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A Note on Developing and Testing Hypotheses

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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. 1Let 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. 2I 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. 3I 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.

References   [ + ]

1. ↩ 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.
2. ↩ 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.
3. ↩ 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.

How an Investor’s Behavioral Traits Might Completely Derail Your Pitch – Part I

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Many startup pitch meetings start out on a promising note, but things fall apart during the conversation between the startup and its prospective investor. Sometimes this could have been prevented if the startup team had studied a little bit of behavioral psychology beforehand. 1Any errors in correctly attributing work to my sources and references is entirely my fault. I’ll make corrections if you point an error out to me.

Traditional finance theory tries to tell us how investors should behave, if they act as rational economic beings. Behavioral finance is based on the observed behavior of investors. Traditional finance is based on economic theory. Behavioral finance is based on psychology. Traditional finance assumes that investors make their decisions based on all available information, that investors are rational, and that markets are efficient. 2The efficient-market hypothesis (EMH) states that financial markets are informationally efficient and that the price of a publicly traded stock incorporates all available information about that stock. Even if you think this is true in public markets, you have to agree that it is not the case in venture capital. It is especially true that the market for early stage venture investments is highly inefficient, prone to extreme uncertainty and severe information asymmetries. Behavioral finance makes none of the assumptions of traditional finance.

In this post, I will describe a few behavioral biases that an investor might exhibit during a pitch meeting with the founder of a startup. 3I am basing the outline of this post on portions of the CFA Institute’s 2013 Level III readings on Behavioral Finance. I will describe how each bias might be exhibited during a pitch meeting. I will also suggest how the entrepreneur might attempt to mitigate each bias. Failure to mitigate a behavioral bias could mean that the pitch gets derailed, and the entrepreneur fails to communicate effectively with the investor about the startup. I have developed the examples on the basis of meetings at which I have been on the side being pitched by entrepreneurs, after-the-fact reports about investor meetings that entrepreneurs I know have spoken with me about, and also from meetings at which I have been on the side pitching an innovation to potential business partners, and investors for a startup that I have been helping to incubate since January 2011. 4I am not a psychologist, so my discussion of this topic will certainly fall far short of even very modest expectations. However, I hope that budding entrepreneurs find this discussion to be a good starting point for some independent work on this topic.

The behavioral biases that I will cover in this post are categorized as cognitive errors. A cognitive error or bias stems from the inherent shortcomings people face as they try to process information that is unfamiliar and complex. Cognitive errors are further categorized as information processing errors or belief perseverance errors. This post will focus on a few belief perseverance biases. Behavioral biases generally can be grouped as cognitive errors, emotional biases, memory errors or social biases.

As you read the rest of this post you will notice that the lines between these biases is somewhat blurry – what one person sees as indicative of one kind of error could be seen by someone else looking at the same information as indicative of another bias. That seems to be the nature of those behavioral biases I have studied – there’s a lot of interconnection and it is difficult to assign an observed behavior to a single bias or error. More likely, the observed behavior arises due to a combination of biases and errors. That is why I think preparation beforehand is key. The entrepreneur can try various approaches to mitigating the observed bias until one approach leads to a break-through that restores the flow of ideas and communication between the entrepreneur and the potential investor.

Cognitive Errors – Belief Perseverance:

  1. Conservatism Bias: This occurs when a potential investor fails to revise his preconceived beliefs about your startup even when there is new evidence that suggests that his beliefs are incorrect. 5This can also be exhibited as a tendency to underestimate high-likelihood events and overestimate low likelihood events.
    • Case: Steve is 20 years old. He has quit college with two of his classmates to focus on building a startup – Disruptive Tech Startup (DTS). He meets with an early-stage venture capitalist to describe the work they have done so far and their vision for the future. Steve does not realize that the investor believes that he’s too inexperienced and too young to accomplish what he wants to accomplish with DTS. More specifically, the investor does not believe that Steve is experienced enough to steer DTS so that the investor realizes the minimum 10x return that the investor’s investment thesis requires. Steve thinks the meeting went well, but the investor later tells him that the fund has decided to pass on making an investment in DTS.
    • Mitigation: Steve should spend more time discussing his background, what he has done to learn how to run the startup, and how he will learn what he needs to learn in order to run the startup in the future. He should explicitly tackle the issue about his youth and relative lack of experience, and openly discuss steps he will take, or has already taken to ensure that his investors’ capital is not put at risk because of his youth and perceived inexperience. He should offer references who prospective investors might speak to about his leadership potential as it relates to managing a startup. He should not assume that the investor will conclude that he will continue to succeed in the future after seeing what he has accomplished at DTS so far.
  2. Confirmation Bias: This occurs when the investor focuses on perceived negative aspects of your startup while ignoring and dismissing your attempts togive an explanation with evidence that will contradict that perception.
    • Case: Steve is pitching DTS to another early stage investor. She thinks that the technology they have developed is trivial after having listened to Steve for about 20 minutes, and she tells them as much. Steve gets frustrated because he feels she does not understand what DTS is about. The meeting is a disaster because she keeps focusing on the notion that “But isn’t this just a simple bot that scrapes the web for data? If I were a software developer I could do this with very little effort.”
    • Mitigation: This investor likely does not understand the full extent of the problem that DTS is solving. 6The unstated assumption here is that DTS is not solving a trivial problem. If Steve is stuck in “Demo Day Pitch” mode he likely has not considered that in a small meeting the dynamic is different. He should “put away the deck” and go into “professor mode” – in this mode he is educating the investor about the problem, about how DTS is solving that problem, and also about the opportunity that presents for potential investors in DTS, all in a conversational setting – like a professor teaching a student a new concept during office hours. He should expect to follow this up with further information for the investor to consider. 7See this post for an example.
  3. Representativeness Bias: 8The Wikipedia entry for the Representativeness Heuristic is here. You should read it if you are building a startup and will be raising capital from investors. This occurs when the investor uses an if-then rule of thumb or mental shortcut toassess your startup because of the high levels of uncertainty associated with the decision the investor must make.
    • Case: Ademola is a Nigerian entrepreneur. He has been building an Internet software startup in Lagos for two years, African Technology Startup (ATS). He grew up in Nigeria and holds a master’s degree in computer engineering from the Obafemi Awolowo University of Science and Technology, one of Nigeria’s leading universities of technology. In order to grow ATS he has moved to New York and is raising capital from investors. ATS has customers from all over the world, and he believes ATS is solving a significant problem for them. Growth has been phenomenal. ATS has accomplished that growth on a shoe-string budget. Ademola has been building ATS with two other people, they are both co-founders of ATS as well. They will remain in Lagos to manage the technology and R&D efforts. Ademola is worried that many of the investors he will meet are ignorant about Africa. He is also worried that they may unconsciously harbor negative perceptions about ATS that they will not verbalize during a meeting.
    • Mitigation: Ademola has to work doubly hard to demonstrate his technical competence because the average early stage investor in the United States does not associate Africa with technical talent and competence. For example, investors might assume that ATS is relying on a contract software engineering consultant in Asia or Eastern Europe. If ATS is building its technology in-house, Ademola has to make that explicit. He has to talk about the technology in a way that demonstrates that he can fulfill the vision that he’s selling to his customers, and potential investors. He has to convince his audience that a software engineer trained in Nigeria can compete on the global stage. He has to remember that his accent could inhibit potential investors’ ability to understand what he is trying to communicate. 9Y Combinator’s Paul Graham inadvertently got embroiled in a pseudo-controversy over this subject. You should read what he has to say, and also read what others have said. Paul’s post on the subject is here. One response is here. Instead of hoping that they will ask him to clarify something, or explain something they do not understand he should practice speaking clearly and communicating effectively to people who have never encountered someone with his accent. He should avoid using colloquial terms and idioms that are used in Nigeria, but may not be commonly used elsewhere. Investors in NYC will not understand those terms. He should be friendly, but he should avoid the temptation to be unnaturally funny. His off-the-cuff attempts at humor could back-fire. The representativeness bias at play here could be “If ATS is an African startup then the probability that it is doing all this on its own is zero because all we ever see on TV about Africa is war, starvation, and political corruption and incompetence.” Ademola has to overcome that bias during his conversations. 10This blog post at 59 Ways is a clear, but brief explanation of this bias.

An investor’s cognitive biases play an important role in how that investor will hear and interpret the information that an entrepreneur is trying to convey. Time spent understanding this phenomenon and how to mitigate any possible negative effects of a prospective investors behavioral biases will invariably lead to more productive pitch meetings.

Wikipedia’s entry on cognitive biases is here. Wikipedia also has a much more extensive list of cognitive biases here. If you have the time, you should invest in a copy of Daniel Kahneman’s 11If you purchase it through this link I will receive a small portion of the sales proceeds from Amazon to help me maintain this blog. Thinking, Fast and Slow.

References   [ + ]

1. ↩ Any errors in correctly attributing work to my sources and references is entirely my fault. I’ll make corrections if you point an error out to me.
2. ↩ The efficient-market hypothesis (EMH) states that financial markets are informationally efficient and that the price of a publicly traded stock incorporates all available information about that stock.
3. ↩ I am basing the outline of this post on portions of the CFA Institute’s 2013 Level III readings on Behavioral Finance.
4. ↩ I am not a psychologist, so my discussion of this topic will certainly fall far short of even very modest expectations. However, I hope that budding entrepreneurs find this discussion to be a good starting point for some independent work on this topic.
5. ↩ This can also be exhibited as a tendency to underestimate high-likelihood events and overestimate low likelihood events.
6. ↩ The unstated assumption here is that DTS is not solving a trivial problem.
7. ↩ See this post for an example.
8. ↩ The Wikipedia entry for the Representativeness Heuristic is here. You should read it if you are building a startup and will be raising capital from investors.
9. ↩ Y Combinator’s Paul Graham inadvertently got embroiled in a pseudo-controversy over this subject. You should read what he has to say, and also read what others have said. Paul’s post on the subject is here. One response is here.
10. ↩ This blog post at 59 Ways is a clear, but brief explanation of this bias.
11. ↩ If you purchase it through this link I will receive a small portion of the sales proceeds from Amazon to help me maintain this blog.

What If Q&A: What Should I Do To Spread My Idea?

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Rajoshi Gosh in Chennai, India sent me this question via email; I am a startup founder in the developing world. 1She is the co-founder of 34Cross. Their first product, Owlink, is a browser extension that is now available for Chrome. Rajoshi tells me versions for Firefox and Safari are in the pipeline. We have known one another since 2011 when we worked together while she was a teaching fellow at the Meltwater Entrepreneurial School of Technology in Accra, Ghana. You can check out Owlink here. How should I go about making the right connections in the developed world for:

  1. A product that is global in nature?
  2. A product that is specific to the developing world?

I will paraphrase Rajoshi’s question, and try to outline an answer to a not dissimilar question; How do I help to spread my idea?

But, I digress. Because I know what Rajoshi has been working on, I know why she asked that question; Her startup is launching its first product, and she wants to generate some awareness.

Jason L. Baptiste, author of The Ultralight Startup, penned a great blog post that answers Rajoshi’s more specific question. 2Jason L. Baptiste, How I Pitched @TechCrunch and 13 Ways to Get Press When You Launch Your Startup, accessed at http://jasonlbaptiste.com on August 3, 2013. I suggest you read that post for an outline of the tactics that worked for him, and that you might use yourself. There are a few very minor bits of insight I would add, but overall Jason does a phenomenal job of laying out how one might go about solving the kind of problem that is confronting Rajoshi and her colleagues at this moment.

Now, back to the more general conversation about how one might spread one’s ideas. I will start with the general notion that ideas spread because people care about the ideas, and start talking about them to other people. Ideas are spread by people. Sometimes it is easy for us to forget that. In the following discussion I will use Rajoshi as my protagonist.

Epidemiology 3 I have adapted this description of disease transmission from the description provided online at http://www.epidemiologyschools.com/intro/index4.html which I accessed on August 3, 2013. Any similarities are deliberate. is the study of how disease outbreaks occur. An epidemic occurs when a disease outbreak exceeds what one might expect under normal conditions, within a relatively small area and group of people, over a given period. Disease causation, transmission and propagation need a number of factors. First, there must be an agent capable of causing the disease. Second, there needs to be a host that is vulnerable to the agent. Third, the agent and the host must meet one another in an environment that allows them to interact with one another.

The disease-causing agent must have three characteristics: infectivity, pathogenicity, and virulence. Infectivity refers to the agent’s capacity to cause infection in the host. Pathogenicity describes the capacity of the agent to cause an outbreak of disease in the host. Virulence describes with which the disease occurs, and the speed with which it spreads. 4It is not a coincidence that the term viral marketing and viral coefficient have been adopted widely.

Potential hosts too must have characteristics that are important to consider. A host might be immune, in which case the agent will fail in its bid to cause a disease outbreak in that specific host. A host might be already infected, in which case there’s no point for the agent to keep trying to cause the disease in that specific host. Finally, a host might be susceptible. This is the kind of host that every disease-causing agent seeks.

So how is this analogous to Rajoshi’s predicament? She is our agent. She has an idea that she wants to spread. Her first task is to find a small group of people who are already talking about the idea she wants to spread. This group of people represents the host. It is likely they are not having a discussion about the concept in a formulation that is exactly the same as Rajoshi’s expression of the idea. That’s okay. This group of people exhibits the quality of susceptibility. They are very likely receptive to listening to Rajoshi talk about her idea. This group is likely to engage her in discussion about her idea, and members of this group are also more likely to volunteer towards helping her spread the idea. Because Rajoshi’s idea is similar to the idea they have already discussed, it is likely that people in this group will talk to other people they know about Rajoshi’s idea without her knowing it. In other words, they are likely to become agents themselves, propagating her idea as they tell their friends about it.

So how does Rajoshi prepare herself to propel her idea once she finds this small group of people who are open to hearing her idea and engaging in an exchange with her about it? As it relates to her idea, how does she cultivate the quality of pathogenicity within herself? Jason says in his blog post that a startup should be prepared technically before it seeks PR. This is also true of Rajoshi. Before finding a group of people who would be receptive to her idea, she should prepare to discuss her idea in substantial detail. Ideally, by the time she does this she and her colleagues should have developed a list of frequently asked questions that they expect will arise when other people encounter their idea for the first time, and they should have prepared answers to the questions they expect to arise often. This is important because many smart people are skeptics at first. They will ask questions about how something works. They will ask about why an alternative solution to a problem was not used. They will challenge some of the assumptions that Rajoshi and her colleagues have made. If this happens, Rajoshi needs to have prepared well-thought answers. Her answers will prove that she’s thought carefully about what she’s doing and why she’s doing it. How she answers such questions can suggest that this is a solution that may indeed work to solve the problem she’s trying to solve, or may suggest that she is not to be taken seriously. No one I know wants to waste their time with something that will not last because the people who should be most invested do not care enough to develop answers to basic questions about what they are doing, why they are doing it, and how they are doing it. It will be easier for others to embrace her idea if they believe that it is an idea that will stand the test of time. If someone raises a question that she and her team had not considered, it makes sense for her to ask for help developing the skeleton of a possible answer. That gets the person that raised the question invested in solving the problem they have pointed out. Rajoshi needs to communicate her idea in a way that is persuasive and convincing if she expects other people to adopt her idea as their own and to spread it to other people who Rajoshi is unable to reach directly.

This brings us to the notion of virulence. How does Rajoshi make sure her idea is adopted and spoken about enthusiastically by other people?

Atul Gawande discussed this in a recent article published in the New Yorker. 5Atul Gawande, Slow Ideas, New Yorker, July 29, 2013. Accessed at http://www.newyorker.com on August 3, 2013 I will try to paraphrase the conclusions that he reaches in that article, but I urge you to read it for yourself.
First, Rajoshi must make her idea simple to understand and explain to other people. While it is okay if the underlying concept is technically complex, the way the idea is communicated must be devoid of unnecessary complexity. It must be simple, and easy to remember. Second, the primary benefits of the idea must be experienced by the group of people who Rajoshi hopes will adopt and then spread her idea. We are all inherently selfish beings. I find it easy to remember to excitedly tell my friend about how much a new app I have downloaded has made my life much easier. I find it more difficult to tell my friend about a new app I discovered which will make her life much easier. Third, for Rajoshi’s idea to gain massive adoption, assuming the first two conditions have been met, it helps if the cost of adopting her idea is relatively low. An app with a single step sign-on and registration will be adopted more readily than one that has a two-step sign-on and registration process. All else equal, an Internet product that allows a prospective user to try the product for free before signing up for a paid subscription for additional features will gain more adoption than one that does not allow a free trial of any kind. Fourth, if people are going to adopt and then spread Rajoshi’s idea she must get to know them and they must get to know her. Mr. Gawande describe’s a pharmaceutical sales rep’s practice of “touching a doctor seven times” in order to get the doctor to change from prescribing one medication to prescribing a new one that the sale rep’s employer wants to sell to the doctor’s patients. 6This might mean that one should not wait till the last minute before one engages with the people one would like to adopt and then spread one’s ideas in the future. Last, people respond positively to others that they consider “nice” – remember that spreading ideas is fundamentally about people. Mr. Gawande’s article ends with an interview between him and a birth attendant in India regarding the extent to which the birth attendant had adopted recommended practices that BetterBirth, an organization dedicated to improving maternal and infant care during childbirth, had introduced to her through a series of site-visits with a much younger nurse, who had only a fraction of the experience of the birth-attendant. As it turns out, contrary to expectations the birth attendant had in fact made a lot of changes based on her interaction with the nurse. The birth attendant said she made the changes suggested by the BetterBirth nurse because the nurse ” . . . was nice”,  among other things.

It is easy to think we should remain faceless, and rely on technology to spread our ideas. It seems easier to craft one email and send it to 500 people from our contact list all at once. It seems easier to simply send one tweet to our hundreds of followers, or to simply post a Facebook status update to our friends telling them about the idea we wish to spread. That assumption is not borne out by empirical evidence. In and of itself, technology is not enough.

Technology in the form of email, social networking, communication tools like Skype, Google Hangouts, etc provide the environment in which Rajoshi can communicate her ideas to other people. However, she must harness that technology in non-scalable ways at the outset – having one-on-one interactions with the people she identifies as most likely to be open to adopting her idea. As this process continues, the group of people who have encountered, accepted and made her idea a part of their world view will grow. They too will spread the idea to other people. As the process continues her idea will begin to spread and gain currency, with technology playing an enabling role.

To quote from Atul Gawande’s article;

“Diffusion is essentially a social process through which people talking to people spread an innovation,” wrote Everett Rogers, the great scholar of how new ideas are communicated and spread. Mass media can introduce a new idea to people. But, Rogers showed, people follow the lead of other people they know and trust when they decide whether to take it up. Every change requires effort, and the decision to make that effort is a social process.

 

 

 

References   [ + ]

1. ↩ She is the co-founder of 34Cross. Their first product, Owlink, is a browser extension that is now available for Chrome. Rajoshi tells me versions for Firefox and Safari are in the pipeline. We have known one another since 2011 when we worked together while she was a teaching fellow at the Meltwater Entrepreneurial School of Technology in Accra, Ghana. You can check out Owlink here.
2. ↩ Jason L. Baptiste, How I Pitched @TechCrunch and 13 Ways to Get Press When You Launch Your Startup, accessed at http://jasonlbaptiste.com on August 3, 2013.
3. ↩ I have adapted this description of disease transmission from the description provided online at http://www.epidemiologyschools.com/intro/index4.html which I accessed on August 3, 2013. Any similarities are deliberate.
4. ↩ It is not a coincidence that the term viral marketing and viral coefficient have been adopted widely.
5. ↩ Atul Gawande, Slow Ideas, New Yorker, July 29, 2013. Accessed at http://www.newyorker.com on August 3, 2013
6. ↩ This might mean that one should not wait till the last minute before one engages with the people one would like to adopt and then spread one’s ideas in the future.