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Strategy

Industry Study: Nanotechnology

July 12, 2015 by Brian Laung Aoaeh

SUNY College of Nanoscale Science and Engineering's Michael Liehr, left, and IBM's Bala Haranand look at wafer comprised of 7nm chips on Thursday, July 2, 2015, in a NFX clean room Albany.   Several 7nm chips at SUNY Poly CNSE on Thursday in Albany.  (Darryl Bautista/Feature Photo Service for IBM)
SUNY College of Nanoscale Science and Engineering’s Michael Liehr, left, and IBM’s Bala Haranand look at wafer comprised of 7nm chips on Thursday, July 2, 2015, in a NFX clean room Albany. Several 7nm chips at SUNY Poly CNSE on Thursday in Albany. (Darryl Bautista/Feature Photo Service for IBM)

Note 1: This is an update of an article that I wrote on a whim in December 2006, while I was on a break from business school at NYU Stern. It was published without update at Tekedia in July, 2011. The announcement by IBM about its new 7nm chip prompted me to dig it up from my archives and update it to reflect more recent developments.

Note 2: KEC Ventures does not specifically invest in nanotech startups, although in the past we have examined startups developing quantum crystals and other nanomaterials.

Introduction

I became exposed to nanotechnology during my days as an undergraduate student at Connecticut College, in New London, Connecticut. I pursued a double major in Physics and Mathematics, and had the good fortune of working as a research laboratory assistant in the Tunable Semiconductor Diode-Laser Spectroscopy lab, which was run by Professor Arlan W. Mantz, Oakes Ames Professor of Physics, and erstwhile chair of the Physics Department. My involvement with the lab spanned three years, and that experience played a critical role in my education. ((Let me know if you feel I have failed to attribute something appropriately. Tell me how to fix the error, and I will do so. I regret any mistakes in quoting from my sources.))

What Is It?

The term nanotechnology refers to a group of scientific processes that enable products to be manufactured by the manipulation of matter at the molecular level – at the nanoscale. One nanometer represents a length of 10-9 meters – one billionth of a meter. ((For perspective, 100nm represents about 1000-1 of the width of a human hair.)) Nanotechnology enables the manipulation of matter at or below dimensions of 100 nanometers. Nanotechnology draws from a multitude of scientific disciplines – physics, chemistry, materials science, computer science, biology, electrical engineering, environmental science, radiology and other areas of applied science and technology.

There are two major approaches to manufacturing at the nanoscale;

  • In the “bottom-up” approach, nanoscale materials and devices assemble themselves from molecular components through molecular recognition – small devices are assembled from small components.
  • In the “top-down” approach materials and devices are developed without the manipulation of individual molecules – small devices are assembled from larger components.

Where is Activity Concentrated?

Research into nanotechnology and its applications is growing rapidly around the world, and many emerging market economies are sparing no effort in developing their own research capacity in nanotechnology.

  • Naturally, the U.S., Japan, Western Europe, Australia and Canada hold an advantage, in the short term.
  • China and India have made significant progress in establishing a foundation on which to build further capability in nanotechnology – A 2004 listing puts them among the top 10 nations worldwide for peer-reviewed articles in nanotechnology. ((Hassan, Mohamed H. A., Small Things and Big Changes in The Developing World. Science,Vol. 309 no. 5751, Jul 1 2005, accessed on Dec 19, 2006 at http://www.sciencemag.org/cgi/content/full/309/5731/65))
  • South Africa, Chile, Mexico, Argentina, The Philippines, Thailand, Taiwan, The Czech Republic, Costa Rica, Romania, Russia and Saudi Arabia have each committed relatively significant resources to developing self-sufficient local nanotechnology industries.

Why Should Investors Care?

Fundamentally, investors should pay attention to nanotechnology because of its high potential to spawn numerous “disruptive technologies.” Nanoscale materials and devices promise to be;

  • Cheaper to produce,
  • Higher performing,
  • Longer lasting, and
  • More convenient to use in a broad array of applications.

This means that processes that fail to provide results comparable to those available through nanotechnology will become obsolete rather quickly, once an alternative nanoscale process has been perfected. In addition, companies that fail to embrace and apply nanotechnology could face rapid decline if their competitors adopt the technology successfully.

The United States Government has maintained its commitment to fostering U.S. leadership and dominance in the emerging fields of nanoscale science. In its 2006 budget, the National Nanotechnology Initiative, a multi-agency U.S. Government program, requested $1.05 Billion for nanotechnology R&D across the Federal Government. ((The National Nanotechnology Initiative, Research and Development Leading To A Revolution in Technology and Industry, Supplement to The Presidents FY 2006 Budget)) That amount reflects an increase from the $464 Million spent on nanotechnology by the Federal Government in 2001.

Applications of Nanotechnology

Nanotechnology’s promise to revolutionize the world we live in spans almost every aspect of human endeavor. Today, nanotechnology is applied in as many as 800 commercial products. ((National Nanotechnology Initiative, accessed online on Jul 12, 2015.))

  • IBM’s new chip “could result in the ability to place more than 20 billion tiny switches – transistors – on the fingernail-sized chips that power everything from smartphones to spacecraft. To achieve the higher performance, lower power and scaling benefits promised by 7nm technology, researchers had to bypass conventional semiconductor manufacturing approaches. Among the novel processes and techniques pioneered by the IBM Research alliance were a number of industry-first innovations, most notably Silicon Germanium (SiGe) channel transistors and Extreme Ultraviolet (EUV) lithography integration at multiple levels.” ((See the announcement from IBM. Accessed online on Jul 12, 2015.))
  • Carbon nanotubes and other nanomaterial additives can be used to fabricate stronger, lighter materials for use in automobile bodies, helmets, sports equipment and other products in which stiffness and durability are important features.
  • Researchers at Stanford University have killed cancer cells using heated nanotubes, while EndoBionics, a US firm, developed the MicroSyringe for injecting drugs into the heart. MagForce Technologies, a Berlin based company developed iron-oxide particles that it coats with a compound that is a nutrient for tumor cells. Once the tumor cells ingest these particles, an external magnetic field causes the iron-oxide particles to vibrate rapidly. The vibrations kill the tumor cells, which the body then eliminates naturally. Other applications in medicine and biotechnology exist.
  • Cosmetics companies are actively engaged in the exploration of nanotechnology as a source of enhanced products. For example, to produce cosmetics that can be absorbed more easily through human skin and that exhibit longer lasting properties.
  • Thebreakthrough by IBM will only acceleratethe development ofnanoscale technologies for computing platforms. According to the National Nanotechnology Initiative:”Nanotechnology is already in use in many computing, communications, and other electronics applications toprovide faster, smaller, and more portable systems that can manage and store larger and larger amounts of information. These continuously evolving applications include:
    • Nanoscale transistors that are faster, more powerful, and increasingly energy-efficient; soon your computer’s entire memory may be stored on a single tiny chip.
    • Magnetic random access memory (MRAM) enabled by nanometer‐scale magnetic tunnel junctions that can quickly and effectively save even encrypted data during a system shutdown or crash, enable resume‐play features, and gather vehicle accident data.
    • Displays for many new TVs, laptop computers, cell phones, digital cameras, and other devices incorporate nanostructured polymer films known as organic light-emitting diodes, or OLEDs. OLED screens offer brighter images in a flat format, as well as wider viewing angles, lighter weight, better picture density, lower power consumption, and longer lifetimes.
    • Other computing and electronic products include Flash memory chips for iPod nanos; ultraresponsive hearing aids; antimicrobial/antibacterial coatings on mouse/keyboard/cell phone casings; conductive inks for printed electronics for RFID/smart cards/smart packaging; more life-like video games; and flexible displays for e-book readers.”
  • Nanotechnology is applied in the garment industry to produce stain resistant fabrics, for example.
  • Nanotechnology companies in the developing world are pursuing solutions to problems peculiar to the developing world – for example, an Indian company is working on a prototype kit for diagnosing tuberculosis. There is great potential for the application of nanotechnology to agriculture.

A more complete listing of the benefits and applications of nanotechnology is available here: US National Nanotechnology Initiative

Threats

In spite of its promise, nanotechnology faces threats that could investors ought to be aware of. Among these;

  • It is not yet clear how nanotechnology will affect the health of workers in industries in which it is applied. For example, how should we assess exposure to nanomaterials? How should we measure the toxicity of nanomaterials?
  • Public agencies and private organizations do not have a clear sense of how further progress in nanotechnology will affect the environment, or of the public safety issues that will accompany an expanded use of nanotechnology in industrial, medical and consumer applications. For example, what factors should risk-focused research be based on, and how should we go about creating prediction models to gauge the potential impact of nanomaterials?
  • The complexity of the science that is integral to nanotechnology makes it a very difficult area to regulate. It is likely that firms involved in the pursuit of nanoscale applications in medicine and pharmaceutics will face long delays in obtaining regulatory approval for the wide scale use of their products.
  • The complexity of nanotech-related patents could lead to delays in obtaining intellectual property protection for nanotech-enabled products.
  • It is not yet clear how society can protect itself from the abuse of nanotechnology. The public sector needs to collaborate with the private sector in developing protective mechanisms to guard against “accidents and abuses” of the capabilities of nanoscale processes and materials.

A Note To Would-Be Investors

The average investor must remain keenly aware that firms involved in nanotechnology will have to assign significant resources to research and development. There is no reliable means of predicting the ultimate outcome of such activities, and the probability that any firm can maintain an enduring edge over its competitors is small. Investors should expect the mantle of leadership in innovation to change with a relatively high frequency. As such, pure-play nanotechnology firms will need to pay critical attention to means of sustaining market dominance that go beyond core competence in the science of nanotechnology.

Lux Research estimates that revenues from products using nanotechnology will increase from $13 Billion in 2004 to $2.6 Trillion in 2014. The 2014 estimate represents approximately 15% of global manufacturing output. ((Gosh, Palash R, How To Invest In Nanotech, www.businessweek.com, Apr 17, 2006. Accessed on Dec 22, 2006.))

In 2005, Lux Research and PowerShares Capital Management launched a nanotech ETF – The PowerShares Lux Nanotech Portfolio (PXN). In addition, Lux Research measures the performance of publicly traded companies in the area of nanotechnology through the Lux Nanotech IndexTM, a modified equal dollar weighted index of 26 companies. The companies in this index earn profits by utilizing nanotechnology at various stages of a nanotechnology value chain; ((Adapted from www.luxresearchinc.com))

  • Nanotools – Hardware and Software used to manipulate matter at the nanoscale.
  • Nanomaterials – Nanoscale structures in an unprocessed state.
  • Nanointermediates – Intermediate products that exhibit the features of matter at the nanoscale.
  • Nano-enabled Products – Finished goods that incorporate nanotechnology.

Companies in the index are further classified as

  • Nanotech Specialists, or
  • End-Use Incumbents.

Investors must note that the investment characteristics of Nanotech Specialists are likely to differ markedly from those of End-Use Incumbents. The end-use incumbents that are part of this index include 3M, GE, Toyota, IBM, Intel Hewlett-Packard, BASF, Du Pont, and Air Products & Chemicals. Because these companies have large, well-established and significant operations in arenas that do not rely heavily on nanotechnology, investors can expect them to achieve financial results that are only moderately volatile. In contrast the financial performance of nanotech specialists will exhibit highly volatile swings, because;

  • With the exception of companies in the “picks and shovels” segment of nanotechnology, much of the work that many nanotech specialists engage in is still in the “trial and error” phase, and
  • There is no reliable means of predicting the results that heavy investment in R&D will yield.

Finally, it is likely that financial valuations of nanotech firms will fail to capture the true value of the intangible assets that provide the bedrock of each company’s ability to sustain innovation, create economic value, and protect its competitive advantage. If nanotechnology is truly the way of the future, then investors must embrace that future with enthusiasm that is layered with caution by;

  • Performing an extra amount of due diligence before committing significant funds to investments in individual nanotechnology companies,
  • Limiting such investments to companies in the U.S., Japan, Canada, Western Europe, and Australia, in the near term, and
  • Following developments in the nanotechnology initiatives of the BRIC block of emerging market economies without committing any funds until a clear assessment of the future prospects of individual investment opportunities becomes possible.

Individual investors must exercise an extra amount of caution in pursuing nanotech investments, and should not commit more than they can afford to lose. Most individual investors with a desire to invest in nanotechnology should do so through PXN and similar instruments. Institutional investors must bring all their resources to bear in assessing the viability of a nanotech investment strategy prior to committing funds to this nascent area. For added security, individual investors that seek to invest in publicly traded nanotech companies should seek firms with the following characteristics;

  • No debt, and positive cash flows, and evidence of an ability to sustain profits.
  • Companies that supply corporate customers must not be too reliant on one customer.

Founders and insiders should have a significant and increasing portion of their net worth at stake in the company, and a track record in multi-disciplinary research.

In a Feb 2014 State of The Market Report update, Lux Research says “Our expanded forecast for nano-enabled products reveals the global value of nano-enabled products, nano-intermediates, and nanomaterials reaching $4.4 trillion by 2018.”

Closing Thoughts

Many risks accompany investments in nanotechnology. However, if nanotech is to be believed, it may yield significant long-term returns to those investors that learn to harness its power while keeping the following caveats in mind;

  • Many nanotech companies face an up-hill task in converting promising research into products that can sustain a steady revenue stream.
  • A considerable number of nanotech companies may be surrounded by “more hype than substance”.
  • There is no guarantee that the price investors pay for an investment in nanotech will be adequate, once all associated risks are taken into account.

 

Filed Under: Industry Study, Science, Technology Tagged With: Business Models, Early Stage Startups, Economic Moat, Strategy, Technology, Venture Capital

6 Things I Have Learned About Building High-Performing Teams

June 29, 2015 by Brian Laung Aoaeh

Chelsea FC - Celebrating victory in the 2012 UEFA Champions League. Image Credit: Chelsea FC
Chelsea FC – Celebrating victory in the 2012 UEFA Champions League. Image Credit: Chelsea FC

I spend a lot of time thinking about teamwork; how teams function, how they operate, how they fail or succeed, and how the most successful teams make their success repeatable in the face of changing conditions. This post is my attempt to synthesize what I have learned so far. ((Let me know if you feel I have failed to attribute something appropriately. Tell me how to fix the error, and I will do so. I regret any mistakes in quoting from my sources.))

What is a team? For the purpose of this blog post I will define a team as: A group of people with complementary skills who choose to work collaboratively together towards accomplishing a shared vision and a common objective, within an environment of mutual support in which each team member is empowered to independently set goals, solve problems and make decisions with support from other members of the team, based on an agreed-upon framework, under severe resource constraints.

A team will perform better than an individual in situations where the task at hand can only be completed through:

  1. the creation of new knowledge, or
  2. a novel and unique application of existing knowledge, or
  3. the meshing of different disciplines and subject matter areas, and
  4. there isn’t a single person who possesses all the knowledge and skills that would be required to accomplish the task within a period of time acceptable to the parties involved.

The presence of the following phenomena help us identify a team ((Adapted from Group Behavior, accessed on Jun 29, 2015 at https://www.boundless.com/psychology/textbooks/boundless-psychology-textbook/social-psychology-20/social-influence-104/group-behavior-393-12928/))

  1. Interdependence and Social interaction: Team members depend one one another in order to meet the teams goals and objectives, their interdependence results in social interaction through communication with one another.
  2. Perception of a group and Commonality of Purpose: Team members agree they are part of the team, and they buy into the team’s purpose, its goals, and its objectives.
  3. Favoritism: Members of the group demonstrate positive prejudice towards one another, and discriminate in favor of other members of the team.

In research using Letters-to-Numbers Problems, a task-grouping that combines elements of hypothesis testing, mathematical and logical reasoning, cryptographic reasoning, and collective induction: Groups of size three, four, and five performed better than the best of an equivalent number of individuals, but groups of size two performed at the level of the best of two individuals. Groups of size three, four, and five performed better than groups of size two but did not differ from each other. These results suggest that groups of size three are necessary and sufficient to perform better than the best of an equivalent number of individuals on intellective problems. ((“Groups Perform Better Than the Best Individuals on Letters-to-Numbers Problems: Effects of Group Size”, Patrick Laughlin, Erin Hatch, Jonathan Silver, and Lee Boh, University of Illinois at Urbana Champaign; Journal of Personality and Social Psychology, Vol. 90, No. 4. Accessed online on Jun 27, 2015.))

Other research found that: “Groups are better than individuals in making difficult decisions, but the opposite effect is found when decisions are easy. The model suggests that the reason lies in the different assessment mechanisms operating at the level of individuals and colonies. For a difficult choice, solitary ants have a relatively high probability of accepting the worse nest, because they rely on quality dependent acceptance probabilities that differ little for similar nests. Successive comparisons cause these probabilities to diverge, but the ant is likely to make her decision before this slow process has had much effect. Whole colonies, on the other hand, do much better at difficult choices, because they use social information to accentuate the quality difference between sites. Rather than rely on individual comparisons, the colony’s choice emerges from a competition between recruitment efforts. Recruitment generates positive feedback on the number of ants at each site, with the better site slightly favored by its higher acceptance rate. The quorum rule amplifies this difference, allowing the colony to settle on the better site more frequently.” ((Takao Sasaki et al, Ant Colonies Outperform Individuals When A Sensory Discrimination Task is Difficult But Not When it is Easy.Proceedings of the National Science Academy of Sciences of the United States 2013 110(34). Accessed on Jun 27, 2015 at http://www.pnas.org/content/110/34/13769.full.pdf+html))


The importance of the team that is working to build a startup cannot be overstated. The team is the most important aspect of a startup during the earliest stages of its existence, while it is searching for a repeatable, scalable, and profitable business model. Once that business model has been found, the startup has a better chance of surviving team instability. Before that, team instability can be fatal. Also, the traits of the people in that early team determine the culture of the company that might evolve out of that startup.


Lesson # 1 – Every team goes through Development Stages: Bruce W. Tuckman’s model of how groups form is the foundational work on which our understanding of how teams develop and function is built. His paper ‘Developmental sequence in small groups’ was first published in 1965. ((Tuckman, Bruce W. (1965) ‘Developmental sequence in small groups’, Psychological Bulletin, 63, 384-399.))

  1. Forming: “Groups initially concern themselves with orientation accomplished primarily through testing. Such testing serves to identify the boundaries of both interpersonal and task behaviors. Coincident with testing in the interpersonal realm is the establishment of dependency relationships with leaders, other group members, or pre-existing standards. It may be said that orientation, testing and dependence constitute the group process of forming.”
  2. Storming: “The second point in the sequence is characterized by conflict and polarization around interpersonal issues, with concomitant emotional responding in the task sphere. These behaviors serve as resistance to group influence and task requirements and may be labeled as storming.”
  3. Norming: “Resistance is overcome in the third stage in which in-group feeling and cohesiveness develop, new standards evolve, and new roles are adopted. In the task realm, intimate, personal opinions are expressed. Thus, we have the stage of norming.”
  4. Performing: “the group attains the fourth and final stage in which interpersonal structure becomes the tool of task activities. Roles become flexible and functional, and group energy is channeled into the task. Structural issues have been resolved, and structure can now become supportive of task performance. This stage can be labeled as performing.”
  5. Adjourning or Mourning: This stage is experienced by teams that go through the process of dissolution; planned or unplanned, voluntary or involuntary. It was added to the preceding four stages in 1977.

Lesson #2 – To sustain success, leadership matters: Anita Elberse conducted research on Manchester United Football Club’s legendary leader, Sir Alex Ferguson. About Sir Alex Ferguson, she writes “Some call him the greatest coach in history. Before retiring in May 2013, Sir Alex Ferguson spent 26 seasons as the manager of Manchester United, the English football (soccer) club that ranks among the most successful and valuable franchises in sports. During that time the club won 13 English league titles along with 25 other domestic and international trophies—giving him an overall haul nearly double that of the next-most-successful English club manager.” Following are some observations based on her research. ((Anita Elberse, Ferguson’s Formula. HBR October 2013 Issue accessed on Jun 27 at https://hbr.org/2013/10/fergusons-formula. Also, Anita Elberse and Thomas Dye, Sir Alex Ferguson: Managing Manchester United. Harvard Business School Case N9-513-051, Sep 2012.))

  1. Sir Alex Ferguson on building an organization that will last, starting with the foundation: “From the moment I got to Manchester United, I thought of only one thing: building a football club. I wanted to build right from the bottom. That was in order to create fluency and a continuity of supply to the first team. With this approach, the players all grow up together, producing a bond that, in turn, creates a spirit.”
  2. Successful teams are led by people who set high standards, and hold everyone accountable to meeting and even exceeding those standards: “He recruited what he calls “bad losers” and demanded that they work extremely hard. Over the years this attitude became contagious—players didn’t accept teammates’ not giving it their all. The biggest stars were no exception.”
  3. Team leaders, and other team members, should encourage one another as often as possible, especially when a team member’s effort has matched or exceeded the group’s expectations. Sir Alex Ferguson: “Few people get better with criticism; most respond to encouragement instead. So I tried to give encouragement when I could. For a player—for any human being—there is nothing better than hearing “Well done.” Those are the two best words ever invented. You don’t need to use superlatives.”
  4. The most successful teams prepare to win. Under Sir Alex Ferguson, Manchester United was always prepared to adapt its tactical play in order to increase its chances of winning the game; how to play if a goal was needed in the late stages of a match, training to force a favorable outcome when the going got tough. They used training sessions as opportunities to learn and improve. Sir Alex Ferguson: “Winning is in my nature. I’ve set my standards over such a long period of time that there is no other option for me—I have to win. I expected to win every time we went out there. Even if five of the most important players were injured, I expected to win. Other teams get into a huddle before the start of a match, but I did not do that with my team. Once we stepped onto the pitch before a game, I was confident that the players were prepared and ready to play, because everything had been done before they walked out onto the pitch.”
Denali  Image Credit: NPS Photo/Tim Rains
Denali
Image Credit: NPS Photo/Tim Rains

Lesson #3 – Great teams learn how to adapt their leadership structure to match the intensity and difficulty of the task at hand: In a study of 5,104 mountain-climbing expeditions that took place between 1905 and 2012 on more than 100 mountains around the world, researchers found that: “In sum, hierarchical cultural values predicted summiting and fatality rates only for group expeditions. Hierarchy did not predict summiting or fatality rates in solo expeditions, providing evidence that group processes are a critical driver of the observed effects.” ((Eric M. Anicich et al, Hierarchical Cultural Values Predict Success and Mortality in High-Stakes Teams. Proceedings of the National Science Academy of Sciences of the United States 2015 112(5). Accessed on Jun 27, 2015 at http://media.outsideonline.com/documents/himalaya-expedition-study.pdf.)) In other words, groups characterized by a higher degree of “command-and-control” style leadership – and a lower degree of egalitarian leadership, were more likely to summit but also faced more deaths than groups with a higher degree of egalitarian leadership – and a lower degree of command-and-control style leadership. Commenting on the study, Cecilia Ridgeway, a professor at Stanford University observed that: ((Devon O’Neil, Summit or Death! Accessed on Jun 27 at http://www.outsideonline.com/1928751/summit-or-death))

  1. The crucial factor in a team’s success or failure under conditions such as those the researchers examined is the leader’s competence. Perhaps that competence is compromised in certain situations due to ingrained social structures, norms and behavioral patterns.
  2. Egalitarian teams are better positioned to survive in the face of potentially dooming conditions which would overwhelm the single decision maker in a non-egalitarian team. “The reason for that is when they hit these complex situations, under best circumstances they share their information, the ideas bounce off, and they come up with things that none of them would have thought of alone about how to survive.”

Related questions raised by this study:

  1. How can a team find an optimal balance between egalitarianism and non-egalitarianism, and
  2. How can the team learn to identify the situations in which it should adopt one leadership approach over the other?

Cecilia Ridgeway offers this advice: “The team would have to know itself well and all the members would really have to trust one another and be willing to go with their boss but also pull back from that in a kind of kaleidoscopic way. It’s not impossible but it wouldn’t be easy to do. It would depend a lot on the interpersonal skills, not just the climbing skills, of everybody involved.”

The best teams shift fluidly from one organizational form to another, depending on the circumstance, and depending on the nature of the task at hand. This is a function of the effectiveness of the team’s leadership, and reflects the complex nature of the environment in which startups and other businesses operate today.

  1. Teams can be organized such that interaction between each member and the team leader is the key characteristic of how the team gets its work done. The degree of collaboration between team members is low. The effectiveness of a team organized in this way is largely dependent on the effectiveness of the team leader.
  2. They may be organized such that responsibilities are shared to a large extent, with each team member exerting significant authority and decision-making responsibility for some aspect of the team’s work. Team leadership is not a shared responsibility. The degree of collaboration is high.
  3. A team can also be self-directed, with no official leader. However, such a team will often have one person responsible for coordinating the activities of team members.

Lesson #4 – Great teams are made up of people who each strive for true mastery in their area of specialization. The greatest soccer teams usually have players who each would be selected amongst the very best players in the world for the position that they fill on the team. ((On such teams young players must commit to trying to become one of the best, and the example must be set by the more experienced members of the team.)) To become the best each member of the team must hold a worldview that is keeping with what the Japanese describe as Shokunin kishitsu (職人気質) – translated roughly as the “craftsman spirit” and commit to the following five principles: ((Adapted from Garr Reynold’s Shokunin Kishitsu & The Five Elements of True Mastery. Accessed on Jun 27, 2015 at http://www.presentationzen.com/presentationzen/2015/05/the-five-secrets-to-mastery.html))

  1. They must be committed to the art, and committed to always functioning in their role on the team at the highest possible level. Commitment to hard work, and dedication to consistently executing at a high level is what sets great teams apart from their peers.
  2. They must aspire to improve themselves and their work, individually and collectively.
  3. They must pay attention to the cleanliness and freshness of their work environment. “Work environment” applies to the physical space in which the team gets its work done, but it also applies to the intangible work environment; Do team members feel free to express opinions that might be unpopular without fear of the consequences? Does every member of the team feel a sense of belonging and inclusiveness? Have cliques formed within the team, how does this affect the team’s overall effectiveness?
  4. The team’s leader is stubborn and obstinate in the pursuit of excellence. This does not mean that the leader has to be a jerk towards other members of the team, but it implies that the team’s standards for excellence, its vision, its mission . . . those are not sacrificed for the sake of consensus building.
  5. They each must be passionate and enthusiastic about mastering their skill, and in doing so they each cause their team to improve and become every day. They must be passionate about their individual and collective pursuit of perfection.

https://www.youtube.com/watch?v=Q78xvcnmIMw

Lesson #5 – A team should strive to become collectively more intelligent than any single member of that team could be acting alone. 

  1. Gender diversity helps, or find team members with high social sensitivity. Researchers assigned subjects randomly into teams after each individual had been administered a standard intelligence test, and then the researchers asked the teams to solve several tasks which included brianstorming, decision making, and visual puzzles, as well as one complex problem. The team’s collective intelligence was scored on the basis of their performance on the tasks. Teams with members with higher IQs did not perform much better than the other teams. However, teams that had more women did. The researchers suggest that the higher social sensitivity of women relative to men, explains the higher scores attained by teams with more women. Teams that include people who have high social sensitivity will perform teams that do not. ((Anita Wooley and Thomas W. Malone, What Makes a Team Smarter? More Women. Harvard Business Review, Jun 2011. Accessed on Jun 27, 2015 at https://hbr.org/2011/06/defend-your-research-what-makes-a-team-smarter-more-women/ar/1))
  2. In the face of complex problems, teams that solve the problem together will improve their chances of success over teams that rely on a star individual performer. “Swarm intelligence, which brings to mind the image of a hive of bees working together, requires people to gather information independently, process and combine it in social interactions, and use it to solve cognitive problems, according to behavioral biologist Jens Krause.  It has an advantage over other systems in that individuals get the opportunity to lead the swarm and affect what it does.  Moreover, because people act collectively, they can consider more factors, come up with more solutions, and make better decisions.” There are 4 things teams can do to accomplish this: ((Wolfgang Jenewein et al, Learning Collaboration from Tiki-Taka Soccer. Harvard Business Review, Jul 2014. Accessed on Jun 27, 2015 at https://hbr.org/2014/07/learning-collaboration-from-tika-taka-soccer/))
    1. Create a common vision,
    2. Leaders should be teachers, not bosses,
    3. Set collective objectives, and
    4. Leaders must be full-time leaders.
  3. Create, maintain, and nurture the team’s identity. Together with culture, identity can provide a powerful means of driving performance. Identity is different from culture. Identity tells a team “who we are.” Culture tells the team “what we do” or “how we behave.” ((Andres Hatum and Luciana Silvestri, What Makes FC Barcelona Such a Successful Business? Harvard Business Review, Jun 16 2015. Accessed on Jun 27, 2015 at https://hbr.org/2015/06/what-makes-fc-barcelona-such-a-successful-business))

A good team has learned how to make one plus one equal two. A great team has learned how to make one plus one equal three.


Lesson # 6 – In order to sustain performance teams should be aware of the problems related to intra-group collaboration and intra-group creativity. 

  1. The process of collaboration can lead teams to perform worse than an individual. Julia A. Minson and Jennifer S. Mueller found that teamwork can exacerbate overconfidence, and lead team members to reject outside information. ((Julia A. Minson and Jennifer S. Mueller, The Cost of Collaboration: Why Joint Decision Making Exacerbates Rejection of Outside Information. Psychological Science, Mar 16, 2012. Accessed on Jun 29, 2015 at http://opim.wharton.upenn.edu/DPlab/papers/publishedPapers/Minson_2011_The%20cost%20of%20collaboration.pdf))
    1. The study examined the assumption that collaboration leads to superior decisions than decisions made by an individual.
    2. The study found that teams of two people were more reluctant to change their judgements when presented with new information than an individual working alone. As a result the teams made poorer decisions than they would have if they had more willingly incorporated outside information in their decision making.
    3. The researchers found that teams’ tended to be more confident in the inherent ability of the team to reach a decision without outside input, this led them to be less willing to accept outside information. They suggest that the process of collaboration itself, not the quality of collaboration, makes team members over-confident in their collective expertise and leads to the higher degree of rejection of outside input.
    4. This is especially detrimental when the team is confronting a novel problem or task, but fails to explore alternatives that might lead to an improved decision.
  2. The best teams are those in which each member of the team shares the same team mental model, and the team mental model is correct. Beng-Chong Lim and Katherine J. Klein found that team performance is enhanced when team members share the same mental model. ((Beng-Chong Lim and Katherine J. Klein, Team mental Models and Team Performance: A Field Study of The Effects of Team Mental Model Similarity and Accuracy.J. Organiz. Behav. 27, 403–418 (2006) Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/job.387. Accessed on Jun 29, 2015 at http://www-management.wharton.upenn.edu/klein/documents/Lim_Klein_Team_mental_models_2006.pdf))
    1. A mental model is “a ‘mechanism whereby humans generate descriptions of system purpose and form, explanations of system functioning and observed system states, and predictions of future system states.’ Mental models are organized knowledge frameworks that allow individuals to describe, explain, and predict behavior. Mental models specify relevant knowledge content as well as the relationships between knowledge components. An individual’s mental model (of, for example, a car, a disease, or a process such as child development) reflects the individual’s perception of reality.”
    2. They found “a direct relationship between team mental model similarity and team performance. This may reflect the context in which the teams that we studied are trained to operate. They are expected to perform under high stress and intense time pressure. Under such circumstances, there is very little time for explicit coordination and communication. To succeed in their tasks (e.g., reacting to an enemy’s ambush), team members must have a shared understanding of the emerging situation and the collective action required. It is precisely in this type of context that shared mental models have been hypothesized to be most predictive of team performance.” Mental model similarity is a measure of the degree to which each team member’s perception of reality differs from the perception of other individuals on the team.
    3. They also found “that team mental model accuracy is also instrumental for team performance. Teams whose average mental models were most similar to experts’ mental models performed better than did teams whose average mental models were less similar to experts’ mental models. We speculate that teams whose mental models were most accurate pursued more effective task performance strategies than did teams whose mental models were less accurate.” In other words, the more correct a team’s mental model, the better the team performed. ((Coincidentally, this is an area of investigation I often pursue when I study early stage startups – does the startup team’s mental model match the mental model that customers have of the startup? What are the implications if it does not?))
  3. The team might fail to benefit from the knowledge of its most knowledgeable member because of pressure to conform with the majority position.  In 1956 Solomon E Asch found that even when one member of a team is more knowledgeable than the rest of the team about a specific task, that individual might choose to agree with the team even if the team is wrong, and that individual would have disagreed with the team’s decision under different circumstances. This happens because that individual feels pressure to conform with the team’s position. This is especially the case if that individual’s self-perception of the power-dynamic on the team places that individual in a position of weakness which makes it advantageous for that individual to protect the social relationships that exist between that individual and the other members of the team. ((Solomon E. Asch, Studies of Independence and Conformity: 1. A Minority of One Against A Unanimous Majority. Psychological Monographs: General and Applied, Vol. 70, No. 9, Whole No. 416, 1956. Accessed online on Jun 28, 2015. I have saved a copy here Minority v. Majority – asch1956. A more accessible discussion of Samuel Asch’s research on Decision Making and Social Conformity can be found here))
  4. The behavioral biases present in individual team members can be amplified within a group setting, particularly, the biases of dominant group members can become amplified by the group. Behavioral psychology is the study of observable and quantifiable aspects of human behavior. Behavioral biases refer to the tendency that people have to behave in certain ways under certain conditions. Behavioral biasescan be divided into two groups; Cognitive biases and Emotional biases. Anindividual’s behavioral biases can interfere with that person’sdecision making, and cause theindividual to makesuboptimal choices. The impact behavioral biases have on the quality ofdecision makingcan be worse in the context of a group asindividual team members might have a multiplying effect on oneanothers’ biases instead of reducing bias within the group. For example, two over-confident people might form a team that exhibits a higher degree of overconfidence than either individual acting alone. ((Adapted from Group Behavior, accessed on Jun 29, 2015 at https://www.boundless.com/psychology/textbooks/boundless-psychology-textbook/social-psychology-20/social-influence-104/group-behavior-393-12928/ ))
    1. Groupthink occurs when teams make consistently suboptimal decisions because members of the team have a strong desire to maintain harmony within the group. Groupthink can lead the team to consistently reject creative ideas.
    2. Groupshift occurs when the group adopts a position that is more extreme than the position that any of the individual members of the team would have taken. It is the example I described above of a team of individually overconfident people forming a team that is even more extreme in its overconfidence than any single individual member of the team would be if acting alone, under any circumstance.
    3. Deindividuation occurs as the group gradually becomes self-unaware as individual members of the group engage in less self-evaluation and self-critiquing, subsuming their self-awareness in the face of the behavior of the group. This phenomenon is exemplified in the real world through phenomena like lynch mobs, peaceful demonstrations that turn violent for no identifiable or obvious reason, or groups that form spontaneously and cause destruction, say while celebrating a sport’s teams victory in some sports tournament like the FIFA World Cup.
  5. As a team grows individuals in the team can perform worse than they would have if they were acting alone. Jennifer S. Mueller found that as teams grow larger the performance of individuals on the team can suffer because the social bonds between members of the team grow weaker. In her study relational loss outweighed extrinsic motivational loss and perceived coordination loss in explaining the tendency for individuals on large teams to perform worse. Relational loss is a measure of the likelihood of one team member to obtain task-related help from another team member when it is needed. Extrinsic motivation is the tendency of team members to perform actions because of the likelihood of recognition from  other members of the team. Coordination loss is the tendency for team members to become less capable of taking synchronistic action towards completing a task as the team grows. She states: “This study identifies that, in modern contexts, coordination losses and motivation losses provide an incomplete story in explaining why individuals in larger teams perform worse. Instead, the current study shows that relational losses play an important role in explaining why individuals experience performance losses in larger teams. Better understanding of process in larger teams moves the field past an obsession with finding the ‘‘optimal team size,’’ a line of questioning which has yielded little understanding about performance in larger groups. Indeed, the optimal team size may be completely dependent upon the exact nature of the group task which may have as many variations as there are teams. Focusing on process also moves the field past blanket recommendations to simply keep group sizes small. The reality is that managers tend to bias their team size towards overstaffing, and theory would suggest that larger teams have more potential productivity that can lead organizations to increased competitive advantage if managed correctly. ((Mueller, J. S. Why individuals in larger teams perform worse. Organizational Behavior and Human Decision Processes (2011), doi:10.1016/j.obhdp.2011.08.004. Accessed on Jun 29, 2015 at https://1318d3f964915c298476-71207924aec76187d46cf4d3ee8ac05a.ssl.cf2.rackcdn.com/or-mueller_2012_obhdp_why-indivdiuals-in-larger-teams-perform-worse.pdf))
  6. Individual team members, and thus teams in general can have an implicit bias against creative ideas. The best teams are those that recognize this and introduce mechanisms to guard against discarding creative ideas that later go on to become the basis for phenomenally successful products and businesses. A study by Jennifer S. Mueller, Shimul Mewani and Jack A. Goncalo suggests that this might happen because people and teams try to reduce uncertainty, and creative ideas are those that confront us with extreme uncertainty.

Concluding Thoughts


If you want to go fast go alone. If you want to go far go together.


The Big 5 of Team Work and The Coordinating Mechanisms of Teamwork: In Is There a “Big Five” in Teamwork? Eduardo Salas, Dana E. Sims and C. Shawn Burke provide a helpful summary of The Big Five and the Coordinating Mechanisms of Team Work. I am reproducing part of that summary below. ((Eduardo Salas, Dana E. Sims and C. Shawn Burke, Is There a “Big Five” in Teamwork?SMALL GROUP RESEARCH, Vol. 36 No. 5, October 2005 555-599 DOI: 10.1177/1046496405277134. Accessed on June 29, 2015 at http://www.uio.no/studier/emner/matnat/ifi/INF5181/h14/artikler-teamarbeid/salas_etal_2005_is_there_a_big_five_in_teamwork—copy.pdf. I have saved the relevant pages Salas et al – Big 5 in Team Work.))

The Big Five of Teamwork

  1. Team Leadership
    1. Definition: Ability to direct and coordinate the activities of other team members, assess team performance, assign tasks, develop team knowledge, skills, and abilities, motivate team members, plan and organize, and establish a positive atmosphere.
    2. Behavioral Markers: Facilitate team problem solving. Provide performance expectations and acceptable interaction patterns. Synchronize and combine individual team member contributions. Seek and evaluate information that affects team functioning. Clarify team member roles. Engage in preparatory meetings and feedback sessions with the team.
  2. Team Orientation
    1. Definition: Propensity to take other’s behavior into account during group interaction and the belief in the importance of team goal’s over individual members’ goals.
    2. Behavioral Markers: Taking into account alternative solutions provided by teammates and appraising that input to determine what is most correct. Increased task involvement, information sharing, strategizing, and participatory goal setting
  3. Shared Mental Models
    1. Definition: An organizing knowledge structure of the relationships among the task the team is engaged in and how the team members will interact.
    2. Behavioral Markers: Anticipating and predicting each other’s needs. Identify changes in the team, task, or teammates and implicitly adjusting strategies as needed.
  4. Mutual Trust
    1. Definition: The shared belief that team members will perform their roles and protect the interests of their teammates.
    2. Behavioral Markers: Information sharing. Willingness to admit mistakes and accept feedback.
  5. Closed-loop Communication
    1. Definition: The exchange of information between a sender and a receiver irrespective of the medium.
    2. Behavioral Markers: Following up with team members to ensure message was received. Acknowledging that a message was received. Clarifying with the sender of the message that the message received is the same as the intended message.

The Coordinating Mechanisms of Teamwork

  1. Mutual Performance Monitoring
    1. Definition: The ability to develop common understandings of the team environment and apply appropriate task strategies to accurately monitor teammate performance.
    2. Behavioral Markers:Identifying mistakes and lapses in other team members’ actions. Providing feedback regarding team member actions to facilitate self-correction.
  2. Backup Behavior
    1. Definition: Ability to anticipate other team members’ needs through accurate knowledge about their responsibilities. This includes the ability to shift workload among members to achieve balance during high periods of workload or pressure.
    2. Behavioral Markers: Recognition by potential backup providers that there is a workload distribution problem in their team. Shifting of work responsibilities to underutilized team members. Completion of the whole task or parts of tasks by other team member.
  3. Adaptability
    1. Definition: Ability to adjust strategies based on information gathered from the environment through the use of backup behavior and reallocation of intrateam resources. Altering a course of action or team repertoire in response to changing conditions (internal or external).
    2. Behavioral Markers: Identify cues that a change has occurred, assign meaning to that change, and develop a new plan to deal with the changes. Identify opportunities for improvement and innovation for habitual or routine practices. Remain vigilant to changes in the internal and external environment of the team.

Filed Under: Critical Thinking, Entrepreneurship, How and Why, Management, Organizational Behavior, Psychology, Sociology, Strategy, Team Building, Venture Capital Tagged With: Early Stage Startups, Explorations, Long Read, Strategy, Team building, Teamwork, Venture Capital

Notes On Early Stage Technology Investing; Art, Science, or Both?

June 18, 2015 by Brian Laung Aoaeh

Conducting market research is an important part of the investment decision making process.
Conducting market research is an important part of the investment decision making process.

Often when I have asked other people this question I get a response that leaves me feeling dissatisfied. It seems most investors are compelled to take one side over the other, and, at least as far as the admittedly small sample  of investors I have asked this question are concerned, insufficient thought is given to the notion that perhaps early stage investing has elements that make it like art in some respects but like science in others.

I am writing these notes on early stage technology investing in order to clarify my own thinking on the subject. ((Let me know if you feel I have failed to attribute something appropriately. Tell me how to fix the error, and I will do so. I regret any mistakes in quoting from my sources.)) Ideally, once I am done I should have a clearer understanding of how my process for arriving at “yes” or “no” decisions should work, in what context certain steps can be truncated or even eliminated altogether, and the risks I am exposing our fund’s limited partners and myself to by the choices I make during the period over which I study and analyse an early stage startup that is an investment prospect.

To ensure we are on the same page, and thinking about the issues from the same starting point . . . first, some definitions.

Definition #1: What is a startup? A startup is a temporary organization built to search for the solution to a problem, and in the process to find a repeatable, scalable and profitable business model that is designed for incredibly fast growth. The defining characteristic of a startup is that of experimentation – in order to have a chance of survival every startup has to be good at performing the experiments that are necessary for the discovery of a successful business model. ((I am paraphrasing Steve Blank and Bob Dorf, and the definition they provide in their book The Startup Owner’s Manual: The Step-by-Step Guide for Building a Great Company. I have modified their definition with an element from a discussion in which Paul Graham, founder of Y Combinator discusses the startups that Y Combinator supports.)) As an investor, I hope that each early stage startup in which I have made an investment matures into a company.

Definition #2: What is art? 

The expression or application of human creative skill and imagination, typically in a visual form such as painting or sculpture, producing works to be appreciated primarily for their beauty or emotional power. ((http://www.oxforddictionaries.com/us/definition/american_english/art, acessed Jun 18th, 2015.))

In an article published in 2010, Marilina Maraviglia says:

This question pops up often, and with many answers. Many argue that art cannot be defined. We could go about this in several ways. Art is often considered the process or product of deliberately arranging elements in a way that appeals to the senses or emotions. It encompasses a diverse range of human activities, creations and ways of expression, including music, literature, film, sculpture and paintings. The meaning of art is explored in a branch of philosophy known as aesthetics. At least, that’s what Wikipedia claims.

Art is generally understood as any activity or product done by people with a communicative or aesthetic purpose – something that expresses an idea, an emotion or, more generally, a world view.

It is a component of culture, reflecting economic and social substrates in its design. It transmits ideas and values inherent in every culture across space and time. Its role changes through time, acquiring more of an aesthetic component here and a socio-educational function there. ((Marilina Maraviglia, What Do We Really Mean By Art? Accessed on Jun 18th, 2015 at http://www.smashingmagazine.com/2010/07/23/what-do-we-really-mean-by-art/))

Lastly, according to Tolstoy:

To evoke in oneself a feeling one has once experienced, and having evoked it in oneself, then, by means of movements, lines, colors, sounds, or forms expressed in words, so to transmit that feeling that others may experience the same feeling — this is the activity of art.

Art is a human activity consisting in this, that one man consciously, by means of certain external signs, hands on to others feelings he has lived through, and that other people are infected by these feelings and also experience them. ((Leo Tolstoy, Art and Sincereity. Accessed on Jun 18th, 2015 at http://denisdutton.com/tolstoy.htm))

I will attempt to extract a few key characteristics that I think qualify something as art on the basis of the preceding quotations. ((Adapted from: What is art? An Essay on 21st Century Art, Sylvia Hartmann. Accessed on Jun 18th at http://silviahartmann.com/art.php))

First, art is initially conceived or imagined entirely in the artist’s mind.

Second, the artist uses an artistic medium to transform what has been an intangible object in the artist’s mind into something tangible that other people can experience.

Third, art evokes a response from the people who experience it.

Finally, art is transformative in nature. Once experienced, art changes how we see and experience the world.

Definition #2: What is science? Conventional, and commonly held notions about what constitutes science often mistake and confuse the “pedagogy of science” with the “practice of science” . . . What does that mean precisely?

When we learn science we do so in a very formulaic manner. This makes sense, the first step in becoming a scientist is learning a sufficient amount of the body of knowledge that man has accumulated over time thanks to the work done by generations of scientists. The same is true for mathematics. That makes sense . . . Structure and process are important if the typical student of science is to make steady progress through the accumulated body of knowledge, until that student has built enough mastery of the subject to begin making new contributions to the knowledge we keep accumulating about the world. Out of necessity, the process of learning science adheres to the “scientific method” . . . It is linear, and simple, and provides structure for how one goes about mastering the accumulated knowledge of science. Generally, the process of teaching and learning science leaves little room for creativity. This leads many to develop and embrace the notion that the practice of science is an endeavor devoid of creativity. The way science is taught and learned also leads to the misconception that science is uniformly precise at every stage, and that it leads to conclusive answers to the questions that scientists investigate.

However, how one learns science is not the same as how one practices science. The following images attempt to illustrate that point.

Real Process of Science (1 of 3) . Image Credit: University of California Museum of Paleontology's Understanding Science
Real Process of Science (1 of 3). Image Credit: University of California Museum of Paleontology’s Understanding Science
Real Process of Science (2 of 3). Image Credit: University of California Museum of Paleontology's Understanding Science
Real Process of Science (2 of 3). Image Credit: University of California Museum of Paleontology’s Understanding Science
Real Process of Science (3 of 3). Image Credit: University of California Museum of Paleontology's Understanding Science
Real Process of Science (3 of 3). Image Credit: University of California Museum of Paleontology’s Understanding Science

In real-life, scientists:

  1. Create knowledge using an iterative process in which new advancements are built on prior work, in relatively small, incremental steps. The process starts with ideas, beliefs, or guesses . . . conceived entirely in the scientist’s mind. Old knowledge is revised, and modified based on new discoveries made possible by advancements in technology.
  2. Conduct research for which there’s no pre-determined outcome. For example, the evidence obtained from observation and experimentation might contradict the researcher’s best before-the-fact guesses and assumptions as well as established and accepted theory.
  3. Always begin with an idea that can be tested through observation, experimentation, measurement, and analysis. Observation, experimentation, measurement, and analysis – together, these constitute the scientist’s medium.
  4. Conduct experiments to test the ideas that they seek to investigate. The process of conducting experiments is the method by which they collect the necessary evidence that leads them to ultimately accept or reject the idea under investigation. To succeed at this they must be willing to reject conventional-wisdom, and scrutinize closely-held and cherished beliefs based on the evidence and observations of the experiments they perform.
  5. Typically work in collaboration with other scientists, or scientists-in-training. For example, as an undergraduate mathematics and physics double major at Connecticut College, I spent three years assisting Prof. Arlan W. Mantz with research on the temperature dependence of molecular absorption line widths and shapes using tunable semiconductor diode lasers. The nature of scientific collaboration can be direct or indirect.
  6. Often say that ” . . . further research needs to be conducted on this topic . . . ” This refrain seems to be a common feature of presentations in which scientists present their work. Yet, if one understands science as the pursuit of a deeper, nuanced, and increasingly sophisticated understanding of the laws that govern the natural world . . . That makes complete sense. Scientific research is ongoing in its search for better answers to questions that non-scientists might consider closed-to-debate.
  7. Transform our understanding of the laws of nature, and in so doing change the relationship that we each have with the world around us.

I can’t find a substantive difference between what we stereotypically call “art” and that which we stereotypically call “science” . . . Can you?

Does science evoke a response from the people who experience it? Each time I use one of the many objects that has become part of modern life, I am filled with awe at what scientists have accomplished. I will grant that there is one difference between “art” and “science”; namely it is that art is related to notions of aesthetic beauty. Yet, one could argue that there is aesthetic beauty in science as well.

Consider the equation:

Mass-Energy Equivalence
Mass-Energy Equivalence

Let’s set dogma aside, for a moment; Can one argue objectively that this equation is not an aesthetically pleasing way to express the relationship that exists between the energy and the mass of an object?

What are the implications for me as an early stage investor, if “art” and “the practice of science” are more alike than they are different?

Here is a scientist’s code of conduct according to the University of California Museum of Paleontology: ((“Participants in science behave scientifically.” Understanding Science. University of California Museum of Paleontology. Accessed on Jun 18th, 2015 at http://undsci.berkeley.edu/article/0_0_0/whatisscience_09))

  1. Pay attention to what other people have already done. Scientific knowledge is built cumulatively. If you want to discover exciting new things, you need to know what people have already discovered before you. This means that scientists study their fields extensively to understand the current state of knowledge.
  2. Expose your ideas to testing. Strive to describe and perform the tests that might suggest you are wrong and/or allow others to do so. This may seem like shooting yourself in the foot but is critical to the progress of science. Science aims to accurately understand the world, and if ideas are protected from testing, it’s impossible to figure out if they are accurate or inaccurate!
  3. Assimilate the evidence. Evidence is the ultimate arbiter of scientific ideas. Scientists are not free to ignore evidence. When faced with evidence contradicting his or her idea, a scientist may suspend judgment on that idea pending more tests, may revise or reject the idea, or may consider alternate ways to explain the evidence, but ultimately, scientific ideas are sustained by evidence and cannot be propped up if the evidence tears them down.
  4. Openly communicate ideas and tests to others. Communication is important for many reasons. If a scientist keeps knowledge to her- or himself, others cannot build upon those ideas, double-check the work, or devise new ways to test the ideas.
  5. Play fair: Act with scientific integrity. Hiding evidence, selectively reporting evidence, and faking data directly thwart science’s main goal — to construct accurate knowledge about the natural world. Hence, maintaining high standards of honesty, integrity, and objectivity is critical to science.
Image Credit: Tasha S. K. Aoaeh
Image Credit: Tasha S. K. Aoaeh

What are the risks I take if I cling to the notion that early stage investing is “all art” and “no science”? For one, I will not subject my own assumptions, hunches, guesses, biases, ideas, visions, opinions to the level of scrutiny to which they should be subjected. Worse yet, I might fail to subject other people’s ideas and assumptions to sufficient scrutiny and testing. Instead; I might rely on decision-making heuristics like “pattern-matching” and I might engage in “groupthink” or succumb to social-proof bias . . . I might fail to maintain a mind that is sufficiently open and flexible to recognise an early stage startup founder poised to transform the world because that founder does not fit my idea of what such a founder “looks like” . . . I might pass on a great startup investment for reasons that are completely irrelevant simply because I have failed to develop my own thinking and ideas about its prospects . . . I might fail to unlock promising new markets before the greatest returns have already been harvested by other early stage investors because I lacked enough curiosity and discipline to ask nuanced questions and challenge myself to acquire new knowledge and insights from other sources and other people – possibly people outside circles within which I am most comfortable . . . I might spend my career in early stage technology investing in a self-imposed exile to the land of piddling mediocrity.

Leonard Mlodinow on human thought and the evolution of science – podcast by Guardian ScienceWeekly #np#SoundCloudhttps://t.co/KjJRVOH8wO

— Brian Laung Aoaeh (@brianlaungaoaeh) June 19, 2015

I find none of those possible outcomes palatable; early stage investing is both an art and a science. The best early stage venture capitalists behave in keeping with that belief. It is their trade secret.

Science is Uncertain - Freeman Dyson
Science is Uncertain – Freeman Dyson

Further Reading

  1. The Pleasure (and Necessity) of Finding Things Out

Filed Under: Business Models, Innovation, Investing, Lab Notes, Lean Startup, Science, Strategy, Technology, Venture Capital Tagged With: #InvestmentPhilosophy, #WorldView, Business Models, Early Stage Startups, Innovation, Investment Analysis, Long Read, Strategy, Technology, Venture Capital

A Note on Developing and Testing Hypotheses

June 10, 2015 by Brian Laung Aoaeh

Working on Problem Sets
Working on Problem Sets

This post is a continuation of the discussion I started in A Note On Startup Business Model Hypotheses. In this post I will describe how one might go about developing and testing a hypothesis about any aspect of a startup’s  business model. ((Let me know if you feel I have failed to attribute something appropriately. Tell me how to fix the error, and I will do so. I regret any mistakes in quoting from my sources.))

I will use a fine-dining restaurant as a motivating example.

To ensure we are on the same page, first some definitions.

Definition #1: What is a startup? A startup is a temporary organization built to search for the solution to a problem, and in the process to find a repeatable, scalable and profitable business model that is designed for incredibly fast growth. The defining characteristic of a startup is that of experimentation – in order to have a chance of survival every startup has to be good at performing the experiments that are necessary for the discovery of a successful business model. ((I am paraphrasing Steve Blank and Bob Dorf, and the definition they provide in their book The Startup Owner’s Manual: The Step-by-Step Guide for Building a Great Company. I have modified their definition with an element from a discussion in which Paul Graham, founder of Y Combinator discusses the startups that Y Combinator supports.))

Definition #2: What is a business model? A business model is the description of how a startup will create, deliver and capture value. Alex Osterwalder’s Business Model Canvas is one framework for describing and documenting the elements of a startup’s business model.

Alex Osterwalder's Business Model Canvas, from the book Business Model Generation
Alex Osterwalder’s Business Model Canvas, from the book Business Model Generation

Definition #3 What is a hypothesis? A hypothesis is a statement, or a group of statements, that proposes an answer to a question, or a solution to a problem, in a manner that is testable through experimentation. The goal of experimentation and testing is to determine if the hypothesis is correct, and to inform the subsequent actions that the startup should take on the basis of that evidence.

A hypothesis is;

  1. A guess about the process underlying a set of observations that have been made by the founder.
  2. A testable guess, in the sense that it attempts to establish and predict the basic relationship between two or more variables that interact with one another to lead to the observed phenomena. This allows the researcher to test what happens when one of those variables is allowed to change, while others are held constant.
  3. Not the same as a research question; in the sense that a research question is broad while a hypothesis is more narrow in scope.

Motivating example; A fine-dining restaurant is experiencing an ongoing slump in revenues. The restaurateur wishes to test a number of possible approaches to reversing that trend. From prior experience they believe that the following factors each has a positive impact on overall sales at the restaurant;

  1. Wine Sales
  2. Liquor Sales
  3. Appetizers
  4. Seasonal Menu Changes
  5. Table Turns
  6. PR, Advertising and Marketing

How should management determine where to make an adjustment in order to improve overall sales without incurring a large capital outlay?

Discussion

Let us assume that Sales can be modeled by the following relationship. ((I am in no way suggesting this is the appropriate model for this problem. I am using this only for the purpose of this discussion. Assume the restaurateur has developed this model after years of experience in the industry, and based on trial and error. This model is supported by the last 5 years of sales data.))

Restaurant Sales as A Function of Various Factors
Restaurant Sales as A Function of Various Factors

Remember that the goal is to try to figure out a course of action without spending too much by way of capital until the restaurateur is fairly certain that any capital deployed to this end will yield a disproportionately positive result.

To keep the discussion brief, let’s focus on two of the factors that management believes play an important role in driving revenues from the preceding list. Let’s focus on Wine Sales and PR, Advertising, and Sales.

Assume the restaurant is laid out such that the front-of-the-house is organized as two nearly identical dining rooms, they are separated by an ornate vestibule. During the experiment one dining room is operated status quo, while the other is operated as part of the experiment. Management feels this makes sense because guests are typically evenly split between the two dining rooms. Let’s call them Dining Room #1 – the control dining room, and Dining Room #2 – the dining room in which the experimental change is implemented.

Possible Hypotheses for Wine Sales Program – Experiment #1

  1. General Hypothesis: Implementing a wine-program will have an effect on sales.
  2. Directional Hypothesis: Implementing a wine-program will have a positive effect on sales.
  3. Testable Hypothesis: If we implement a wine-program we will increase revenue significantly because average check size increases substantially when wine sales increase.

Experiment #1: Management might hold all the factors constant, but implement an in-house training program to get wait-staff more comfortable speaking to guests of the restaurant about it’s current small collection of wine. Waiters in Dining Room # 2 are coached to discuss wines before guests order their meal, and afterwards when a guest might be considering a dessert. Let’s assume this program lasts a month. At the end of the month, management compares data from Dining Room #1 with data from Dining Room #2.

Possible Hypotheses for Inside-Sales Program – Experiment #2

  1. General Hypothesis: Implementing an inside-sales program will have an effect on sales.
  2. Directional Hypothesis: Implementing an inside-sales program will have a positive effect on sales.
  3. Testable Hypothesis: If we implement an inside-sales program we will increase revenue significantly because average check size increases substantially when wait-staff are encouraged to engage with guests.

Experiment #2: In this experiment, which also lasts a month, management trains wait-staff in Dining Room #2 to engage guests in casual conversation and to speak more enthusiastically and informatively about each day’s specials. Wait-staff are trained to highlight upcoming events at the restaurant that regular guests might find interesting enough to return for – a special prixe-fixe Wine Dinner the following week, for example. On occasion, every 90 minutes or so, the restaurant’s chef de cuisine spends about 10 minutes walking through Dining Room #2 and socializes with guests. The restaurateur wishes to determine if making things like this a more consistent part of how the restaurant operates makes sense from the perspective of increasing revenue.

During each experiment management collects the following data for each day, each week, and also for the month:

  1. Total Revenue
  2. Average Check Size – Revenue per Table
  3. Average Wine Sales – Wine Sales per Table
  4. Positive Social Media Mentions
  5. Negative Social Media Mentions
  6. In-restaurant Reservations – future reservations made while the guest is at dinner as a result of learning about an upcoming special event. This only applies to Experiment #2.

 

9-inch tall stack of paper - 6 months worth of studying
9-inch tall stack of paper – 6 months worth of studying

Now that the restaurant has collected some data it is time to test the data to see what conclusions the restaurateur might be able to reach based on each of the experiments.

Stating Null and Alternative Hypotheses

To test the hypotheses our restaurateur must make two different statements that will form the basis of a test; The Null Hypothesis states that the effect our restaurateur thinks exist does not in fact exist. The Alternative Hypothesis makes a statement opposite to that made in the null hypothesis, and typically is the statement we want to prove.

Experiment #1: Null and Alternative Hypothesis

  1. Null Hypothesis: Implementing a wine sales program has no effect on revenue.
  2. Alternative Hypothesis: Implementing a wine sales program has a positive effect on revenue.

Experiment #2: Null and Alternative Hypothesis

  1. Null Hypothesis: Implementing an inside-sales program has no effect on revenue.
  2. Alternative Hypothesis: Implementing an inside-sales program has a positive effect on revenue.

Note that the null and alternative hypotheses stated above are merely examples. The restaurateur could formulate each of those statements in more general terms, or with more specificity than I have done. To some extent that choice depends on the granularity of the data that was collected from the experiments.

At this point our restaurateur can perform a test of statistical inference to reach a conclusion – the outcome would be a “rejection” or “a failure to reject” the null hypothesis. A rejection of the null hypothesis leads us to accept the alternative hypothesis. A failure to reject the null hypothesis leads us to fail to accept the alternative hypothesis.

Assuming that the restaurateur rejects the null hypothesis in both instances, then it makes sense to spend some capital trying to build out a more robust version of each of the experiments we described, with the intention of operationalizing what was done during the experiment and making those practices a permanent part of how the restaurant is managed and run on an ongoing basis.

Closing comments:

  1. I have glossed over a significant amount of detail. That was deliberate. The goal of this post is not to discuss statistical theory, but to think about how statistical thinking can help startup founders who need to make important choices about how to utilize scarce resources. More detail can be found in any good introductory level business statistics text book.
  2. While going through this process our restaurateur needs to ask more questions than I did in this post. For example, what does “significant” mean? Is a 5% increase in revenue significant? Why? Is a 20% increase in revenue significant? Why? Or, why not? Are the costs associated with implementing the changes necessary to make what was done during the two experiments a permanent operating practice of the restaurant justified by the restaurateur’s forecasts of the long term benefit of doing so? Why, or why not?
  3. It is always important to think about sources of error whenever one is conducting an experiment that is supposed to yield data that decisions like the one I described in this post will be based upon. In this instance, one concern might be that the restaurateur is not collecting the appropriate data on which this decision should be based. Or, for example, that a single month is not a wide enough window of time to determine if the effect observed during this period of the experiment persists during the remaining 11 months of the year.

Notwithstanding those concerns, I believe that when it is possible, this kind of analysis should always complement management’s intuition.

Filed Under: Business Models, Customer Development, Entrepreneurship, How and Why, How To, Innovation, Investing, Lean Startup, Startups, Venture Capital Tagged With: Business Model Canvas, Business Models, Early Stage Startups, Hypothesis Testing, Long Read, Probability, Statistics, Strategy, Venture Capital

Case Study: Fab – How Did That Happen?

November 27, 2014 by Brian Laung Aoaeh

A 2012 iteration of Fab's "About Page"
A 2012 iteration of Fab’s “About Page”

Recent reports in the press about the problems Fab is facing got me thinking about the lessons one might learn from its meteoric rise and spectacular crash. Professional investors always give this disclaimer; Past performance does not necessarily guarantee or predict future results. Similarly, Fab’s experience is not necessarily a precedent that will always be proven true. However, it is my responsibility as an investor to try to update my understanding of how what has happened to Fab should influence the investment choices I make in the future, after all that is one aspect of my fiduciary responsibility to the KEC Ventures’ limited partners. ((Any errors in appropriately citing my sources are entirely mine. Let me know what you object to, and how I might fix the problem. Any data in this post is only as reliable as the sources from which I obtained them.))

In this case study will I examine; Fab’s market, Fab’s business model and some risks inherent to that business model, the competitive landscape it faced, Fab’s history as reported in the press, and last I will outline some lessons early stage investors might draw from Fab’s experience. ((I do not have the benefit of knowing anyone at Fab. This post is based entirely on public information. My employer KEC Ventures is an investor in JustFab. We made that investment in 2014. Fab and JustFab sued one another in 2013.))

Fab was founded as a social network for gay men in 2010 by Jason Goldberg and Bradford Shellhammer. It pivoted in June 2011 and adopted an e-commerce business model focused on selling a wide range of products to individuals on the basis of daily design inspiration. It became immensely popular, and within a month of its launch reports suggested it had more than 350,000 members and that it processed more than 1,000 orders each day. It’s membership was growing at a rate of about 5,000 new members per day. ((Tricia Duryee; Flash Sales Site Fab.com Raises $8 Million to Be a Step Up From Etsy. Accessed online on Nov. 27, 2014.))

The E-commerce Market

Fab entered the e-commerce market selling directly to the individual consumer. E-commerce is an enormous market according to estimates from eMarketer. The chart below provides a sense of the size and scale of the market.

Global B2C Ecommerce Sales: 2012 - 2017
Global B2C E-commerce Sales: 2012 – 2017

The following table provides an estimate of the distribution of b2c e-commerce sales by region.

B2C E-commerce Sales: 2012 - 2017 by Region
Global B2C E-commerce Sales: 2012 – 2017 by Region

Evidently, e-commerce is a large, and quickly growing market. Global growth will be driven by increasing economic prosperity in China, India, Argentina, Brazil, Russia, Italy and other markets in which e-commerce has not yet reached maturity. At the time it launched Fab seemed well positioned to exploit the expectations of future growth in e-commerce sales in North America, and in other parts of the world.

You might guess that e-commerce is a fiercely competitive market. Fab faced direct competition from Fancy, Achica, Hay Needle, Rue La La, Open Sky, Etsy, Beyond The Rack, Gilt Groupe, Rent The Runway, Zulily, Ideeli, Privalia, and Touch of Modern, among others. ((There are too many indirect competitors to count.))

Fab’s Business Model

Fab built a classic e-commerce market-place. It acted as a curator of products characterised by “great design” where what qualified for that categorization was determined by Bradford Shellhammer’s taste and design sensibility. For example, consider this description of the process by which products featured on Fab were selected; ((Tomio Geron. From Gay To Pay: How Fab.com Became The Hottest Online Retailer. Accessed Nov. 27th, 2014.))

He’s demonstrating the “Beach Thingy,” a flat, brightly colored plastic back of a chair with two spikes that anchor it in the sand. One of the buyers says it’s just a piece of plastic. Shellhammer disagrees. “It’s perfect because you can lay your towel down and slide up or down. And with a stomach like mine I hate getting in and out of the chair and having to suck it in. It’s perfect. This is why I’m a good salesperson,” he says, laughing.

The buyers take turns pitching Shellhammer stuff they think belongs on Fab.com, which means almost anything: cutlery, T-shirts, dog food bowls, wall planters and wine refrigerators. The common denominator is that it has to be something Shellhammer likes, which usually means brightly colored, whimsical, something you haven’t seen before, from a young designer no one’s heard of. When a buyer strikes his fancy, approval comes punctuated with “Love!,” “Holy sh-t!”, “Very nice!” or all three.

Simply put, on one side of the market Fab created a community of product designers from whom it curated the products that it would present to its members. On the otherside Fab developed a community of users or members who were the consumers of the products produced by the designers. Fab ensconced itself between these two groups and acted as a platform facilitating the interactions between them. In exchange, Fab collected fees for each transaction that it facilitated. Fab took care of logistics, transaction processing, customer relations, and marketing. It could do this by building proprietary systems, or by partnering with providers of modular solutions and other services which it could incorporate within its “platform” for a fee. For example, it might use the payment processing technology developed by another startup, and rely on drop-shipping services from UPS, FedEx, USPS or another shipping service. Fab’s business model combined product curation with flash-sales. Presumably, the trend that made such business models popular between 2010 and 2013 was based on consumers’ desire to acquire social status through the discovery of unique, exciting, scarce, exquisitely designed, and affordable products. The flash-sale mechanism was used to create a sense of urgency in order to drive purchasing behavior. Every day Fab would send an email to its members in which the flash-sale of that day was announced.

In 2012 my colleagues and I were studying an investment in Ideeli. As part of our analyses we debated the merits of the business model I have described. On one occasion, we were discussing Fab with an early stage venture capitalist based in New York City who was paying us a visit at our office in Manasquan, New Jersey. He told us Fab would easily justify its status as a billion dollar company and even exceed that by a mile because of its design-centric approach. In his words; “I actually want to open my email from Fab because I know I will always get great design.” In his defense, Fab’s founders confidently echoed that sentiment in interviews; Forbes reported that Jason Goldberg, the CEO, said;

What hadn’t been done is bringing excitement to ecommerce. Not just commodity items. ((Tomio Geron. Fab.com Aims For Amazon-Sized Business. Accessed on Nov. 27th, 2014.))

The same article quotes Bradford Shellhammer as saying;

The product assortment is very emotional. That’s why we feel this business is very hard to replicate.

I had severe doubts about that aspect of the business model. The question I asked myself was this; Do people open the daily emails from Ideeli impulsively as a form of transient self-entertainment in the absence of a more interesting alternative, or is there something fundamental about human nature that ensures that people will continue to behave in ways that make that kind of business model viable over the long term? No one could give me a satisfactory answer so I argued against making an investment in Ideeli. ((My concerns were amplified once I performed some financial statement analysis. That exercise confirmed my suspicions; among other things, it failed my tests of its earnings quality. Each of my two other colleagues at the time had also reached a “Don’t invest.” conclusion by looking at Ideeli from other angles.))

Fab integrated social networking in its business model. At one point as many as half of its users had come through social networking. People would make a purchase on Fab.com and then share the news about that purchase with their friends on Facebook and Twitter. It incentivized people with a $5.00 credit to use on the website if they allowed their purchases on Fab to be automatically shared to their social networks. ((Kristen Nicole. How Fab.com Brainwashed Me Into Broadcasting What I Buy. Accessed on Nov. 27th, 2014.))

The results were phenomenal. Fab reported $100 million of sales within a year of going into business, after growing to more than a million members between June and September of 2011. A year later Fab removed the requirement that individuals had to become registered members of Fab.com in order to browse products. This happened even though it had grown its membership to 1M faster than Facebook, Twitter and Groupon and said it had more than 10M members by December 2012.

On the strength of that performance, Fab raised a $1M seed round in July 2011. That was followed by a $7.7M Series A at a $20M valuation and then a $40M Series B at a $200M valuation, in August and December respectively of that year. It raised a $105M Series C tranche in July 2012, and a $15M tranche in November that year, at a $600M valuation. Between June and August 2012 it raised a $165M Series D in three tranches. The valuation for the Series D was reportedly between $1B and $1.2B. Its sales at the time were reportedly around $115M, giving it a price-to-sales multiple between 8.7x and 10.4x. Altogether, it raised $336.3M. ((I obtained this data from CBInsights and Crunchbase.))

External signs that Fab’s rocket engines were beginning to sputter and fail started appearing in 2013; in July it laid off 100 employees in Berlin and moved others to New York, in October it cut 20% of its worldwide workforce by eliminating 101 jobs. It also made a pivot from the flash-sales model. Bradford Shellhammer left the company around that time. In May 2014 rumours about further lay-offs began circulating in the press, suggesting that as many as 60 of its 305 employees would be let go. Most recently it is reported that Fab has slimmed down to 25 employees, that it was burning $14M per month at its peak, and that some of its problems began because it only secured half the amount of capital it originally set out to raise in its last round. Yet other reports suggest Fab might be sold for as little $15M.

So what went wrong?

What questions might have suggested to Seed and Series A investors that the realities Fab is facing today had more than 50% probability of becoming the reality within their investment horizon? ((In performing my assessments of early stage startups KEC Ventures might invest in I assume we will hold a Seed or Series A investment for at least 10 years. Also, I assume we will not be in a position to engineer a favorable exit within that horizon.))

The study of economic moats is useful in trying to avoid making investments that evaporate into thin air before the investor has harvested returns. In this case the investor would be performing a forward-looking assessment. But first, what is an economic moat? An economic moat is a structural feature inherent to a company’s business model which protects it from the deleterious effects of competition. Economic moats help companies preserve and sometimes enhance the advantages they enjoy over their competitors. There are five ways in which a startup can build an economic moat; Branding, Intellectual Property or Intangibles, Efficient Scale or Cost Advantages, Network Effects, and Switching Costs or Buyer Lock-in.

Let’s quickly go through each of these for Fab, remember that we are pretending this is 2010/2011 and that we are studying Fab for a Seed or Series A investment.

  • Brand: High; because consumers have come to associate Fab with great curation, great content, and great design.
  • Intellectual Property or Intangibles: Low, or non-existent; because there’s nothing proprietary about what Fab is doing, contrary to the claims made by Messrs. Goldberg and Shellhammer. Other great designers would be able to replicate what Fab is doing. The notion that Fab could develop a monopoly on emotionally inspiring design is specious at best. Also, Fab failed to fully harness technology to its advantage by operating with a relatively low level of automation.
  • Efficient Scale or Cost Advantages: Low, or non-existent; the use of social channels only confers an advantage for as long as other startups do not incorporate those methods into their marketing and sales processes. Moreover, there is no way to create exclusive relationships with the social networks through which Fab acquires users without incurring high sales and marketing expense.
  • Network Effects: Low, or non-existent; popularity in-and-of itself does not reflect the existence of network effects. Neither does virality point to the existence of network effects. In fact, one might argue that businesses like Fab exhibit a negative network effect – each individual user has a less enjoyable experience as the user base grows if social status is in fact a key driver of Fab’s relationship with its members or users.
  • Switching Costs or Buyer Lock-in: Low, or non-existent; because members suffer little to no pain if they decide to stop using Fab and instead take their consumption to one of its competitors.

The conclusion one draws from the preceding analysis is that although Fab appeared to have found a way to grow remarkably fast, that growth could end in very short order. Of equal concern, Fab’s business could deteriorate just as fast as it had grown, if not faster.

Another way to look at the issue is this; In the absence of its near-irresistibly seductive revenue and user growth would Fab be the kind of business that the Seed or Series A investor would be comfortable owning for 10 years or more?

Conclusion

So perhaps one is asking the wrong question when one asks “What went wrong?” or “How did that happen?” in reference to Fab. A more appropriate question might be; “Why did investors believe a startup with no inherent advantage over its direct and indirect competitors could defy the gravitational forces of competition and changing consumer tastes?”

For that answer, you’d have to speak with one of Fab’s investors.

Filed Under: Business Models, Case Studies, Entrepreneurship, Funding, Innovation, Venture Capital Tagged With: Business Models, Case Study, Early Stage Startups, Economic Moat, How Did That, Strategy, Switching Costs, Venture Capital

Revisiting What I Know About Switching Costs & Startups

October 24, 2014 by Brian Laung Aoaeh

Switching costs are another aspect of a startup’s business model that I pay attention to. Together with Network Effect, Intangibles, Cost Advantages, and Efficient Scale they form the source of economic moats. ((Any errors in appropriately citing my sources are entirely mine. Let me know what you object to, and how I might fix the problem. Any data in this post is only as reliable as the sources from which I obtained them.)) In this post I will discuss switching costs; what they are, how they develop and evolve, and how switching costs can help or hurt a startup.

To ensure we are on the same page, I will start with some definitions. In the rest of this discussion I am primarily focused on early stage technology startups. Also, the customer I have in mind is one whose present known needs are adequately served by the current product. Finally, I assume government intervention is not a significant factor.

Definition #1: What is a startup? A startup is a temporary organization built to search for the solution to a problem, and in the process to find a repeatable, scalable and profitable business model that is designed for incredibly fast growth. The defining characteristic of a startup is that of experimentation – in order to have a chance of survival every startup has to be good at performing the experiments that are necessary for the discovery of a successful business model. ((I am paraphrasing Steve Blank and Bob Dorf, and the definition they provide in their book The Startup Owner’s Manual: The Step-by-Step Guide for Building a Great Company. I have modified their definition with an element from a discussion in which Paul Graham, founder of Y Combinator discusses the startups that Y Combinator supports.))

Definition #2: What is an economic moat? An economic moat is a structural barrier that protects a company from competition. ((Heather Brilliant, Elizabeth Collins, et al. Why Moats Matter: The Morningstar Approach to Stock Investing. Wiley. Hoboken, NJ. 2014; p. 1))

Definition #3: What are switching costs? Switching costs refer to the expense in cash, time, convenience, risk, and process disruption that a customer of one product or service must incur if they change from one product from an incumbent Producer A to another product from Producer B. Switching costs can be explicit or implicit, and confer the benefit of customer lock-in to incumbent suppliers if the customer perceives the cost of switching to outweigh the benefits that would be obtained by making the switch. ((In economics switching costs are defined as the disutility that a customer experiences in switching between products.))

How do switching costs develop? 

Switching costs develop and become stronger when an incumbent product becomes “mission-critical” for the purpose for which the customer acquired the product in the first place. An incumbent that combines network effects with high switching costs in the same product line is well positioned to build a durable moat around its business. Economists describe at least three main assumptions about switching costs. Exogenous switching costs are believed to evolve without any intentional influence from the incumbent producer – for example; customers independently create or enhance switching costs by becoming more skilled and experienced in applying and adapting the incumbent product towards solving a wider variety of problems than the producer had originally envisioned. Endogenous switching costs evolve through deliberate actions by the incumbent – for example; volume discounts to encourage wide adoption within a company of a new software product, coupled with long-lived license agreements and punitive charges if the license is terminated between license renewal dates. Also, deeply entrenched incumbents will typically opt for incompatibility with competing products while new entrants will prefer to build in compatibility with the incumbent product that they seek to displace. Lastly, switching costs are symmetrical between all the producers competing within a given market. ((Pei-yu Chen and Lorin M. Hitt. Information Technology and Switching Costs. September 2005; p.9. Accessed online on Oct. 19, 2014.))

What are the types of switching costs that lead to buyer lock-in? 

One might consider switching costs to exist along a continuum that is characterised most distinctly by how intertwined each of the categories identified by economists is tightly intertwined with nearly every other category below. ((The following discussion is based largely on; Paul Klemperer. Competition When Consumers Have Switching Costs: An Overview With Applications to Industrial Organization, Macroeconomics, and International Trade. Review of Economic Studies, 1995; p. 515 – 539. Accessed online on Oct. 19, 2014.))

Compatibility Requirements make it difficult and expensive to switch between products. Consider an individual or organization running MS Windows contemplating a decision to switch to Linux. The implications of this choice are generally non-trivial. Compatibility requirements are largely an implicit cost that is borne by the customer.

Transaction Costs impose an explicit cost on customers who decide to switch from one product to another. For example, cable-tv subscription agreements typically impose a high penalty on subscribers who decide to terminate their agreement before it has run its full course. Any cost than can be measured explicitly and that has to be considered in switching between products falls under this category.

Cognitive Costs are the perceived hurdles customers feel they will have to overcome when they switch from one product to another. One example is the dichotomy between people who prefer Mac OS and those who prefer MS Windows. I understand the practical reasons one might have for preferring one operating system over the other; for example, one platform is more compatible with a wider variety of products in a certain category of software applications than the other. What often surprises me is the speed with which conversations between those two groups quickly devolve past anything one might consider rational, logical or practical to become an exercise in name-calling and ad-hominem attacks. Such episodes suggest that in some situations there are significant psychological issues at play that have nothing to do with the reality one might face if one tried to switch products. ((Klemperer calls these psychological costs.))

Uncertainty is the apprehension that the customer has to face regarding the quality of the new product. Uncertainty is minimized only if the customer believes that, at a minimum, the new product will match the old product in quality. As an example, consider a small business that is trying to decide if it should migrate from MS Exchange Server to Google Apps for Business at a time when its license for the former is up for renewal. Uncertainty works in favor of the incumbent product when customers have very little information about the relative performance characteristics of the new product. ((Ibid. Pei-yu Chen and Lorin M. Hitt. p. 4.))

Learning Costs are the known hurdles that a customer must overcome in order to attain mastery of the new product that is at par with that customer’s mastery of the incumbent product required to accomplish the tasks the customer needs to complete. Learning costs need to be considered on their own, independent of other categories of switching costs. High learning costs tied to adopting a new product increase switching costs in favor of the incumbent. Minimal learning costs tied to the adoption of a new product lower switching costs in favor of the new product. On one hand, an individual customer might be willing to face high learning costs in situations where the consequence if things go wrong is non-fatal; for example switching from one messaging app to another. On the other hand, enterprise or small business customers who face loss of business and revenue if things go wrong will exhibit high levels of inertia in the face of high learning costs; for example switching from one company-wide CRM system to another.

Lost-Benefit Costs are costs suffered by the customer because certain benefits that have been earned but not yet consumed by the customer as a result of its historical relationship with the incumbent are non-transferrable in nature – the customer who decides to make a switch suffers a significant loss and must start to earn such benefits from scratch with the new provider. An example of this is found in the various loyalty programs that are used to induce customers from switching from one product to another; airline travel points, for instance. Mobile phone subscription roll-over minutes are another example – my roll-over minutes accumulated on AT&T’s cell phone network are not transferrable to another carrier if I choose to switch. ((Klemperer calls these discount coupons and other devices.))

How might switching costs become a disadvantage? 

Switching costs lock in customers who face the highest opportunity costs of switching from an incumbent product to another product offered by a rival. It is often the case that these customers comprise the most profitable segment of customers for the incumbent, and it is not uncommon for the incumbent to continue optimizing the product to meet their requirements, with each improvement in the incumbent product being reflected in an across-the-board increase in prices for all the incumbent producer’s customers. This iterative cycle of feature upgrades and attendant price increases will continue until it gets to a point at which the following things happen; first the product becomes too advanced for a large number of customers with “only moderate” needs and therefore, commensurately moderate switching costs. Second, at this point the medium- to long-term cost of switching is perceived by this group of customers to be less than the commensurate benefit of remaining locked into the incumbent product. Last, the rival product has matured such that it satisfactorily meets the needs of customers considered to be low-margin customers based on the business model implemented by the incumbent producer. However, these same customers comprise an attractive, high-margin customer cohort for the rival product because the rival producer is pursuing a business model that features significantly lower overhead costs than the comparable costs reflected in the incumbent’s existing business model. ((This is the process described by Clayton Christensen in The Innovator’s Dilemma.))

As a result the incumbent producer faces a dilemma; stay and fight for low-margin customers, or cede that ground to the rival product? The most typical response from incumbents is to cede the unprofitable customers to the rival. This gives the rival a toe-hold in the market, a position from which the rival can gradually strengthen its position and eventually migrate upstream until it poses a direct and powerful threat to the incumbent. The effect high customer switching costs have on an incumbent producer is that they lock the incumbent into a pattern of sustaining innovation. Sustaining innovation improves on already existing products, and focuses on squeezing more out of a large base of existing and and a comparatively small base of new customers. An example of sustaining innovation is Microsoft’s line of Windows and Office products; annual sales to new customers is small compared to sales generated from the installed base of Windows and Office users. Disruptive innovation seeks to satisfy non-consumption by developing products with features so simple and inexpensive in comparison to the status quo that a disproportionately large number of new customers enter the market. ((Or, a large number of “unprofitable customers” abandon the incumbent product for the new, disruptive product.)) The key is that the customers that flock to the disruptive product are very unattractive to established incumbents. With time, the disruptive innovation matures to the extent that it becomes a viable substitute for the incumbent’s most profitable customers at a price point that is extremely hard for them to resist. It is at this tipping point that the incumbent’s fight for its survival begins. It is easy to dismiss disruptive innovations at the outset because the performance measurements that have become customary for the market in question do not apply in the same way for the new wave of consumption that the disruptive innovation enables. For example, consider an investor trying to decide if an investment in Facebook was a good idea in 2006. On the basis of CPMs this investor would probably have decided to pass on the opportunity to invest in Facebook, reasoning that it did not have the qualities necessary to build a highly valuable business. Except, CPMs were the wrong metric by which to judge Facebook at that point. ((Read: Andrew Chen. Why I Doubted Facebook Could Build A Billion Dollar Business, and What I learned From Being Horribly Wrong. Accessed online on Oct. 19, 2014.))

What are the competitive strategies at play in markets in which switching costs matter. 

It is important for technology startups and early stage technology startups to understand the dynamic that might evolve as they seek entry into a market characterized by an incumbent who benefits from customer lock-in. Fortunately, substantial economic research exists on that topic. ((Joseph Farrell, Carl Shapiro. Dynamic Competition With Switching Costs. RAND Journal of Economics; Vol. 19, No. 1, Spring 1988. and Joseph Farrell, Paul Klemperer. Coordination and Lock-in: Competition With Switching Costs and Network Effects; Handbook of Industrial Organization, Volume 3. Ed. M. Armstrong, R. Porter. Copyright 2007, Elsevier B.V. Accessed online on Oct. 23, 2014.))

The incumbent sells to existing customers, rival new-entrant serves new buyers. This happens in markets that are relatively mature. The incumbent focuses its efforts on its existing customer base, with growth in revenues arising from endogenous growth within that customer base – e.g. Bloomberg revenues increasing because the average number of employees of each of its existing customers is increasing with time. New entrants meanwhile utilize new technology to serve new customers, initially ignoring the incumbents existing customer base. This is especially true in markets in which the incumbent producer has a high level of power relative to customers in that market – typified by dominant market share, giving it pricing power over its existing customer base. To use the parlance of Farrell and Shapiro (1998) the incumbent sells to the oldsters while the entrant sells to the youngsters. ((See for example; Aaron Timms. The Race To Topple Bloomberg; Institutional Investor, Jan. 30, 2014 and Startups Estimize and Kensho Take Aim at Bloomberg; Institutional Investor, Jan. 30, 2014.))

The incumbent excludes the new entrant. This happens when the incumbent’s fixed costs per customer are greater than the switching costs per customer. The strategy works under conditions in which the incumbent is in a position to set a price that makes it unattractive for any new entrant to enter the market. Where this is not possible the incumbent will choose to set a price that allows the market to be shared between the incumbent and the new entrant. This is why freemium business models are so powerful, especially when a freemium business model is coupled with a product that embodies network effects and switching costs. For example, think about how dominant Facebook has become because it gives its product away to users for free. Clearly, it not possible to compete with Facebook on the basis of the price users pay in exchange for the value they derive from it. The startups that will ultimately compete with Facebook do not have a cost-leadership strategy available to them, and so must instead seek an alternate path. ((Examples; Whatsapp, Instagram, Pinterest, Snapchat, Line, Kik etc. Most recently Ello has tried to carve a niche for itself by emphasizing privacy. It is too early to tell if that tactic will work.))

New customers are won with bargains, then they are “ripped off”. This happens when customers are offered low “introductory offers” in order to entice them to adopt a product. Prices increase once lock-in has been established. As an example, consider a product that is free up to a certain usage threshold but for which continued use beyond the set threshold requires customers to pay. In this scenario, various mechanisms might be used to ensure the onset of customer lock-in, and improvements in the product’s features and capabilities are designed to nudge users over the threshold beyond which they have to become paying customers. ((Examples; Google Apps for Business, now renamed Google Apps for Work started used this tactic to build a beach-head in a market dominated by Microsoft. This scenario excludes predatory pricing practices.)) This tactic is common among cable TV and satellite TV providers, and also among internet service providers.

Customers are paid to switch. Consider three segments of an incumbent producer’s customers; Existing locked-in customers, unattached or new customers, and customers locked into a rival. In this situation, rival producers will implement price discrimination. Existing locked-in customers get one set of prices, new or unattached customers get another set of prices, while customers locked into rivals are paid to switch. ((Ibid; Farrell and Klemperer.)) Recent reports of the battle for market share between Uber and Lyft are a great example of this tactic being applied in the real world. ((See for example; Alison Grisworld, Uber Rival Gett is Making a Risky, Clever Play in The Ride-Sharing Game, Oct 15, 2014. and Avi Asher-Schapiro, Is Uber’s Business Model Screwing Its Workers?, Oct 1, 2014.)) This tactic is common with cellular phone service providers and credit card issuers.

A portfolio of products is bundled together in order to increase total switching costs. This tactic is especially effective because in order to make a switch, the customer must deal with nearly all the switching costs we have previously considered at the same time and it works especially when the incumbent producer offers a product line that is so broad that most customers simply deal with the incumbent as their single supplier for the entire line of products that they use. ((Ibid; Farrell and Klemperer.)) For example, Microsoft’s strategy of giving away Internet Explorer in a bundle with Microsoft Windows reportedly led to the demise of Netscape Navigator. I would guess that beyond merely bundling Explorer with Windows, Microsoft built-in a number of features that made Navigator less compatible with the Windows operating system than Explorer. ((Wikipedia; United States v. Microsoft Corp. Accessed online, Oct 23rd, 2014.))

Switching costs play an important role in retaining customers, and motivating repeat purchases in the future. Technology startups can’t survive without user lock-in and incumbent suppliers with strong customer lock-in typically earn monopoly profits. Early stage startups thinking about spend some time understanding the features that create value for the customer while building customer lock-in for the startup early in product design process. The existence, or lack thereof, of switching costs amongst the incumbent’s customers will play an important role in determining the competitive response that is likely to occur once the new-entrant’s intentions become undeniable. In which case speed of market entry is critical for the new-entrant. In a market with low switching costs, one might expect vicious price wars to ensue. Generally, such price wars will always favor the presumably better capitalised incumbent. Moreover, price wars are a bad idea for the incumbent as well as the new entrants. In a market where the incumbent enjoys significant customer lock-in with ensuing monopoly profits, one generally expects new entrants to find a foothold from which they can eventually migrate up-market.

 

Filed Under: Business Models, How and Why, Innovation, Startups, Technology, Venture Capital Tagged With: Early Stage Startups, Economic Moat, Long Read, Strategy, Switching Costs, Venture Capital

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