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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?