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LABS
[Metrics] Do you need that feature?

The hardest part of product management is deciding which features NOT to build, especially when they seem like great ideas.  When you have a product in the market (or are getting it out there), you want to spend your engineering team’s time delivering value to your customers.  With this logic in mind, building anything has a cost associated with it (and more importantly, an opportunity cost).

So let’s say your marketing team advocates implementing auto-login for customers by following links in their email.  Sounds useful, but certainly has some security and privacy concerns.  Surely someone’s built this already?  One quick Google search later reveals a promising start.  You start writing the story and prioritize it in the backlog.

But wait — do you need that feature?  Product management by “wouldn’t it be cool if…” happens, but I wouldn’t recommend it.  Instead, channel your inner-lean and ask how you could validate that this idea has merit.  I.e., what hypothesis would this feature prove, and is there a way to test without building it?

In this case, I’d create an assumption like: “Customers aren’t re-engaging with our site from emails because they can’t remember their sign-in credentials.”  That leads to a hypothesis along the lines: “Customer engagement should increase by including automatic login in all emailed links to our site.”

This thought exercise points us at a conversion to track — how often do customers follow an emailed link and log in?  Luckily we’ve already implemented transactional email funnel metrics, which look something like this:

Hmm.  I see that we’re getting a 33% open rate on transactional emails; potentially good given the type of product.  Of those that view, 26% clicked to follow a link.  30% of those that followed a link weren’t part of the last step (visited site) because our tracking cookie didn’t recognize them; they didn’t log in and hence didn’t stitch their session into this funnel.  These numbers don’t scream for an auto-login feature, do they?  Sure, we’re churning 42 customers between click and login, but that only represents 2.6% of the customers that received emails.

Given that we’ve fit auto-login into the universe of engagement, I would look to prioritize any feature work could deliver a larger lift to this KPI.  Perhaps you could build more targeted emails that drive more customers to open their email?  A 16% bump in opens will have a larger effect than a near-impossible bump on click-to-visit conversions of 30%.

While far from perfect, data-driven product management will force you to evaluate any new feature from the context of increasing customer value on some axis.  In this example we were fortunate to draw upon historical email funnel metrics.  This won’t always be the case, but I encourage you to find cheaper, quicker approaches to [in]validating the merit of an idea before it becomes a feature story.

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