Today, we falsely assume that our conversations and our images are not by default recorded by other people in proximity. Not having a persistent record allows us to present a nuanced identity to different people, or groups of people; it provides the space to experiment with what we could be. The risk that what we say will be broadcast, or narrowcasted, to people we don’t know, or may bubble up at some point in the future in the hands of someone serving up ads, fundamentally changes what we want to talk about. The challenge for Glass is that the costs of ownership fall on people in proximity of the wearer, and that its benefits have yet to be proven.
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In hindsight it’s easy to see that consumers understood words like “follow” or “friend” for content they wanted delivered regularly rather than “subscribe,” “RSS” or “Atom feed.
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What is really confusing to the world is that engineers, and most product people and actually most people in business get trained on the development half of things. We actually don’t know a thing about research; we do it completely wrong. Development is all about narrow and deep; I want to be as efficient as possible with the resources that I have to build a specific thing in the shortest time possible. Let’s not waste a bunch of time. Research is the opposite. It’s broad and shallow. And if you go narrow and deep too early, you’re effectively wasting time.
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Most of the processes that we learn as product managers are actually pretty detrimental to the research side of things. So if you want to make a breakthrough, you need to create a space for something like this.
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@david_z streampunk is a home-grown system developed by Google to live stream, record, and archive lab study sessions.
— Tomer Sharon (@tsharon) September 25, 2012 -
The most valuable tech companies in the world — Apple and Google — have huge audiences, but not the biggest. Rather, they are valued so highly because they are better at extracting value from those relationships.
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The question isn’t how we hack our way to a site with two billion users. It’s how we do a better job at making 1 billion — or even 10 million — have greater value. -
Of Google’s 20 [usability] labs worldwide…
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A ‘surveywall’ may be more palatable than a paywall, especially for younger users
[…] Google showed publishers a new transaction system for inexpensive products such as newspaper articles. It works like this: to gain access to a web site, the user is asked to participate in a short consumer research session consisting of a single question – a set of images leading to a quick choice.
— How Google could reshape online market research and micropayments
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Corporate control of data could give preferential access to an elite group of scientists at the largest corporations.
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A glimpse at one of Google’s usability labs (begins 2:50)
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In this case study we describe a four-step process for eliciting and analyzing user behavior with products over an extended period of time. We used this methodology for conducting a comparative study of two mobile applications over a period of seven months with 17 participants.
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The participants’ ultimate impressions of the applications differed markedly from their first impressions, lending further evidence that longitudinal study … is essential in evaluating product usability and usefulness.
— Case Study: Longitudinal Comparative Analysis for Analyzing User Behavior (pdf)
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One could argue that seeing the future through a developer lens and not one of digital anthropology, sociology or ethnography is why Google has wrestled with opportunities to socialize new and existing products. While Facebook builds and ships it must also continue to explore the intersection of technology and liberal arts to build and ship in ways that continue to define or redefine how people discover, connect, and share.
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Early on, changes are expected and the risks of alienating users are often outweighed by the benefits of a better product experience for users. But keep in mind that at some point the balance may shift, and people who are happily using your product will react negatively to changes — unless you’ve planned ahead to minimize their change aversion.
Change aversion: why users hate what you launched (and what to do about it)
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The HEART framework
While helping Google product teams define UX metrics, we noticed that our suggestions tended to fall into five categories:
Happiness: measures of user attitudes, often collected via survey. For example: satisfaction, perceived ease of use, and net-promoter score.
Engagement: level of user involvement, typically measured via behavioral proxies such as frequency, intensity, or depth of interaction over some time period. Examples might include the number of visits per user per week or the number of photos uploaded per user per day.
Adoption: new users of a product or feature. For example: the number of accounts created in the last seven days or the percentage of Gmail users who use labels.
Retention: the rate at which existing users are returning. For example: how many of the active users from a given time period are still present in some later time period? You may be more interested in failure to retain, commonly known as “churn.”
Task success: this includes traditional behavioral metrics of user experience, such as efficiency (e.g. time to complete a task), effectiveness (e.g. percent of tasks completed), and error rate. This category is most applicable to areas of your product that are very task-focused, such as search or an upload flow.
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Comparing Collaboration and Individual Personas for the Design and Evaluation of Collaboration Software
Collaboration personas are a tool that can be used to design for groups. Prior work posits that collaboration personas can improve tool adoption by helping designers create collaboration tools that are better targeted to the goals, needs, and interactions between members of collaborative groups. We present a comparative study of design and user experience practitioners who used both collaboration personas and individual personas. Participants conducted a cognitive walkthrough and provided redesign suggestions for a collaboration tool. Our results show that the focus of the cognitive walkthrough and redesign task differed, with collaboration personas showing more group focus. Collaboration personas led to a more complete discussion, as indicated by a greater amount of time spent on the task compared to individual personas. Despite prior experience and training with individual personas, collaboration personas were preferred and better supported the task, since they focused on groups of people and their interactions.
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Talking in Circles: Selective Sharing in Google+
Online social networks have become indispensable tools for information sharing, but existing ‘all-or-nothing’ models for sharing have made it difficult for users to target information to specific parts of their networks. In this paper, we study Google+, which enables users to selectively share content with specific ‘Circles’ of people. Through a combination of log analysis with surveys and interviews, we investigate how active users organize and select audiences for shared content. We find that these users frequently engaged in selective sharing, creating circles to manage content across particular life facets, ties of varying strength, and interest-based groups. Motivations to share spanned personal and informational reasons, and users frequently weighed ‘limiting’ factors (e.g. privacy, relevance, and social norms) against the desire to reach a large audience. Our work identifies implications for the design of selective sharing mechanisms in social networks.
