Hulu UX teardown: 5 user experience fails and how to fix them

Hulu is the first major streaming platform to offer a social watching experience. And with most major league sports now being allowed to resume behind closed doors, Hulu’s combined proposition with ESPN will likely help entertain the service’s 30+ million users over the winter months.

But users have a surplus in choice of streaming services right now, so how will Hulu stay competitive?

With the help of UX expert Peter Ramsey from Built for Mars, we’re going to give Hulu an Extra Crunch UX teardown, demonstrating five ways it could improve its overall user experience. These include easy product comparisons, consistent widths, proportionate progress bars and other suggestions.

Comparing features inside packages

If your product/service has different tiers/versions, ensure that the differences between these options are obvious and easy to compare.

The fail: Hulu has four different packages, but the listed features are inconsistent between options, making it incredibly difficult to compare. Instead of using bullet points, they’ve buried the benefits within paragraphs.

The fix: Break the paragraphs down into bullet points. Then, make sure that the bullet points are worded consistently between options.

 

Steve O’Hear: I’m really surprised this one got past the marketing department. Not a lot to say except that I would argue that when UX, including layout and copywriting decisions, become decoupled from business goals and customer wants, a company is in trouble. Would you agree that’s what has happened here?

Peter Ramsey: Honestly, this happens all the time. I think it’s just a symptom of the designers building things that look nice, not things that work nicely. I probably raise this issue on about one-third of the private audits I do — it’s that common.

Keep a consistent width

Try to maintain a consistent page width throughout a single journey — unless there’s a major benefit to changing the width.

The fail: During the Hulu sign-up process, the page width doubles at a totally unnecessary point. This is disorienting for the user, with no obvious rationale.

The fix: Hulu has a pretty consistent first-half of their journey and then it drops the ball. I’d redesign these “extra-wide” pages to be the default width.

#developer, #entertainment, #hulu, #media, #peter-ramsey, #streaming-services, #tc, #usability, #user-experience, #ux, #video

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5 UX design research mistakes you can stop making today

A recent article in Entrepreneur magazine listed “inadequate testing” as the top reason why startups fail. Inadequate testing essentially means inadequate or sub-par user research that leads to poor UX design which, not surprisingly, usually ends in failure. While working with startups and tech companies, I have also seen how even when people know how important user research is, they may not necessarily know how to conduct it in optimal ways.

Let’s look, then, at some of the biggest UX research mistakes companies make and what I wish I had known when I first started.

Conduct UX research early and throughout product development

When considering any potential product or service, it’s best to get certain questions answered as soon as possible. Is it actually going to be something useful and feasible for the target users and their organizations? Are your initial; assumptions correct? Ideas that seem good at first may not seem so great after research, and many commonly criticized failures were likely results of insufficient research. This is why it’s vital to begin user research early before product development has even begun.

While it is important to conduct foundational research early on, you also want to make sure to conduct evaluative research by continuously testing your product as you build or upgrade it. One of the reasons why Google products product like Gmail or YouTube are relatively easy to use for most people is that Google has teams continuously testing their products, making sure that their users know where to find what they’re looking for.

Don’t do all of the user research yourself

One of the mistakes I see many startups and entrepreneurs make (and that I myself made early on) is doing all of the UX research themselves. In some ways, books like Lean Startup” have bolstered this tendency by stressing the need to “get out of the building” and get to know your users. In itself this isn’t a bad idea—it’s good to know who your users are and to build empathy for their experiences. Likewise, this isn’t to say that you should not do any research yourselves.

However, you also want to be sure to complement that by having professional, third party UX researchers do research for you as well. When you are heavily invested in your research, as you invariably would be if it is your own product, it is difficult to conduct it in an unbiased way. And when your research participants know that you are asking them about your own project, they are not likely to provide you with good signal that can actually help you improve your product.

#column, #customer-experience, #developer, #entrepreneurship, #market-research, #product-management, #startups, #tc, #user-interfaces, #user-research, #ux

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Disney+ UX teardown: Wins, fails and fixes

Disney announced earlier this month that it’s going all-in on streaming media.

As part of this new strategy, the company is undergoing a major reorganisation of its media and entertainment business that will focus on developing productions that will debut on its streaming and broadcast services.

This will include merging the company’s media businesses, ads and distribution, and Disney+ divisions so that they’ll now operate under the same business unit.

As TechCrunch’s Jonathan Shieber reports, Disney’s announcement follows a significant change to its release schedule to address new realities, including a collapsing theatrical release business; production issues; and the runaway success of its Disney+ streaming service — all caused or accelerated by the national failure to effectively address the COVID-19 pandemic.

So what better time than now to give Disney+ the Extra Crunch user experience teardown treatment. With the help of Built for Mars founder and UX expert Peter Ramsey, we highlight some of the things Disney+ gets right and things that should be fixed. They include zero distractions while signing up, “the power of percentages,” and the importance of designing for trackpad, mouse and touch outside of native applications.

Zero distractions while signing up

If the user is trying to complete a very specific task — such as making a payment — don’t distract them. They’re experiencing event-driven behaviour.

The win: Disney have almost entirely removed any kind of distractions when signing up. This includes the header and footer. They want you to stay on-task.

Image Credits: Disney+

Steve O’Hear: This seems like a very easy win but one we don’t see as often as perhaps we should. Am I right that most sign-up flows aren’t this distraction-free and why do you think that is?

Peter Ramsey: Yeah, it’s such an easy win. Sometimes you see sign-up screens that have Google Adwords on it, and I think, “You’re risking the user getting distracted and leaving for what, half a penny?” If I had to guess why more companies don’t utilise this technique, it’s probably just because they don’t want to deal with the technical hassle of hiding a bunch of elements.

The power of percentages

Only use percentages when it makes sense. 80% off sounds like a lot, but 3% doesn’t. Percentages can be a great way of making a discount seem larger than it actually is, but sometimes it can have the reverse effect. This is because people are generally bad at accurately estimating discounts. “What’s 13% off £78?”

The fail: If you sign up to a year of Disney+, then you’re offered 16% free. But 16% of a £60 bundle isn’t easy to calculate in your head — so people guess. And sometimes, their guesses may be less than the actual value of the discount.

The fix: In this instance, it would be far more compelling (and require less mental arithmetic), if it was marketed as “60 days free.” Sixty days is both easy to understand and easy to assign value to.

Image Credits: Disney+

Percentages may be harder to process or evaluate in isolation as an end user but they are easy to compare with each other i.e., we all know 25% off is better than 10% off. Aren’t you advocating obscuring the actual saving in favour of what sounds better on a case-by-case basis and therefore actually working against the end user? Of course I’m playing devils advocate a little here.

So, it’s actually a really complex dilemma, and there’s no “easy” answer — this would probably make a great dinner time conversation. Yes, if you’re offering two discounts, then a percentage may be the easiest way for people to compare them.

#apps, #disney, #entertainment, #media, #netflix, #peter-ramsey, #streaming-media, #tc, #the-walt-disney-company, #ui, #user-experience, #user-interface, #ux

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Coinbase UX teardown: 5 fails and how to fix them

Digital currency exchange Coinbase has probably done more than most to push cryptocurrencies closer to the mainstream, earning an $8 billion valuation by private investors along the way. The company is reportedly eyeing a public listing next year, and is inarguably doing a lot of things right. However, that doesn’t mean its product experience is perfect. In fact, far from it.

In our latest UX teardown, with the help of Built for Mars founder and UX expert Peter Ramsey, we highlight some of Coinbase’s biggest user experience failings and offer ways to fix them. Many of these lessons can be applied to other existing digital products or ones you are currently building, including the need to avoid the “Get Started” trap, the importance of providing feedback, why familiarity often wins and other principles.

The ‘Get Started’ trap

Only use CTAs like “get started” or “learn more” if you’re actually teaching users something.

The fail: Coinbase doesn’t actually have any onboarding — but it looks like it does. It has a very prominent “get started” CTA, which actually just puts bitcoins in your basket. This isn’t helping you get started, it’s nothing more than an onboarding Trojan horse.

The fix: It’s simple: Don’t lie in your CTAs. You wouldn’t have “Email Support” as a CTA, and then just show the user a bunch of FAQs.

Steve O’Hear: This feels like another classic “bait and switch” and reeks of dark pattern design. However, what if it actually works to get users over the line and purchase their first bitcoin? Growth hackers, rejoice, no?

Peter Ramsey: You’re absolutely right, this may convert better. From a business point of view, this could be a brilliant little growth hack. However, something converting well doesn’t mean it was a good experience for the user. Look at clickbait-y journalism — it gets more eyeballs, but people aren’t generally happy with what they read.

I’m convinced that in the long term having a great product will perform better than frustrating short-term growth hacks.

Feedback architecture

As a general rule of thumb, all “states” — e.g., success/failure of an action — need to provide feedback to the user.

The fail: After adding a card, you click “Add Card,” and … it takes you back to the homepage. There’s no notice if it was successful or not. The user has no awareness if the action they were trying to do failed and they need to do it again. This is a real problem with digital products: All feedback needs to be thought of and built.

The fix: During the design phase, consider statuses and what the user will want feedback on. For example, if they’ve just added an item to their “wishlist,” how will you show them that the action was successful?

#apple, #articles, #bank, #banking, #bitcoin, #coinbase, #cryptocurrencies, #digital-currencies, #feedback, #ipod, #macintosh, #money, #netflix, #onboarding, #peter-ramsey, #product-design, #san-francisco, #steve, #tc, #usability, #ux

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Zoom UX teardown: 5 fails and how to fix them

Valued at over $60 billion and used by millions each day for work and staying in touch with friends and family, the COVID-19 pandemic has helped make Zoom one of the most popular and relevant enterprise applications.

On one level, its surge to the top can be summed up in three words: “It just works.” However, that doesn’t mean Zoom’s user experience is perfect — in fact, far from it.

With the help of Built for Mars founder and UX expert Peter Ramsey, we dive deeper into the user experience of Zoom on Mac, highlighting five UX fails and how to fix them. More broadly, we discuss how to design for “empty states,” why asking “copy to clipboard” requests are problematic and other issues.

Always point to the next action

This is an incredibly simple rule, yet you’d be surprised how often software and websites leave users scratching their heads trying to figure what they’re expected to do next. Clear signposting and contextual user prompts are key.

The fail: In Zoom, as soon as you create a meeting, you’re sat in an empty meeting room on your own. This sucks, because obviously you want to invite people in. Otherwise, why are you using Zoom? Another problem here is that the next action is hidden in a busy menu with other actions you probably never or rarely use.

The fix: Once you’ve created a meeting (not joined, but created), Zoom should prompt and signpost you how to add people. Sure, have a skip option. But it needs some way of saying “Okay, do this next.”

Steve O’Hear: Not pointing to the next action seems to be quite a common fail, why do you think this is? If I had to guess, product developers become too close to a product and develop a mindset that assumes too much prior knowledge and where the obvious blurs with the nonobvious?

#design, #enterprise, #interface-design, #peter-ramsey, #tc, #ui, #usability, #ux, #video, #zoom

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A look inside Gmail’s product development process

Google has long been known as the leader in email, but it hasn’t always been that way.

In 1997, AOL was the world’s largest email provider with around ten million subscribers, but other providers were making headway. Hotmail, now part of Microsoft Outlook, launched in 1996, Yahoo Mail launched in 1997 and Gmail followed in 2004, becoming the most popular email provider in the world, with more than 1.5 billion active users as of October 2019.

Despite Google’s stronghold on the email market, other competitors have emerged over the years. Most recently, we’ve seen paid email products like Superhuman and Hey emerge. In light of new competitors to the space, as well as Google’s latest version of Gmail that more deeply integrates with Meet, Chat and Rooms, we asked Gmail Design Lead Jeroen Jillissen about what makes good email, how he and the team think about product design and more.

Here’s a lightly edited Q&A we had with Jillissen over Gmail.

Google has been at email since at least 2004. What does good email look like these days?

Generally speaking, a good email experience is not that different today than it was in 2004. It should be straightforward to use and should support the basic tasks like reading, writing, replying to and triaging emails. That said, nowadays there is a lot more email, in terms of volume, than there was in 2004, so we find that Gmail has many more opportunities to assist users in ways it didn’t before. For example, tabbed inboxes, which sorts your email into helpful categories like Primary, Social, Promotions, etc. in a simple, organized way so you can focus on what’s important to you. Also, we’ve introduced assistive features like Smart Compose and Smart Reply and nudges, plus robust security and spam protection to keep users safe. And lastly, we’ve made deeper integrations a priority: both across G Suite apps like Calendar, Keep, Tasks and most recently Chat and Meet, as well as with third-party services via the G Suite Marketplace.

How has Google’s hypothesis about email evolved over the years?

We see email as a very strong communication channel and the primary means of digital communication for many of our users and customers for many years to come. Most people still start their workday in email, which is still used for important use cases, such as more formal or external communications (i.e., with clients/customers), for record-keeping or easy access/reference, and for communications that need a little more thoughtfulness or consideration.

#developer, #diversity, #extra-crunch, #gmail, #google, #market-analysis, #product-design, #tc, #usability, #ux

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Five ways to bring a UX lens to your AI project

As AI and machine-learning tools become more pervasive and accessible, product and engineering teams across all types of organizations are developing innovative, AI-powered products and features. AI is particularly well-suited for pattern recognition, prediction and forecasting, and the personalization of user experience, all of which are common in organizations that deal with data.

A precursor to applying AI is data — lots and lots of it! Large data sets are generally required to train an AI model, and any organization that has large data sets will no doubt face challenges that AI can help solve. Alternatively, data collection may be “phase one” of AI product development if data sets don’t yet exist.

Whatever data sets you’re planning to use, it’s highly likely that people were involved in either the capture of that data or will be engaging with your AI feature in some way. Principles for UX design and data visualization should be an early consideration at data capture, and/or in the presentation of data to users.

1. Consider the user experience early

Understanding how users will engage with your AI product at the start of model development can help to put useful guardrails on your AI project and ensure the team is focused on a shared end goal.

If we take the ‘”Recommended for You” section of a movie streaming service, for example, outlining what the user will see in this feature before kicking off data analysis will allow the team to focus only on model outputs that will add value. So if your user research determined the movie title, image, actors and length will be valuable information for the user to see in the recommendation, the engineering team would have important context when deciding which data sets should train the model. Actor and movie length data seem key to ensuring recommendations are accurate.

The user experience can be broken down into three parts:

  • Before — What is the user trying to achieve? How does the user arrive at this experience? Where do they go? What should they expect?
  • During — What should they see to orient themselves? Is it clear what to do next? How are they guided through errors?
  • After — Did the user achieve their goal? Is there a clear “end” to the experience? What are the follow-up steps (if any)?

Knowing what a user should see before, during and after interacting with your model will ensure the engineering team is training the AI model on accurate data from the start, as well as providing an output that is most useful to users.

2. Be transparent about how you’re using data

Will your users know what is happening to the data you’re collecting from them, and why you need it? Would your users need to read pages of your T&Cs to get a hint? Think about adding the rationale into the product itself. A simple “this data will allow us to recommend better content” could remove friction points from the user experience, and add a layer of transparency to the experience.

When users reach out for support from a counselor at The Trevor Project, we make it clear that the information we ask for before connecting them with a counselor will be used to give them better support.

If your model presents outputs to users, go a step further and explain how your model came to its conclusion. Google’s “Why this ad?” option gives you insight into what drives the search results you see. It also lets you disable ad personalization completely, allowing the user to control how their personal information is used. Explaining how your model works or its level of accuracy can increase trust in your user base, and empower users to decide on their own terms whether to engage with the result. Low accuracy levels could also be used as a prompt to collect additional insights from users to improve your model.

3. Collect user insights on how your model performs

Prompting users to give feedback on their experience allows the Product team to make ongoing improvements to the user experience over time. When thinking about feedback collection, consider how the AI engineering team could benefit from ongoing user feedback, too. Sometimes humans can spot obvious errors that AI wouldn’t, and your user base is made up exclusively of humans!

One example of user feedback collection in action is when Google identifies an email as dangerous, but allows the user to use their own logic to flag the email as “Safe.” This ongoing, manual user correction allows the model to continuously learn what dangerous messaging looks like over time.

Image Credits: Google

If your user base also has the contextual knowledge to explain why the AI is incorrect, this context could be crucial to improving the model. If a user notices an anomaly in the results returned by the AI, think of how you could include a way for the user to easily report the anomaly. What question(s) could you ask a user to garner key insights for the engineering team, and to provide useful signals to improve the model? Engineering teams and UX designers can work together during model development to plan for feedback collection early on and set the model up for ongoing iterative improvement.

4. Evaluate accessibility when collecting user data

Accessibility issues result in skewed data collection, and AI that is trained on exclusionary data sets can create AI bias. For instance, facial recognition algorithms that were trained on a data set consisting mostly of white male faces will perform poorly for anyone who is not white or male. For organizations like The Trevor Project that directly support LGBTQ youth, including considerations for sexual orientation and gender identity are extremely important. Looking for inclusive data sets externally is just as important as ensuring the data you bring to the table, or intend to collect, is inclusive.

When collecting user data, consider the platform your users will leverage to interact with your AI, and how you could make it more accessible. If your platform requires payment, does not meet accessibility guidelines or has a particularly cumbersome user experience, you will receive fewer signals from those who cannot afford the subscription, have accessibility needs or are less tech-savvy.

Every product leader and AI engineer has the ability to ensure marginalized and underrepresented groups in society can access the products they’re building. Understanding who you are unconsciously excluding from your data set is the first step in building more inclusive AI products.

5. Consider how you will measure fairness at the start of model development

Fairness goes hand-in-hand with ensuring your training data is inclusive. Measuring fairness in a model requires you to understand how your model may be less fair in certain use cases. For models using people data, looking at how the model performs across different demographics can be a good start. However, if your data set does not include demographic information, this type of fairness analysis could be impossible.

When designing your model, think about how the output could be skewed by your data, or how it could underserve certain people. Ensure the data sets you use to train, and the data you’re collecting from users, are rich enough to measure fairness. Consider how you will monitor fairness as part of regular model maintenance. Set a fairness threshold, and create a plan for how you would adjust or retrain the model if it becomes less fair over time.

As a new or seasoned technology worker developing AI-powered tools, it’s never too early or too late to consider how your tools are perceived by and impact your users. AI technology has the potential to reach millions of users at scale and can be applied in high-stakes use cases. Considering the user experience holistically — including how the AI output will impact people — is not only best-practice but can be an ethical necessity.

#artificial-intelligence, #column, #cybernetics, #design, #developer, #machine-learning, #personalization, #startups, #tc, #user-experience, #user-interfaces, #ux

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(How to fix) 5 common UX mistakes in online banking

Customer support is a huge part of a user’s experience, and one that every bank likes to say they’re great at. But there is a lot we can learn from the mistakes that U.K. banks have made.

Based on his latest research report into the user experience of a dozen leading British banks — including Barclays, HSBC, Santander, Monzo, Starling and Revolut — Built for Mars founder Peter Ramsey shares his top five UX tips for customer support.

We dive deeper into each tip, including discussing the thorny topic of call decision trees (press 1 for … press 2 for … etc.), which Ramsey advises should be depreciated in the age of mobile apps, how push notifications might be employed to provide a more Disney-like queuing experience, why hold music is bad as a concept and why it’s time to ditch the live chat bait and switch.

Get rid of call decision trees

Call decision trees are annoying to use and unnecessary for users who have access to an app. Instead of asking customers to navigate via their telephone’s numeric keypad, use in-context questions inside the app, and then put the full number, including the correct extension, behind a button.

TechCrunch: Perhaps we should clarify what you mean by “call decision trees” and — considering they’ve been an industry standard for years — why is now the time to get rid of them?

Peter Ramsey: The decision tree is that automated “press 1 for … press 2 for … ” process you sometimes have to go through at the beginning of a call. I should clarify: It’s not time to eradicate them entirely, because it’s pretty useful for people who only use telephone banking. But for anyone who has access to an app, it’s totally unnecessary.

#argos, #barclays, #ecommerce, #europe, #extra-crunch, #finance, #financial-services, #fintech, #hsbc, #market-analysis, #monzo, #peter-ramsey, #revolut, #startups, #tc, #usability, #ux

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