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UX Study: The Search for Wine

It all began one night at the Press Club in San Francisco, where I was attending my first company event. As the youngest guy in the company, I was the least knowledgeable when it came to alcohol (debatable) and wine. When I was asked which wine I preferred, the only two things that came to my mind were red and white.

Let’s be honest, we’ve all experienced a similar situation, and it didn’t help that Press Club has a 17-page long wine list. I waited until everyone ordered, and kindly said “I will get that one too.” After that night, it’s been my goal to better understand wine. I found Vivino, an app for wine lovers and it has been my best drink companion ever since.

So What’s Vivino?

Vivino is a mobile app that allows users to photograph any wine, and instantly see ratings, price, and reviews. While my experience with the label scanning feature has been amazing, I have always struggled with the search (explorer) feature. Trying to find a bottle that suits both my taste and my wallet is difficult. Putting on my UX designer hat, I conducted usability tests to discover pain points and redesigned this feature of my beloved Vivino.

What I Accomplished from This Study

How I did it

I used guerrilla usability testing, affinity mapping, and persona creation for my initial user research. During the redesign process, I discovered two pain points that I wanted to focus on, then created task flows and wireframes for the changes I wanted to make. Through building an interactive prototype, I was able to validate my assumptions. To better emulate a real-world startup time frame, I gave myself only a week for this case study. Now let’s go deeper into each step.

User Research

Guerrilla Usability Testing

To eliminate any personal bias, I carried a bottle of wine to Yerba Buena Gardens. I then asked five people who had never used Vivino to complete the following tasks, “Imagine it’s Thursday’s evening, and you are looking for a bottle of cheap, good wine to bring to a housewarming party. How would you go about it?” I filmed their interaction with the app (with permission), so I could analyze their interactions later.

Affinity Mapping

To organize my findings from the conversations, I watched the user interactions with the app, and I jotted down insights on a pile of sticky notes. I then categorized similar insights into an affinity map and weighed them against the Importance to User vs. Importance to Business on a 2×2 metric.

Users need to be able to find the list of wine quickly and accurately to help them make their purchasing decision. Since I don’t have access to Vivino’s business goals, I made the assumptions that user engagement and satisfaction are the most important things to the company.

Persona Creation

From the usability tests, I learned the following:

To better understand the typical user, I collected the information above, and created a persona that reflects the characteristics of those I had interviewed.

I also created a scenario that my persona might go through.

Redesign

Define & Analyze

While none of users had a problem finding a wine, most of their interactions showed that they were either confused or frustrated with the inability to complete a certain action. I mapped these interactions out using the task flow below.

With this task flow, I was able to discover the two pain points that users were having. Below are my proposed solutions based on each of the pain points.

Pain Point 1

Users struggled to move the minimum rating slider

Pain Point 2

Users expressed frustration when they couldn’t sort the result and list of wine by price

Bringing It All Together

In order to make sure that my changes are reflected well in the overall user experience, I revisited the task flow created earlier, and made an update to it.

Validation Testing

After a week of user research, analysis and redesign, I was able to validate the assumptions and changes I had made. I did this by testing my interactive prototype with five new users. The results are:

• The average time taken on using and reusing the filter was reduced from 11.4 seconds to 3.7 seconds

• All users were confident and happy in selecting the wines that fit their price range

Conclusion

This UX case study has been a challenging and rewarding experience for me. Not only did I get to exercise my UX skills, I was also able to make a positive impact with the proposed changes for my wine buddy, Vivino. With these changes, I hope that people will be able to search for their perfect wine with ease and become a wine connoisseur at their company events and business meetings! For now we all need…

I’m currently consulting startups on growth, product and UX strategy. If you’d like to chat about growth, product, UX or startups, please reach me at casper.sermsuksan@gmail.com, Twitter or LinkedIn =)

from Sidebar http://sidebar.io/out?url=https%3A%2F%2Fmedium.com%2F%40casper.sermsuksan%2Fux-study-the-search-for-wine-492be5bb3b77

Vivaldi Browser, the brainchild of Opera co-founder Jon von Tetzchner, is launching a new feature that’s downright historic. Today, the browser reveals its History feature, which provides users with detailed insight into their browsing behavior.

This isn’t just your average history record. Instead of users simply looking at what websites they visited—line-by-line and row-by-row, like other browsers—Vivaldi gives them visual clues instead.

According to the company’s latest press release, the new feature means users can conduct a full-fledged analysis of their browsing patterns, all supported by stats and a visually friendly interface.

As von Tetzchner puts it:

Instead of having to scroll through hundreds of lines, Vivaldi gives a comprehensive overview of history, presented in a visual way. This lets our users analyze their online activity and helps them find what they are looking for.

So say goodbye to the days of monotonously scanning your browser’s history until you finally find what you’ve been looking for. This new feature lets Vivaldi’s users efficiently locate what they’re searching for by allowing quick scans through visited sites and offering helpful hints when searching for older URLs.

The end result is a better user experience.

Users will also be pleasantly surprised by the use of a calendar view to present all this history data. Changing to a calendar view provides users with a more user-friendly interface that’s easier to look through than having to scan line-by-line, as with traditional history views.

In addition, a color-coded heat map and graphs to the right of the calendar give users a further layer of depth to their history browsing. Key browsing trends and the user’s online-activity peak round out the data that’s available for analysis.

These changes allow users to locate previously visited webpages even if they fail to remember the exact search term. That’s because this new feature puts searches in context. For instance, it will help users find an old URL if they see it show up on a specific day when they were more active on the web.

Users also have full control over their history search: They’re able to narrow down their search to a range of dates from the monthly view or just from the Day Picker Calendar. Just for good measure, users also have the power to filter their search results by title, date, views and addresses.

Perhaps the best part of this update is the emphasis on privacy rights. Vivaldi never collects the user’s history data because all of this data is local to a user’s browser.

Using the history feature is straightforward. Vivaldi has incorporated History into the browser’s Side Panel, so simply clicking on the History icon will show the user the list of previously visited URLs, right next to the open sites. This design allows users to efficiently search through their history without having to leave their current page.

Mega Action Bundle of 4 Photoshop Add-Ons – only $19!

Source

from Webdesigner Depot http://www.webdesignerdepot.com/2017/03/vivaldi-browsers-new-feature-makes-history/

from The Next Web https://thenextweb.com/google/2017/03/29/googles-new-site-showcases-its-open-source-projects-and-favorite-tools/

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What is AI? Even Elon Musk Can’t Explain

Artificial intelligence is hard to define — because the field is broad and the goals keep moving

Word leaked Monday via The Wall Street Journal that Tesla / SpaceX industrialist Elon Musk has been funding a company called Neuralink— allegedly with some of his own money — attempting to connect computers directly into human brains. This is the same Musk profiled in this month’s Vanity Fair, where he tells journalist Maureen Dowd in all seriousness that humanity needs a Mars colony to which we can escape ‘if AI goes rogue and turns on humanity.”

Which side is he on?

In short, Musk is one of many big thinkers who believe a human-computer hybrid is essential to allowing humans to keep their own machines from marginalizing them. Neuralink’s technology is said to be a neural lace, which Musk has spoken about for over a year.

But for most people, the first question isn’t whether artificial intelligence will usurp our planet. The first question is: What exactly is AI?

Let’s skip the science fiction and get to the science: AI research and development spans a broad range of fields and myriad goals, as befits the concept of mimicking the vast breadth and depth of a human mind as compared to a calculator. Even people who work in AI, and reporters who’ve covered it for years, can’t agree on what doesn’t and doesn’t count as “intelligence,” or how to group all AI projects into a few understandable categories. We tried asking.

Five Types of AI

The explanation-friendly people at Tutorials Point have done a tidy job of breaking AI research into an understandable graphic with five major categories. (Their tutorial is a good next step to learn more details about AI research areas.)

Expert Systems

These are computer systems which are programmed with vast histories of human expertise on a topic, so that they can quickly examine far more options, scenarios and solutions than a team of experts could ever come up with. Google Maps, which solves the apocryphal traveling salesman’s dilemma before you’ve realized you have one, is a familiar example. Air traffic control systems juggle even bigger arrays of data points, options and restrictions.

Clinical systems, which dispense medical advice, are one of the promising areas for AI — what doctor can keep on top of all medical knowledge today, even in one field? But doctors point out that these systems are a long way from replacing a human clinician — they’re helpful advisers, not successors.

Neural Networks

Artificial neural network systems somewhat mimic the neurons in the human brain. They are already far superior to humans at tasks that involve pattern-matching. They can spot a face in a crowd from old photos, or tell you not only what someone said or wrote, but who like said it or wrote it (or didn’t!) based on language patterns too subtle and complex for mere mortals to spot.

Fuzzy Logic

Traditional computer programs work with hard logic — true or false, 1 or 0, yes or no. Fuzzy logic allows an array of possible values, a sliding scale of trueness, a “truthiness” rather than the inflexible numbers of a spreadsheet. It’s much more like how humans think.

Your washing machine may already use fuzzy logic — that’s how it can do one-touch cleaning. But more advanced fuzzy logic is what will enable a self-driving car. If it’s going to run over one baby or five old people, which will it choose? Silly human, you’re looking for there to be one right answer.

Robotics

Most babies learn to walk in less than a year. Many animals can scamper up, over, and around terrain where humans wouldn’t dare. Researchers are learning a simple truth: Walking is hard. Getting a robot to take a single step under its own balance took years. That second step is a doozy.

But robotic systems are already much better than people at many precision tasks, such as assembling products or shipping boxes from an Amazon warehouse. Not only are they precise, they’re smart — they scan spot production glitches or incorrect parts, and can compensate for variances from one product to the next.

Even products that require craftsmanship, like building guitars, can often be done better, faster and more reliably by CNC manufacturing now. It’s getting harder for experienced players to tell a $400 robot-made G&L guitar from Indonesia — or is it China now? — from a $1,500 one built by hand in California.

AI for Chatbots: Natural Language Processing

NLP, as everyone calls it, is the corner of AI most applicable to chatbots. The goal of natural language processing is to hold everyday conversations with humans, without them needing to speak in restricted syntaxes and vocabularies, or speak (or type) special commands. Researcher Alan Turing proposed a simple test for a successful program: “A computer would deserve to be called intelligent if it could deceive a human into believing that it was human.” By that standard, the Twitterbots have already won.

But as Pandorabots founder Lauren Kunze, who built her first chatbot at age fifteen, told us recently, “Walking is complex, language is far beyond that.” Human languages are far more complex than computer programming languages in complexity, flexibility, malleability and nuance. As a human, you can read a 500-year-old play by Shakespeare and sort of tell what’s going on. Try typing some Shakespeare at an Internet chatbot.

Musk told Vanity Fair that he believes a human-brain interface is four to five years away. But IBM researchers once claimed they would have software that could not only understand any human language, but translate it into any other language, in three years. That was in 1954.

A Never-Ending Quest

Artificial intelligence — hardware and software that performs functions once believed possible only by a living brain — has been a concrete goal of technologists for more than 150 years, since Charles Babbage drafted the design for his Difference Engine (never built until the 1990’s, as a museum piece) and Ada Lovelace realized it could manipulate not just numbers, but musical notes or anything else with a formulaic system.

But the path from vision to reality is one that continues to get longer and longer the further we travel down it. Dead ends, imminent breakthroughs that never happen, and algorithms that almost work have become pretty much expected of any direction AI research takes.

The people who pay for that research frequently become disillusioned. Scientists talk about the AI Winters of the 1970’s and 1980’s, when one institution after another refused to pour more money into projects that were neither sticking to schedule nor delivering the hoped-for results.

AI took a mini-hit this past year in the world of chatbots. Facebook’s announcement of an AI-enabling platform for its Messenger communication channel spurred a slew of investments in automated friends, assistants and services (including Octane AI, which publishes Chatbots Magazine.) A year later, reports claimed that Facebook’s M project, advanced AI designed to understand Messenger users, could comprehend and complete fewer than one in three requests.

Moreover, AI research today is unlike the Internet R&D that pawns thousands of products from a never-ending stream of small startup companies. Modern AI research uses an almost unimaginable amount of computing power and time — beyond the financial reach of many startups. And engineers who understand sub-disciplines like machine learning — whereby one doesn’t program software directly, but instead gives it an ocean of example data from which to learn on its own — are rare and therefore expensive, even for computer programmers.

That’s why startup investment firm Y Combinator recently announced a special funding track for AI startups. Y Combinator plans to provide entrepreneurs whose ideas seem financially promising with extra computational credits for cloud computing, and with experts in machine learning consultants who will make office hours to help young founders. Those are what Google, Facebook, IBM and other big-budget firms can afford that two Stanford dropouts in a loft can’t.

So What Are Musk and Others Afraid Of?

Many discussions of artificial intelligence skip past what it is or isn’t to an apocalyptic worry: That a sufficiently complex computer system will develop self-awareness, literally thinking for itself. And that one or more such artificial superminds will then decide the pesky humans who built them are in the way. The Vanity Fair article is a well-written primer on who worries about what among leading tech thinkers.

But one reason world-conquering AI seems to always be just a few more years out is that to us humans, advanced software only earns the title of “artificial intelligence” until it becomes part of everyday life. What we once imagined only a human mind could deduce, like finding the fastest driving route through five spots across Los Angeles, loses its mystique once Lyft does it. Dude, it’s just an app.

It’s been deemed the AI effect, tidily summarized as Tesler’s Law: “AI is whatever hasn’t been done yet.”

This series continues Wednesday with “What is NLP?” and Thursday with “What is Machine Learning?

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Click the ❤ below to recommend this story to other Medium readers looking to learn more about the world of AI and chatbots.

from Chatbots Magazine – Medium https://chatbotsmagazine.com/what-is-ai-even-elon-musk-cant-explain-1070b492d3d5?source=rss—-d6dc2c824f17—4

What do the countries and continents of the world really look like? If we look at a map on a piece of paper or a computer screen, we are viewing a flat, two-dimensional representation of the world. It’s inevitably wrong – to get from three dimensions to two, some accuracy of the real dimensions of the world’s land and seas is lost.

There are different ways to compromise between the actual shape and size of land masses in the world in order to get them down in two dimensions. The most common way that the round world is shown on a flat map is the Mercator projection, first devised to aid sea navigation in 1569.

This projection tries to maintain the right overall shape of land masses, at the expense of accurately representing their actual size. This means that the further you get to the equator, the more land masses are stretched. So a square mile close to the North Pole appears far larger on this map than a square mile at the equator.

It’s such a familiar map that it’s easy to forget that countries don’t in fact look like this at all. Our ideas of how big a country or continent is are often very far from reality.

There are other ways to compromise: the Gall-Peters projection preserves size at the expense of shape. This makes land at the equator appear elongated and land toward the poles appear squished.

To an untrained eye, the Galls-Peters projection looks very peculiar, but that’s just because it never really caught on in and is seen so rarely compared with the famous Mercator projection.

Here are 10 ways that show just how wrong the much-loved Mercator projection is.

Related Articles

• Here’s what the visible universe looks like – scientists produce map of 3 billion space objects
• Scientists unearth the secrets of the ocean using Google Maps-style mapping
• Just how wrong is our map of the world?

from IBTimes UK Editor’s picks http://www.ibtimes.co.uk/just-how-wrong-our-map-world-1605618

Using Data As Part Of A User Centred Design Process

The Definition. User-centered design (UCD) is a framework of processes (not restricted to interfaces or technologies) in which the needs, wants, and limitations of end users of a product, service or process are given extensive attention at each stage of the design process.

When I started out as a web designer, I had no real understanding of what user experience design (UXD) meant, or even that it existed as a term. I was led into the industry through my passion for both e-commerce and graphic design.

As an entry level designer I realised I spent all my time diving into analytics to discover what I could about users of the site. I was really trying my hardest to undertake UX design without even realising I was doing so.

As my career has progressed I managed to move a lot closer to UX design and begun to undertake user testing as part of my design process. This is where my fascination with UXD & UCD really began and where I started to understand that analytics data can only be fully understood when the user provides context and true qualitative insight.

In this article I want to share a few things I’ve learnt along the way into how to use both the web analytics and user testing data throughout the design process and as part of continuous learning and improvement cycle to create the best possible products for your customers.

The Quantitative Insight

Quantitative insight can be gained by using analytics in the form of statistics, figures and reports to collate trends and patterns on user behaviour. Data leaves assumptions behind and provides us with facts.

In God We Trust, all Others Must Bring Data. W. Edwards Deming

Data is insightful. All data however, needs context and needs to be validated with qualitative insight, from your users. You can collect all the data in the world, but in the wrong hands, assumptions will be made and you won’t know what to do with it.

Data is like garbage. You’d better know what you are going to do with it before you collect it. Mark Twain

Diving into the analytics and quantitative research can often be a can of worms and will more than likely throw up more questions than you get answers. It’s important to try to stick to the specifics and try to focus on certain areas that are of the most significance.

The data you hold will then be more actionable and you will be able to use your findings to make decisions and to influence design.

Facts Do Not Cease To Exist Because They Are Ignored. Aldous Huxley

You cannot ignore data. Data rings true, it can help you get your point across. It can prove right or wrong and can enable you to be able to act upon your findings.

With the above said, it’s important to communicate data in a simple and understandable way to effectively to help get your point across to different areas of the business and senior stakeholders. When doing so however, the importance of backing up this qualitative insight with input from users must be stressed, the what must be framed with the why.

It’s important to balance between the quantitative data you have collected and use qualitative data through user centric research to not only validate your findings but to also either reinforce them or prove them wrong.

The Qualitative Insight

Qualitative insight is the best way to creating user centric designs and can be gained through many varying methods of data capture. There really is no substitute for one on one time with your users, however user testing doesn’t need to be so costly of time, money or resource. Insight can be gained through remote, online user testing, gaining voice of customer insight through surveys and questionnaires and so on.

You Are Not Your User And You Cannot Think Like A User Unless You’re Meeting Users Regularly. Leisa Reichelt

Having been involved in many user testing sessions both in-person and remote I have never once failed to be completely surprised by something that is said or done. Users will always be completely irrational and highlight problems, it’s all part of the test and learn cycle.

No matter how much user testing you do, you will never fail to be surprised, however you will also never be able to answer every single question or cater for all users. It’s important to stick to what’s most important for you, whether that be designing for personas or answering specific questions.

Not Everything That Can Be Counted Counts, And Not Everything That Counts Can Be Counted. Albert Einstein

Qualitative user research techniques provide invaluable insight that go far beyond the analytics. It’s important to test and validate at every stage of the design process. Then to continue to iterate and re-evaluate once a product has been shipped. Your first solution will almost certainly not be your final solution.

When People Talk, Listen Completely. Ernest Hemingway

One of the best pieces of advice I’ve ever heard used in the context of conducting user research is the above quote. No matter how proud or attached you are to a design, forget it, it’s useless unless you listen to the voice of the customer. Listen to their feedback and understand it to be able to shape your design progressively.

In Conclusion…

I’ve only scratched the surface here, although hopefully that has shared some insight. Using data can highlight the importance of key issues and can provide key insights. You can use it to influence and shape your designs. Though you must always ensure you are testing and validating with your users. Although you will never be able to design to suit the needs of every user, you should use personas and key demographics to help you target your designs. Once your product is live you must implement data and user research as part of a continuous test and learn cycle to keep improving and learning about your product.

Data for Design was originally published in uxdesign.cc – User Experience Design on Medium, where people are continuing the conversation by highlighting and responding to this story.

from uxdesign.cc – User Experience Design – Medium https://uxdesign.cc/data-for-design-f33fd7419cc8?source=rss—-138adf9c44c—4