I am a big fan of lean startup, and will therefore tell you why. Still, the method is not universal, and has its limits, I will tell you about that too. If there is one thing you should remember is that lean startup gives you a simple recipe that can shape a new product both to meet the needs of its customers and to be profitable. To do that, the method needs customer interaction, and without enough of it, the method is likely to fail.

Lean startup is an iterative process. Yet unlike other development processes that only focus on the "how", lean startup gives you some very precise rules to follow on the "what". Once you understand how the method works, these rules make sense. Here are the important "whats":

- Value is defined relative to a reverse income statement (defined as the "engine of growth"). In effect you are expected to model both your product and your customers. You can use a simple model of fix price values per events (such as client clicks), or you can be more creative and use some real financial engineering tricks to build your notion of value.
- Risk is to be minimized, and as risk is hard to measure, it is up to you to judge your uncertainties, and then to diminish your largest know risk/uncertainty at each development iteration.
- Customers and products are understood through the data that can be collected out of them (this is the notion of "measure"). All value assumptions must be validated through the collection of data. Risk assumptions are not validated.
- Continuous deployment means that software changes go directly into production (with some smart test automation in between).
- Software is "staged" into production. So new changes go into production, but normally only to be initially used by few. As data comes in that validates the code change and expected value improvements, the change will be made available to more users. Staged deployment makes it possible to simultaneously collect data on different versions of your product. This is the notion of split testing. If you have done enough math you will know that split testing, well implemented, gives you the most reliable measure of how the customers are effected by your code changes. Other ways to collect data will include much more noise..
- The careful observer will have noted that split testing within a "learn and change" loop is in effect a quasi-Newtonian optimization process. That is, we a looking for the best way to improve our product (equivalent to the steepest gradient). As we do not know that best way, and only know "a way", when the split testing confirms that the new version of the product is better than its previous version, we have a "quasi" method. As Newtonian methods are susceptible to getting stuck in local minimas, we need something more, and that is inspired by linear programming with the the notion "pivot": To pivot is to swivel variables into constraints and constraints into variable. Said differently, to pivot in business terms, is to change assumptions, to change your beliefs.
- Data and data collection are core values in Lean Startup, and therefore the notion of pivot. In lean startup you must note all your assumptions down, and any change to these assumption (any "pivot") must be noted down too. This type of detail may sound pedantic but that is how smart learning happens. And more importantly that is how good team learning happens, by making the effort to express and note down your assumptions, to validate them, and make the effort to update your notes (data) when you change these assumptions.

Expressed in this way, lean startup is one big algorithmic machine. People still have a role to play as they decide the changes they might want to bring to their income model and to their product. The lean startup algorithm makes sure that changes really do have impact on revenue and on product perception. The originality of the method is to be expressed within one recipe.

I do not know what made the creator of lean startup put his method together, but if you have worked in finance, and especially market making and algorithmic trading, then you have already been using very similar methods. The big differences is that the method is meant for material products, and not immaterial financial products, and that these material products normally only have a single sided market, unlike financial markets where you can buy and sell. These two aspects do make the method a bit tricky when the income model is not obvious. For example, when income comes from advertisement, you can equate each advertisement screen refresh with income, therefore it is pretty simple to map customer events to income values. But suppose you want to move to a subscription model, now your customer is paying a monthly fee, how to map your products features to this income? If you have done enough math and worked in finance, you can think up a few ideas on how to do this, but nothing is obvious when you do not have this math experience. And that can make the method tricky to apply.

I like the method's smart tie into financial theory. For example, simple portfolio theory say that it equivalent to maximize return under fixed variance, as to minimize variance under fixed return. So lean startup chooses the second option: minimize risk (variance) while having asked you to express return (inverse income statement, growth engine). To then make sure that you validate that return model with your collected data. As a full notion of variance would be impossibly complicated, lean startup says: focus on the largest risk. That is a simplest approach, but also the smartest approach.

Another tie to financial markets is that you need to make a market in your product for lean startup to exist. Making a market means that you attract customers to use your product. And because lean startup relies on a "market fitting" approach, that is, you fit your product and income model to your market of customer, the type and amount of customers you have will make all the difference in how your product evolves. This is a real issue because the good clients are not the ones that want to spend their time playing with small applications that only have a subset of functions. Therefore, a critical difficulty with lean startup is in how to bring valuable customers to a product that is not yet really valuable enough to them. This bootstrap phase can be most difficult and that explains why you need to cheat at bit and sometimes offer what you do not have to get the necessary feedback to validate your product's evolution. Unfortunately, customers will hesitate to come back to your product if the first time they came they felt deceived. So offering what you do not have, in order to get a feed back on the demand, will also tarnish you ability to grow later. This is especially true with small niche markets.

Market dynamics are much the higher order properties of a product. Many just ignore them and hope for the best when launching their new product. Lean startup makes the effort to try to learn about these market forces while you develop the product. It is not easy, it is not obvious, but it is still the right thing to do.