One thing many people forget when dealing with data: outliers.
Even in a controlled online A/B test, your data set may be skewed by extremities. How do you deal with them? Do you trim them out, or is there another way?
Your users will make mistakes. It’s inevitable. That’s what error messages are for—but so many companies fail to follow best practices, and they’re pissing off potential customers in the process.
So, how can we better design error messages to improve the user experience and increase conversions?
Friction is “the psychological resistance that your visitors experience when trying to complete an action.” It’s also a conversion killer.
You can optimize your value proposition or call to action buttons all you want, but if your sign-up flow contains too much friction, you’re leaving money on the table.
Analyzing the customer journey is pivotal to conversion optimization. But how do you track user journeys in a way that is digestible, visual, and useful?
With funnels, of course! Funnel tracking in Google Analytics is one of the best ways to identify—in detail—where you’re going wrong.
You’d like to think that you’re a completely rational person making completely rational decisions, right? It’s nice to believe that you haven’t made major life decisions based on how you were feeling.
Well, you have. Many times.
Good user research asks the right questions to the right people. If you fail on either account, you may make million-dollar decisions on bad data.
Think you’re sitting on the next big idea for a product or a product feature? Before you spend dozens of hours and tens of thousands of dollars on the idea, you need to validate it.
In other words, you need to make sure your audience is just as excited about the idea as you are.
So, how can you validate the ideas sitting in your startup notebook or your product feature backlog without wasting resources? By running a smoke test.
User experience is a nebulous term. What defines a “good” UX from a “bad” UX, and what do the gradations look like between the two poles?
In the early 2000s, DVDs were the primary way to watch videos. Netflix streaming launched in 2007, and the DVD player is now a technological antique.
Products, much like humans, live on borrowed time. From the moment they launch, they’re on a journey towards decline.
How this journey plays out is what marketers try to predict by using the product lifecycle as a model.
No one is better at building anticipation ahead of a launch than Apple. New product launches trigger publicized spec leaks and reveal events that draw crowds in the millions (over 2.7 million people watched the iPhone 12 presentation live).
In the iPhone 13’s first quarter, it generated $71.6 billion in revenue (despite parts shortages and a global pandemic).
You don’t have to create Apple-level hype to see a successful new product launch. Trading app Robinhood launched with almost one million users thanks to a pinpointed market need and waitlist pre-launch campaign.