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

0

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

0

Conversational analytics are about to change customer experiences forever

Companies have long relied on web analytics data like click rates, page views and session lengths to gain customer behavior insights.This method looks at how customers react to what is presented to them, reactions driven by design and copy. But traditional web analytics fail to capture customers’ desires accurately. While marketers are pushing into predictive analytics, what about the way companies foster broader customer experience (CX)?

Leaders are increasingly adopting conversational analytics, a new paradigm for CX data. No longer will the emphasis be on how users react to what is presented to them, but rather what “intent” they convey through natural language. Companies able to capture intent data through conversational interfaces can be proactive in customer interactions, deliver hyper-personalized experiences, and position themselves more optimally in the marketplace.

Direct customer experiences based on customer disposition

Conversational AI, which powers these interfaces and automation systems and feeds data into conversational analytics engines, is a market predicted to grow from $4.2 billion in 2019 to $15.7 billion in 2024. As companies “conversationalize” their brands and open up new interfaces to customers, AI can inform CX decisions not only in how customer journeys are architected–such as curated buying experiences and paths to purchase–but also how to evolve overall product and service offerings. This insights edge could become a game-changer and competitive advantage for early adopters.

Today, there is wide variation in the degree of sophistication between conversational solutions from elementary, single-task chatbots to secure, user-centric, scalable AI. To unlock meaningful conversational analytics, companies need to ensure that they have deployed a few critical ingredients beyond the basics of parsing customer intent with natural language understanding (NLU).

While intent data is valuable, companies will up-level their engagements by collecting sentiment and tone data, including via emoji analysis. Such data can enable automation to adapt to a customer’s disposition, so if anger is detected regarding a bill that is overdue, a fast path to resolution can be provided. If a customer expresses joy after a product purchase, AI can respond with an upsell offer and collect more acute and actionable feedback for future customer journeys.

Tap into a multitude of conversational data points

#artificial-intelligence, #automation, #cloud, #column, #customer-experience, #cx, #ecommerce, #extra-crunch, #growth-marketing, #machine-learning, #market-analysis, #natural-language-understanding, #saas, #sales, #startups, #tc, #user-experience

0

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

0

This UX specialist opened 12 UK bank accounts and ‘logged everything’

“I’ve got a really high attention to detail, which might sound great, but it’s possibly a curse because I can’t help but spot problems with everything around me,” says Peter Ramsey .

He’s the founder of Built for Mars, a U.K.-based UX advisory, and he has spent the last three months documenting and analyzing the user experience of a dozen leading British banks — both incumbents and challengers — including Barclays, HSBC, Santander, Monzo, Starling and Revolut.

“Quite literally, I opened 12 real bank accounts,” he explains. “You remember the stress of opening one account? I did that 12 times, [and] it was probably a terrible idea. But I really needed to control as many variables as possible, and this was the only way of doing that.”

Next, Ramsey says he “logged everything,” recording every click, screen and action. “I saved every letter, and made a note of when they arrived. I recorded pretty much everything I could,” he recalls. “At one point I even weighed all the debit cards to see if some were heavier. That was a total waste of time though, because they all weighed the same amount. But you see what I mean, I just thought about making it as scientific as possible. Also, UX is really quite subjective, so I wanted to back up my opinions with some more quantifiable metrics.”

The resulting analysis — covering opening an account, making a first payment and freezing your card — supported by individual bank case studies, is being published on the Built for Mars website over the month with a new interactive chapter released weekly.

After being given early access to the first three chapters and an initial series of case studies, I put several questions to Ramsey to understand his motivation, methodology and what he learned. And if you’re wondering which bank came out on top, keep reading.

TechCrunch: Why did you choose to do this on banks?

Peter Ramsey: My background is in fintech, and I think the banks are just in this weird place right now. When they first came out I think consumers were surprised at how much better the apps were. Banking was renowned for having old software, it was almost acceptable for an old bank to be buggy. But now that these challenger banks have been out for five years, I think that perception has changed. So I chose the banks because they represent this industry of “challenger” versus “legacy.” Plus, for billion-dollar companies, you’d expect them all to really care about experience.

#banking, #challenger-bank, #challenger-banks, #design, #europe, #extra-crunch, #finance, #financial-services, #hsbc, #market-analysis, #monzo, #online-banking, #payments, #peter-ramsey, #revolut, #starling, #tc, #user-experience, #user-interaction-design

0