@andrewchen

This is reposted from my answer on Quora here.

Question: What UX considerations were built into Google+?
The most interesting design choice I’ve seen for G+ has been deploying it across all the Google properties within a navbar, and via the notifications – I’m talking about this thing right here:

(btw, looking at it now, I notice it’s the same coloring scheme as Quora, hilarious)

Building G+ on top of pre-existing, high-retention products
Obviously this is a smart decision because it lets them build on top of their own high-retention, pre-existing products: Google Search and Gmail, in particular. Contrast this to an approach where they would have started up G+ as its own independent property, which Google users could choose to adopt or not- but then that looks like Orkut.

Anyway, as a result of adding this new global navbar across all the Google properties, they have to deal with a very small amount of real estate to create some pretty rich interactions. Thus, it was very interesting to then see them building a mobile-like interface for interacting with comments, follows, etc., inline, without leaving whatever experience you’re already in:

And if you click on any of these, you see a quick sliding motion that lets you interact with the different notifications inline, without going anywhere:

Contrasting with Facebook
In comparison, the Facebook notifications dropdown is almost more like a inbox of “pointers” to the actual content. As a result, while you can see what’s new, you can’t actually do anything about it without leaving where you are. I found this a nice interaction on G+’s part given that they are building on top of things like email or search where you may not want to leave yet.

Someone should obviously do a much longer design discussion of the G+ main site, but I personally found the new navbar and notifications system pretty interesting so I thought I’d write a bit about it.

Like this post?
If you liked this post, please subscribe or follow me on Twitter. You can also find more essays here.

Simple products aren’t only better designed, they’re easier to market too.
Marketing and product UX are seen as conflicting with one another, but there are, in fact, many opportunities for the two to work together. Some of the best tools for increasing metrics are the same ones that are used to create effective interaction designs. These techniques include things like adding “soft” onboarding experiences, stripping out unnecessary features, having clear visual hierarchy and calls to action, and many more tactics. Ultimately, these tactics serve to create simple product experiences that are both desirable and well-optimized.

Let’s explore the different reasons why simplicity is a virtue for both designers and marketing quants.

Highly optimized flows make it easy to understand “what do I do next?”
Every product lives and dies based on how well new users are able to sign up and get oriented with the product’s core value. High signup and onboarding rates depend on a large % of users completing each step.

As a result, it’s important for each page to be as simple and directed as possible, so it’s constantly obvious what to do next. If each page gives the user too many options, thus distracting from the primary goal of the funnel, then the %s will decrease. As a result, some of the best landing pages and funnels fundamentally depend on extremely simple, stripped down designs. Here, removing things like navigation chrome, extraneous links, etc is not only simpler, but also better performing from a metrics standpoint.

More data and faster learning cycles
A metrics-informed team depends on deploying A/B tests and evaluating the results as the core of their product iteration process. Early on however, you often don’t have enough users to quickly evaluate tests at a statistically significant level. This data is then further diluted when you have a complex featureset, since only a small % of users interact with each option. However, if you have a simple product, where almost 100% of the users go through the same signup, invite, and sharing flows, then you’ll be able to collect data sooner and thus make decisions faster too.

This is a huge advantage because when you can run A/B tests in 3 days instead of 9 days, for instance, you can learn 3x faster and find product breakthroughs sooner. Think about this like compounding interest in the bank- finding 10% improvements faster leads to exponentially better performance.

Simple products are easier to optimize and pivot
Ultimately, it’s the optimized flow through your product that wins – you don’t get any credit for complexity. One optimized funnel beats any number of unoptimized funnels, because you only get credit for average conversion rate across all the funnels. Thus, more funnels means that on a practical level, it’s harder to keep them all optimized. It’s easier and better to push users through a small number of signup flows that you can keep well-designed and well-optimized, so that the overall quality stays high.

This is especially true if you decide to make some product changes in a classic “pivot,” or otherwise test significant new additions in a signup funnel like adding Facebook sign-on. If you have a simple product with a small number of onboarding flows, then it’s easy to experiment to see if it’ll work, collect data quickly, and then add it to 100% of new users’ experiences. Contrast this to a complex product where shifting the design takes a lot of time because you have to update so many different places in the product.

Keeps the focus on top of funnel rather than low-impact add-ons

When a product isn’t working, often the knee-jerk response is to “fully bake” the product by adding more features. However, I’ve found that when examining the data of new startups, the problem most often lies on the first couple pages of a product- often an unattractive value proposition, or clunky signup flow that kills the new user experience. Adding metrics to simple products often makes it clear exactly what’s going on, and most of the time, it’s a fundamental issue that needs to be fixed on the first page.

In this way, simple products with the “right” value prop will end up with better signup rates- this lets you put your attention on top-of-funnel issues rather than low-impact feature add-ons that won’t 10x the destiny of your product.

Short funnels result in more conversions
One of the most powerful things you can do to a key product flow is to shorten it*. Generally, because you lose a % of users at each step, reducing the amount of work to get started is a highly effective tool- rather than presenting a complicated homepage and asking for tons of information upfront from a user, perhaps you just let them signup with Facebook- that might reduce the number of steps, leading to a simpler product and better metrics too.

Ultimately, this all aligns with the highly opinionated design ethos that prioritizes what users most often want to do, rather than presenting many options equally. As is discussed in the Palm story in the book “Designing Interactions” the features of a product are used in a Power Law distribution- a small number of features are used constantly and the rest are long tail. As a result, you want to make the most commonly used features convenient while putting the unused features available but hidden.

(*in some outliers, lengthening signup flows with the right steps can help too)

Increasing the prominence of high-value actions by removing low-value actions
One of the most common (bad) design patterns I see among metrics-oriented products is continually layering more and more prominent calls to action for sharing or other viral mechanics. This got especially bad in early Facebook apps. The problem is that the user’s attention is easily diluted, and each new feature competes with the last- as a result, after a few iterations of this, it’s pretty easy to end up with a frankenstein of a product that’s cluttered and messy.

Instead, a compelling tool is to remove features in order to make what remains more prominent. Instead of making the high-value actions bolded and highlighted in yellow, simply remove the actions that are no longer necessary. This leads to both a simpler product experience as well as raised prominence for whatever actions you want to emphasize.

Conclusion- let’s make design and metrics work together
Ultimately, the key to the tools above are that they increase the effectiveness of the UI while simultaneously increasing the metrics. This can happen because highly optimized products are dead simple to use- they have landing pages that communicate a compelling value, soft onboarding flows, clear calls to action, and simple mechanics that drive a lot of value. The same things that make it a highly marketable product are the same things that make it well-designed, and a great thing for which every product should strive.

To use these tools effectively, those who are metrics-informed must also become design-informed. While it’s obvious that you can increase the prominence of something by making it blink and highlighted in red, there are many more tasteful tools that lead to less visual clutter and provide an even greater metrics benefit. Even Dave McClure!

Like this post?
If you liked this post, please subscribe or follow me on Twitter. You can also find more essays here.

There’s more than one way to use this tool
A/B testing is a very useful tool that can be used to develop better product designs, rather than just evaluating landing pages.

In a classic A/B test, you’re metrics-driven and want to pick whatever test variant ends up with the higher numbers. This is a useful tool, but is only applicable to scenarios like signup flows where the conversion is obvious. This post will describe some different tactics that are metrics-informed and end up as an aid to your product design process, rather than driving it.

The tactics I’ll describe are for:

• Updating your product without negatively impacting numbers
• Streamlining your product by measuring and removing unused features
• Designing for the right level of prominence

Updating your product without negatively impacting numbers
Product teams are constantly pushing small updates to their products in response to customers and what’s happening to the market. When an update affects a key part of the product, particularly to the main signup flow or core viral loop, it’s often important to ensure that it doesn’t hurt the numbers.

For example, let’s say you’re building a new social site and you have a Facebook-integrated “friend finder” option that you want to add. If you build this and test it, you’ll likely find that since it’s unoptimized, it’ll have worse initial numbers. A classic A/B test will often eliminate the new design because it performs worse. But instead of killing it prematurely, you can use an A/B test to iteratively “bake” the new design with a small % of users until it’s ready to replace the old one.

If you know that it’s important to have this type of Facebook integration in your product design, what you do is leave it in, but only expose 10% of your users to it. Then keep making small updates to the design, working on the copy, call to action, and other aspects, until the new design performs as well as the original.

In this way, you can update your product without impacting the numbers negatively. And unlike a classic A/B test where you aim to just pick a winner, instead you are using it to incrementally benchmark a new design until it’s ready to replace the existing one. For this, you are design-led because you know you want to execute this product in a particular way, but you use the A/B test as a safety net to make sure you don’t push out something that’s not ready.

Streamlining your product by measuring feature usage
There’s an important design principle that says, “Do less, but better.” I’ll elaborate on my POV of this philosophy more in a future post, nevertheless many product teams struggle to remove features, or even to quantify unused features.

For example, you might have a legacy feature that suggests people to follow on your social site, which you’d like to replace with a Facebook-based “friend finder” screen instead. Sometimes it can be difficult to get rid of navigation on something like this because it’s not clear how many people are really using it and how that affects their behavior overall, especially new users

A nifty way of using A/B tests to handle this is to run an A/B test to remove the feature, and get the following information back:

• How many people actually get exposed to this feature? (Based on what % of people get added into the experiment versus your active users during the test’s time period)
• What metrics are affected by people who have this feature removed? (As long as the metrics are neutral to positive, then you can remove it safely)
• If some metrics are bad, can you counteract it by adding something else to the new design?

Similar to the process of updating your product, the important notion here is that you have a particular action you want to take on a design level (simplify the UX) and you use the A/B test as a tool to aid that design goal. In this case, rather than going with whatever has better metrics, instead the goal is to go with the better design as long as it’s neutral or better on the numbers.

Designing for the right level of prominence
As you model out the key metrics for your product, there’s often important assumptions that need to be made on things like what % of your users invite their friends, or how many friends they invite, etc. Oftentimes, entire product strategies hinge on making sure that certain kinds of metrics get hit- it could mean the difference between being a viral eyeballs business versus one based on lifetime value and ad spend.

From a product standpoint, this manifests itself as trying to figure out how prominent to make things like “Invite friends” or “Import your addressbook” or “Subscribe to the Pro version.” To build a great UX, you often want to make something as low-prominence as possible while still making sure it’s easy and accessible for users.

A/B testing can help a lot here since you can test multiple versions of prominence and see where it takes you. If you want to prove that a model is even possible (for example, in the very best case could we get 20% of our users to invite their friends?) then you can make a popup that asks for friend invites constantly and see if you are even close. The point here isn’t that you would ever actually close the experiment with the obnoxious popup, but rather, it helps you do a sensitivity analysis of what might even be possible, to see are realistic values within your model.

You can use this technique hand-in-hand with the other ones listed above so that you eventually take a high-prominence version of it and iterate until it’s acceptable to show to 100% of the users.

Final thoughts
The thing that all of these ideas share is that you are using A/B testing as a tool to aid in a broader and stronger design POV rather than slavishly following whatever has the better metrics outcome. As others have discussed before, it’s the difference between data-informed versus data-driven. Many features you’ll want to do in your product have lots of qualitative value, even if the short-term quantitative benefits are difficult to measure or not there at all- using these advanced tactics lets you continue to push out dramatic new designs but without hurting the metrics your business depends on.

Like this post?
If you liked this post, please subscribe or follow me on Twitter. You can also find more essays here.