What does economics or military warfare have to do with Product Development?

In this article, I’m gonna share with you what makes this book particularly interesting and how we can apply these principles to web product development.

In the first chapter, this book already provides brilliant insights for the reader interested in product development and in particular in how to make the right decisions in this field.

It starts with a quick analysis of 12 problems of “traditional” product development:

I hope that with such a list, your curiosity is already piqued!

What’s really excited me with this book is how the author intends on providing a deep and almost scientific analysis of each subject.

Here are a few examples of the big ideas the book is based on:

I can see that most of the problems the author raises have already been discussed a lot in resources about Agile or Lean. This is the first time however that I see so many key issues systematically analysed through the lense of “economics”, or all the economical mechanisms involved that impact profits.

The first key idea defended by the author is that we fail to understand the “economics” of our projects: identifying and analysing all the mechanisms involved and how they ultimately impact profits.

Taking as a known fact that the purpose of product development is to increase profits, the author states we should always evaluate the impact on profits of our decision. (I would discuss that idea by saying that some companies have a different core mission than generating profits — e.g. having a societal impact — but you could just replace “profits” with “mission” and do the same economically-based analysis).

Most product developers indeed use “proxy variables” (e.g. time-to-market, capacity utilization…) to take decisions. The issue is that these proxy variables are an incomplete representation of the project’s economics and you generally fail to detect incompatibilities between objectives on these “proxy variables”.

Let’s take an example and say we have selected 2 objectives (based on proxy variables):

Now, let’s analyze maximizing capacity utilization. At full capacity, a system is not really capable of dealing with unanticipated workloads. We can quickly see that it increases the risk of delays in a system that has to deal with unpredictable events (which systems don’t?). Don’t unexpected delays negatively impact predictability?

We saw the problem with only 2 proxy variables. The point behind this is that it will get worse by increasing the number of proxy variables and objectives.

Another issue is that we often ignore some variables that may have a critical impact on profits. Consider the following chart:

If you take your development cost as the proxy for your impact on profit, you will probably want to take the decision early to reduce the cost. But, what if you add another variable?

We can now see that looking at both development and opportunity costs, we may not want to take the decision as early as before!

The solution: always go back to the common unit of measure, the final objective: increasing profits!

We tried many times to introduce business value estimation to prioritize product development. Reading the book however, I understand how our way of “estimating” business value is similar to taking just another proxy variable that does not truly represent the underlying economics. But in our context, doing a correct analysis of profit impact seems particularly hard. Why? Our business model is a multi-sided one, and we mostly provide our platform for free:

In this context, evaluating the impact on profit of product decisions never seemed easy. For example, how would you estimate the impact of:

You get the idea.

When the author tells we fail to correctly quantify economics, he explains that we need to be able to evaluate the impact on “product life-cycle profit”.

Since we will be doing economical analyses, we need a limited time-span. Most functions on profit will vary according to time, so you must do the analyzis according to a defined period!

I can’t remember having ever seen the concept of product life-cycle for web platforms.

I also think that the current Lean and Agile inspirations blur and even hide the product life-cycle. These approaches are strongly based on iterative improvement loops that cycle almost indefinitely. If we do not take a step back to have a look over the product life span, we risk ignoring its limit in time and thus hinder our capacity to do a viable economic analysis.

A solution to this problem may be to define an arbitrary life span for web products. It seems sensible to do so, because anyway, after enough time, either most of the product will have been rewritten by developers, or it will be replaced by newer products on the market that better solve the needs of users (and your product would be in the “decline” phase…).

In the first part of the article, they explain why they chose Lead and Cycle Time as metrics of success for product-development. It’s funny to see that they do like most product-developers and use proxy variables!

However, they explain in details why they are the appropriate variables from an economical point of view:

Their strategy of shipping before competitors to improve capture of market share is clearly their strategy to increase profits. Lead and Cycle Time are the proxy variables they decided would better serve this strategy.

I advise you to read the SoundCloud’s article too. It’s really interesting to see how they analyse the effects of other variables and objectives on Lead and Cycle Time.

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