GEOFF RUDDOCK

A blog (mainly) about data science

Pieces of contradicting advice

Posted at — Jun 26, 2015

One of the problems with abstracted tidbits of advice is that they lose much of their meaning when divorced from their context. The correct decision can be heavily weighted by the nuances of the specific scenario. As a result, you often receive seemingly conflicting pieces of advice. The easy example is with contradicting proverbs, which are humorously documented here. But the contradictions also occur in more serious advice given around technology, business strategy, and product development. Here are a couple I have been thinking about recently.

Should you strive to be well-rounded (full-stack?) or should you focus on your strengths?

This can be viewed as a version of the classic generalist–specialist dichotomy. But it is more interesting when applied to the “micro” skill level rather than “macro” level career advice. When it comes to your skills and capabilities, should you focus on your strengths, or invest the time to round-out your weaker skills? This is loosely related to the multi-armed bandit problem, and to the concept of local maxima. What is the optimal mix of breadth and depth?

Should you apply the 8020 rule, or should you focus on the details?

Ellen Chisa pointed out this contradiction on her blog, specifically in the context of product development. It ties into the concept of Minimum Viable Product (MVP) which is unfortunately often cited as an excuse to cut corners and ship half-baked products into the market. 8020 style prioritization lets you achieve more output with fixed time/money. But it makes an implicit assumption that you are optimizing for raw efficiency. What if that is not true?

Imagine you are playing Super Mario for a moment. If you get 95% through a level but then die, you start again from the beginning. You are rewarded not for your average performance, but for the number of absolute wins you achieve. You can fail at that 95% over and over, and walk away with a 90% average but without making any real progress to the next level. In the context of product development, you are not optimizing for average happiness of a user but rather number of users happy enough to sign-up / buy. In this sense, users are fungible unit of success.

If you spread your resources out with the 8020 rule, you could launch 5x the number of features, but at an 80% quality level. This could get you 5x the exposure, or perhaps 5x the engagement, but it does not necessarily lead to 5x the sales / conversions. Imagine a user has some intrinsic standard for how well a solution must fit their needs to sign-up or buy. If this “bar” falls above 80%, then you might lose all your 5x users to a bunch of niche competitors that serve their specific needs at a theoretical 90% level.

It may make more sense to focus your resources on developing something at a 95-100% level but with only 20% of the scope. This involves saying no to 80% of opportunities/features. As a result, you might get objectively fewer users into the start of your funnel. But assuming that your product is well-executed—that you didn’t waste these theoretical resources—then you should have a far higher conversion than in the 8020 scenario.

Is it better to have the time lead of being first-to-market or the lower risk of being a close second?

Using the “first mover advantage” is a classic business school strategy. It is completely logical in industries such as telecom or social networks, where customers are locked-in and there are strong network effects at play. Yet many first-mover activities center around creating a market, and are not always defensible to a specific company. Competitors can get a “free ride” on your push for regulatory change or established supply chains. When does it make sense to be a trailblazer, and when does it make sense to tuck yourself into the slipstream of the current leader?

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