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Tag: startup development

User feedback: A startup perspective

Introduction – the first-order problem

The first-order problem for startups often is, they are not making something people want enough to pay for. As you can see from the CB Insights data, founders identify this as the most common reason for failure.

Figure 1 Reasons for startups failure

Notice the connection between 1 and 2: We can paraphrase that as “there was no market need, therefore the startup ran out of cash.” Investor hype aside (think of Twitter), most startups don’t have the luxury of living years with negative profitability. This is why I emphasise the part ‘enough to pay for’ – contrary to Andrew Chen and others who advice to first get users and then figure out how to make money [1], I’m of the small but growing ‘direct monetization’ school of thought [2].

The solution for this problem is evident: find out from users what they want or need, and then build it.

Second-order problem

But, this is followed by a second-order problem: How should you learn from the users? It’s not evident at all; let me elaborate.

  • First, if you ask users what they want, you get inaccurate feedback because the users don’t know all the possibilities. In other words, people don’t know what they want (feel free to insert a Henry Ford quote here).
  • Second, if you show them a demo, you get inaccurate feedback because your product is not ready, and the users cannot magically “imagine” how it would solve their problems, if it was ready.

Lean to the rescue?

Eric Ries (video), and a large number of his followers (video), advocate ‘Minimum Viable Product’ (MVP) as the solution. The theory goes that “it’s enough your MVP demonstrates the solution”, as potential customers is shown how the product essentially solves their problem, and for the rest they fill in the gaps. The key difference to a demo is the tight connection to the problem – we only need to show the logical connection between problem and solution (this is referred to as product-solution fit [2]), and for that we necessarily do not need even a laptop.

However, the MVP approach has two major problems. First of all, for many problems you cannot create an effective MVP. Consider Apple Pen, or many other products of that company. “See, here’s a pen – would you use it?”. It’s not very effective – you miss all the subtleties that the final product has and that the people pay for. Oftentimes, they pay for fine details, not for the hard crude solution. For this reason, the final product often ends up being very different from an MVP which is closer to a prototype. Second, there are complex problems which have, say, one main problem and two sub-problems: For example, to speeden up the set-up of a manufacturing plant, you need to solve logistical bottlenecks. But how do you capture that complexity in your MVP? For this kind of problems, it’s all or nothing: a partial solution won’t do. Moreover, they require the kind of deep customer understanding of the customer’s circumstances which is not usually part of the MVP gospel, centered on simple consumer software products as opposed to, say, B2B industry solutions.

I grant that the MVP approach has advantages, as technically, you could solve a complex problem on a flowchart, or communicate your solution as a video (as Dropbox did). I’m just highlighting that it has shortcomings, too. Most importantly, the final product that the people end up buying is often something very different from the MVP. So, maybe MVP could be used as a starting point, but not as the end solution.

How, then?

The best solution, as far as I can see, is this:

Learn and much of the nature of the problems, and then bridge that knowledge with the technical possibilities.

As you can see, this approach closely follows Steve Blank’s customer development.

The main difference is that Blank argues strongly it’s “not focus group” (read: market research), in my opinion it’s exactly that. In fact, you can apply both traditional and novel methods of market research to get to the bottom of users needs and wants. These include etnography, surveys, qualitative interviews, etc. I wrote a separate blog post about market research for startups.

At the core of Blank’s idea is the notion that the founders are testing their hypotheses by customer development. However, those hypotheses originate from innate assumptions about the customer’s reality, and are likely to be biased and flawed. Challenging the hypotheses is therefore must, and not a bad solution at all. However, we can also start by learning about the problem, not from the hypothesis formulation. Ultimately, I believe you can reach the same outcome either by starting from the founders’ hypotheses, or by inducing them from market research [3]. Which is faster and more efficient, probably depends on details of execution. Given the execution is equal, then the accuracy of the original hypotheses is the determining factor — if they are far off, more adjustment has to be done. In comparison, inductive market research, in theory, arrives straight away to the core of the user problems.

Conclusion

In the proposed approach, we take any means necessary to find out what is needed or wanted, and then combine that with the information of what is possible. If you look close enough, this is what marketing is all about – matching supply and demand. Consequently, the role, or competence, of a market researcher, is crucial for a startup organization. They need someone to bridge the technical knowledge, existing in developers’ heads, and customer knowledge, existing in customers’ heads. Often, these two groups don’t speak the same language, so the individual who is mediating is acting as a kind of an interpreter. (S)he has to have the ability to understand both languages — that of technology, and that of ordinary people.

Endnotes

[1] It’s the well-known Y-Combinator motto: “make something that people want”. This can be interpreted as getting users being the priority, which is why I like to re-phrase it as “make something that people want to pay for.”

[2] The major exception for foregoing direct monetization is subvention: e.g., in platforms that seems to be the de facto necessity to even enter the market, while for all startup is may be when users are recruited to learn about them. From an economic point of view, this equals subventing one group of users (early adopters) to improve access to another group (main market).

[2] Problem-solution fit precedes product-market fit, which essentially deals with having a product with a lucrative market.

[3] The same separation exists in the academia: There are hypothetico-deductive studies, and inductive studies.

My other writings on startup problems:

Startups! Are you using a ‘mean’ or an ‘outlier’ as a reference point?

Introduction

This post is about startup thinking. In my dissertation about startup dilemmas [1], I argued that startups can exhibit what I call as ‘reference point bias’. My evidence was emerging from the failure narratives of startup founders, where they reported having experienced this condition.

The reference point bias is a false analogy where the founder compares their startup with a great success case (e.g., Facebook, Google, Groupon).

According to this false analogy: “If Facebook did X and succeeded, we will do X and succeed, too.

A_x | B_x –> S

or doing A ‘x’, given that B did ‘x’, results in success.

According to a contrary logic, they ought to consider the “mean” (failures) rather than the “outlier” (success) because that enables better preparation for the thousand-and-one problems they will face. (This is equivalent to thinking P(s) = 1- P(f), or that eliminating failure points (f) one can achieve success (s); which was a major underlying motivation for my dissertation.)

Why is this a problem?

Firstly, because in the process of making decisions under the reference point bias, you are likely to miss all the hardship left out from the best practices outlined by the example of your outliers. In other words, your reference point suffes from survivorship bias and post-hoc rationalization.

But a bigger, and a more substantial problem in my opinion, is the fundamental discrepancy between the conditions of the referred success case and the startup at hand.

Let me elaborate. Consider

A{a} ≠ B{b},

where the conditions (a) taking place in your startup’s (A) market differ from the conditions (b) of your reference point (B). As a corollary, as the set of market conditions of A approach B, the better suited those reference points (and their stories & best practices) become to your particular scenario. But startups rarely perform a systematic analysis for discovering how close the conditions whereupon certain advice or best practice were conceived match those at hand.

As a result, discrepancies originating from local differences, e.g. culture, competition, etc., emerge. Some of these dimensions can be modeled or captured by using the BMC (Business Model Canvas) framework. For example, customer segments, distribution channels, value propositions — all these can differ from one geographical location or point in time to another, and can be systematically analyzed with BMC.

In addition to BMC, it is important to note the impact of competitive conditions (a major deficit in the BMC framework), and especially that of the indirect competition [2]. At a higher level of abstraction, we can define discrepancies originating from spatial, temporal, or cultural distance. Time is an important aspect since, in business, different tactics expire (e.g., in advertising we speak of fatigue or burn indicating the loss of effectiveness), and there are generally “windows of opportunity” which result in the importance of choosing the correct time-to-market (you can easily be too early or too late).

So, overall, reference point bias is dangerous, because you end up taking best practices from Twitter literally, and never end up making actual money. In particular, platform and freemium businesses are tricky, and based on my experience something like 90% of the reference point outliers can be located to those fields. It should be kept in mind that platforms naturally suffer from high mortality due to winner-take-all dynamics [3].

In fact, one of the managerial implications of my dissertation was that platform business may not be a recommended business model at all; at least it is one order of magnitude harder than a your conventional product business. The same goes for freemium: giving something for free in the hopes of at some point charging for it turns out, more often than not, wishful thinking. Yet, startups time after time are drawn towards these challenging business models instead of more linear ones.

That is why the general rule “This is not Google, and you’re not Sergey Brin.” is a great leveler for founders overlooking cruel business realities.

But, when is outlier a good thing?

All that being said, later on, I have realized there is another logic behind using reference points. It is simply the classic: “Aim for the stars, land on the moon.”

Namely, having these idols, even though flawed ones, encourage thousands and thousands of young minds to enter the startup scene. And that’s a good thing, resulting in a net positive effect. Sometimes it’s better not knowing how hard a problem is, because if you knew, you would never take on the challenge.

Conclusion

In conclusion, my advice to founders would be two-fold:

1) Use reference points as a source of inspiration, i.e. something you strive to become (it’s okay wanting to be as successful as Facebook)

2) But, don’t apply their strategies and tactics literally in your context.

Each context is unique, and the exact same business model rarely applies in a different market, defined by spatial, temporal and cultural distance. So the next time you hear a big-shot from Google or Facebook telling how they made it, listen carefully, but with a critical mind. Try to systematically analyze the conditions where they took place, not only “why” they worked.

End notes

[1] Salminen, J. (2014, November 7). Startup dilemmas – Strategic problems of early-stage platforms on the internet. Turku School of Economics, Turku.

[2] That is, how local people do things differently: A good example is WhatsApp which was not popular in the US because operators gave free SMS; the rest of the world was, and is, very different.

[2] Katz, M. L., & Shapiro, C. (1985). Network Externalities, Competition, and Compatibility. The American Economic Review, 75(3), 424–440.

On complexity of explaining business failure

Introduction

During the research period for my dissertation based on startup failures, I realized there are multiple layers of failure factors associated with any given company (or, in reverse, success factors).

These are:

  1. generic business problems (e.g., cash-flow)
  2. individual-level problems (e.g., personal chemistry)
  3. company type problems (e.g., lack of funding for startups)
  4. business model problems (e.g., chicken-and-egg for platforms)

Only if you combine these multiple layers – or perspectives – can you understand why one business venture fails and another one succeeds. However, it is also a relative and interpretative task — I would argue there can be no objective dominant explanation but failure as an outcome is always a combination of reasons and cannot therefore be reduced into simple explanations at all.

A part of the reason for the complexity is the existence of parallel root causes.

For example,

  • A company can said to have failed because it runs out of money.
  • However, why did it run out of money? Because customers would not buy.
  • Why didn’t they buy? Because the product was bad.
  • Why was the product bad? Because the team failed to recognize true need in the market.
  • Why did they fail to recognize it? They lacked such competence.
  • Why did they lack the competence? Because they had not enough funding to acquire it.

Alas! We ended up making a circular argument. That can happen with any failure explanation, as can coming up with a different root cause. In a team of many, while also considering several stakeholders, it is common that people’s explanations to cause and effect vary a great deal. It is just a feature of social reality that we have a hard time of finding unambiguity.

Conclusion

In general, it is hard to dissect cause and effect. Human beings are inclined to form narratives where they choose a dominant explanation and discard others. By acknowledging a multi-layered view on failure, one can examine a business case by applying different lenses one after another. This includes interviewing different stakeholder groups and understanding multiple perspectives ranging from individual to structural issues.

There are no easy answers as to why a particular company succeeds or fails, even though the human mind and various success stories would lead you to believe so!

Why do I love startups?

I’ve dedicated plenty of time for studying and coaching startups. But why do I care? Not only care, but be passionate about them, enough to say I love startups.

I got to think about that, and here are the results of that quick reflection.

1. Startups are about technology

Novelty, innovation, progress… call it what you want, but there is something endlessly exciting about startups. It’s to see them take something which exists and turn it into something completely new. This sounds melodramatic but at its core it’s close to creation, close to being a god. Not meaning to blaspheme, but honor the impact startups have on people’s lives. Of course there is a lot of hype and failure associated with this progress because the process of creation is not linear, but nevertheless the renewal of daily lives is to a great extent driven by startup innovations. We can see them all around us, never stopping to be amazed of what we humans can accomplish.

2. Startups have rebel spirit

They are the anti-thesis of corporations. As much as I love startups, as much I hate (bureaucratic) corporations. Startups are about freedom, creativity and independence — and about power to execute. Some other small organizations share these traits, which is why working with small tends to be easier than working with the big. Even big companies create innovative stuff from time to time, but I’ve seen plenty of cases where new ideas are strangled to death by internal politics. Many large organizations don’t want to change — truly — but they just pay lip service to change and new management fads. They also don’t need to change, because in the short-term the world actually remains quite stable. The change in any given industry does not come over-night which gives corporations plenty of time to adapt (i.e., they can hire and fire many CEOs until they have gradually shifted their focus to something that works).

The rebel spirit of startups can be seen in their desire to take on the world, solve big challenges (not only create vanity apps), and relentless execution and elimination of waste. Indeed, there’s a small optimization maniac inside me who loves startups because they aim for optimal use of resources – that’s the economic ideal. And they have to operate under strict scarcity which fosters innovation – much more exciting of a challenge to solve a major problem when facing resource constraints. You wouldn’t believe they are able to do it, but history shows otherwise.

3. The people and culture are amazing

Anyone who have been bitten by the startup bug know what I’m talking about. It’s energetic, young people that want to change the world for the better. Who wouldn’t get excited about that? On a side-note, it’s actually not to do with age; I’ve seen many mature people get excited about startups as well — so it’s more about mentality than age, gender or any demographic factor. The love for startups is universal – you can see that e.g. in the rapid diffusion of student-run entrepreneurship societies around the world. Startup people care about their surroundings, want to make a change, and are super helpful to one another. Again, this is the anti-thesis of “normal business” where dominant paradigms are rivalry and secrecy.

Startups openly share their ideas, invite new people to join them and are geared more towards collaboration than strategic thinking and self-interest. Even sometimes, coming from a business background, I think they are too nice (!) and neglect profit-seeking to their own demise (this shows e.g. in the monetization dilemma which I examined in my dissertation). However, it’s part of the startup magic at least in the early-stage: purely commercial motives would undoubtedly destroy some of the appeal. Ultimately, it’s the people of diverse backgrounds — IT, engineering, art, business, marketing, corporations — that make the startup scene such an interesting place to be.

4. Startups are never done

This relates to the first point of innovation. Joseph Schumpeter, a famous economist, had the idea of creative destruction which startups almost perfectly embody. When interacting with startups, you can see the world is never ready. The turnover of new companies coming and going, making small, medium and large impacts to their surroundings, is baffling. It’s analogous to research community, where scholars stand on the shoulders of those who came before them, and strive to make contributions, even small ones to the body of knowledge in their disciplines. Startups aim to make a contribution to the society, and are never finished at that.

In conclusion, startups are a fascinating topic to study and interact with. Startups are endlessly inspiring and embody the spirit of progress in daily lives of people. Startup people are a special group of people that willingly share their ideas and experiences to elevate one another.

Startup dilemmas: Feature priority problem

Introduction

It is a common issue for startups applying customer development to discover many customer problems and either relating to that or to their vision include many, many features in their product development roadmap. However, as we know, it is not about the number of features but their quality, i.e. usefulness in solving the customer pain points.

Why is this important?

Having too many features introduces several problems: feature creep for end-users, loss of time and money, etc. One can perceive it as a form of premature scaling, but it is also has negative marketing consequences for positioning and differentiation: what is the startup all about? Too many features can easily cloud the answer. In brief, too many features pose a problem for both the end-user and the startup’s internal operations.

How to address the issue?

Well, it can be framed as ‘feature priority problem’, i.e. what features should the startup focus on. Say you are a “dance app startup”. Based on a prior assumptions and perhaps some customer development activities, you have defined a set of features reflecting what customers want. This set includes:

  • buy dance event tickets
  • get notifications on dance events
  • see dance events in map
  • get driving directions to events
  • learn to dance better (dancing instructions, videos)
  • search dance partner

You could focus on developing all these features, but according to the above logic, it makes more sense to choose the most relevant ones and solve them. If one problem is large enough, solving only that one would be enough to provide real value.

So, how do you determine which features are more important than others – i.e. how do you prioritize? It’s a very simple process:

  1. Tie them to problems (e.g., “I always miss the interesting dance events in my city” –> Get notifications on dance events).
  2. Prioritize problems through customer development (rank-order questions, customer understanding, relative solution gap).
  3. Thus, you have the features prioritized as well.

The customer development activities should therefore focus on setting the problems (and thus features) in order of preference from customer perspective. The more problems there are, the more useful it is to rank them. You can directly ask customers to rank the presented problems. You can also form a general understanding of the magnitude of different problems by interviewing customers.

Finally, ‘relative solution gap’ refers to the ratio between the magnitude of the problem and competing solutions effectiveness in solving it. The ideal would be a combination of MAGNITUDE: high, EFFECTIVENESS OF COMPETING SOLUTIONS: low. The least feasible problem to solve, according to this logic would be where the perceived magnitude (rank) of the problem is low and, moreover, extant solutions are effective in solving it.

Conclusion

To avoid feature creep, startups should prioritize their customer’s problems and associated solutions. Based on this, they are able to create a “market-oriented” product development roadmap.

People vs. business models: Warren Buffet’s dilemma

In Quora, somebody asked why Warren Buffet prefers not to invest in startups [1]. One of the answers that resonated with me was this one:

“In an interview several years back, Warren Buffet said that he does not like to invest in companies whose success is based on the smartness of its people.

His reasoning was that all companies hire from the same pool of talent, so smart people by themselves do not provide long-term competitive advantage or “moat” (because a competitor can hire the same or similar talent). He thought of a company’s processes (not just operating processes, but also processes for new product creation, developing new business models etc) as the place where its value resides.”

So, I got to think of this question:

Which is more important for business success, people or business model?

At critical extremes, the answer splits like this:

People are critical, so talented people can make any business model work.

versus

Business model is critical, so even non-talented people can make a good business model work.

The question is quintessential for startup entrepreneurship — should we be
chasing the best people or the best combination of business model parameters?

In other words, can we find business models combinations that even an idiot could use to succeed? Or, is it like most lean startup advocates argue, that any business model parameters are just guesses and the success rests only on the team’s ability to execute them?

There are examples of smart people turning around business that would have otherwise failed. There are equally examples of poorly managed companies that still thrive because they have a killer business model at place.

However, facing the market dynamics often involves shaping the business model parameters that therefore cannot be seen static but dynamic in nature. But who are the ones shaping them? It’s the people — ultimately everything in companies can be abstracted to human actions. But, without the right “recipe” of business model components at place, the actions of even the smartest people can become futile. As such, we may not be able to examine the team and business model separately – business model and people are not isolated but interacting factors.

The truth, therefore, lies somewhere in between and in the mix of both. Oftentimes in dichotomous questions like this end up in a structurally similar conclusion that was made here. Almost every time, an extreme argument can be shot down. The fallacy of believing in extremes can therefore save you time, but lead astray.

As for Warren Buffet, the explanation given in the Quora post sounds plausible — for an investor, it may be an efficient strategy to focus on business model parameters and macro-competitive factors (and finding opportunities against logical basis) instead of betting on startups with risky ideas and people.

[1] Here’s the Quora discussion: https://www.quora.com/Why-doesnt-Warren-Buffett-invest-in-startups

Problem/Solution Space: A Startup Perspective

I was inspired to write this post by the following pictures that I’d included in my lecture material a few years. Writing it in a bit of a hurry since the class starts soon! (but it’ll good enough to make the point)

(You can find the original source for the pictures by googling.)

Okay, a couple of things.

First, it’s highly important for a startup to define both the problem space and the solution space relating to their product. This includes the particular pain points that the customer whose problems we’re solving is experiencing – at minimum, solving one pain point, if substantial enough, suffices to make a successful business. The solution space includes the competition — here, it is super important to consider not only the direct competition (a common mistake) but also the indirect competition.

I call it the “pen and paper” test — can the problem you’re solving, most often with a high degree of technological sophistication, solved with a simpler, non-technological way?

And more importantly, how are the customers solving it now? It takes a lot for them to change their habits, much more than what founders typically think. The customer will not download an app to solve the problem — no matter if it’s free or not — unless the app provides a solution several magnitudes better than what he currently has. So, bear this in mind.

Second, once the gravity of the problem we’ve set to solve has been “validated” by more trustworthy means than guessing (such as customer development), the problem dimensions need to be tied formally into the product features the team is building (the second picture depicts this).

This way, we avoid waste in the startup development process (remember, waste is your biggest enemy because you’re always on borrowed time).

Third, after this the usage of these features needs to be backed up real usage data — in other words, the product needs to be exposed to real users whose behavior is analyzed based on engagement metrics (e.g., time they spend with the product, what features they use, how frequently, etc.). For this, there needs to be a good analytical system built into the product. Follow the Facebook guideline here: you don’t know what data you might later need, so store everything. This enables maximum flexibility for subsequent analyses.

And finally, of course when we get feedback on the usage of the product, we tie it back to the problem we’ve set out to solve and conclude whether or not we’re actually solving it. If the data suggest low engagement, we need to start over and make radical changes to the core of the product. If the data gives us a nice depiction, we’ll still continue with further adjustments to improve the user experience (which, of course, is by definition never good enough).

That’s it. Thank you for reading (and I’m off to class!)

Dr. Joni Salminen holds a PhD in marketing from the Turku School of Economics. His research interests relate to startups, platforms, and digital marketing.

Contact email: [email protected]

I hate to see investors coming into a growing startup… here’s why

I hate to see, from a customer’s perspective, investors coming into a growing Web startup.

Because it only means rising prices.

The logic is this: 1) the investors need a positive return, and 2) the startup is growing because it has created something valuable, in most cases significantly more valuable than what it is charging from the customer.

Therefore, the investor logic is to raise the price and narrow down the extant value gap, i.e. charge according to the value provided (or, closer to it). However, most customers will still stay, because they keep getting more value than what they pay, even with increased prices, and therefore the startup can maximize its revenue. In addition, there would be a switching cost associated with finding a new provider, such as learning the new tool, configuring it, exporting/importing data, etc. So basically, this strategy is a form of value transfer from the customer to the startup — or, more correctly, to the investor.

Next, we’ll explore what this means for investors and founders.

1. Implications for investors

The major implication for an investor of course is that it makes sense to identify startups which are growing fast but have not optimized the value capture part of their business model.

However, a major difference lies in between having some revenue and not having revenue at all; in the latter case, the growth might be just an indicator of popularity, not business potential. (See my dissertation on startup dilemmas for a thorough elaboration of this topic.)

2. Implications for founders

The major implication to a startup is that if you seek funding, price your product well below the value provided, thereby sacrificing unit-level profitability for growth. But if you want to stay away from investors, experiment with rising prices – that way, it’s only you keeping the surplus. Obviously, this is “ceteris paribus”, so it excludes the potential revenue uplift from scaling with investor money. As we know, the only reason to bring in an investor is to grow the size of the business and thereby also increasing the founder’s personal profit, regardless of stock dilution.

Dr. Joni Salminen holds a PhD in marketing from the Turku School of Economics. His research interests relate to startups, platforms, and digital marketing.

Contact email: [email protected]

Startup syndromes: “The Iznogoud Syndrome”

1. Definition

The Iznogoud Syndrome can be defined as follows:

A startup strives to disrupt existing market structures instead of adapting to them.

In most industries, existing relationships are strong, cemented and will not change due to one startup. Therefore, a better strategy is to find ways of providing utility in the existing ecosystem.

2. Origins

The name of this startup syndrome is based on the French comic character who wants to “become Caliph instead of the Caliph“, and continuously fails in that (over-ambitious) attempt. Much similarly, many startups are over-ambitious in their attempt to succeed. In my experience, they have an idealistic worldview while lacking a realistic perspective on the business landscape. While this works for some outliers – for example Steve Jobs – better results can be achieved with a realistic worldview on average. The world is driven by probabilities and hence it’s better to target averages than outliers.

3. Examples

I see them all the time. Most startups I advise in startup courses and events aim at disintermediation: they want to remove vendors from the market and replace them. For example, a startup wanted to remove recruiting agencies by making their own recruiting platform. Since recruiting agencies already have the customer relationships, it’s an unrealistic scenario. What upset me was that the team didn’t even consider providing value to the recruiting agencies, but intuitively saw them as junk to be replaced.

Another example: there is a local dominant service providing information on dance events, which holds something like 90% of market (everyone uses it). Yet, it has major usability issues. Instead of partnering with the current market leader to fix their problems, the startup wants to create its competing platform from scratch and then “steal” all users. That’s an unrealistic scenario. All around, there is too much emphasis put on disintermediation and seeing current market operators either as waste or competitors as oppose to potential partners in user acquisition, distribution or whatever.

Startups should realize they are not alone in the market, but the market has been there for a hundred years. They cannot just show up and say “hey, I’m going to change how you’ve done business for 100 years.” Or they can, but they will most likely fail. This is all well for the industry in which it doesn’t matter if 9 out of 10 fail, as the one winning brings the profits, but for an individual startup it makes more sense to get the odds of success (even average one) greater. So you see, what is good for the startup industry in general is not the same as what is good for your startup in particular.

4. Similarity to other startup syndromes

The Iznogoud syndrome is similar to “Market education syndrome”, according to which an innovation created by the startup falls short in consumer adoption regardless of its technical quality – many VC’s avoid products requiring considerable market education costs. Whereas the Market education syndrome can be seen a particular issue in B2C markets, the Iznogoud syndrome is more acute in B2B markets.

5. Recommendations

Simply put, startups should learn more about their customers or clients. They need to understand their business logic (B2B) or daily routines (B2C) and how value can be provided there. In B2B markets, there are generally two ways to provide value for clients:

  • help them sell more
  • help them cut costs

If you do so, potential clients are more likely to listen. As stated previously, this is a more realistic scenario in doing business than thinking ways of replacing them.

I’m into digital marketing, startups, platforms. Download my dissertation on startup dilemmas: http://goo.gl/QRc11f