Juice Analytics

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.”

As our followers know, for the past few years Juice has been creating software applications that solve customers’ real information visualization problems in purposeful, understandable, and beautiful ways. In doing this, we have found ourselves reusing quite a few components over and over again – which has made our jobs a lot easier. It occurred to us that others might like to benefit from using these components to achieve great results too.

We’re proud to announce the open source release of Juice Analytics’ JuiceKit™ SDK.

The JuiceKit™ is a toolkit built on Adobe’s Flex SDK to make it easier for web designers and software developers to build visually compelling Information Experiences™. It contains a wide variety of development components from individual data renderers such as a single “small multiple”, to a large visualization component such as a treemap or US Map, to fine grained “helpers” that provide handy capabilities such as copying data to the computer’s clipboard. These components can be used independently, within other applications, or assembled together to create full applications.

What can I do with it? (Show me the money)

Because we’ve been using the JuiceKit™ for quite a while, we have a number of customer proven applications based on the SDK that we thought you’d be interested in seeing.

Here is a screenshot of an application that we built to help our client see trends in their internet search and traffic activity. We used the JuiceKit™ to create the small multiples data visualization component of this application.

We’ve also frequently used JuiceKit™ to create dashboard prototypes. If you haven’t seen our recent application of our treemap component to the incomprehensible Federal Stimulus Plan, here is a nice example (click to explore):

And here is a very quick one we did for an IVR monitoring application where we assembled multiple different components together into one view:

Finally, we’ve used JuiceKit™ many times to build full enterprise applications such as this sales pipeline tracking dashboard:

How do I get it?

Now it’s time for you to have a go. Here’s how you do it:

• Go to the JuiceKit™ SDK web page at juicekit.org and catch up on the current status of the project
• Check out the JuiceKit™ discussion group on Google Groups
• Download the JuiceKit™ library from github
• Contribute back to the JuiceKit™ community to make the JuiceKit™ even better

While Juice continues to focus on designing and providing software solutions (as opposed to toolkits) for our clients, we believe offering the JuiceKit™ as open source will benefit the information visualization community we try to serve. In the future we will continue to extend the JuiceKit™ with other components and technologies.

Good luck, and make sure you share how you’re using the SDK so we can continue to drive it in the right direction not only for us, but for you as well.

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.

Here at Juice we build fewer Excel dashboards than we used to. Excel itself is a decidedly imperfect vessel for any serious development–it’s simply too easy to veer off of the disciplined track onto the underbrush.

Even so, Excel remains a playground where we can do surprising things. For instance, check out our Excel lightbox and an Excel tagcloud. We could appropriate everything that you find on the webbiest of Web 2.0 websites and build our Uruk-hai equivalents.

The key to staying on the rails when building Excel tools–either dynamic dashboards or simply to explore data–is discipline. At Juice, we use a methodology that we call “DTP” (Data Tansform Present). The foundation of DTP is the rigorous separation of data from presentation. This is similar to a well-known approach when building computer user interfaces called Model-View-Controller.
I’m going to cover some of the key principles and we’ll follow up with an example later on the blog.

Data

Data is the raw material of any visualization or report. It needs to be easy to add data or change data without having to change anything else about your dashboard.

We store raw data with dimensions preceding metrics in blocks in separate worksheets. If you want to sound pretentious, you can call this “first PivotTable normal form”. Key points:

• Have one worksheet for each data source.
• Call these sheets “Data”, or “{Title} Data”.
• Place them at the end of your workbook.
• Data is snug to the top left of the spreadsheet. This allows us to use dynamic ranges. Dynamic ranges let you add data and have it automatically incorporated in all PivotTables.
• Ensure that column names are in the first row.
• Place your dimensions before metrics.
  

Transform

We use PivotTables to transform the data into the structure we need.

• Call these sheets “Transform” or “XXXXXXX Transform”.
• Create one sheet for each issue that you are exploring. This doesn’t mean that you will only create one PivotTable. You may have multiple PivotTables to support different views or perspectives on an issue.
• Turn on “show items with no data” for row and column dimensions.
• We are seeking predictability, we want to the PivotTable to always be the same size regardless of what the PageField filters are.
• Place all the dimensions that aren’t used as rows or columns in the PivotTable as page fields. Every dimension should have a home.
  
  • Set all PivotTables to not store data and refresh on open.
    

Present

The Presentation page copies data from the Transform page(s) and formats it for display. It also allows users to control what data is being displayed.

• Build a user interface to interact with your data. There are many ways to let people interact with your data, but one of the easiest is to use a PivotTable as your interface. This is described below.
• We use an in-house style guide for graphs that you can see in our Chart Chooser.
• If the Presentation page is likely to be printed, preset the print range.
• When copying data from the transformation page to the presentation page, blank values will come out as zeros. We use a simple formula, =if(’Transform!A2’<>"",’Transform!A2’, ""), to ensure that blanks remain blanks.

Using a PivotTable as your interface

A simple way to let people manipulate your data is place a PivotTable containing only PageFields but no data on the presentation sheet. A Visual Basic macro triggered to run whenever the PivotTable changes then pushes out any changes to the master PivotTable to all the PivotTables on your Transform sheet.

Here is the code to make this happen.

This drives our PivotTables in concert and ensures they stay in sync.

That’s a basic overview of our DTP technique. You can try a simplified version of DTP here.

DTP Example.xls

We’ll be back soon to talk through this example.

Recently we wanted to show how Concentrate, our new long-tail search analytics tool, could give you a view of search patterns across travel websites.
As political junkies, we were inspired by this chart from our friends at the NY Times.

The first tool we tried, simply on principle, was Excel 2003. As expected, making a NY Times quality bubble chart in Excel 2003 is a hard problem. Here’s a draft of how far I got before giving in to label fatigue.

The bubbles themselves aren’t tough, but getting the labels right is hard. I’d love to see a solution, so if any reader wants to tackle it eternal fame can be yours. Here is a CSV if you want to try.

travelpatterns.csv

Another of the tools we use at Juice is NodeBox, which we used to make this:

Here’s the code that made the graph.

The power of a programmatic approach like this is that by changing a line or two, you can get the following. Click for a larger version. Click the text for the code..

Vary formatting by value

Drop the labels, tighten the spacing

Display the largest item in each row as a red square

With great power comes a great need to exercise restraint. Otherwise you end up like these poor chaps. Must… flex… restraint… muscles…

This is a follow up to “Target Long Tail Searches with Keyword Patterns”

To get a sense of the scale of the long tail in search, Dustin Woodard recently put together an analysis of U.S. search data collected by Hitwise over a 3 month period, during which they measured 14 million different search terms. How did these break down?

• Top 100 terms: 5.7% of the all search traffic
• Top 500 terms: 8.9% of the all search traffic
• Top 1,000 terms: 10.6% of the all search traffic
• Top 10,000 terms: 18.5% of the all search traffic

This means if you had a monopoly over the top 1,000 search terms across all search engines (which is impossible), you’d still be missing out on 89.4% of all search traffic. There’s so much traffic in the tail it is hard to even comprehend. To illustrate, if search were represented by a tiny lizard with a one-inch head, the tail of that lizard would stretch for 221 miles.

Yesterday, we described the concept of search patterns and how you can use them to summarize this type of long tail text data. Today, we will walk through a case study we put together to explain how Concentrate’s pattern discovery feature will help you find new competitive insights.

You can replicate this study yourself by signing up for the Plus version of Concentrate and loading competitive search data from providers like Hitwise, Compete, Keyword Discovery, or comScore. The input search data used in our analysis consisted of a sample of unique queries leading to clicks on top travel domains during Spring 2006, along with their frequency of occurrence (the chart is truncated after the 20th query):

Raw search data: most frequent queries by site

We loaded the full dataset of queries into Concentrate to generate summary patterns for each of 5 top travel sites. After each file of unique queries and associated metrics is loaded, the application generates reports which include summary statistics based on the head (top 50) and tail queries for each site. This is a good way to start looking at the data if we want to get a sense of each site’s long tail search strategy:

Head vs. tail queries for top travel sites

It appears that the long tail makes up the overwhelming majority of traffic for the travel planning and review sites, but is a much smaller percentage for transaction focused sites like Expedia and Travelocity. Measuring the size of the head and tail gives us a rough idea what is going on, but we need to dig deeper if we want to benchmark where we stand in various categories and produce actionable insights. Inspired by a recent New York Times infographic “Words They Used”, our data visualization guru, Chris Gemignani, downloaded the Pattern CSV file that Concentrate generated for each of these sites and created the following view of competition in the travel search sector:

Comparing travel searches by pattern

This chart compares the proportion of searches that go to each travel site for the top 25 patterns in the travel sector. The site getting the most traffic for each pattern is highlighted. Only searches that wound up at one of these five travel sites are considered.

The difference in search pattern profiles for these sites is striking. Tripadvisor leads the pack in the long tail, which makes sense given the huge amount of long tail user generated content on the site. TripAdvisor owns most of the pattern categories, but Yahoo Travel and Hotel-Guides take the lead in niche areas like maps and hotels. Traffic to Expedia and Travelocity is largely composed of navigational and branded queries (not shown). The only long tail patterns they have significant share for are “[x] ticket”, and “cheap [x]“.

The input data we used reflects referrals to these sites from a sample population of users who clicked on search engine result pages. Factors which will affect the number and type of search referrals a site received in this data include: how representative the sample is of the population of U.S. searchers as a whole, how much relevant content a site has for a given query pattern, and how well that content ranks in google and other search engines.

If a travel website repeated this study with Concentrate using current competitive data, then uploaded additional search data for their own site including other metrics beyond search frequency (see our demo using Google Analytics), the results might reveal that “things to do in [x]” queries lead to high quality visits and their site has a chance at winning more searches for that pattern. Based on this information they might decide to make a move on TripAdvisor in that content category. Mark Jackson describes some strategies to apply within the travel sector in an article at Search Engine Watch:
Should Your SEO Strategy Target the Head or the Long Tail?. Using Concentrate, a travel website could streamline the process by downloading thousands of real queries for this pattern sent to their competitor:

Some queries in TripAdvisor pattern: “things to do in [x]“

Take Action: Some ideas for next steps

• Use Concentrate patterns to segment your sites query referrals, then estimate revenue opportunities for underperforming long tail categories and target your efforts accordingly
• Expand into new content areas based on trends discovered in long tail search patterns over time and ensure your site’s information supply (PDF) matches customer demand
• Get a subscription to Concentrate Plus and repeat this case study for your industry
• Use your industry search patterns as a guide to optimize internal website architecture
• Jumpstart your SEM efforts by using Concentrate to automatically create tight adgroups containing similar queries found within long tail each pattern
• Use your patterns as templates for dynamic filters within your website to show targeted ads, content, related links, or content recommendations based on query referral strings
• If you are a financial analyst or product planner, spot emerging trends in the long tail with Concentrate and use the information to guide decision making

On Friday, we launched our new search analytics product: Concentrate. One of its key features is a scalable algorithm that automatically discovers patterns in large amounts of search data and clusters long tail queries into manageable groups. This post will explain how using Concentrate’s pattern discovery feature can simplify search data analysis and give you an edge on the competition.

To explain how valuable Concentrate’s pattern discovery can be, we put together a case study of the travel sector using the Plus version of Concentrate and the type of competitive search data available from commercial providers like Hitwise or Compete. We will go into the details tomorrow, but here is a sneak peek at the results. This chart shows the share of travel searches by site in Spring 2006 and was generated using reports downloaded from Concentrate pattern discovery:

Travel Sector Searches: Comparing sites by pattern share

The Long Tail of Search

Search analytics starts by looking at the most frequent search queries driving traffic to your site or that of your competitors (these are often called the “head queries”). For most sites, these queries are a fraction of your total search traffic and just the tip of the iceberg in terms of insight about your audience. Queries like “cheap hotels in liverpool ny” may only occur once or twice in a given month, but when aggregated with other rare phrases can make up the bulk of your traffic.

The concept of the long tail in business intelligence has been a topic of debate over the last few years. One area where the long tail is alive and well is in search. The landscape of user search queries is dominated by the long tail, and most studies indicate that referrals from these long tail phrases are more likely to lead to purchases on your site. Natural search isn’t the only area where the long tail turns out to be critical. Paid search efforts which ignore the long tail are potentially missing out on a large chunk of revenue. The challenge of the long tail is that dealing with massive amounts of query data quickly becomes unmanageable.

Traditional Search Reports: head queries for some top travel sites

If you have hundreds of pages of unique queries to sort through manually, forming a actionable view of that data is a painful process. This is why most people only look at the first few pages of queries.

Categorizing Queries using Patterns

Finding frequent search patterns is the key to making search data understandable. Patterns let you to treat groups of long tail searches like popular individual queries.

Our concept of patterns is similar to an example described by Brian Brown in a recent SEOMoz post. Patterns are templates for searches that have a similar structure. For instance, the pattern “jobs in [x]” represents searches for jobs in some location. The “[x]” is a wildcard that can stand for one or more words. These “masked terms” are often variants of a similar concepts, like locations or celebrity names. Depending on the nature of your site, up to 80% of your long tail search traffic could be summarized using just the top 20 query patterns.

Concentrate Pattern Summary View for TripAdvisor.com

The next iteration of Concentrate’s learning algorithms will replace many of these wildcards with named entity labels. For example: “hotels in [x]” will become “hotels in [City]“. See our FAQ for more details on special pattern categories like navigational queries. Tomorrow, we’ll cover the travel case study in detail.

We are pleased thrilled to introduce Concentrate™, an innovative long-tail search analytics 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)