Juice Analytics

Twitter’s wild popularity hasn’t obscured the fact that the service needs to eventually make money. The concept of “Twitter analytics” as a revenue stream has come up often enough to make my ears itch and my nose burn.

Twitter’s new business development lead explains that the company is “developing a range of analytics and metrics products and services built around the information contained in tweets”…and “trying to figure out what are the appropriate metrics around engagement and how to convey those.”

Web Strategist Jeremiah Owyang raises the concept of a Twitter CRM solution, in which Twitter would offer their own analytics system to brands, that will help them to track and manage the conversations.

The Twitter ecosystem has responded with a wide range of tools for analysis of Twitter data. Web analytics behemoth Omniture recently announced the integration of Twitter data into their platform. At the same time, web analytics consultant Eric T. Peterson has been vigorously marketing Twitalyzer, a tool to evaluate individuals’ use of Twitter and metrics of influence. Google’s Chrome Experiments released a cool visualization tool called Social Collider that reveals cross-connections between conversations on Twitter. Here are a few more Twitter analytics tools that I’ve run across:

• TwitterAnalyzer
• TwitterFriends
• TweetEffect
• Tweetstats
• Twitturly
• Thummit Quickrate
• Twist
• TweetVolume

Despite all the activity, I haven’t yet seen a solution that offers the kind of valuable analytics that a company could use to understand the Twitter conversation relevant to their business. The applications above are either focused on the measurement of individual Twitter users or offer a high-level tracking of words and phases in the general conversation. They treat tweets as transactions — How many? How valuable? Who’s listening? Who’s responding?

To me, the great and more rewarding challenge in Twitter analytics is to synthesize the substance of those conversations. Imagine if you went to a party and could overhear everything that everyone else was saying. Who talked the most and who had the greatest audience is less interesting than what topics people were discussing and what was said.

I wanted to take a shot at this type of Twitter analytics.

Analysis Approach

First I had to define a particular domain or topic area. For expediency, I focused on all the tweets that included the word “analytics.” Using the Twitter search API, I pulled the first 500 tweets for each day in March and parsed the results to pull out users, urls, and other characteristics of the tweets.

To analyze the words and phrases being used, I uploaded the resulting 11,300 tweets into Concentrate, our search analytics tool. Concentrate is optimized for search query text (i.e. short phrases without a lot of punctuation). Nevertheless, it has a number of features that make text analysis easier, including breaking out the most common words, phrases and patterns. It also allows for filtering by words to create frequency statistics.

There were two main questions I wanted to address:

1. What topics are people discussing?
2. What is the structure of the conversation?

Topics of Conversation

The content of the Twitter conversation can be analyzed as words, sites/links, people/groups, and company/products.

Words

I used Concentrate to find the most common words, then I dumped those words into Many Eyes to create this “Wordle-brand” word cloud. Many Eyes has a nice feature that takes out the “common English words.” Clearly Google dominates the conversation, and I even had to artificially reduced the value to make the other words legible.

Below are the top 10 (non-common) words that show up in the analytics conversation

Sites and Links

Twitter has become a mechanism for sharing interesting links (I’ll get to data on that in a bit). Looking at the most popular sites and specific links gives a sense for what people in this community are reading and talking about.

People and Groups

Twitter users have a few conventions for connecting tweets to people or groups:

• ”#” (i.e. hashtag) associates the message with associated with a group, topic or event.
• “RT” (or “via”) is to repeat or “retweet” something someone else has said.
• ”@” associates a tweet with another user, whether retweeting their message or directing a comment to them.

Here are the most common groups and people referenced in the Twitter data.

And the people with the most tweets using the word “analytics”

Companies and Products

I was also interested in what companies or products were referred to most frequently. It is no surprise that Google dominates the conversation. Microsoft gets on the board with the recently closing of their adCenter product. I think we can safely assume they won’t be showing up that often in the future.

Conversation Structure

Beyond the specific content of the conversation, I was also curious about how people who are talking about analytics tend to use Twitter.

Types of Tweets

Eric T. Peterson has four things he considers “signal” (versus “noise”) in the Twitter conversation:

• References to other people (defined by the use of “@” followed by text)
• Links to URLs you can visit (defined by the use of “http://” followed by text)
• Hashtags you can explore and participate with (defined by the use of “#” followed by text)
• Retweets of other people, passing along information (defined by the use of “rt”, “r/t/”, “retweet” or “via”)

While I’m not fond of this definition, examining these different types of tweets (along with question-based tweets) provides a good lens into the nature of the conversation. The following chart shows the percentage of tweets that fall into each of those categories.

It would be all the more interesting if you could follow the types of tweets across time and compare against other topic areas. I suspect that the URL linking within Twitter is on the rise and is turning Twitter into a Delicious-style bookmark sharing service — without the functionality to save, tag, annotate, and view the bookmarks at your leisure.

Link Evolution

Given all the sharing of links, I wanted to get a clearer picture of what happens when a link becomes popular. The graphic below shows some of the top links during the month and the amount they showed up in tweets by day. The red bars represent days where ten or more tweets included the link. A couple links demonstrated popularity over a week or so, but the rest sizzled then disappeared in a day or two.

Activity Distribution

Finally, I took a look at the distribution of users by the number of tweets including the word “analytics.” It was no surprise that the vast majority of the 7,700 twitterers only used the word once in March (of course this doesn’t tell us about their other twittering activity). Obviously there is a small population of people at the core of the discussion.

While you’d have to go into more depth to answer detailed questions, there are a number of interesting take-aways for me, including:

• “Analytics” means “web analytics”, not business intelligence or general reporting about sales, operations, or marketing.
• Google Analytics is the star of the party. Of course, the fact that the brand name includes “analytics” is an advantage, but I didn’t see a giant “Juice” in the word cloud.
• Twitter is an echo-chamber. The content clusters around particular subjects, with people retweeting and sharing links about the big news of the day. There are a dozen or so stories that dominated the conversation over this time period.

What’s next?

There are a lot more views of this data that could be enlightening for a company interested having a real-time understanding of their marketplace. For example, it would be interesting to provide more insight into:

• Who is at the center of these conversations?
• What is the positive or negative tone of the discussion (Twitter actually offers this information as part of their API)?
• How has is the conversation changing over time?
• What is the best way to define the boundaries of a domain-specific conversation?

These are the types of questions that I’d like to see addressed in a more complete Twitter analytics tool.

Update: A more recent guide to the Juice website can be found here.

With almost 300 blog posts and dozens of free tools and demos, we thought it would be useful to offer some of the highlights from the Juice blog and website.

Our Views on Analytics and Communicating Data

• A perspective on business intelligence
• The last mile problem
• “Business intelligence isn’t a technical problem”
• Why analytical applications fail

Information Experiences™, Dashboards and Metrics

• Designing Information Experiences™
• Features of successful real-time dashboards
• Choosing the right metric

Demos

• JuiceKit™ Economic Census Treemap (introductory post)
• Examples of JuiceKit™ visualizations
• Contact us to see examples of our client work

Analytics Tools (Free stuff!)

Visualization

• JuiceKit™, an open-source visualization SDK (introductory post)

Web analytics

• Concentrate, a long-tail search analytics solution (and how it works)
• Enhanced Google Analytics Firefox add-on (introductory post)
• Google Trends API
• Google Analytics API

Excel and charting

• Chart Chooser (a fan favorite)
• Dynamic Excel reporting framework
• Chart Cleaner

Mapping

• Excel geocoding tool
• Census heatmaps in Google Earth

Excel Tricks

• Lightweight data exploration in Excel and More on in-cell Excel graphing
• Exploring data in Excel with conditional formatting
• Essential Excel skills and Excel training worksheet
• Why make 100 charts when one will do?

Just for Fun

• Proof of the Colbert Bump
• Baby Dashboard and Baby Dashboard v2.0
• A Presentation breakup letter
• AnalyticsTime

Now that I’ve got treemaps on the brain, I keep noticing how many things could be better understood using this visualization technique. A few examples:

We thought it would be a nice demonstration to use data from the 1997 and 2002 US Economic Census (unfortunately 2007 isn’t out yet) to see what kind of stories bubble forth. The demonstration was built using a component from JuiceKit™, our recently open sourced Software Development Kit (SDK) for building Information Experience™ applications. The SDK can be used by web designers and developers to build graphically rich and interactive information displays. JuiceKit™ currently integrates with Adobe Flex to create components that are easy to implement and aesthetically pleasing.

Check out the treemap here.

Here are a few of the macro-trends that I found:

• The rise of CostCo, Amazon, and Home Depot: This time period saw strong growth in warehouse clubs and superstores, online retailers (“electronic shopping”), and home centers.
• From manufacturing to services economy: Most of the growth was in service sectors (financial services, healthcare, professional services) while manufacturing was shrinking.
• Productivity gains, even in adversity: For struggling sectors, the employee declines almost always outpaced the sales declines — squeezing more sales per employee.
• Demographic shifts: Homes and services for the elderly were among the strongest areas of growth in the category of “healthcare and social assistance.”

And there were lots of little insights as well:

• No wonder hospital TV shows are so popular: Hospitals are the largest single employer as a business-type.
• Starbucks and Krispy Kreme steal the unhealthy food dollar: Cookies and frozen yogurt retail saw a rapid decline while coffee and donut shops flourished.
• Goodbye stand-alone pump: Gas stations with convenience stores overtook the just-plain gas station.
• It can’t last, can it?: Mortgage broker payroll up 177%.

Once you understand how to read treemaps, they are great for exploring data like this: hierarchical with both quantity and quality-type measures. In a true testament to their power, my wife admitted this visualization was “kinda interesting.”

In the information visualization world, treemaps are on the rise…and justifiably so. Treemaps simultaneously show the big picture, comparisons of related items, and allow easy navigation to the details.

However, treemaps aren’t easy to get right. In contrast to basic charts where Stephen Few, Edward Tufte, and the Chart Chooser have laid down the law, treemaps roam the Wild West of interface design, obeying few rules, breaking many, and contributing to much infovis lawlessness.

Over the last year or so we’ve been building treemaps for our clients using our (recently open-sourced) Flex-based JuiceKit™ SDK. Over the course of these projects, we’ve thought a lot about the best way to make treemaps easy to understand and use. I won’t claim we have “cracked the code,” but we have gotten a feel for what works and what doesn’t. I want to share some examples of the good and the bad in treemap design, and hopefully gather some feedback so we can continue to evolve our thinking.

1. Choose the right measures for size and color

Each box in a treemap can show two measures:

• Size of the boxes should be a quantity measure. The measures should sum up along the hierarchical structure of the data. The sum of all the elements in one branch need to sum to the value of the branch as a whole. Therefore, you can’t use ratios or dates or any other measure you wouldn’t use in a pie chart.
• Color of the boxes is best suited to a measure of performance or change such as growth over time, average conversion rate, or customer satisfaction.

The King of Treemaps — Smart Money’s Map of the Market — offers a classic set of measures: size represents market cap; color represents change in market cap.

2. Space matters

Like a pie chart, size represents value in a treemap. In the following example from LabEscape, the category labels use space — almost as if you added slices to a pie chart for labeling. This approach distorts the values by arbitrarily using space, making it harder for the viewer to visually compare sizes.

3. Labels should add value

Labels are hard to get right in a treemap. If you aren’t careful, labels can clutter up the treemap without adding useful information. This Macrofocus treemap wasn’t careful. Notice how the majority of labels get reduced to just a few letters or simply an ellipses (“…”). It would be better to show nothing until the user rolls over a box.

4. Labels must stand-out against treemap colors

One of the unique challenges of a treemap is that the labels need to stand out against a multicolored background. The ILOG Elixir treemap chooses to put the labels in a white text box. Unfortunately these text boxes look clunky, obscure some of the data, and don’t always fit into the allotted space.

To neutralize the contrast of the label to the background and ensure legibility, we created a “glow” around the text.

5. Explanatory legends

The New York Times folks know what they are doing when it comes to visualizations and the explanations around them. Below is the legend for a treemap about automobile sales. The meaning of size and color aspects are articulated in a small space.

6. Color ranges fit the data

The nature of your color measure should determine whether you need a one-sided or two-sided color range. In situations where the color measure has both negative and positive values (e.g. period over period growth), we typically use a two-sided color range with a light grey at the middle. A one-sided color range is a better fit when the measure starts at zero. The Hive Group treemap below offers an example where a two-sided color range (red to green) doesn’t make as much sense. This treemap is using color to show geographic area rank from 1 (largest) to 195 (smallest).

7. Show correlation by highlighting

One of the nice advanced features treemaps can offer is highlighting items that meet a user-specified criteria. In the Many Eyes treemap below, a search features identifies that companies that include the selected search term. Not only does this aid the navigational capabilities of the treemap, it allow allows you to see color, size, and location correlations for the selected items.

8. Show changes with animation

When you want to show variations in the data (e.g. changing time periods, filtering, changing measures), we’ve found that animation effects can help emphasize the differences. In our stimulus plan treemap, flipping between “cost” and “votes” to size the boxes results in an animated reorganization of the boxes. The boxes that get bigger move to the upper left and those that shrink move down and to the right. The effect helps the user track where things are moving and get an understanding of the overall differences in the treemap.

9. Simple presentation of node detail

When a user selects a node in a treemap, they should see the available detail either in a tooltip window or in the sidebar. If the detail is substantial in size, it is best to push it into a sidebar as we did with our Stimulus Plan Explorer. Simpler data can show up in a tooltip box like the beautifully designed tooltip created by MIX Online (notice how it flips around to stay within the borders of the treemap).

ILOG Elixir’s demo recognizes the need to see detail, but the execution is flawed. Selecting a box in their treemap highlights rows in a table, but the rows are not consolidated so you are lucky to see only one or two rows of highlighted data. Users need to scroll through a massive table to be able to see the complete details.

10. Gradually reveal detail

Panopticon has a powerful treemap offering, but their demo treemap has some missteps in showing the detail. In particular, they choose to show as much detail as possible, but in a faint grey text. When you roll-over a box, this text becomes legible just as a redundant pop-up box appears. Detail is shown before the user has even expressed any interest in the box. Better to wait until the user rolls over or clicks on a box, then show the details. In the meantime, let the size and color do the talking.

These are just a few of the design lessons we’ve considered in our work. Treemaps offer an opportunity to make vast and complex data accessible — but they depend on thoughtful, user-friendly design.

How about you? What are some of the design features you have seen in treemaps that you think are particularly effective in making the communication of information stronger?

Update: Thanks for checking this out! However, we have taken this visualization down. For more recent examples, please check out our gallery page.

We’ve seen a lot of anxiety about the huge price tag of the stimulus bill winding its way through Congress. Some of the complaining is about the difficulty in understanding the contents of this complex legislation. Certainly the stimulus bill looks impenetrable if you try to sift through 700 pages of details or even a 25-page summary. In response many people evaluate it based on their gut feel.

To help out, we’ve created the Juice Stimulus Bill Explorer – a treemap visualization that summarizes the House version of the stimulus bill and let’s you vote on its pieces.

The data in this treemap comes from the 1/15/09 summary (pdf) of the House of Representatives version of the American Recovery and Reinvestment Act. Selecting any box will show a description of the individual program, the price tag, and an opportunity to express whether you like or dislike the idea. The treemap boxes are sized by the proposed cost of each program. The color is based on the average level of support for the program from user votes.

Thanks to Scott Love for encouraging us to put this together.

We are pleased thrilled to introduce Concentrate™, an innovative long-tail keyword tool. Concentrate is for SEO and paid search professionals who want to make sense of search keyword data and make the most of search investments.

Check out the demo here. Or try out the free version here (you’ll need admin access to a Google Analytics account).

We built Concentrate because we saw a fundamental conflict in the world of search analysis:
On the one hand, search keyword data is terrifically interesting and valuable. It can tell you what your visitors and customers want and how they think about you and your products.

Unfortunately, search query data is also big, messy, and hard to get your hands around. In a typical month, the Juice site gets over 10,000 visits from over 7,000 unique keywords.

Even if I could somehow wrap my head around our top 100 keywords, I’d only understand 25% of the visits. For people spending money on search engine optimization or paid search campaigns, that’s a big blind-spot to accept.

We want you to understand and act on all your search data. Concentrate ingests data from sources that most sites already have available (e.g Google Analytics, Omniture, Coremetrics, Hitwise, Compete, etc.), enhances this data by finding common patterns and query types, and visualizes search phrases for exploration and analysis.

Over the next couple of weeks, we will share examples of some of the interesting things you can do with Concentrate, including:

Pattern identification to condense the long tail into keyword phrases with similar structures. For example, here are some common search patterns from a cooking web site (the “[x]” represents a wildcard).

Keyword visualization to show the connections between keywords and the relative performance of phrases. This wordtree shows the frequency of words within phrases (size) and average time spent on site (color).

Congratulations to Chris, Pete, and Sal for all their hard work, diligence, and creative problem solving to launch this solution.

Presentation guru Andrew Abela recently published his first book Advanced Presentations by Design. Abela shares his 10-step technique for developing influential business presentations. Before reading this book, I thought I had a pretty good idea how to make a compelling presentation; it turns out I mostly knew how to throw together a bunch of non-boring slides. There are a few key themes that summarize the book for me:

1. Focus on your audience.

“Your presentation should be all about serving your audience. You need to show them that you see everything from their perspective — their problem, in their terms, their motivation and issues. This also means that you have to be bound by their constraints. There is no point in raising an important problem and proposing new investments to solve it if your audience just does not have any money to spend this year.” (p55)

2. Solve a problem.

“Focus your entire presentation deliberately and undividedly on solving an important problem of theirs (the audience)” (p6)

“Your objectives should be about how your audience will change as a result of your presentation: how they will think and act differently after they leave the room.” (p5)

3. Tell a story.

“An effective way to reframe your evidence and involve your audience is to present your information in the form of a story…Stories are a coherent whole, where one thing flows to the next, so we tend to remember the whole thing.” (p65)

“By presenting your information in the form of a story, by setting up a tension and resolving it, and repeating as necessary, you can create this physical desire in your audience for your message.” (p77)

If you make presentations for a living or just as a hobby, I can wholeheartedly recommend this book. Abela does an impressive job of teaching his process and keeping it interesting. My one point of concern is that I felt he didn’t offer much help with the critical transformation from story outline (he recommends you shouldn’t open up PowerPoint until you are most of the way through the process) to presentation slides.

I also enjoyed this book because it connects to, and expands upon, the messages we emphasize in our design of Information Experiences for reporting, dashboards, and analytical tools. (Even the introduction gives us a nod: “I’ve become convinced of how crucial the last mile of communication is to driving organizational impact.”) Here is a short checklist of considerations articulated by Abela that bridge any communication of complex information:

• When presenting data, pay particular particular attention to what is new or different.
• Drive action. Or in Abela’s words: “What does it allow them to start doing, stop doing, or continue doing that would be difficult or impossible without this information.” (p47)
• Respecting the challenges faced by users. Understand what problems and levers the audience has available to them.
• Consider your audience “type”. How does the audience best absorb information?
• Consider the presentation environment. In what context will the audience be engaging with the information?
• Use different types of data (e.g. statistical, anecdotal). Sometimes specific data points can help focus attention better than an aggregate metric.
• Identify problems, then give people the tools to address the problem. This parallels Abela’s storytelling technique of creating and resolving tension.
• Users before technology. Usability before features. Abela notes: “Presentation and advice and tools have been developed for the benefit of the presenter, not the audience.” (p5)

via Dashboard Spy

Real-time dashboards — the kind that show up on a big screen in a call center — are an entirely different beast than your standard management dashboard. Their job is to support immediate decision-making. As a result, the information must be easy to interpret, alert users to problems, and make the next action obvious. In addition to key success metrics, real-time dashboards may show detailed data about the action “on the ground.” Here are eight characteristics that can make a real-time dashboard effective:

1. A summary status that indicates how things stand overall. Users need to be able to tell at a glance whether they should worry or not. Here’s a great example from the folks at Superblock. The “Is it going to rain?” site tells you the single most important thing you need from a weather report.



2. Reflect a well-understood structure of the business. By the time you design a real-time dashboard, you should have a strong theory for how the pieces of the business fit together (i.e. the relationships between key measures, drivers, and available actions). For example, in the call center business, there are clearly defined success measures (e.g. wait time), a mathematical relationship between these measures and their underlying drivers (e.g. call volume), and known levers to address problems (e.g. staffing levels).

3. Support quick diagnosis of problems. The data presentation should point directly to the likely source of the problem. Real-time dashboards aren’t the place for deep analysis or introspection into the drivers of the business.

4. Simple data presentation. In my view, real-time dashbaord’s aren’t the place for complex or advanced data visualizations. Imagine you were Napoleon and you had to use a half-completed version of this chart to make a battlefield decision in the next 5 minutes.



5. Granular view of the “unit of action.” Real-time dashboards are often about tracking activity. It may be useful to show the raw data around these events in the form of a ticker, scroll or RSS feed. We use at a real-time tracker for our website called Sitemeter. It does a nice job of tracking the basic unit of action — visitors.



6. Appropriate time window. Getting time right on an operational dashboard is critical. If the measures and trends represent too long a time period, users may not react to changes quickly enough. On the other hand, very small time windows encourage frantic reactions to changes that may not represent real trends. Ideally, the dashboard should offer the ability to configure this time range and “freeze” a moment in time.

7. Prominent but balanced alerts. Naturally, alerting users to problems is a central mission for real-time dashboards. The challenge (as always with alerts) is to balance between “the sky is falling” hysteria and “don’t worry, be happy” apathy. I’ve written before about alerts, but one item to emphasize is the need to show a sense of relative importance. Not all problems have the same impact on the business, and finding a way to communicate this relative importance is valuable.

8. Point to specific action. If real-time dashboards are about identifying and responding to issues, the tool should point users to what they can do about a problem. This can be as simple as displaying the phone number of the right person to call.

Real-time dashboards can be ignorable, create mayhem, or drive great behavior in an organization. Thinking carefully about the design and functionality will make a huge difference.

A couple years ago we released our first baby dashboard design. I’ll admit it was a bit rudimentary. It tracked only the most basic measures and offered little insight into your baby’s current mindset. I was a new father and had a relatively superficial understanding of the nuances of babies, not to mention actionable baby metrics.

With the arrival of my second child, I set to work designing a dashboard that would give a parent all the important information they need, presented in ways that let them react to baby data even in a harried household. Let me present the prototype of our new Baby Dashboard 2.0, modeled by my daughter Maya.

We use the same heads-up display technology as in our first release, but now with more sophisticated data collection techniques we’ve included a meltdown prediction chart and real-time translation engine.

There are a few features in here that I believe demonstrate important fundamentally design principles for great Information Experiences:

• Choose metrics and information that a user can act on. Information that is just interesting isn’t worth a random pile of ones and zeros. You need information that you can act on. In BD 2.0, we wanted to deliver news you could use, in the moment. The “meltdown fuse”, for example, is a way to measure how close your baby is to freaking out. As she gets tired, sick, or hungry, her fuse shortens to the point that a simple disruptive act — a loud noise, Mom walking out of the room — will set off a meltdown. You need to know how close you are to this threshold so you can minimize the smallest of disruptions.

• Draw attention to the information that is most urgent. While the dashboard provides detailed trend breakdowns, the most important thing for a parent is the current state of things. The top bar of the dashboard answers the most critical questions always on a parent’s mind: 1) How close is my baby to melting down? 2) Does my baby need any of the basics: food, sleep, or clean diaper? 3) What is my baby trying to say to me?

• Progressively reveal data as the user expresses interest. Like a busy executive, a parent doesn’t have time for all the information at once. They are on a need-to-know basis. If a parent needs to get a better sense of the potential meanings of a baby word (“daaah”), a single click will give a breakdown of the most likely interpretations.

• Different views for different audiences or perspectives. BD 2.0 provides distinct views for baby status and parent status. The parent status (not shown) was added because we recognized that the mental state of the parent was as important to a happy child as a clean diaper.

For those of you who expressed interest in licensing our Baby Dashboard 1.0 technology, please be patient while we work out the bugs in this next release.

When people contact us at Juice, they sometimes don’t have a complete picture of what we do. Our obsession with finding better ways to communicate information is obvious, but how it adds up to something relevant to their business isn’t always as clear.

The answer: We design, prototype, and develop great Information Experiences™.

Information Experience™ is our way of describing the intersection between user experience and information-intensive applications, where success is how effectively a user can consume, understand, and apply that information.

Like sitting behind the wheel of a BMW or my two-year-old flipping through photos on an iPhone, great Information Experiences have less to do with features and more to do with an intimate connection between human and device. Great information experiences tell stories where data is the primary medium for communication. The information appears when it is needed and the device or application seems to anticipate the next question or action. These are the objectives that we apply to the solutions we design and build.

Designing Information Experiences spans from the highest architectural model of a system to the specific details of user/interface interaction and data visualization. Across these levels, we consider four objectives:

1. Support the achievement of organizational objectives. How can the information experience fit into users’ existing decision-making and work processes? How can we influence decision-making with the right information at the right time?

2. Direct the user to likely actions in order to “get it done”. What are the important questions a user is trying to answer or tasks the user wants to accomplish? How can the application make it as easy and intuitive as possible to get to results? Does the navigation and user flow feel like an extension of users’ thought process?

3. Present only the information that needs to be seen. For any given view of data and situational context, what is the most critical information to share with the user? How can information be progressively revealed to give the user what they need to know at any given time?

4. Present the information in a way that produces understanding and action. For any given data and situational context, what is the most effective information visualization? What are the best ways to present information given users’ experience and sophistication with interpreting information? What is the appropriate level of detail to be displayed given the context and user needs?

When we talk about the social rather than technical challenges of Business Intelligence, it is motivated by the belief that too many vendors are more comfortable tackling technical details rather than evaluating how users can interact and gain value from information. Which is to say: design better Information Experiences.

That’s what we do here at Juice. And we have people skills! We are good at dealing with people! Can’t you people understand that!