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4 Reasons Customer Development in Wealth Management is Different and How to Deal With it

I noticed that very little has been written about how to do customer development in the world of wealth management and FinTech. I was searching for advice and couldn’t really find any when I jumped into my adventure. Now that I have gained some experience, I decided to write a post and share some of my observations and ideas.

In my previous blog post, I explained that I had been doing customer development for some new ideas related to wealth management. I did have earlier experiences in doing customer development in less regulated markets. I had gained my experiences from both being a real founder (Disruptive Media) and as a member of the founding team (Kiosked). Thus, I only had an understanding of how to do this stuff in less regulated markets, like photo sharing, e-commerce and online advertising. I thought the same process can directly be applied to wealth management as well. However, wealth management is a bit different.

First, let’s recap what these methodologies are. Customer Development, invented by Steve Blank, helps startup founders systematically search for a profitable and scalable business model and ensure there is product-market fit before putting all chips on the table and scaling up the company. The key idea is getting out of the building and validating whether customers really have the problem and whether they are willing to pay enough for the solution. Lean Startup is a similar approach, invented by Eric Ries, which is perhaps better suited for typical consumer web apps, where most experiments can be run online.

What both of these methodologies have in common is that they aim to minimize waste. A startup often needs to try out things quickly and learn from each experiment. When something does not work, most code (and work) goes to trash. It makes sense to minimize waste and only do what is absolutely necessary to validate or invalidate a hypothesis (or idea). The more efficiently a startup executes this process of coming up with hypotheses and testing them, the more likely the startup is to find product-market fit before it goes bankrupt.

The word hypothesis is used to distinguish ideas and assumptions from facts. They become facts once validated. Typical business hypotheses can be for example: what price the customers are willing to pay, what channels are used for distribution and cost structures. These are often easiest to document lightly in a Lean Canvas or similar.

Whichever process you use, in the end it all comes down to two steps: 1) coming up with business hypotheses and 2) validating them. Usually hypotheses are validated by conducting experiments. Sometimes this can be done by going out and meeting the customers and test selling the product. Other times it is necessary to build an MVP and measure conversion, retention and whatever else that is necessary for success.

Success in the search for a profitable and scalable business model will be determined by how well a startup performs both of these steps. The better a startup is at coming up with the right hypotheses, the fewer iterations will be needed. In the best case scenario, the first set of hypotheses happens to be right and the product immediately hits a homerun. On the other hand, the better a startup is at performing the second step (testing of hypotheses), the more iterations it can do before reaching the end of its runway.

Most of the startup literature focus on the second step, which is mostly about process and execution. It is easier to become good at this step. Learn to design experiments which will result in maximum learning with minimum effort. Good engineers also develop stuff faster and the whole engineering team can also improve its process. There are tons of books about these things so there is no point discussing it any further in this post.

It is much harder to create any formal method to come up with good hypotheses (in other words great ideas). It is a combination of experience, knowledge and creativity. It helps to have a long background in the industry or being a heavy user of similar kind of apps. A heavy user of some specific apps may have some painful unsolved problem and a vision about a solution.

I used to think that ideas don’t matter and that it’s all about execution. This is the typical mantra that is repeated everywhere. Books, like Lean Startup, give the impression that if you do not know something you just test it and find out. This is all very good advice and also applies to wealth management and FinTech. However, in certain businesses testing and validating hypotheses is harder and more expensive. Wealth management and many FinTech businesses unfortunately belong to this category.

Why is this the case for wealth management?

1. Trust is everything

One reason is that it is a business where the customer’s trust is everything. This means that your sales conversions are completely dependent on how much people trust you and your brand. Hence, putting up a landing page to test conversions will not necessarily be a reliable experiment to validate whether a wealth management solution will have enough interest.

Since it’s all about trust, a startup’s success will depend a lot on how successful the company will be in establishing the trust within the core audience. Most likely building such trust is going to take some time and this is very hard to test in advance. Likewise with conversion rates and willingness to pay, they are both dependent on people’s trust.

Of course trust in brand is important in any business and has huge impact on conversion rates everywhere. Still, I think wealth management is at the high end of the scale. I feel that when doing customer development in some other markets, it is much easier to find earlyvangelists and visionary customers who are willing to be the first to try new things more eagerly. In fact, they often prefer that others have not yet discovered the new product and therefore do not want to see references either. However, in wealth management also earlyvangelists and visionary customers want to be sure it’s a trustworthy service and say that they would like to hear that a friend used it or something similar.

2. People don’t want to share financial information with strangers

Another thing to keep in mind is that people are not really open to share all financials with strangers. Additionally, people are a bit embarrassed to admit how unprofessionally they handle their own investments and often try to give a better picture of what they are actually doing. This adds up to the uncertainty.

3. Regulation

A third difficulty is regulation. In many countries FinTech startups can run small closed and controlled experiments without all the necessary licenses. This is unfortunately not the case in Finland. Depending on what you want to do, licenses can take a very long time to obtain. There may also be requirements for the company’s staff and its financials. This is extremely bad news if you just want to test something quickly with the help of an MVP.

4. Things happen slowly

Last but not least, things happen very slowly in this field. Banks and other financial institutions move very slowly and it can take ages to get any partnerships done with anyone. The situation is even worse if you need any integrations with them.


Since the 2-step iterative process of coming up with hypotheses and validating them can be extremely slow and expensive, we either have to succeed with fewer iterations or accept greater risk. Either you run the iterative process and make decisions based on very inaccurate data or you just accept bigger risk and hope you are right. I have a strong feeling that in this business one just needs to take a bigger leap of faith and have a lot more funding at an earlier stage.

What does this mean?

I think it means that startups need to put way more value on the idea phase (coming up with good hypotheses). I think it is important to get the initial “guess” close enough, so that potential pivots will not need new licenses or new deals and integrations with slowly moving banks and other big players.

How to ensure that the initial “guess” is close enough?

I think that especially in wealth management and FinTech, it is vital to have people on the team and advisors who have a background in the industry. People who can help make sure some details have not been overlooked, which could kill the business later. Additionally, I think it is a good idea to make sure there is room for error in the initial hypotheses because there probably is. In other words, the business must look extremely profitable on the spreadsheet, so that the actual reality will also be acceptable.

I want to still say that what I just wrote also depends largely on what kind of a startup we are talking about. There may also be some concepts in wealth management which can easily be pivoted in any direction without that much hassle. Stock pickers, alternative data services and various calculators and expert tools are not under regulation and fall into this category.

Founders should be aware of the stale nature of the field and understand that pivots here and there are not that easy. When I got in, I underestimated the impact of the regulation and how slowly things go forward in this field.


So as a conclusions, this business is very stale and trying out anything is expensive and time consuming. Therefore, startups cannot as easily run experiments and pivot here and there. Instead, startups need to have an experienced team, preferably with industry background. This way it can improve its odds of being close enough with its initial hypotheses, so that no major pivots are needed.

This is my current understanding of this business, I’d be happy to hear what you think?

Scratching the surface of FinTech

It’s time for me to give an update on what I have been up to lately. I was lucky and had the opportunity to take a break from my daily work and take a look at a few market opportunities in FinTech.

Already during 2014 and 2015 I tried out some new concepts related to trading stocks, mainly TradingDrill and some ideas related to sentiment and alternative data. We launched a low-fidelity MVP to test initial demand and how the channel to reach customers worked. Thereafter we continued doing customer development and interviewed everyone who signed up and could be reached. Unfortunately, we learned that the business model was not profitable and the customer acquisition costs were quite high.

We turned a few stones and investigated some related ideas which came up during the customer development process. Ultimately the interest faded away. Regular work had the highest priority and almost required our 24/7 attention. So the ideas were put on hold sometime late 2015.

At a startup event, a few months ago, I happened to meet Petri Asunmaa. He was pitching his idea about a tool to help stock investors. We started discussing after the event and eventually decided to join forces and figure out what could be done. This time it was about investing and not trading. There was huge potential in the field.

We started doing customer development almost by the book. So far during this process we have interviewed about 100 potential customers, banks, brokers and various players in the industry. Petri’s blog post summarizes quite well our findings. We also pivoted our concept a few times, starting from a tool for non-professional “do it yourself” investors to a wealth management solution. We gained a significant understanding of the business and how customers really behave and the reality is quite surprising.

Additionally, we learned a lot about the difficulties of FinTech startups entering the market. Typical Lean Startup methodologies are difficult to execute and they need minor adjustments due to the nature of the field. Partnerships and integrations with existing big players take a very long time, not to mention the regulation and applying for all necessary licenses. You cannot just one morning decide you want to test if customers are interested in buying stocks through you and deploy your MVP code in the afternoon, measure and learn. No, you need a different approach in this business.

I will write a separate blog post about lessons learned about customer development in wealth management and FinTech. The biggest takeaway is probably that one needs to take a bigger leap of faith than in other software businesses. This usually comes in the form of bigger initial investments as well as larger and slower MVPs.

During the process, many other good ideas came up. Some related to trading or wealth management, others related to disruptions in the industry, like PSD-2. We are now working at a better plan how to move forward with a few smaller leaps of faith instead of a gigantic one.

We are interested to continue discussing with other companies within wealth management and see if we could find ways to validate our ideas more easily and do something together. Both with new startups and existing players.


The Disruptive Adventure – A Happy Ending After All

A long time ago I wrote a series of posts about my own startup. For the readers with more time and patience, I suggest you pour a glass of scotch (or whatever your favorite drink is) and start reading from part 1/3. I certainly had my fair share of scotch when I wrote it.

For the rest of you, I’ll just give a quick summary:

Together with my co-founder Jarl Törnroos, we built a photosharing website, which allowed users to gather their photos around shared events. All events together formed the collaboratively documented story of each and every user’s lives.

This did not become a major success for various reasons explained in more detail in the full post. At that time we thought monetization was our biggest problem to solve. We could not have been more wrong and monetization should have been the least of our worries with the small user base we had. Anyway, we wanted to monetize our small user base by selling print products (like photobooks) from the photos people had uploaded to the service, and that is what we did.

There were not really good 3rd party solutions we could use for this purpose, so we set up to build our own. That eventually led to building a full blown e-commerce solution specialized for selling print products. Here we kind of pivoted, although in practice it was more like branching or spinning off a new business.

We productized the e-commerce solution and sold it (as SaaS) to a few photo shops who needed an online store. Unfortunately this was not really a good business either and it was hard to compete with existing big players like Fuji, who already had way over 50% market share and sold a full service, including an e-commerce solution, photo machines, paper, etc.

We had to try something and the next thing on the list was the hypothesis that other websites and photosharing sites would also want to monetize their images by allowing users to buy them, just like we wanted and did with ours. We launched together with Riemurasia, which was one of Finland’s biggest websites at that time. Now this generated real revenue at first. Unfortunately we encountered various difficulties, which we were unable to overcome with the money we had left.

That forced us into doing consultancy, which actually is nothing else than a safe way to fail. So we built whatever software anyone was willing to pay us for. We took projects from various clients. One of the projects was for the guys who are behind Kiosked. Eventually, we put aside all our own other businesses and joined them as partners when they founded the company Kiosked.

This is where the story ended in my previous posts and a lot of things have happened after this.

The e-commerce solution had been running for itself for about 4-5 years. Almost no maintenance required and very few incidents as long as we remembered to upgrade the servers before the huge peak in usage before Christmas. Perhaps the bureaucracy caused the biggest headaches, which consisted of sending invoices to customers, filing VAT reports, etc.

Now last year (2015), we sold the whole e-commerce solution to MV-Kuvat, a photo shop who had been our customer ever since the beginning. Their engineers are taking care of all future development work and will be able to customize it better for their own needs.

Also worth to mention is that today we closed the Pix’n’Pals photosharing community, which was founded 9 years ago. Although that is very sad, it frees up room in our minds to focus on other things. Sometimes it’s good to cut your losses and move on.

The exit for our e-commerce solution was far from anything you usually read in Techcrunch. No Ferraris or private jets for us. Still, it was an exit, which provides some kind of closure and recognition that what we had built was really something of value. That feels good and that is worth to celebrate!

Refurbished my Blog - No More Wordpress

It’s way over a year ago since I wrote anything here. Not a single post in 2015 and only one in 2014.

Life has been quite hectic lately. Hard work at Kiosked and four kids at home leave very little room for additional hobbies. Still, I have had the time to write two posts on Kiosked’s blog about microservices:

Discussing pros and cons of microservices:

Kiosked’s approach to microservices:

Now it’s time to get back on track and continue writing. But first things first. It was time to get rid of the damn Wordpress. Wordpress has been a liability. It is all the time under attack. Mostly various DDoS attacks on weak points like xmlrpc.php, etc. But there’s more. The technical design of wordpress is really flawed. All PHP files are public, including all the plugins which may have their own security holes as well. It’s a nightmare. I don’t have time to deal with that kind of shit.

Now I have switched to Hexo. Thanks to Perry and Daniel for the recommendation. Although I am not too keen on writing in markdown, I will sleep a lot better during nights when I am just running plain HTML files instead of a buggy Wordpress.

Ok, this was a short post. I have learned that the best way to get started is to just do something quickly, even if it is small. So more posts will follow…

The 3 Same Demons that Startup Founders and Stock Traders Face

You would think that startup founders and stock traders have nothing in common, but when it comes to characteristics needed for success, it turns out that they have to overcome many of the same psychological difficulties. I was reading the book Trading for a Living, partly because I am involved in a software project related to it and partly because I have been doing some trading as a hobby myself. A major part of the book is about trader psychology, like how to defeat your inner demons to succeed. I noticed that this stuff really applies to startups as well.

1. Dishonesty and Postponing Losses

Honesty to oneself is important in any field. The book advocates keeping records of all trades and planning ahead. Before doing a trade, one should have a plan when to exit the trade and always put a stop-loss to limit the amount of money that can be lost on the trade. Amateur traders sometimes neglect this and get stuck with their shares when prices decline. Their hope of some magical event turning the price trend around keeps them from selling. For them it is always possible to find some indicator giving false hope. It is human nature to postpone losses.

The record keeping and planning in the startup world is covered by basic customer development, where you write down your business hypotheses and your plan to test them. This includes what the result must be in order for the hypothesis to be valid. Steve Blank, the inventor of the customer development model, also speaks for the importance of writing down the exit criteria (pass/fail) of a test before executing it in his Startup Manual. It is too easy to find some other metric showing positive signs. Founders need to be honest with themselves, otherwise they will hurt themselves just the way traders do when they hang on to their shares to the bitter end.

It is very easy to fall into this trap. For example a simple MVP (Minimum Viable Product) with a landing page may be used to test general interest in a new product. One might set a specific requirement for the signup ratio of new visitors coming to the page. If there are too few signups but instead some social shares with discussions and long visits to the page, the founder can easily find a way how to interpret this as a success even though it is not.

The new observations about how users shared and discussed the page is still valuable insight. It should be used to plan new tests. However, it cannot be used to pass the current test, that had a different requirement. It is as easy for a founder as it is for a trader to find false proof justifying the current strategy. In practice, both are just postponing the losses.

I fell into this trap in my own startup. We did not plan our tests well enough with clear exit criteria and we were not fully honest to ourselves either. We ended up hanging to the same idea too long, hoping for some miracle to happen while implementing more features to better support the wrong idea. The pain we went through is actually quite similar to what a trader feels like when his stock is going down and he keeps buying more in hope of a change to the trend to get even.

2. Repeating Mistakes

Traders should try to look for repetitive success and failure patterns in their trading records. They should write down why they thought the trade was good, what they where feeling, etc. A startup that has been running for a longer time and tried out several different things can probably find similar patterns in all experiments they conducted. Even though all ideas would have failed, it still would be possible to find common mistakes that were repeated or common reasons why stupid things were done.

A startup should be able learn which types of experiments were unnecessarily expensive and could have been tested in a more efficient and cheaper way. I bet many failed startups wasted most of their time on a few very bad ideas/features, just like failed traders usually destroy their accounts with a few really bad trades. If traders can find out what the common denominator is, why couldn’t startups?

3. Irrational Goals

Another very interesting point in the book is that many traders are consciously or unconsciously trying to satisfy irrational goals when they trade. The one and only rational goal a trader should have is to make money. Typical irrational goals a trader has are getting some excitement into their otherwise boring lives or honor from a big win. These irrational goals hurt traders. I think the problem of having irrational goals is even worse for startup founders, who can have quite many of them.

When I think back of the times I had my own startup, I remember how good it felt when Mashable, TheNextWeb and lots of other tech blogs wrote about our first product. In terms of product-market fit, we were not ready for a full launch at all. By doing a big press release, we satisfied an irrational goal of getting acknowledgement for all our hard work. Doing a big launch before product-market fit is often considered bad, because potential pivots and other corrections to positioning, etc. are going to be very expensive. Unfortunately it is very hard to keep oneself from pulling the trigger too early.

Additionally, I remember many times thinking what completely irrelevant people (to the business) are going to think about our product or some feature. What if we spent too long time iterating and experimenting? What would people think of us after 6 months if we only had built something small? To look good in front of other people is an irrational goal. Adding more features is an easy way to satisfy that irrational goal. Allowing this kind of thoughts in your brain is dangerous and may take you one step closer to featurism.

Irrational goals must be satisfied elsewhere. Maybe it should be some hobby or something less important. Actually, to be really honest, I use this blog to satisfy some of my irrational goals.

Photo edited from original creation by L. Whittaker

Can anyone learn programming?

There has been a lot of discussion about whether kids should be taught programming in school, even as early as in preschool. Like always, there are different opinions on the subject. Many based on the question whether everyone can learn programming or not. Some say it is only for certain kind of people. Usually at basic programming classes in universities, it seems as if some people just “gets it” and others don’t. What could be the reason? I have my own theory.

I remember reading from Malcolm Gladwell’s book, Outliers, that there had been a study showing that the success of a math student is directly proportional to how much time the student is willing to spend on a problem before giving up. I think the same applies to programming as well. Even though most programming tasks do not require any knowledge in math at all, programming and math have one thing in common; they both require the same kind of logical thinking.

A lot of people try to learn math in school by trying a little bit on one problem and then giving up and moving to the next one. Almost as if they would be practicing throwing darts. You throw a dart, you miss the target, you throw another one, you might get closer, a third one… and so it goes on. They think that they should know how to solve a problem immediately. If they fail by not seeing the solution immediately, they move to the next problem. They will go through all problems but fail on each one.

During my studies, I remember seeing people desperately trying to learn programming exactly this same way. They got a problem and thought that they should be able to just write the code immediately. Sometimes they were able to write something but realized that it did not compile or it did not work for some other reason. They almost immediately gave up and thought that they failed, after which they moved on to the next problem and did the same all over again. One completed task is better than having tried on 10.

I believe anyone can learn programming, if they just accept the fact that it is something that requires time. That first exercise takes as long as it takes, but once it is done you have learned a lot. Having this right attitude will make sure you will later master any more difficult language construct, library or anything else. Even though it is always frustrating at first, once you understand how something works, a lot of things start to make sense. You will see that there are patterns. Bigger pieces of information will start coming easily. In the end you may not need to spend that much time after all. You just need to accept that the initial effort required is huge and you need to be prepared to sacrifice as much time as is needed.

Now if they plan to teach programming in school, how are they going to get people to do this in the right way. I think they have failed to do that in math. There is a huge risk that this wonderful subject is going to be taught the wrong way. Imagine that there is a 2 hour class and the teacher will naturally want to stuff in lots of exercises. Students who get 10 exercises for 2 hours will not feel good about spending 3 hours on the first one. They will ask for help immediately or copy-paste from examples. That is the only way to get something done so it looks like you tried, while a few students are going to spend 20 hours during the weekend to get the initial boost and will therefore easily fly through the rest of the course.

I am going to teach my kids programming and I hope I will do it the right way!

Photo by Paul L Dineen

High Performance or High Scalability Website?

Scalable vs performing website

Do you know the difference between scalability and performance? Which one do you need? Whether you are building, buying or selling web-based software, this post will teach you what you need to know in order to make the right choice.

I touched the surface of this topic in one of my previous posts, Scalable, Flexible and Cheap, Pick 2 you can’t have All 3, when discussing how to understand customer requirements regarding scalability. I learned that when a customer orders a software project from you and says “It must scale”, it can basically mean anything.

A few questions should help understand whether the customer means scalability from a technical- or business point of view. Business scalability means whether the business model can be scaled for a bigger audience. This is a bigger concept, covering market, process, localization and various other properties, including the technical scalability. Engineers often forget that scalability is not always only technical. However, in this post I will only discuss the technical one.

Having narrowed down to technical scalability, there is still room for misunderstanding. The customer may actually mean performance. If you are the subcontractor, you need to ask the right questions to figure this out. If you happen to be sitting on the other side of the table and you are outsourcing the software development to some other company, it is very good to know the difference too. There are plenty of subcontractors out there who just implement whatever was originally requested.

In various discussions, I hear all the time people say “this does not scale” when something is not as fast as expected or “Wow, this is really scalable” when something is fast. This is another reason for writing this post. Scalability and performance are not synonyms.

A high performance website is what one could call a “fast” website. Sometimes this is all you need. You might know in advance how many users are going to use it but require that the page responds quickly. This is usually achieved by code optimizations, bundling/packing/minifying resources and doing various other configurations.

A high scalability website is a website, which performance-level can be maintained by adding more capacity when the load increases. Some websites need to scale for growing traffic, others for growing amount of data. Many need to scale for both. A website can be slow and perform badly even if it scales well.

Regarding scalability, there are two types of scalability: vertical and horizontal. Vertical scalability means that capacity is increased by upgrading the server(s) to more powerful ones. Horizontal scalability means that capacity is increased by adding more servers of the same kind to the system. Horizontal scalability is preferred, since costs will often grow somewhat linearly to capacity. When scaling vertically, costs will often grow exponentially to capacity, since hardware gets very expensive at the high-end.

In practice, many systems only scale horizontally to a certain point. This is usually because they consist of many components, of which some cannot be scaled horizontally. When these components become bottlenecks, they need to be scaled vertically, resulting in a non-linear cost structure.

As earlier said, a website can perform badly even if it scales well. For example a website having a response time of 2 seconds could be considered slow and performing badly. If this website can handle 1000 users with one server, 10000 with 10 servers and 100000 with 100 servers, still maintaining its performance level (2s response time), it would be considered a highly scalable website.

Scalability and performance can have dependencies, both with positive and negative impact on each other. Consider a website, which architecture is changed into a more scalable Service Oriented Architecture (SOA), resulting in many more layers and web services. Requests may have to pass several layers causing a performance drop. On the other hand, some performance optimization tricks (or hacks) may be hard to scale. However, performance optimizations will often save resources, which will let you handle much more traffic with the same servers; hence, you will not reach your bottlenecks as early.

So, back to the question, which one do you need?

If you expect traffic or amount of data to grow, you need an architecture that can be scaled to meet the future needs by investing in proportion to the need. Hence, you need a horizontally scalable website.

If you know the traffic and amount of data is going to be within some reasonable known boundaries, but expect the website to be fast, you need a high performance website, designed for the specified amount of load.

Whichever you go for, remember that premature optimization is the root of all evil.

10 Reasons Why Features Can Poison a Software Startup

Swiss army knife with a lot of features

This post is about one common mistake startups often do. We did it when we built the PiX’n’PaLs photosharing website and later kind of repeated it in our e-commerce solution (read the story here). The mistake I am talking about is implementing more features without fully understanding what all the implications are. There are always a lot of great ideas popping up, but you can probably only pursue 1% of them. Trying to do to much is a common cause of failure; thus, the saying “Startups don’t starve, they drown“ has become so popular. In order to help others avoid drowning, I decided to list 10 things for startups to think about before implementing another feature. These are based on my own experiences, others’ stories I have heard or read about and a lot of common sense.

Experienced product managers use the word NO often. When I was younger I didn’t get them. Now, having been an entrepreneur myself, I understand them perfectly. I have really started to like Steve Job’s famous quote “Innovation is saying no to 1000 things”. Focus is essential and when you have one good idea you always find thousands of other ideas that would be cool. It is so easy to fall into the trap of implementing ideas here and there.

It is vital to understand that there are huge hidden costs in implementing a new feature. A feature may be really cool and seem to take only a few days to implement. An inexperienced product manager may happily keep pushing this kind of features into production even before product-market fit is found. The feeling is good when progress is being made and there is a lot of positive feedback from others. If the product gets poisoned with various different features that don’t fit well together before product-market fit is found, the company will be in a “feature hell”. At this stage it will be hard to find product-market fit, since all changes are slow and require a lot of work.

Let’s look a the different reasons why features can be poison:

1. Every new feature implemented requires more effort (the obvious one)

Every new feature obviously needs implementation effort. Additionally, a lot of documentation, manuals, FAQs, legal documents, etc. need to be updated. Some of these documents can be seen as features as well. Do you really need an FAQ or a manual? Will they become liabilities and slow you down? Furthermore, startups often put a lot of effort into analytics, which is used to learn about customers. This means that usage of the new feature need to be tracked. Finally, there may be a need to monitor the health of the feature in some cases, which will also result in more implementation work. What the developers initially thought would be a one day task suddenly became a huge operation that requires several people to get involved.

2. Every new feature may need maintenance and support

Having a new feature requires more maintenance work; someone needs to analyze all measurements collected, monitor the health of the system, etc. The new feature may also require more sales- and customer support.

3. Every new feature uses resources

New features may use more memory, hard disk or some other resources. All these have a cost.

4. Every new feature increases complexity

New features will increase the complexity of the system. This is dangerous for a startup still in search for product-market fit. Very often the complexity grows exponentially, because features have dependencies. This means that when you specify (or implement) feature X, you need to take into account that features A, B, C, etc. may or may not be used together with feature X. Sometimes it is possible to design everything in such a way that they are independent. But often when we solve real world problems that real customers have, we may not have that luxury. A well thought through spec and good software design will help deal with the complexity, but only to some degree.

5. Every new feature will cause some loss of flexibility

When there are more features and more complexity (indirectly), it will obviously be harder to do changes. Both technical- and business/logical complexity will require that a lot of time is spent on analyzing the impact of a change or new feature. All combinations must be thought through. This is very bad for a startup that has not yet found product-market fit and may still have to pivot.

6. Every new feature increases risk of bugs

Complexity will make it harder for developers to understand what the impact of a change is going to be. Also, high logical complexity will often also result in underspecification of new features and everybody will not share the same understanding of how the new feature should work together with everything old. Misunderstandings will lead to bugs.

7. Every new feature may introduce security holes

As complexity can lead to bugs and low quality of code, these can in their turn lead to security holes. Like with bugs, if the complexity is high and it is unclear how something is supposed to work, you will easily end up with some security holes in both code and application logic.

8. Every new feature must scale

The more features you have, the more potential bottlenecks you have. Complexity and dependencies between features are going to make it even harder to scale. Consider a consumer web application, which hits product/market fit and starts to grow rapidly. You have to cache content shown to end users. You have a requirement that the data must be fresh (changes visible immediately). The more different features there are that can indirectly have an impact on the data that is going to be shown to end users, the harder it is going to be.

9. Every new feature results in a longer learning curve

The more features and complexity there are, the longer it will take for new team members to be able to work independently and become productive. This applies to both developers and other employees.

10. Usability & Value

Every feature that is not used by the user, will distract the user from achieving his/her goal. Many studies show that a lot of options may result in the customer not choosing any because making the decision becomes so hard.

Additionally, when new features are added that do not perfectly fit into the software or are not fully thought through, the overall experienced completeness of the product will decrease. This happened to us in PiX’n’PaLs. If you thought our first version felt 50% complete, after we had added some more features that were not really core, you probably felt it was 40% complete.

At the time we realized that we should have been spending 80% of our time fixing old and 20% developing new, we had already implemented so many features that raising the degree of completeness required a lot more effort.

What happened in PiX’n’PaLs was actually that new features introduced new requirements. First our puzzle was missing a few pieces. Instead of looking for these pieces, we started building new puzzles next to the core puzzle, so the result was that we were missing even more pieces to the puzzle than when we started.

A startup should build a Minimum Viable Product (MVP). The product is either viable (can be used to validate a hypothesis) or it is not viable. The product cannot become any more viable by adding more features.

What to do then?

A startup needs to try out things and they may be complex, since customers need real world problems to be solved and the real world is complex.

Here’s some advice:

  • Seriously try to keep things simple before product-market fit is found. Look for different trade-offs to reduce dependencies between features if possible.
  • Narrow down the target customer segment.
  • Focus on only one revenue stream, one Growth Engine, one X, one Y, etc. at a time. And do it well. Fail fast and move to next one.
  • Avoid automating everything. Experiment by doing manual work.
  • Really force yourself to think 80/20.
  • Remove features and old stuff that do not add enough value.
  • Focus on the goal and how to get there. Is this new feature really the way to get to the goal? There is most likely one biggest problem at the moment that you must solve in order to succeed. That problem is so hard and frustrating that it feels good to concentrate on some other tasks instead, like implementing some other new cool features. Don’t fall into this trap!
  • If possible, keep things modular and reduce dependencies between features
  • If you can identify key components that are unlikely to change during pivots, go for a Service Oriented Architecture (SOA). This is easier said than done ;)
  • If you find yourself in the “Feature Hell”, try a Zoom in pivot, which means detecting one feature that is used, and making that the product and hiding everything else. If that is too hard to do, due to complexity, take the knife, be really aggressive and cut off all dead meat (features) from the product. This is what I would do if I would continue PiX’n’PaLs or any of my other old projects.

Picture Source: Jesse Sneed / Flickr (Creative Commons)

Should startups do TDD?

All over the web you find debates whether early stage startups should do Test-Driven Development (TDD). TDD is a development process where developers first write a test before writing any code. When the test is there, they first run the test to prove that it fails when the functionality is not there yet. After that they start implementing the functionality and continue until the test passes. TDD should result in higher quality and more maintainable code. TDD takes more time in the beginning, compared to just writing code directly. But when the complexity increases and there are no tests, things start to break and the velocity of the team will drop. However, with TDD the team will keep going with a somewhat more constant velocity for a much longer time. This is illustrated in the figure below with the green and orange curves.

There are plenty of people who feel that a startup should not do TDD, because it slows down the development in the beginning. The only goal of a startup is to find product-market fit as quickly as possible. Probably 90% of the code is anyway going to trash in the beginning. Progress is measured by validated hypotheses and not features, lines of code or anything else. The usual argument is that things change too quickly and the built MVP is anyway so simple that the benefit from TDD will not show off, since the code will be in trash before the velocity starts to drop. The blue curve in the diagram below shows theoretically the progress made when all code is thrown away after each experiment. One will have the opportunity to start fresh each time with no technical debt, so the average velocity will be quite high. This can probably only be done for the first experiments, after which there will be a better understanding of the users/customers and code reuse will start to pay off.

The diagram illustrates the idea that TDD saves time in the long run when the codebase grows, except when almost all code is thrown away many times during development

Some TDD fanatics argue that TDD actually speeds up development no matter what. The developers just have to learn to do it efficiently. Additionally, maintainability of the code is considered important in a chaotic environment where things change quickly. A final argument is that once product-market fit is found, it will be hard to take things forward and scale up when there is an enormous technical debt. This holds true for most software. The only exception would be apps, where the market risk is so high that one needs to start validating hypotheses with some really simple tests that go to trash immediately like illustrated with the blue curve in the diagram above. Completely new concepts for new markets meet these criteria.

Another common argument is that the software can be a bit buggy as long as it is good for validating a hypothesis. I read an interesting comment in Hacker News from a user called DanielBMarkham, that “technical debt can never exceed the economic value of your code, which in a startup is extremely likely to be zero”. I think this should be understood so that the cost of a rewrite is very low in a startup. This should not be be mixed with quality. Low quality with bugs will be a lot more costly than what was invested in writing the software. Even if the software is just a simple test of a business hypothesis, a bug may result in wrong measurements, which will eventually result in wrong business decisions.

How high quality do you need in your MVP? Let’s first look at what quality means. Colin Kloes wrote a good blog post about the importance of a team’s shared understanding of quality. He said “Software quality is characterised by how well the software has solved the user’s problem.”. Now think about Steve Blank’s definition of a startup: “A startup is a temporary organization designed to search for a repeatable and scalable business model”. The only purpose of the software is to help the founders find a repeatable and scalable business model. The software itself is the test; testing the business model that the founders have come up with. This means that quality is defined by how well the software is able to help the founders test their business model hypotheses.

In order to be able to test hypotheses, it is vital that all analytics are correct. A bug in the tracking of some important event that we are testing will result in wrong measurements, which may result in the wrong decisions taken by the founders. Also if there is a bug making the software unusable, the founders will never know if users were not using it because of a bug or because they were not interested. By putting it this way, quality is suddenly more important than ever.

Usually MVPs do not have much complex code, mostly just getters and setters. On the web they may sometimes be mostly html/css with some data retrieved from the database. In these cases there will not be a need for many unit tests. However, it is still a very good idea to have the TDD attitude from start. You do not know in which direction the MVP is going to evolve. You might want to have a couple of automated acceptance tests around the most important features to be sure they work as supposed to, ensuring your experiments are conducted correctly. Also, by doing this you will have the necessary infrastructure already in place to scale up operations when product-market fit is found.

In many cases the first MVP can be just an almost static HTML-page as an experiment to see how many users would sign up for a new concept. Some concepts can also be tested with something built on a CMS or even just static HTML-pages updated manually by a user. In these cases it would not make any sense to do TDD. However, I think these are special cases and at some point the founders are going to need own code to validate hypotheses further.

The software a startup is building, no matter whether it is an MVP or not, will have a purpose. If the software fails to deliver what it is meant to do, it is useless. Thus, bad quality is not acceptable. My conclusion is that the software is an important tool for the founders to validate their hypotheses. If the tool breaks or the technical debt becomes so high that it is no longer possible to use it for validating hypotheses with quick experiments, the founders will not be able to do their job of finding a repeatable and scalable business model.