MDN Product Talk: The Case for Experiments

This is the third post in a series that is leading up to a roadmap of sorts — a set of experiments I will recommend to help MDN both deepen and widen its service to web developers.

The first post in the series introduced MDN as a product ecosystem. The second post in the series explained the strategic context for MDN’s product work. In this post I will explain how an experimental approach can help us going forward, and I’ll talk about the two very different kinds of experiments we’re undertaking in 2015.

In the context of product development, “experiments” is shorthand for market-validated learning — a product discovery and optimization activity advocated by practitioners of lean product development and many others. Product development teams run experiments by testing hypotheses against reality. The primary benefit of this methodology is eliminating wasted effort by building things that people demonstrably want.

Now my method, though hard to practise, is easy to explain; and it is this. I propose to establish progressive stages of certainty.

Francis Bacon (introducing the scientific method in Novum Organum)

Product experiments all…

  • have a hypothesis
  • test the hypothesis by exposing it to reality (in other words, introducing it to the market)
  • deliver some insight to drive further product development

Here is a concrete example of how MDN will benefit from an experimental approach. We have an outstanding bug, “Kuma: Optional IRC Widget“, opened 3 years ago and discussed at great length on numerous occasions. This bug, like so many enhancement requests, is really a hypothesis in disguise: It asserts that MDN would attract and retain more contributors if they had an easier way to discuss contribution in realtime with other contributors.

That hypothesis is untested. We don’t have an easy way to discuss contribution in realtime now. In order to test the bug’s hypothesis we propose to integrate a 3rd-party IRC widget into a specific subset of MDN pages and measure the result. We will undoubtedly learn something from this experiment: We will learn something about the specific solution or something about the problem itself, or both.

Understanding the actual problem to be solved (and for who) is a critical element of product experimentation. In this case, we do not assert that anonymous MDN visitors need a realtime chat feature, and we do not assert that MDN contributors specifically want to use IRC. We assert that contributors need a way to discuss and ask questions about contribution, and by giving them such a facility we’ll increase the quality of contribution. Providing this facility via IRC widget is an implementation detail.

This experiment is an example of optimization. We already know that contribution is a critical factor in the quality of documentation in the documentation wiki. This is because we already understand the business model and key metrics of the documentation wiki. The MDN documentation wiki is a very successful product, and our focus going forward should be on improving and optimizing it. We can do that with experiments like the one above.

In order to optimize anything, though, we need better measurements than we have now. Here’s an illustration of the key components of MDN’s documentation wiki:

metrics_status Visitors come to the wiki from search, by way of events, or through links in online social activity. If they create an account they become users and we notify them that they can contribute. If they follow up on that notification they become returners. If they contribute they become contributors. If they stop visiting they become disengaged users. Users can request content (in Bugzilla and elsewhere). Users can share content (manually).

All the red and orange shapes in the picture above represent things we’re measuring imperfectly or not at all. So we track the number of visitors and the number of users, but we don’t measure the rate by which visitors become users (or any other conversion rate). We measure the rates of content production and content consumption, but we don’t measure the helpfulness of content. And so forth.

If we wanted to add a feature to the wiki that might impact one of these numbers, how would we measure the before and after states? We couldn’t. If we wanted to choose between features that might affect these numbers, how would we decide which metric needed the most attention? We couldn’t. So in 2015 we must prioritize building enough measurements into the MDN platform that we can see what needs optimization and which optimizations make a difference. In particular, considering the size of content’s role in our ecosystem, we must prioritize features that help us better understand the impact of our content.

Once we have proper measurements, we have a huge backlog of optimization opportunities to consider for the documentation wiki. Experiments will help us prioritize them and implement them.

As we do so, we are also simultaneously engaged in a completely different kind of experimentation. Steve Blank describes the difference in his recent post, “Fear of Failure and Lack of Speed In a Large Corporation”. To paraphrase him: A successful product organization that has already found market fit (i.e. MDN’s documentation wiki) properly seeks to maximize the value of its existing fit — it optimizes. But a fledgling product organization has no idea what the fit is, and so properly seeks to discover it.

This second kind of experimentation is not for optimization, it is for discovery. MDN’s documentation wiki clearly solves a problem and there is clearly value in solving that problem, but MDN’s audience has plenty of problems to solve (and more on the way), and new audiences similar to MDN’s current audience have similar problems to solve, too. We can see far enough ahead to advance some broad hypotheses about solving these problems, and we now need to learn how accurate those hypotheses are.

Here is an illustration of the different kinds of product experiments we’re running in the context of the overall MDN ecosystem:

optimization_vs_learningThe left bubble represents the existing documentation wiki: It’s about content and contribution; we have a lot of great optimizations to build there. The right bubble represents our new product/market areas: We’re exploring new products for web developers (so far, in services) and we’re serving a new audience of web learners (so far, by adding some new areas to our existing product).

The right bubble is far less knowable than the left. We need to conduct experiments as quickly as possible to learn whether any particular service or teaching material resonates with its audience. Our experiments with new products and audiences will be more wide-ranging than our experiments to improve the wiki; they will also be measured in smaller numbers. These new initiatives have the possibility to grow into products as successful as the documentation wiki, but our focus in 2015 is to validate that these experiments can solve real problems for any part of MDN’s audience.

As Marty Cagan says, “Good [product] teams are skilled in the many techniques to rapidly try out product ideas to determine which ones are truly worth building. ┬áBad teams hold meetings to generate prioritized roadmaps.” On MDN we have an incredible opportunity to develop our product team by taking a more experimental approach to our work. Developing our product team will improve the quality of our products and help us serve more web developers better.

In an upcoming post I will talk about how our 2015 focus areas will help us meet the future. And of course I will talk about specific experiments soon, too.

MDN Product Talk: The Series

  1. Introduction
  2. Business Context
  3. The Case for Experiments
  4. Product Vision
  5. Reference Experiments
  6. Learning Experiments
  7. Services Experiments
MDN Product Talk: The Case for Experiments

One thought on “MDN Product Talk: The Case for Experiments

  1. I like this experimentation approach. We do, however, have to be careful not to fall into a mode where we experiment with every.little.thing until nothing gets finished.

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