Frans Ekman's blog

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In Search of the Ultimate Product Analytics Playbook

Over the past two decades, I’ve ventured through the entrepreneurial landscape, serving as a CTO and Interim CTO for five distinct companies, and offering advisory roles to a few others. Through it all, one consistent challenge has stood out: product analytics. It has always been a headache, consuming more hours than I’d care to admit.

In my journey, I’ve experienced the dynamics of both small and large teams. When starting out and the teams were small, the responsibility of product analytics invariably landed on my desk. As our operations expanded, we onboarded professionals to ease the load – initially business analysts, and then a mix of seasoned analysts and data engineers. Yet, despite these additions, navigating the analytics terrain remained a challenging, costly endeavor, often falling short of perfect execution.

Beginning a venture with limited funds often brings its unique set of challenges. One of the primary dilemmas is resource allocation. It’s tough to justify hiring analysts when the immediate need revolves around either developing or selling the product. However, understanding the product’s usage is paramount; it feeds into critical decision-making processes. A common misconception is that every bit of data is already tracked, just waiting to be queried. Many assume that any developer with database access can instantly provide answers to questions like, “How many users who signed up in March are still using the product weekly?” But in reality, it’s rarely that straightforward.

I completely grasp the complexities behind analytics, but I’ve always been hesitant about pouring substantial resources into it. Whether it’s shelling out for premium tools or hiring specialists, it’s been a tough call. So, more often than not, I find myself dedicating a ton of my own time to get the insights we need.

Throughout the years, I’ve been on a continuous quest to refine my analytics approach. I’ve tested various tools, delved into numerous books and blog articles, and gained insights from some truly brilliant minds in the field. While I’ve also made my share of missteps, it’s clear to me that things are gradually becoming more manageable. Yet, there’s still this lingering challenge. I can’t shake the feeling that many of the routine aspects of analytics could be streamlined further.

I’m confident that the insights I’ve gathered over the years could spare many startups from the pitfalls I’ve encountered. At the same time, I’m ever-curious and believe there’s a wealth of knowledge out there that can further hone my expertise and take me forward.

We’re navigating through some truly fascinating times in the tech world. The landscape is teeming with groundbreaking tools and advancements. Among these, the potential of AI in revolutionizing the field of analytics stands out. I’m eagerly watching its progression, curious about how it’ll mesh with what we know and perhaps transform our established methods.

I’m eager to dive deeper into this realm. Connecting with fellow entrepreneurs, CTOs, product experts, and business analysts is high on my agenda. There’s so much value in understanding best practices and swapping ideas. As I journey through, you can expect to see my thoughts and learnings shared on Twitter, my blog, and other platforms. It’s a way for me to both seek insightful feedback and contribute back to our vibrant community.

Seasoned developers, especially those indie hackers who’ve crafted multiple apps, often have their go-to templates. These are typically skeleton apps filled with essential functions, integrations to services like Stripe, marketing automation tools, and embody proven workflows. I’m inspired to create something similar, but with a focus on product analytics. Over the years, I’ve developed systems and methods that I’ve recycled across projects. With some refinement and expert feedback, I believe there’s potential to mold these into a valuable resource for others. Naturally, a product analytics template would center less on code and more on best practices and tool utilization.

If you share an interest in this domain and are keen on swapping ideas, I’d love to connect. Please reach out!

Why Good Developers are Always Lucky

When I was younger, I used to play a lot of chess. There is a famous quote from a former world champion in chess, Capablanca: “A good player is always lucky”. I have come to realize that this applies to software development as well.

Now let’s look at what the real meaning of this quote is. If you thought it meant chess is a game of luck, you couldn’t be more wrong. What it truly means is that good players will (sometimes unconsciously) make good moves, placing their pieces on squares where they are more active. For example, placing rooks on open files and pawns on opposite color squares than the remaining bishop are both basic strategies. Following them will eventually lead to more opportunities and perhaps one of the opportunities will lead to victory. When an opportunity like this presents itself and some tactic can be used to win the game, it sometimes feels like luck.

In software development, following certain principles and patterns will keep more opportunities open in the future. An unlucky developer will often feel that it is hard to add new features and that the features do not really fit into the existing legacy. A lucky developer will often realize that there is a very easy way to add the new feature. Part of it is just because the lucky developer was following good principles when he wrote the old code. Many years of experience will also give a good gut feeling on which way to implement certain things.

Less experienced developers often do object oriented programming wrong. Even experienced developers sometimes break SOLID principles and that may lead to various difficulties when the codebase grows. Juniors sometimes get basic inheritance completely wrong and then wonder why they have programmed themselves into a corner. Many juniors do not know when to use composition and when to use inheritance, so they inherit classes just to get access to methods they need. Not only does this become a testing hell due to lack of possibilities to inject dependencies (stubs or mocks), but it will often lead to various problems later.

Let’s look at a completely hypothetical example, one that I was able to quickly come up with. Let’s say that we are building a racing game and have decided to model how the engine accelerates at different speeds; high acceleration at the lower speeds and then slowly decreasing when it gets closer to the top speed. We have a class Engine, with method:

public double getAcceleration(double speed);

Now a junior will start implementing the Car class, which has the properties: coordinates and currentSpeed. Additionally, it has the following methods used to control the car:

public void setGasPedalDown(boolean down);

public void runUpdateCycle(int time);

The runUpdateCycle() method is supposed to run an update cycle of the game, by first getting the acceleration and applying it to the speed (in case gas pedal was down). Then calculating the new coordinates based on the speed and time.

A senior developer will obviously use composition and let engine be a member of Car, and in fact most likely program against an Engine interface in the car class instead of the concrete class. Some juniors would perhaps jump at the opportunity of just changing the Engine class to be the car class and add the missing functionality there. Some junior developers might (although perhaps not that likely) inherit Engine class to get access to the acceleration method. Both of these approaches will of course work fine, but what happens the day when the producer of the game asks for a feature to let the user switch engine?

The senior developer would just implement a setter method, unless the senior developer already had implemented such a method for for dependency injection and unit testing purposes. Now the junior developers will either have to do some refactoring or continue on a path that will require a lot more effort and eventually will lead to bigger problems. The juniors will experience a lot of bad luck.

This idea of luck can probably be applied to many other professions as well. Or why not for all things you we do in our lives. When we have constantly bad luck, perhaps we should ask ourselves: are doing something wrong?

Fully committed to new adventures

It’s quite a while since I wrote a blog post. Life has been quite hectic and that will not change any time soon, so I might just as well give a quick update on what I have been doing lately.

At the time I wrote my last post, about one and a half years ago, I was still at Kiosked and as a side project looking into a few FinTech ideas. There were two ideas I considered very seriously and almost ended up founding a company for either one of them.

The first one I considered was an investment assistant app. Most people’s life savings are going to waste because of mutual fund fees and we wanted to help them. For those unaware of this fact, let me give quick explanation. I could easily write 20 pages about this topic, but I will summarize the key points:

  • The stock market is at least somewhat efficient (if not even fully efficient), so by randomly picking stocks you are likely going to do as well as most fund manager on average.
  • Very few funds can beat the market enough to justify their fees and you cannot know in advance which fund it will be, so throwing the dice and investing in random stocks is actually a much better alternative (or to invest in the index through ETFs for example).
  • The typical 1-2% fees don’t sound high, but the total loss over a 10-20 year period can be surprisingly high due to our world’s eighth wonder, which is compound interest. After 20 years, you may easily have 30% less wealth.

So we wanted to build a tool which serves as your investment assistant so anybody can do this themselves quickly at a reasonable fixed fee. The tool was going to help users choose a strategy and pick stocks fitting the criteria as well as help reduce risk. I won’t go into too much detail if we one day end up building this product ;)

Although most people we interviewed seemed to get the idea, it seemed as if it would be hard to sell to the masses through an online channel. Selling financial products is so much built on trust and a face to face meeting is needed to sell to the mainstream market. However, when meeting customers face to face, one would almost have to sell a product with a high fee just to break even.

Regulation in Finland is very strict compared to for example Sweden, which means that piloting a product like this would be an extremely expensive operation. Moreover, there were no easily accessible APIs to brokers in Finland (this is typical in FinTech anyway), so it is a long road ahead to negotiate API access (and possibly even development) with brokers or alternatively become a broker ourselves.

Piloting abroad was an option but not an easy one with our funding situation. The market is very small here in Finland. We looked at other companies who were doing something in this field or had been doing previously. We even met with a few of them to learn a bit more. Unfortunately it turns out that every one of these companies were extremely unprofitable or already bankrupt years ago. The only exception is brokers and companies selling mutual funds and various investment instruments with very high fees (exactly what we did not want to become).

Perhaps the last nail to the coffin for this idea was that my partner decided to drop out. This venture was not something I could pursue myself alone and the the initial funding was obviously lost when there was no team. I looked a bit for co-founders or ways this could be piloted with a reasonable investment, but then another idea seemed more lucrative.

Just like we lacked proper broker APIs and that was a problem for us, it seemed to be a problem for almost any FinTech company. Plenty of FinTech companies out there would like to access bank account data for various purposes, for example personal finance analysis, lender credit score evaluation, etc.

We all knew that the PSD-2 (EU directive) was going to force banks to open their APIs by January 2018 (this date has been pushed forward and is September 2019 at the time of writing this). Many FinTech companies were doing screen scraping to access this data and some lucky ones had special deals with the banks. We also knew that the date for PSD-2 would possibly be postponed, some banks would apply for extension and even after that there would be banks choosing to accept the fines just to avoid opening up their APIs. Finally, even if the new directive would help a lot, there would be fragmentation; many banks implementing their APIs their own way. No small FinTech startup would like to integrate with 5000 different banks and different APIs in EU.

I guess most of you can expect what we wanted to build. We wanted to build a so called “API Hub”, which is one standardized endpoint to all banks. We wanted to launch this pre-PSD-2 times, solve the bank integrations with screen scraping and try to get a big enough market share before PSD-2 comes.

Always with good ideas I do customer development, contact a lot of companies who I think could be potential customers and try to learn as much as possible. Many seemed to be interested in paying a decent price for this and there seemed to really be a market for this.

The biggest drawbacks were that there was competition, some companies had a head start already and had raised quite big rounds. Competition validates the market, however, in this type of business it is a bit tricky to differentiate from the competition. There were some options though but I suspect competition will push prices down and additionally FinTech companies will start implementing some integrations with common banks themselves to save on costs.

It was a tough decision to make. I wanted to do this so badly but I did not have a co-founder for this yet. I had two advisors and some developers who would be potentially interested in helping me out at a reasonable price.

As always, one adventure leads to another. I ended up dumping this idea for an even better opportunity. One of the companies I had met for customer development was Arkkeo. The founder, Tuomas Kohila, said he has been looking for a technical co-founder for his company. Arkkeo had been building a document bank for receipts, tickets and various documents one receives from companies. He had a new big vision and plenty of interesting ideas how we could take it forward. After some meetings, emails and phone calls, I ended up joining him instead.

So far it has been really exciting. We pivoted, launched a small pilot app in NYC to validate some key assumptions and have now landed back in the FinTech field. I will write more about this soon. In short, we are on a mission to create the standard how shops and restaurants can distribute appreciation and gratitude!

Also from a tech point of view this has been super fun. We modernized the tech stack and are using React Native for the app and React for the merchant dashboard. We also built validation as a key cornerstone in our agile development process, so that everything we build will result in learning and validation of hypotheses. I will write another post explaining this process, which I think is the best way for a startup to really make sure all development produces “validated learning”. We are going as “Lean Startup” as one possibly can in the field of FinTech.

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