquisition.
6) Architect your product and processes for speed and
“Software is eating the world” said Netscape founder Marc Andreessen in his Wall Street Journal 2011 op-ed to describe how digital technology has transformed the world of business. We divide the disruption into two stages; efficient pipelines disrupting inefficient pipelines and platforms disrupting pipelines. Most Internet applications during the 1990s involved the creation of highly efficient pipelines—online systems for distributing goods and services that out-competed incumbent industries. Online pipelines tended to have very low marginal costs of distribution—sometimes as low as zero. This allowed them to target and serve large markets with much smaller investment. We are now in stage two where platforms disrupt pipelines. They bring news sources of supply to market, change value consumption by facilitating new forms of consumer behavior, change quality control through crowd sourced curation, and bring new market middlemen by aggregating fragmented markets.
Platform Revolution - Ch 01 Intro: How Platforms are Changing CommerceMarshall Van Alstyne
Content: (1) Evidence platforms beat products in value, recognition, speed (2) Platform definition (3) Firm implications
These slides provide complimentary course materials for the Ch 1 of Platform Revolution - How Network Markets are Transforming the Economy and How to Make Them Work for You. Final slides provide reading supplements and links to other chapters for industry and academia.
Review of the most important topics of an online business: Acquisition, how to improve targeting new users,
Retention, how to make them stay the most,
Monetization, how to earn money from them to feed the funnel
The document discusses platform business models and digital ecosystems. It defines a platform business model as one that builds value for multiple sides in a market by consolidating customers and simplifying processes. Examples of digital platform businesses include desktop operating systems, game consoles, and payment systems. The document outlines that platform businesses are built on network effects, and their openness is critical. It also discusses how platform models can generate profits through first and third party usage and build digital ecosystems through virtuous cycles of competition and collaboration.
This document analyzes the strategy of Netflix using various frameworks. It provides an overview of Netflix, including its founding in 1997 as a DVD rental service and transition to an online streaming platform. A PEST analysis identifies political, economic, social and technological factors. A five forces analysis examines the intensity of rivalry, threat of new entrants, bargaining powers of suppliers and customers, and threat of substitutes. A SWOT analysis outlines Netflix's strengths, weaknesses, opportunities, and threats. The document also includes a market analysis and identifies problems around high competition and recommendations around content creation and live sports streaming.
The document discusses social media trends for 2023. It notes that TikTok has cemented itself as the dominant platform and is rewriting industry rules by prioritizing organic content and participation. Organic and earned efforts are making a comeback as platforms like Facebook and YouTube see declining revenues and engagement. Brands are taking a more channel-agnostic approach and focusing on engagement and community building rather than uniform strategies across platforms.
This is the successful pitch deck of the Bettery company that Angel and Seed investors positively appreciate. The pitch deck has 15 key slides and 13 appendix slides that describe Bettery aspects in more detail. Bettery is a play-to-earn social betting platform to bet with friends and influencers using social media content, gamble-free & based on blockchain.
Lululemon faces challenges from a difficult holiday retail season and increasing competition from ecommerce retailers. However, new leadership, expanded product lines, and focus on direct-to-consumer sales provide opportunities for continued growth. The company aims to expand its store base and broaden its customer demographic while growing its ivivva brand. Lululemon has strong margins and cash flow, positioning it for long-term success if it can effectively address changing market conditions.
Presentation on Lean Analytics at MicroConf 2013. Understanding what metrics are the most value, when, for your type of business.
* What makes a good metric?
* Types of metrics (qualitative vs. quantitative, vanity vs. actionable, etc.)
* Lean Analytics framework
Shared a number of case studies: Airbnb, Buffer, ClearFit, OffceDrop and others.
This document provides an overview of the Business Model Canvas tool. It describes the 9 building blocks that make up the Business Model Canvas, including Customer Segments, Value Propositions, Channels, Customer Relationships, Revenue Streams, Key Resources, Key Activities, Key Partnerships, and Cost Structure. It then explains how to use the Business Model Canvas to map out a business model and identify relationships between different elements. The document also introduces other related tools like the Value Proposition Canvas and presents examples of how well-known companies like low-cost airlines and Skype have applied business model thinking.
Digital Class Journals is a communication platform that allows kids to upload photos of their schoolwork using iPads. Teachers then approve the submissions to create a digital class journal. Parents can engage with their kids around the schoolwork. School directors get increased visibility into class activities. The platform aims to increase parent engagement through digitally capturing kids' work, building pride and facilitating learning at home. It generates revenue through school subscriptions and additional parent services like printed portfolios and yearbooks.
Product Led Growth (PLG) is a go-to-market strategy that relies on product usage as the primary driver of acquisition, conversion and expansion. Learn why we're now in the end user era and how your org can adapt.
Platform Shift: How New Business Models Are Changing the Shape of IndustryMarshall Van Alstyne
Companies that can transform their traditional business models into network models will have a competitive advantage based on new insights into pricing, network effects, supply chains, and strategy. These principles show how dotcom companies like Airbnb, Amazon, Apple and Uber managed, in a relatively short time, to attract millions of clients worldwide. But they apply also to traditional product companies like Sony, shoe companies like Nike, and spice companies like McCormick. New business models help these companies extend existing transactions to new, associated products and services. Platforms beat products every time. This talk reveals the secret of Internet-driven platforms, why they happen, and what changes they imply.
Platform Revolution - Ch 02 Network Effects: Power of the PlatformGeoff Parker
Contents: (1) Two sided market definitions (2) How demand- and supply-side economies of scale differ (3) Free goods: when and why to subsidize one side or the other (4) How switching and homing costs affect winner take all outcomes.
These slides provide course materials that complement the second chapter of Platform Revolution: How Networked Markets are Transforming the Economy and How to Make Them Work for You. The final slides provide additional reading suggestions for industry and academia.
How to add social share buttons to pdf documentsJan Kearney
Make it easy for people to share your PDF reports, ebooks, whitepapers and more. Add Facebook, Twitter, LinkedIn and Google Plus share icons and links.
This tutorial walks you through how to add social share buttons to your PDF reports.
There is no point in drawing a distinction between the future of technology and the future of mobile. They are the same. In other words, technology is now outgrowing the tech industry.
by Benedict Evans. Please see this link for full description, slides, AND version with talk track: http://a16z.com/2016/12/09/mobile-is-eating-the-world-outlook-2017/
Habits at Work - Merci Victoria Grace, Growth, Slack - 2016 Habit SummitHabit Summit
Presented at the 2016 Habit Summit at Stanford (see: www.HabitSummit.com)
Merci Victoria Grace leads the Growth team at Slack.
Prior to joining Slack, she started a venture-backed game company, designed The Sims Social at Electronic Arts, and worked at a range of consumer, mobile and enterprise startups.
Here she shares insights on putting "Habits to Work at Work".
Looking to scale something up? Depending on how you're going after your market/ acquiring users, you may need to build a sales organization that's optimized for a top-down or bottom-up sales process (or perhaps both).
Watch the video overview at http://a16z.com/2015/03/06/go-to-market-bootcamp/ and then check out this slide deck, which shares some concrete tips and tools for accelerating time to market -- from the go-to-market experts at a16z, led by 'sales savant' Mark Cranney.
Because selling to enterprises is a lot like getting a bill passed through Congress: it can get stuck. And getting stuck -- or going down the wrong path -- can mean death to startups in a competitive market. Here's how to avoid that.
10 Best Practices of a Best Company to Work ForO.C. Tanner
What does it take to be named a Best Company to Work for by FORTUNE magazine? For starters, a winning culture, collaboration, and creating an environment for learning and growth. Take a look at these slides for more ideas!
In this update of his past presentations on Mobile Eating the World -- delivered most recently at The Guardian's Changing Media Summit -- a16z’s Benedict Evans takes us through how technology is universal through mobile. How mobile is not a subset of the internet anymore. And how mobile (and accompanying trends of cloud and AI) is also driving new productivity tools.
In fact, mobile -- which encompasses everything from drones to cars -- is everything.
This document summarizes a presentation about using Piwik, an open source web analytics platform, in enterprise environments. It discusses Piwik's advantages over Google Analytics for enterprises, including data ownership and privacy compliance. It provides examples of how Piwik is used by enterprises for intranet tracking, data warehousing integration, and more. The presentation includes case studies of how governments and businesses like HP and a TV company use Piwik to meet their analytics needs. It also covers some of the challenges of working with enterprises and going global with a web analytics product.
Piwik elasticsearch kibana at OSC Tokyo 2016 SpringTakashi Yamamoto
1. This document discusses using Piwik, fluentd, elasticsearch, and kibana to analyze Piwik web analytics data. Fluentd is used to collect and parse Apache access logs from Piwik and forward the data to elasticsearch for storage and indexing. Kibana queries and visualizes the elasticsearch data.
2. Instructions are provided for installing fluentd, elasticsearch, and building RPM packages for kibana on Red Hat 6 and 7. Configuration details explain how to setup fluentd input, filtering, and output plugins to ingest Piwik log data from Apache logs and store it in elasticsearch.
3. The elasticsearch and kibana installations provide visualization of the Piwik web analytics data for
The Nintendo Wii video game console was created to focus on motion-based gameplay, representing a revolution in the industry. It was developed under Shigeru Miyamoto and first released in Japan in 2006. Within a week of its US release that November, over 600,000 units had been sold. The Wii remote allows players to control games using physical movement. Its active games and accessories like Wii Fit have helped promote physical activity for both young and elderly. Over 24 million units had been sold worldwide by 2008, generating large profits for Nintendo. However, some injuries have also resulted from enthusiastic gameplay.
The responsibilities of EASA include to:
Giving advice for the drafting of EU legislation, implementing and monitoring safety rules (including inspections in the member states),
Giving type-certification of aircraft and components as well as the approval of organizations involved in the design,
Authorizing foreign operators,
Manufacture and maintenance of aeronautical products.
Europe & Israel 1Q17 Venture Capital ReviewGil Dibner
A review of Venture Capital investment trends across Europe and Israel in 1Q17. Breakdowns by country, sector, stage, business model, and target market.
A data-rich dive into the state of education technology from the leading and most active edtech fund. We focus here on school-based education technology with case studies of emerging frontier tech.
The document discusses Nintendo's change in strategy from competing in the existing video game market to creating a new market with the Wii console. Nintendo was losing market share to competitors and focused the Wii on a more casual audience by introducing innovative motion controlled gameplay, game packs for fitness and entertainment, and targeting a broader consumer base beyond traditional gamers. This new "blue ocean" strategy helped Nintendo grow its market share and sales.
Beyond Personalisation: The Customer Conversation with SitecoreSitecore
Key takeaways include:
- Aligning your personalisation and content strategies.
- How Sitecore’s personalisation compares to retrofit or overlay products and why that matters for omnichannel optimisation.
- How aggregators and disruptors win with personalisation.
- Measuring the impact of personalisation.
- Low-effort high-impact personalisation.
- Goals? Rules? How to understand the difference and when to use them!
Today, I’m happy to release a data-driven review of VC investment trends in Europe and Israel in 2016. I’ve tried to put some new and useful data points into the deck, so let me know what you think of the new stuff. And please let me know what else you think I should be tracking and showing in the deck next time.
Some key highlights:
Overall, investment volume was up in 4Q, but still below 2Q’s record high
Total VC investment volume into Europe and Israel in 2016 was $14.5B, up from $12.1B in 2015, and increase of 20%
There were six mega-deals (over $100M) in 4Q, and 15 in total in 2016
Excluding the megadeals, investment volume declined in 4Q, the second quarterly decline in a row
Israel saw more VC investment activity than any other country in Europe with $3.9B in 2016. The UK and Germany were next with $3.0B and $2.3B, respectively
US VCs invested in around 11% of European/Israeli venture rounds. Israel, the UK, and Germany led in terms of US VC participation
Fintech was the most frequently funded vertical, with 178 investments. Marketing was second with 109
The categories that showed the most growth in frequency from 2015 to 2016 were Imagining (+400%), Agtech (+475%), and Automotive (+1100%)
You’ll find all that and more in the 61-page report
Introductory workshop to content modelling and personalisation. Learn how to design content for deliver contextually relevant, personalised experiences.
Europe & Israel 3Q17 Venture Capital ReviewGil Dibner
Today, I’m happy to release a data-driven review of VC investment trends in Europe and Israel in the third quarter of 2017.
Some key highlights:
Overall, dealflow volume continues to hit new highs
Total VC investment volume into Europe and Israel was $5.5B in 3Q17 and $5.8B in 2Q17 (a record)
The number of deals peaked in 1Q17 at over 500 and has been trending gently downward, but still at very high levels.
Deal size is trending up again
In the past two quarters, there were 13 mega-deals (over $100M)
There is still zero evidence that Brexit has impacted the UK venture environment. Both deal numbers and investment volumes are up since the Brexit vote
In both 3Q and 2Q, the UK led the region in investment volume. Israel was in second place
France continues to boom, with 4 quarters of consecutive growth in investment volume
After six quarters of consecutive growth, Swedish VC investment volume declined in 3Q
Fintech was the yet again most frequently funded vertical, with 48 investments in 3Q. Marketing was second (31) and Electronics tied with Lifestyle for third (26)
The categories that showed the most growth in frequency from 3Q16 to 3Q17 were Entertainment (+171%), Health (+133%), and Employment/HR (+111%)
The categories that showed the most decrease in frequency from 3Q16 to 3Q17 were industrial (-50%), communication (-46%), and Fashion and Adtech (both down 43%).
You’ll find all that and more in the 52-page report, so download it here or view it on Slideshare below. I’d recommend also taking a look at the more detailed 2016 year in review report, which can be found here.
As always, I welcome your comments, questions, and feedback. Please let me know if there are additional slices of data you think I should add into the report.
Startup Metrics for Pirates (SF, Jan 2010)Dave McClure
The document discusses metrics for startups, focusing on the AARRR framework of Acquisition, Activation, Retention, Referral, and Revenue. It provides examples and recommendations for measuring key metrics at each stage, including number of visitors, time on site, page views, and conversions. The document emphasizes testing marketing channels and optimizing for user experience and conversion rates through iteration and A/B testing.
Digital Evolutions: Startups, Platforms and EcosystemsSimone Cicero
Digital platforms and ecosystems are experiencing exponential growth and disruption. Understanding their dynamics is important for startups, platforms, and ecosystems. Platforms go through phases from pioneers seeking opportunities, to settlers addressing demand, to town planners optimizing operations. Innovation occurs both on top of and below interfaces as value increases. Designing platforms requires understanding value exchanges between roles to overcome challenges like liquidity. Growth involves working like a startup, nurturing relations like a platform, and always disrupting yourself like an ecosystem.
This document discusses using Hypersoft OmniAnalyser to analyze social networks and collaboration within an organization. Some key benefits of social network analysis mentioned include promoting transparency, preventing isolation, promoting mentoring, and educating employees about collaboration. The solution can analyze communication data from various sources to discover communities, measure metrics like degree and betweenness, and provide insights about leaders, followers, and how collaboration tools are used.
This document discusses the economics of open source software. It explains that open source software is not just about sharing or giving things away for free, but is actually closely tied to capitalism. Open source software development spreads costs and risks across many contributors. Companies that adopt open source can benefit from lower costs and more customized software that is improved through peer review. The open source model is economically viable and may be applicable to other fields beyond just software.
Discovering Influential User by Coupling Multiplex Heterogeneous OSN’SIRJET Journal
This document proposes a framework for modeling and analyzing influence diffusion in multiplex online social networks (OSNs). It introduces coupling plans to represent how data spreads across overlapping users in multiple OSNs. Specifically, it proposes both lossless and lossy coupling plans to map multiple networks into a single network. Extensive tests on real and synthetic datasets show the coupling plans can effectively identify influential users by considering their roles across multiple OSNs. The framework provides insights into influence propagation in multiplex networks and can solve the minimum cost influence problem by exploiting algorithms for single networks.
Testing Vitality Ranking and Prediction in Social Networking Services With Dy...reshma reshu
Social networking services have been prevalent at many online communities such as Twitter.com and Weibo.com, where millions of users keep interacting with each other every day. One interesting and important problem in the social networking services is to rank users based on their vitality in a timely fashion. An accurate ranking list of user vitality could benefit many parties in social network services such as the ads providers and site operators. Although it is very promising to obtain a vitality-based ranking list of users, there are many technical challenges due to the large scale and dynamics of social networking data .
This document discusses social network analysis (SNA) and its application to analyzing relationships between organizations in the machine-to-machine (M2M) sector. It defines key SNA concepts like nodes, ties, and centrality measures. It also outlines benefits of SNA like identifying influential actors, and limitations such as difficulties collecting all relationship data. Visualizations of sample M2M networks show operators have highest degree centrality while application providers have highest closeness centrality. SNA provides a framework for describing complex networks but has limitations like not capturing relationship attributes or motives.
The document discusses the need for new development tools to support the future Internet of Things platform. It notes that as everyday objects become connected, they will generate vast amounts of data and behave more like social networks. New tools are needed to handle the scalability demands and allow for mashups and processing of schema-less sensor data. Cloud computing will also be important for dynamically obtaining resources to serve fluctuating request levels from billions of connected devices.
Sucess of Open Source - Steven Weber (Book Review)Ritesh Nayak
The document summarizes the history and success of open source software. It discusses how open source software is governed by licensing that allows anyone to access, modify, and distribute source code. It then examines the social and economic factors that motivate individual participation in open source projects and allow for coordination between large numbers of developers. Finally, it explores some common business models that generate economic returns from open source software while still sharing source code openly.
Various Types of Vendors that Exist in the Software EcosystemPallavi Srivastava
This document discusses various types of vendors that exist in the software ecosystem. It begins by defining software ecosystems and describing different roles within software companies. It then provides background on the major types of vendors: hardware vendors, software vendors, service vendors, telecommunications vendors, and cloud vendors. For each type, it lists and describes the top 10 vendors in that category. The document concludes by discussing advantages and disadvantages of cloud computing.
An improvised model for identifying influential nodes in multi parameter soci...csandit
Influence Maximization is one of the major tasks in the field of viral marketing and community
detection. Based on the observation that social networks in general are multi-parameter graphs
and viral marketing or Influence Maximization is based on few parameters, we propose to
convert the general social networks into “interest graphs”. We have proposed an improvised
model for identifying influential nodes in multi-parameter social networks using these “interest
graphs”. The experiments conducted on these interest graphs have shown better results than the
method proposed in [8].
Chat Application using Java which is based on Socket Programming java , there is Software managed (SEPM) file ppt based for gudence on project using life cycle of project ,like Feasibility study and steps of Project life cycle that how 1 software faces the phases of development . socket based programming in java ,based on client server technology .
Elements of Innovation Management in Computer Software and ServicesMichael Le Duc
IAMOT 2000, The Ninth International Conference on Management of Technology
February 20-25, 2000, Miami, Florida, USA. Track 4: Industrial Innovation see http://www.iamot.com/
Metalayer now Colayer - Part 3/3 - full PresentationMarkus Hegi
The document discusses the need for knowledge management and collaboration technologies that go beyond email. It proposes a "metalayer" approach using XML to create personalized portals that aggregate content from different communities. This allows integrating internal and external communities to create a global network for sharing knowledge across organizational boundaries.
The document provides an overview of a 7-step process for building an information system. The 7 steps are: 1) Identify and list stakeholders, 2) Identify and list actors, 3) Identify and list use cases, 4) Identify and list scenarios, 5) Identify and list steps, 6) Identify and list classes/objects, and 7) Manage work products. It describes each step in the process, including defining stakeholders, actors, use cases, scenarios, and mapping analysis to design. The process emphasizes discovery, iteration, and developing a shared understanding between stakeholders.
Check out Treesaver. An open-source solution for nonprofits who are tired of their static reports, plain pdfs, or printed materials and want to present their stories in a mobile optimized way that's friendly across devices and platforms.
How to measure Social Media conversations - an overview. Detailed notes are on Broadstuff over here http://broadstuff.com/archives/2144-Measuring-Social-Media-Conversations.html
ENG 102 Unit Six Page 1 of 1 ENG 102 Composition II .docxSALU18
ENG 102 Unit Six Page 1 of 1
ENG 102 Composition II
Unit Six
Drafting Research
Although a draft, this is a formal piece to your final research and it must
appear as such.
Your draft should represent a full essay and not scattered thoughts. It must
include and be reflective of:
9�An explicit claim
9�Proper citation—in-text and end of text (MLA)
9�Varied evidence throughout incorporating blended writing modes (cause and
effect, description, etc.)
9�Revised writing
Heading:
o Name, date, course #, and instructor’s name in the upper left hand corner
o Label the page: “Draft, Final Research” include your working title
o Double spaced, typed, size 12 font in proper paragraphed form
o Aim for roughly 5-7 pages
o Proofread carefully
Find an article that focuses on managerial advice or trends relating to Cloud Computing, Open Source Software, Service-Oriented Architecture (SoA), Enterprise systems, ERP Software as a Service (SaaS), or;
Look for an article that covers user interface design and/or trends/advances in the user interface (speech, gesture recognition, mobility, etc.), or;
Find an article that discusses new system development or implementation in an organization--Success or Failure; or
Information Systems: A Manager's Guide to Harnessing Technology, v. 3.0
Digital All Access Pass
by John Gallaugher
11.1 Introduction
Learning Objectives
1. Recognize the importance of software and its implications for the firm and strategic decision making.
2. Understand that software is everywhere; not just in computers, but also cell phones, cars, cameras, and many other technologies.
3. Know what software is and be able to differentiate it from hardware.
4. List the major classifications of software and give examples of each.
We know computing hardware is getting faster and cheaper, creating all sorts of exciting and disruptive opportunities for the savvy manager. But what’s really going on inside the box? It’s software that makes the magic of computing happen. Without software, your PC would be a heap of silicon wrapped in wires encased in plastic and metal. But it’s the instructions—the software code—that enable a computer to do something wonderful, driving the limitless possibilities of information technology.
Software is everywhere. An inexpensive cell phone has about one million lines of code. [1] Ford automobiles actually have more lines of code than Twitter and Facebook combined. [2] Software might even be in grandpa. The average pacemaker has between 80,000 and 100,000 of code. [3] In this chapter we’ll take a peek inside the chips to understand what software is. A lot of terms are associated with software: operating systems, applications, enterprise software, distributed systems, and more. We’ll define these terms up front, and put them in a managerial context. A follow-up chapter, Chapter 12 "Software in Flux: Open Source, Cloud, and Virtualized and App-driven Shifts" “Soft ...
The document provides an overview of text-based games and includes a sample output from a text-based dragon adventure game. Text-based games preceded modern video games, using only text for interaction through command line prompts. The sample output shows the player moving between rooms and interacting with items to complete the goal before encountering the villain, demonstrating how these early games worked without graphics. It also provides guidance for a class project to design and develop a original text-based game.
1 IT 140 A Mini History of Text-Based Games TextSilvaGraf83
1
IT 140 A Mini History of Text-Based Games
Text-based games were the predecessor to the reality-based video games we play today. They were
"interactive fiction" where words came to life as players read text and made decisions about what to do.
These text-based games simulated environments where players used text commands to control their
characters and influence the gaming environment.
Imagine a current action-adventure video game where, instead of using a controller or touchscreen to
give your character directions, you enter text on a command line. There are no graphics on the screen,
forcing you to use your imagination. Commands you enter might be “open door”, “go west”, or “fight
troll”. These commands change the way the story plays out.
While it may be hard to imagine a video game without any videos, these text games were very popular
in the 70s and 80s. Many programmers and computer technicians played role-playing board games, like
Dungeons and Dragons, with their friends. A text-based game allowed them to take their adventures to
the digital realm. They could play their games on the mainframes at work, submitting commands with a
teleprinter and receiving the output on paper.
T100S Teleprinter by Jens Ohlig under CC BY-SA 2.0
https://commons.wikimedia.org/wiki/File:T100S_teleprinter.jpg
https://www.flickr.com/people/[email protected]
https://creativecommons.org/licenses/by-sa/2.0/deed.en
2
Eventually, monochrome monitors allowed players to see their input and output in real time, right
before their eyes. Players were able to enjoy playing Lunar Lander and Star Trek using displays like the
following:
GT40 Lunar Lander by Brouhaha under CC BY-SA 3.0
Star Trek Text Game by James Gibbon under CC BY-SA 3.0
You can still find playable versions of these games online, such as Lunar Lander, Star Trek, and Zork.
They will help you see how far game development has come. (Note: Links may change over time. Search
for the game name and “simulator”.)
In this class, you will have the opportunity to create your own version of a text-based game. You will be
able to see your code come to life as it becomes interactive. Through the use of conditionals and loops,
you will be able to guide adventurers through your world in the same way these early text-based games
did several decades ago.
https://commons.wikimedia.org/wiki/File:GT40_Lunar_Lander.jpg
https://en.wikipedia.org/wiki/User:Brouhaha
https://creativecommons.org/licenses/by-sa/3.0/deed.en
https://commons.wikimedia.org/wiki/File:Star_Trek_text_game.png
https://en.wikipedia.org/wiki/User:Jamesgibbon
https://creativecommons.org/licenses/by-sa/3.0/deed.en
http://www.lunarmissionsimulator.com/
http://mtrek.com/play-now/
http://www.web-adventures.org/cgi-bin/webfrotz?s=ZorkDungeon
3
References
McIntosh, J. (2018, July 20). A brief history of text-based games and open source. Opensource.com.
https://opensource.com/article/18/ ...
A Beginner's Guide to Carbon Credit Standards for Indian IndustryMurugesh Siva
This book, "A Beginner's Guide to Carbon Credit Standards for Indian Industry," offers a comprehensive exploration of the mechanisms that drive emission reductions and sustainable development in India. It covers key standards such as the Clean Development Mechanism (CDM), the Gold Standard, the Climate Action Reserve (CAR), and Indian Standards, providing in-depth explanations and real-world case studies. The book demystifies the complex processes involved in carbon credit projects, from project design and implementation to monitoring and certification.
Each chapter is meticulously crafted to answer essential questions like what these standards are, why they are important, and how they work. By examining successful projects across various sectors, the book showcases the tangible benefits of carbon credits, including economic, social, and environmental gains. It also delves into financing mechanisms, offering insights into how projects can be funded and managed effectively.
This guide is an invaluable resource for industry professionals, policymakers, students, and anyone interested in understanding the potential of carbon credits to combat climate change and promote sustainable growth. With contributions from experts and practitioners, the book not only informs but also inspires action towards a more sustainable future. Whether you're new to the concept of carbon credits or looking to deepen your understanding, this book provides the knowledge and tools needed to engage with this vital aspect of environmental stewardship.
A Proven Affiliate Marketing Strategy That Works in 2024.pdfTarik Badri
Gone are the days of generic promotions and cookie-cutter affiliate websites. Today's successful affiliates adopt a strategic approach focused on delivering targeted solutions to specific audiences. By understanding the principles of modern affiliate marketing, individuals can position themselves for long-term success in this competitive industry.
AI-Powered Affiliate Marketing_ Your Escape Plan (1).pdfEsther White
AI Powered Affiliate Marketing: Your Escape Plan
The digital age has ushered in a new era of economic opportunity, where individuals can break free from traditional employment and build thriving online businesses. At the forefront of this revolution is affiliate marketing, amplified by the power of artificial intelligence (AI). With Google reporting a staggering $15.7 billion in affiliate marketing revenue, it's clear that this industry is a goldmine waiting to be tapped.
The Allure of Affiliate Marketing
Affiliate marketing is a performance-based marketing strategy where individuals or businesses promote other companies' products or services in exchange for a commission on each sale or lead generated. It's a win-win for everyone involved: the affiliate earns a commission, the merchant gains exposure, and the customer discovers new products.
The AI Advantage: Your Secret Weapon
AI is revolutionizing the affiliate marketing landscape, making it more accessible and profitable than ever before. By automating tasks, providing data-driven insights, and personalizing customer experiences, AI empowers affiliates to achieve unprecedented success.
Efficiency and Automation: AI tools can handle routine tasks like content creation, social media management, and email marketing, freeing up your time to focus on strategic growth.
Data-Driven Decision Making: AI analyzes vast amounts of data to identify trends, optimize campaigns, and predict customer behavior.
Personalized Customer Experiences: AI helps deliver tailored recommendations, increasing conversions and customer loyalty.
Scalability: AI enables you to expand your business rapidly without compromising quality.
The Rise of AI Affiliate Millionaires
A new breed of entrepreneurs is emerging – young, tech-savvy individuals who are leveraging AI to build million-dollar affiliate empires. These digital pioneers have demonstrated that age is no barrier to success in the digital age.
Real-Life Success Stories
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2. What are
network effects?
Properties, terms,
and laws of
networks
Strategies for
building
network effects
What aren’t
network effects?
Case studies of
companies with
network effects
3. Simply put, a network effect* occurs when a
product or a service becomes more valuable to
its users as more people use it
*also known as:
demand-side economies of scale
4. Because understanding network effects helps build
better products and businesses
Especially since network effects are the key
dynamic behind many successful
software-based companies
Why does this matter?
5. Create barriers to exit for existing users and
barriers to entry for new companies
(help build moats)
Protect software companies from competitors’
eating away at their margins
Can help create or tip winner-take-all markets
Network effects
6. What are
network effects?
Properties,
terms, and laws
of networks
Strategies for
building network
effects
What aren’t
network effects?
Case studies of
companies with
network effects
7. Networks, which are basically just a set of nodes
connected by links, have various properties
Some of those relevant properties include:
1. Whether the nodes are homogeneous or heterogeneous
2. Their type of clustering and degree of connections
3. Directionality of those connections
4. Whether they have (or are) complements
Putting the ‘network’ in network effects
8. 1. Homogeneous or heterogeneous?
Homogeneous
Composed of similar types of nodes
Heterogeneous
Skype is an example of a homogeneous
network where most of the value is
derived from a single class of users, all
interested in placing a phone call
Composed of different types of nodes
OpenTable is an example of a
heterogeneous network with two distinct
categories of participants: one side is
restaurants, the other side is diners
Image source (Skype): http://letsbytecode.com/security/skype-the-phantom-menace/
9. 2. Degree of connections and type of clustering
Source: Albert-László Barabási, Linked: The New Science of Networks
Source (original chart): https://griffsgraphs.wordpress.com/tag/clustering/
Degree: Measures number of
connections to a single node
Clustering coefficient:
Measures degree to which nodes
in a graph (e.g., social graph,
interest graph, intent graph, etc.)
cluster together
Type of cluster: Can range from
hub-and-spoke (star) to
connected (clique)
Example of Facebook friends connections clustering
(high school, college, significant other’s, etc. clusters)
10. Zooming in a bit further on those terms
Degree
Number of connections
to a single node
Homogeneous
Type of Cluster
The resulting CC(A) represents the
fraction of possible interconnections
between the neighbors of A
0 ≤ CC(A) ≤ 1
[For operating systems, Microsoft
Windows is regarded as a central hub,
with 85% share of the network!]
Node A has 4 connections,
therefore its degree is 4
Clustering Coefficient (CC)
How likely are two nodes that are
connected part of a highly
connected group of nodes?
If A is the node,
and d is the degree A = 4,
then n is the # of links between
neighbors (blue dots) of A = 1
For example:
CC(A) = 2*N/D(D-1) = 1/6
In hub-and-spoke or star networks, the fittest
node (central hub) grabs all connections,
leaving very little for the rest of the nodes
In clique networks, node A is connected
to its neighbors and all those neighbors
are connected to each other
cliquehub-and-spoke
Source: https://youtu.be/K2WF4pT5pFY
See also: Albert-László Barabási, Linked: The New Science of Networks
11. 3. Connections: Unidirectional or Bidirectional?
Friends
Facebook, for example, is one place where
connections tend to be bidirectional
With bidirectional or two-way friending, you are
more likely to have balanced connections:
Follower
Twitter, for example, is one place where
connections can more easily be unidirectional
Unidirectional or one-way following leads to
asymmetrical connections (e.g., the asymmetric
follow)
Note: You could still have balanced connections here where
you both or neither of you follow each other
• You are friends
• You are not friends • People follow you, but you
don’t follow them back
• People don’t follow you, but
you do follow them
12. 4. Complementary Networks
More usage of the
MS Windows
operating system
More usage of the
MS Office suite of
applications
Increase in usage of one product
by a set of users reinforces and
increases the value of a
complementary (but separate!)
product, which in turn, increases
the value of the original
Source: http://cdixon.org/2009/08/25/six-strategies-for-overcoming-chicken-and-egg-problems/
T W O P R O D U C T S A R E C O M P L E M E N TA RY W H E N T H E Y A R E S E PA R AT E B U T A R E M O R E U S E F U L T O U S E R S
T O G E T H E R
operating systems have strong
network effects of their own (via
developers) as do productivity apps
(via file formats), but in this case they
also reinforce each other
13. Besides those properties, networks
(more specifically, communication networks)
can also exhibit the following laws:
14. 3 common laws for assessing the
value of communication networks
Sarnoff’s law
Value of a network is
proportional to the number
of viewers
Broadcast
Yahoo
1
Value of a network is
proportional to square
of number of connected
users
Peer to Peer
Facebook
Metcalfe’s law
2
Value of a group-forming
network is proportional to
number and ease with which
groups form within it (subgroups
grow faster than sheer number
of P2P participants)
Group Forming
Slack or WhatsApp groups
Reed’s law
3
Sources: Andrew Odlyzko et al http://www.dtc.umn.edu/~odlyzko/doc/metcalfe.pdf
15. Facebook is a classic example of Metcalfe’s law
Every new user connecting to
other peers in the network (peer-
to-peer) non-linearly increases
the number of connections
Source: Bob Metcalfe/ IEEE Computer 2013, via Bill Krause
16. And finally, let’s define some commonly used
terms in the context of network effects
17. Terms and definitions for our purposes
Network is a group of interconnected people (social network) or system of things
(telephones, printers to computers)
Marketplace is a network where money/transactions flow between two or more
sides with distinct (i.e., heterogeneous) groups of users on each side; a successful
marketplace is where supply and demand are attracted to the same place
Platform is a network of users and developers; the multi-sided feedback loop
between those users, developers, and the platform itself creates a flywheel effect
increasing value for each of those groups. It can also be thought of as a network
that can be programmed, customized, and extended by outside users—often meets
needs and creates niches not defined by its original developers at the outset
18. Examples
Platform
Operating systems
Messaging app like
WeChat
Marketplace
Online auction
marketplace
Dating sites (can be
heterogeneous or
homogeneous!)
Network
Social network
Telephone network
Office printer &
computer network
19. So why do any of these details—topologies, other
properties, precision of terms—matter to startup founders?
20. Because these details suggest what questions to ask
(e.g., is this network defensible?) and what the
corresponding entrepreneurial strategy should be
21. For example, if it’s X, then ask Y:
Platform
Will the market we’re eyeing
eventually be served by a
single platform and will it be
shared (Ethernet) or will it be
a fight for proprietary control
(MS vs Apple)?
Marketplace
How do we build liquidity in the
marketplace/solve the
chicken-and-egg (which side
comes first) problem?
Which is the money side vs
subsidy side of the marketplace?
Network
What should the entry point be (to
build a network effect)?
What are the growth
levers/tactics/hacks to get to critical
mass?
What’s the critical mass inflection
point (at which a network
effect occurs)?
How do you drive engagement?
How do we take advantage of
irregular topologies to find clusters
and sub clusters?
22. Most of these questions really boil down to
What’s the initial growth lever or tactic
to help us get to scale?
Another way of thinking about this is:
What’s the deterministic (not-so-random) solution
to the bootstrapping problem?
These questions help counter the wishful thinking
and sometimes faulty assumption behind the belief that
if we build it, they will come
23. We’ll share specific strategies for
attacking all these questions
First, let’s look at some examples
24. But there’s one more question/definition
to know before doing so…
25. Is the product single or multiplayer mode?
Source: http://cdixon.org/2010/06/12/designing-products-for-single-and-multiplayer-modes/
Single Player Mode
The product has immediate
utility for a single user
Examples
Multiplayer Mode
The product has no utility
for a single player
(especially true for communication
products—a phone is useless without
someone at other end)
Examples
(early days):
tool to store
private photos
(early days):
bookmark restaurants
you’ve been to need connections
to other users to
make calls
messaging for teams
26. Note: You can sometimes have both
single and multiplayer mode for a single product
Single player mode is more powerful when accompanied by an initial
‘hack’ or other bootstrapping of early network growth.
(e.g., Instagram’s cool photo filters was a way to post photos
on Twitter before there was enough critical mass)
Single player mode can also help with adoption in the early stages
of a product, when network effects aren’t strong enough yet
come for the tool, stay for the network.
(e.g., Medium offering a beautiful publishing tool before
it built its network of people and ideas)
27. What are
network effects?
Case studies of
companies with
network effects
Strategies for
building network
effects
What aren’t
network effects?
Properties, terms
and laws of
networks
28. Facebook
T H E U LT I M AT E C A S E S T U D Y I N N E T W O R K E F F E C T S
Started off as a social
network (peer to peer
connections)
Became a platform
(with developers)
Has elements of a
marketplace
(users/advertisers,
Instant Articles)
29. So what led to network effects for Facebook?
Mode/product value Growth tactic Engagement trigger Network effects
Began as online student
directory with information
that was immediately useful
even to a single player
(user)
Became a way for college
students in courses and
clubs to connect with other
(multiplayer mode)
Accessed the entire
Harvard directory early
on; critical in driving
early adoption
And because the product
had inherent virality, it
spread from one user to
another as an organic
consequence of use
Identified early on that
connecting a new user to
10 friends within 14 days
of sign up was critical to
improving retention
So they used email
contact imports,
suggested friends and
embedded widgets to
drive that engagement
Continued tweaking
product (relationship
status, timelines, etc.) to
get everybody to join and
stay on board
So made sure there was
an increase in usage
even as the number of
their users grew
C O R R E S P O N D I N G Q U E S T I O N S : W H AT ’ S T H E E N T RY P O I N T ? W H AT A R E T H E G R O W T H L E V E R S ?
W H AT ’ S T H E C R I T I C A L M A S S I N F L E C T I O N P O I N T ? H O W D O W E D R I V E E N G A G E M E N T ?
Sources: Mark Zuckerberg interview with James Breyer, https://youtu.be/WA_ma359Meg
Chamath Palihapitiya interview https://youtu.be/raIUQP71SBU
30. By the numbers: Early growth as key (but not sole) indicator
1 6 12
58
145
360
608
845
0
100
200
300
400
500
600
700
800
900
2004 2005 2006 2007 2008 2009 2010 2011
(millions,year-end)
MAUs
Founded at
Harvard
800+ college
networks
FB mobile
and share
on partner
sites
Launched
platform
with
developers
and apps
Introduced
chat
Introduced
Like button
and
payments
Introduced
Graph API
for easy
integration
Introduced
Timeline
Source: Facebook S-1
31. C O N S TA N T LY R O L L E D O U T N E W F E AT U R E S A N D O P T I M I Z E D T H E S I T E T O I N C R E A S E E N G A G E M E N T, D A I LY
0
200
400
600
800
1000
Mar-09 Jun-09 Sep-09 Dec-09 Mar-10 Jun-10 Sep-10 Dec-10 Mar-11 Jun-11 Sep-11 Dec-11
Inmillions
Hyper growth in DAUs and MAUs pre-IPO
MAUs
DAUs
By the numbers: Focus on daily usage to help grow network
Source: Facebook S-1
32. By the numbers: A sign of network effects
I N C R E A S E I N U S A G E E V E N A S N U M B E R O F U S E R S G R E W
45% 47%
51% 54% 53% 53% 54% 55% 56% 57% 57%
0%
20%
40%
60%
80%
100%
Mar-09 Jun-09 Sep-09 Dec-09 Mar-10 Jun-10 Sep-10 Dec-10 Mar-11 Jun-11 Sep-11 Dec-11
DAUS/MAUs(%)
Source: public company data
33. While many social networks today start off launching to everyone,
Facebook’s entry strategy was taking a clustered approach (get Harvard)
before rolling it out to other clusters (Stanford, etc.)
More importantly, they were focused on engagement, not just growth
Contrary to popular belief, Facebook kicked off offering immediate utility in
single player mode (the online school directory), but people started
connecting with each other (multiplayer mode) right away too
Some takeaways
34. Airbnb
T W O S I D E D M A R K E T P L A C E W I T H O V E R L A P I N B O T H S I D E S
More
guests
More
hosts
Network effect from both
sides of the network
More hosts attract more
guests and vice versa
More hosts = more
availability for guests
More guests = more
business/$ for hosts
35. A unique aspect of some peer-to-peer
marketplaces like Airbnb is overlap between
supply (hosts) and demand (guests)
In other words, guests also become hosts and
hosts also become guests!
36. How did Airbnb achieve its network effects?
Critical mass on
both sides
Network effects
Airbnb capitalized on an
existing problem/need—
very limited or expensive
hotel space
Turning homes to lodging
provided immediate value
to users: 30%-80%
cheaper than hotels and
highly differentiated type of
inventory (less sterile and
more personal/social than
hotel rooms)
As more guests stayed in
more places (demand), more
hosts got more business and
more hosts offered their
places which in turn created
more supply for guests
As measured by number
of room nights
Mode/product value
Airbnb targeted cities with
sold-out events and
constrained hotel supply (such
as during the Democratic Party
national campaign or World’s
Cup) with traditional
marketing and other methods
to advertise its alternative
Growth tactics
Launched photography
services to make offerings
more appealing to guests
Also added ability for mutual
social connections to see who
else had stayed to help build
trust in the marketplace
Critical mass Network effects
C O R R E S P O N D I N G Q U E S T I O N S : H O W T O B U I L D L I Q U I D I T Y / S O LV E T H E C H I C K E N - E G G P R O B L E M ?
37. By the numbers: There was no viral growth in the early days.
launched photography
program
launched social
connections
# of new listings (early days of Airbnb: March 2008 to May 2011)
# of transacting users (early days of Airbnb)
traditional marketing around
targeted events
Note: Y-axes masked for confidentiality
38. But then there was a sign of network effects
I N C R E A S E I N N U M B E R O F G U E S T S T H AT S TAY E D E A C H Y E A R , C R E AT I N G M O R E S U P P LY A N D M O R E D E M A N D
-
2
4
6
8
10
12
14
16
18
2008 2009 2010 2011 2012 2013 2014
Millions
took nearly 36 months to
build sufficient liquidity
and to start seeing signs
of network effects
Source: Company data
39. Airbnb focused early features on building the demand side and in a
marketplace, supply will always go to where the demand is
(and will stay if you help grow their business)
Note: a product or service does not necessarily have to have viral
growth to lead to network effects
Traditional marketing methods—branding, design,
targeting, direct advertising—can help
Trust and safety is paramount in all marketplaces
Some takeaways
Source: Jeff Jordan in http://a16z.com/2015/02/24/managing-tensions-in-online-marketplaces/
See also http://www.forbes.com/sites/valleyvoices/2015/10/21/how-to-guard-your-marketplace-against-fraudsters/
40. Medium
T W O - S I D E D N E T W O R K W I T H C R O S S A N D S A M E - S I D E N E T W O R K E F F E C T S
More
writers
More
readers
Network effect from
both sides of
the network
More writers = more time
readers spend on
Medium
More readers = More
writers begin to write
But can be on
the same side of
the network,
too!
When readers invite other
readers (via highlights,
mentions, replies, and
annotations), the overall
value of the entire
network increases as
more ideas are shared in
that network itself
41. What is leading to network effects for Medium?
Critical mass on
both sides
Network effects
Provided immediate,
single-player utility—in the
form of an elegant and
easy-to-use publishing tool
Often described as the “best
web editor I’ve ever used”
for both experienced and
inexperienced writers
More writers writing directly
on Medium and more readers
spending more time reading
directly on Medium
Becoming a network of
people and ideas
Mode/product value
Curated special content
collections/star contributors to
create perceived exclusivity
and as a beachhead to attract
other influencers
Used the 1-9-90 internet
rule—where 1% users actively
write, 9% participants edit,
90% read—to invite those
who engaged to also
become writers
Growth tactic
As they built critical mass,
Medium designed the
platform itself to optimize for
engagement—through
“in-content interactor”
features such as highlights,
recommends, responds,
and mentions
Used taxonomy of collections
and publications to cluster
highly engaged community
around topics of interest
Engagement trigger Network effects
C O R R E S P O N D I N G Q U E S T I O N S : H O W T O B U I L D L I Q U I D I T Y ? H O W T O D R I V E E N G A G E M E N T ?
Source: Ev Williams https://medium.com/the-story/medium-is-not-a-publishing-tool-4c3c63fa41d2
42. By the numbers: Early signs of network effects
N O N - V I R A L S T O R I E S G E T A M A J O R I T Y O F T R A F F I C F R O M W I T H I N M E D I U M
12%
30%
48%
54%
35%
23%
34%
35%
29%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Top
Middle
Tail
Medium Social OtherTTR (total time reading) by
referrer increasingly coming from
Medium for long tail and middle—
represents the audience that writers
can’t easily reach on their own
without Medium
Source: https://medium.com/data-lab/quantifying-network-effects-7e6bc167aea5 , company data
43. Some takeaways
Reminder that single player mode can help get to multiplayer mode. The
appeal of the tool attracts users initially to help build enough critical mass, and
then getting those users to participate over time creates the network
come for the tool, stay for the network
They didn’t just built the tool and wait for users to come; a lot of up-front work
went into curating and editing early content and community
See also: http://cdixon.org/2015/01/31/come-for-the-tool-stay-for-the-network/
44. WhatsApp
A N E V E N M O R E H I G H LY C L U S T E R E D N E T W O R K T H A N FA C E B O O K
1 WhatsApp user
had ≈20 connections
compared to ≈980
friends on Facebook
While this is fewer
connections, they
were highly clustered
among close family
and friends or
WhatsApp Groups
and therefore led to
more engagement
45. What led to WhatsApp’s network effects?
Critical mass on
both sides
Network effects
Single-player utility:
Initial product enabled
“what’s up” status
updates of phone
contacts that were useful
even without interaction
Multi-player utility:
Provided instant
messaging—essentially
better, simpler, sand free
SMS in international
markets (now 50% bigger
than global SMS)
WhatsApp didn’t just have
growth, it had more
engagement (as indicated by
% DAU x % MAU growth)—in
other words, more users
added more value for other
users (that engagement is
high at over 70%)
Mode/product value
Was one of the early apps
to leverage the phone
book as social graph:
Each user invited users
from their phone contacts
(“closest family and
friends”)
Started small with close-
knit Russian community in
West San Jose to build
initial critical mass before
spreading to
other subclusters
Growth tactic
Group Chat feature
helped it go beyond
pairwise connections
Multimedia (MMS) helped
it be used like Facebook
(family photo sharing, etc.)
in India and other places
where people didn’t use
web-based apps as much
Engagement triggers Network effects
C O R R E S P O N D I N G Q U E S T I O N S : W H AT S H O U L D T H E E N T RY P O I N T / S T R AT E G Y B E ? H O W D O W E TA K E A D VA N TA G E
O F I R R E G U L A R T O P O L O G I E S T O F I N D C L U S T E R S A N D S U B C L U S T E R S ?
Sources: http://www.forbes.com/sites/parmyolson/2014/02/19/exclusive-inside-story-how-jan-koum-built-whatsapp-into-facebooks-new-19-billion-baby/
http://www.businessinsider.com/whatsapp-engagement-chart-2014-2 https://growthhackers.com/growth-studies/whatsapp
46. WhatsApp Growth vs Other Popular Platforms
Source (WhatsApp): http://www.forbes.com/sites/parmyolson/2014/02/19/exclusive-inside-story-how-jan-koum-built-whatsapp-into-facebooks-new-19-billion-baby/
FA S T G R O W T H : H I T 6 7 M M A U S I N 2 Y E A R S ( 5 . 5 X B I G G E R T H A N FA C E B O O K A N D 1 7 X B I G G E R T H A N T W I T T E R Y E A R
T W O )
0
50
100
150
200
250
300
350
400
450
Year 0 Year 1 Year 2 Year 3 Year 4
(millions)
MAUs
Facebook: 145m
WhatsApp: 419m
(800m+ today)
Gmail: 123m(1)
Twitter: 54m(2)
Skype: 52m(3)
47. By the numbers: Sign of network effects
P E O P L E A R E N ’ T J U S T R E A D I N G / R E C E I V I N G M E S S A G E S B U T W R I T I N G / S E N D I NG M E S S A G E S
0
5
10
15
20
25
30
35
Sep-11 Mar-12 Sep-12 Mar-13 13-Sep Mar-14 Sep-14 Mar-15 Mar-15
WhatsApp outgoing messages/day (bn)
Sources: WhatsApp, a16z
See also: http://ben-evans.com/benedictevans/2015/1/11/whatsapp-sails-past-sms-but-where-does-messaging-go-next
48. Some takeaways
Remember, usage—not just growth—is what helps indicate
network effects
Unlike Facebook, WhatsApp launched globally at outset but still pursued a
clustered approach by making sure product was working in one subcluster
first...Product continued to grow in clusters, not just peer to peer
“No ads, no gimmicks, no games”—focused on simplicity first which tends
to viral before adding extra features
Also, phone as login provided a very low barrier to entry for users
(especially internationally, where more people
have phone numbers than email addresses)
49. What are
network effects?
Strategies for building
network effects
What aren’t
network effects?
Properties, terms
and laws of networks
Case studies of
companies with
network effects
50. How do you build—and maintain—network effects?
Product should provide
inherent value,
whether in single or
multiplayer mode
Growth tactics to
drive adoption
Engagement
trigger
Sustain network
effects
Viral growth
note that viral growth, while very helpful, is
not necessary for critical mass
51. Some strategies for building network effects
What is your entry
strategy?
Bowling pin strategy
1
What are the growth
levers to drive adoption?
Growth strategy
2
What is your critical
mass inflection point?
Critical mass goals
3
What are the
engagement triggers?
Engagement strategy
4
How can you leverage an
irregular network?
Irregular networks
5
52. 1. Bowling pin strategy to overcome chicken-egg problem
Segment
Segment Segment
Segment Segment Segment
Facebook did this well by starting with Harvard before moving to other schools and then
opening up to everyone
Should I build supply first
or demand first
And how much of each
do I need?
Where do I start?
One way to overcome
that tension is to use
Geoffrey Moore’s
(Crossing the Chasm)
Bowling Pin strategy:
Start with a niche
segment where the
chicken and egg can
both be easily overcome,
then eventually move to
other niches and the
broader market
53. 2. Bootstrapping growth to drive adoption early on
Source: https://www.quora.com/What-are-some-growth-strategies-used-by-Reddit
See also: https://medium.com/@nishrocks/why-we-created-the-yelp-elite-squad-b8fa7dd2bead
Accessed the entire
Harvard directory early
on to provide immediate
utility for early adoption
Canvassed friends and
family for early feedback
and reviews; also found
and nurtured the
top 100 super users
as tastemakers
Made it “the” place to find
and discuss all things hip
and cool related to a
particular city
Created several
accounts on their own
and submitted a lot of
interesting content link to
make the site feel alive
for new users and thus
quickly helped create
a community
54. 3. Setting goals to help attain ‘critical mass’ more quickly
Connect a new user to 10
friends within 14 days of sign up
Tag at least 3 friends
to each campaign
Facebook realized
early on that it was
important to connect
each new user to at
least 10 friends for
them to stay engaged
on the platform
(repeat usage)
This focus from
Facebook on repeat
engagement is what
drove network effects
Similarly, Tilt
observed in the
early days that
campaigns were
75% more likely to
tilt if at least 3
friends were tagged
55. 4. Having specific triggers to sustain engagement in
network
Constantly rolled out new
features (Like button,
news feed, chat) to keep
engagement on
the platform
First of its kind to
leverage “phone book
contacts”; the stickiest
cluster coupled with the
high utility of the product
(free SMS in international
markets) helped keep
engagement high
56. 5. Leverage irregular network topologies
B Y F I N D I N G C L U S T E R S , C O M PA N I E S C A N R E A C H C R I T I C A L M A S S W I T H I N T H O S E S U B C L U S T E R S A N D E X PA N D
B E Y O N D
Real life networks are
often very different
from the uniform
distributed networks
pictured in textbooks
WhatsApp took
advantage of the fact
that social
connections are
highly clustered in
your phonebook and
used that as a
“beachhead” to
launch groups
They also targeted
the international
communities (e.g.,
Russian community
in bay area) that
found WhatsApp a
cheaper alternative
to expensive SMS
57. Strategies for creating network effects
How do you attract the
harder side of the
marketplace?
6
Subsidizing strategy
M A R K E T P L A C E S
Via cdixon.org
58. 6. Attracting the harder side of the marketplace
In almost every
two-sided market, one
side is harder to
acquire than the other
Common way to attract
the harder side is to
subsidize that
harder side
For example, single bars
often have special
ladies’ nights promotions
on slower nights offering
women discounts
on drinks
More
men
More
womenBars
59. Reducing prices for the hard side of the market (e.g., Adobe Flash and
PDF for end users) can help build critical mass
But solving the chicken-egg problem only by subsidizing has to be
considered carefully in the context of the overall business model—i.e.,
there’s a difference between building initial critical mass and building a
sustainable business (can’t ignore overall unit economics)
This is why understanding which is the money side of the marketplace
and the side of the marketplace where the most value is coming from
matters so much because then you know
which side to carefully subsidize
See also: Thomas Eisenmann https://hbr.org/2006/10/strategies-for-two-sided-markets
60. However, this dynamic may play out a little differently in
so-called “sharing economy marketplaces
Because such marketplaces can be supply-constrained due to
unfamiliarity with the sharing economy model
So those marketplaces have to work harder to get more supply
as well, and hence may also subsidize that side or build other
features to address these issues in other ways
61. Strategies for creating network effects
Show long-term
commitment to platform
7
Provide stand alone
value of the base
8
Vertically integrate when
supply uncertain
9
P L AT F O R M S
Via cdixon.org
62. 7. Showing a long-term commitment to the platform
W H E N FA C E B O O K A C Q U I R E D O C U L U S , T H E Y S I G N A L E D T H E I R L O N G - T E R M C O M M I T M E N T T O H E L P D R I V E
P L AT F O R M S U C C E S S ; O C U L U S A L S O A N N O U N C E D I T W I L L P U M P $ 1 0 M I N T O I N D E P E N D E N T G A M E - D E V E L O P M E N T
E F F O R T S
Getting to critical mass in
indirect networks
can be challenging
Because you are a
platform you are
dependent on 3rd party
developers to remain
engaged and grow
your platform
See also: http://cdixon.org/2009/08/25/six-strategies-for-overcoming-chicken-and-egg-problems/
W H E N T H E Y L A U N C H E D T H E X B O X , M I C R O S O F T
D I D S O M E T H I N G S I M I L A R I N P R O M O T I N G T H E I R
P L AT F O R M A N D S I G N A L I N G T H E I R C O M M I T M E N T
63. 8. Providing standalone value of the base
T H I S C O M P L E M E N TA RY N E T W O R K E F F E C T I M P R O V E D T H E VA L U E A N D I N C R E A S E D S A L E S V E L O C I T Y O F
B O T H T H E B A S E P R O D U C T ( V C R ) A N D C O M P L E M E N T ( V I D E O C A S S E T T E S ) A N D R E M A I N E D T H E
S TA N D A R D F O R Q U I T E A L O N G T I M E !
The standalone value of
the VCR—“time shifting”
of TV programming”—
was strong enough to get
>1M people to purchase
one early on
This installed base
enticed entrepreneurs to
develop a market for pre-
recorded videocassettes,
creating an indirect
network effect improving
the value of the VCR and
protecting it from
incremental alternatives
Source: http://cdixon.org/2009/08/25/six-strategies-for-overcoming-chicken-and-egg-problems/
64. 9. Integrate vertically into critical complements
By vertically integrating
the complement
product (game) as well
as the base product
console), a company
can attempt to ensure
adequate supply of
both goods
For example,
Nintendo is the
leading developer of
games for its own
consoles and
Microsoft and Sony
also fund many of
the most popular
games on their
platforms as well
In platforms, one
doesn’t necessarily
have to be
dependent only on
outside
developers—
companies can
ensure critical
complements are
built by themselves
as well
Source: http://cdixon.org/2009/08/25/six-strategies-for-overcoming-chicken-and-egg-problems/
65. What are
network effects?
What aren’t
network effects?
Properties, terms
and laws of
networks
Case studies of
companies with
network effects
Strategies for
building network
effects
66. Debunking some common misconceptions
Network effects and
virality are NOT the
same thing
1
Viral growth is NOT
necessary for
network effects
2
Just because a platform
has scale does NOT
mean you have
network effects
3
67. 1. Network effects and virality are not the same thing!
Network effects increases value as more users join a network,
whereas viral growth increases just the speed of adoption
(of a particular network’s product/service)
These two concepts are often co-occurring so are sometimes
conflated, but they’re not the same thing
68. What’s the difference?
D E F I N I T I O N S
Network effects
Product becomes more valuable
as more users use it
Network effects help build a moat
for the business, leading to high
engagement/ repeat rates and
higher margins
Represented by Metcalfe’s Law:
value of telecom network is
proportional to square of number
of connected users of system (n2)
Value
Virality
Product that spreads from one
user to another through direct
customer to customer contact
Viral growth implies low CAC
(customer acquisition cost)
Often measured by viral
coefficient (K factor): [average
number of invitations sent by each
existing user] * [conversion rate of
invitation to new user]
Speed
See also: Sangeet Chaudhary http://platformed.info/virality-viral-growth-network-effects/
69. This is something that
spreads without
financial or other
sharing incentive due
to being exclusive,
invite-only, or other
Example: Gmail
created buzz (the hot
thing with 1GB
storage that was
available only to a
few) and encouraged
existing customers to
send invites slowly
Distinguishing between various flavors of viral growth
Referrals with no incentivesNetwork effects
A product that has
inherent virality—i.e.,
spreads from one user
to another as an
organic consequence
of use—will have a
network effect
(referred to as a ‘direct
network effect’ in
academic literature)
Example: Facebook
without friend
connections is
not useful
Word-of-mouth
This is where
customers
recommend the
product to other
customers or
distribute it via other
platforms (like
Facebook and Twitter)
due to a positive
experience with it
Example: games like
Angry Birds;
BuzzFeed ‘The Dress’
Casual contact
This is where a product
spreads virally via
customer to customer
contact (not via users
intentionally inviting
other users)
Example: Hotmail
acquired users by
including footers for free
accounts at bottom of
every email; DocSend
acquires customers
when users email links to
view/download files
TRADITIONAL VIRALITY‘PRODUCT VIRALITY’
See also: Thomas Eisenmann http://platformsandnetworks.blogspot.com/2011/07/business-model-analysis-part-5-virality.html
70. So why do those distinctions
matter?
Because product virality (a product that is
inherently viral) leads to network effects
But traditional virality does not always
lead to network effects
71. By the numbers: Product virality leads to network effects
FA C E B O O K I S T H E C L A S S I C E X A M P L E O F T H I S
Product spread from one user to another as an
organic consequence of its use, allowing Facebook
to acquire users at $0 CAC
The platform became more valuable as more users joined
The signpost of network effect in this case is high
engagement even as number of users increased
1 6 12
58
145
360
608
845
0
100
200
300
400
500
600
700
800
900
2004 2005 2006 2007 2008 2009 2010 2011
(millions,year-end)
MAUs
MAUs CAGR ‘04-’11: 162%
45% 47%
51% 54% 53% 53% 54% 55% 56% 57% 57%
0%
20%
40%
60%
80%
100%
DAUS/MAUs(%)
Engagement (DAUs/MAUs)
Product virality = Connections with friends Network effects
Source: public company data
72. This distinction also explains cases where things that
(seemed to) have viral growth did not lead to network effects
In most cases, that viral growth was really word-of-mouth
73. By the numbers: Word of mouth ≠ Network effects
A N G RY B I R D S I S A N E X A M P L E O F T H I S
Product spread from one user to another via
word of mouth referrals/ brand popularity as
people started playing on their own—did not
spread as an organic consequence of its use
Users do not get incremental value when other
users download and play the game
So Angry Birds does NOT have network effects
(and has a weak competitive moat as a result)
The key difference: Organic consequence of use
Key question: Does value increase for users?
30 100
225
350
400
500
0
100
200
300
400
500
600
Angry Birds (est. downloads in millions)
Chart source: https://www.macstories.net/news/angry-birds-reaches-half-a-billion-downloads/
74. And remember, you can have network effects
without (product or traditional) virality
75. A I R B N B I S A N E X A M P L E O F T H I S
Early days required traditional marketing and
numerous growth hacks to build liquidity on
both sides of the marketplace
# of guests that stayed at Airbnb saw hyper growth
3 years after launch
Leads to more money for hosts and
more availability for guests
No virality…. …yet strong network effects over time
# of new listings between 2008 and 2011
# of transacting users between 2008 and 2011
-
5
10
15
20
2008 2009 2010 2011 2012 2013 2014
Millions
By the numbers: Network effects without virality
Source: Company data
76. To clarify one final term/misconception:
Just because a platform has scale does NOT
mean it has network effects
77. What’s the difference?
Economies of scale
Product becomes cheaper to
produce as business increases in
size and output
Increasing scale leads to lower
cost per unit of output (cost per
unit decreases as fixed costs are
spread out over more units)
Network effects
Product becomes more valuable
as more users use it
Network effects help build a
moat—leading to high/repeat rates
of engagement, higher margins
ValueCost
D E F I N I T I O N S
See also: Sangeet Chaudhary http://platformed.info/virality-viral-growth-network-effects/
78. To sum up: Network effects vs Virality vs Economies of
scale
Network effects
Virality
Economies of scale
1P
79. SUPPLY SIDE
Economies of scale (also referred to as just ‘economies of scale’) is
a function of production size; so scale leads to lower cost per unit of
output (unit economic efficiency)
DEMAND SIDE
Economies of scale (also referred to as network effects) is a function
of users, so with scale leads to more utility for users
They’re both competitive moats, but network effects tend to be
stronger—users have higher barriers to exit
Zooming in on economies of scale
80. Amazon’s ecommerce site (1P) has supply-side economies of
scale—shared warehouse facilities, cheaper shipping options, etc.—
that benefit Amazon in the form of purchasing power and buyers in
the form of lower costs
Amazon’s peer-to-peer marketplace (3P) has demand-side
economies of scale—aka network effects—which help make it the
winner-take-all… is growing much faster than the ecommerce
aspect of the site
Example: amazon.com
81. Strategies: Don’t forget these less obvious, but no less
important, sources of network effects
Hardware Infrastructure
1 2
Data
3
82. “
MAX LEVCHIN:
The defensibility of these businesses lies in their ability to build…a network effect of
data.
MATT TURCK:
Data network effects occur when your product, generally powered by machine
learning, becomes smarter as it gets more data from your users. In other words: the
more users use your product, the more data they contribute; the more data they
contribute, the smarter your product becomes (which can mean anything from core
performance improvements to predictions, recommendations, personalization, etc.);
the smarter your product is, the better it serves your users and the more likely they
are to come back often and contribute more data—and so on and so forth.
Simply put, a data network effect is
a network effect that results from data
Sources: http://max.levch.in/post/41116802381/dld13-keynote
http://mattturck.com/2016/01/04/the-power-of-data-network-effects/
83. But simply having a lot of data does not a data network
effect make!
The data needs to not only benefit from/extract that data,
but also add value back to the network of users
84. A missing graph we’d love to see in pitches that posit data
network effects is something that answers this:
How much data do
you have over time?
How much does the
value of the product or
service increase as a
result of the data?
85. Chris Dixon
Jeff Jordan
Sonal Chokshi
Kathy Wang
A D D I T I O N A L A C K N O W L E D G E M E N T S ( N O T I N C L U D I N G E X P E R T S & A R T I C L E S C I T E D A S S O U R C E S T H R O U G H O U T )