Many analytical applications fail for a simple reason: they assume users know precisely what they need before they’ve begun the analysis. There are cases where this assumption holds and the user has a specific end-point in mind. But more often, users depend on the tool to track down an answer with only a vague idea of where to start. The exploratory analysis that follows can feel like swimming upstream when the application isn’t designed to facilitate the journey.

The source of this mismatch is partly rooted in the technical perspective of database developers. The simplest path to providing data access is to let users fill out a form to define a SQL query. It is a linear mindset that isn’t well-suited to ambiguous problems.

I’d like to offer a couple examples that illustrates the difference between the common, form-based approach and a more dynamic, interactive approach. Then I’ll explain the implicit assumptions behind the different models and why it matters.

At its heart, Travelocity is a travel analysis tool intended to help you find the best flight (or hotel, car rental, package, etc.) given a complex set of parameters. The relative importance of each of these parameters (departure day/time, return day/time, airports, connections, preferred airlines, price, etc.) is a personal preference… but not one that is explicitly or fully known even to the user. For example, it would be hard for me to say exactly how much more I would pay for a non-stop flight or what is the relative value of a more convenient airport versus a more reliable airline. These preferences are hard to understand prior to seeing specific trade-offs.

Travelocity approaches this complex problem in the way that so many analytical problems do: it asks for all your preferences first then offers a static list results for the specified query.

A few things to note about this search results page:

1. On a busy web page, “Change Your Search” is not emphasized.
2. The “tracker” across the top shows a linear five-step process. The user is expected to flow through this sequence in order.
3. Getting results for a new search takes more than ten seconds.

I’ve been a loyal Travelocity user for years, and I don’t want to imply that this site is poorly designed or difficult to use. The problem is more subtle than that.

By way of comparison, let’s take a look at a more recent entrant to the online travel business, Kayak. This site is designed with a different usage model in mind. Kayak starts by asking for the same information as Travelocity, but the results pages is designed to support further analysis:

The biggest difference is the prominent filtering functionality on the left side of the page. The filters allow users to narrow down their original search without leaving the results page (it takes less than a second to view refreshed results after changing a filter—no “run report” button required). In addition, Kayak places more emphasis on the start-over option. The designers of this site did not assume your first search would be enough to get you to the perfect flight option. Finally, notice the different “views” of the data that are available for a given result set. The views help support different types of decisions based on the same search parameters.

Analytical applications for business have similar underlying structures and usage models. The analysis process in Omniture SiteCatalyst, the leading web analytics platform for large sites, offers a typical example:

This application offers lots of functionality, and it feels like featuring functionality is the primary purpose of the start page. If you want to get to useful data rather than view an advertisement for Omniture products and events, you can start by selecting the “Report Builder:”

Now, it is form-filling time. Like Travelocity, the user is expected to choose the precise parameters before they get to see anything. The resulting report requires a 10 second wait, and the result is static. Any additional filtering will require you to run a new report

Now let’s look at how Google Analytics chooses to structure the user experience:

In contrast to SiteCatalyst, Google Analytics shows you results immediately—no defining or configuring a report before you can get started. Similar to Kayak, the application offers a bunch of options on the report results page to refine parameters (e.g. data ranges, metrics, comparisons).

Travelocity and Omniture make a few assumptions common to analytical applications:

• Users can accurately define their need (i.e. they already know what they are looking for).
• Users can precisely define their need (i.e. they know all the relevant parameters).
• Users’ workflow will follow a linear sequence of events. Going back to the beginning is a failure of the process or user.

More effective analytical applications like Kayak and Google Analytics make different assumptions:

• Users have a general question, but do not necessarily know details about what they're looking for.
• Users need to see results before they can ask better, more detailed questions. These feedback loops provide critical learning.
• Users need to get to data as quickly and easily as possible. A screen without data is delayed progress.
• Different views of the data can provide different insights about results.
• Users want the application to keep up with their trains of thought. Speed and responsiveness matter. Here’s a framework from Jakob Nielsen’s blog about response time:

0.1 second is about the limit for having the user feel that the system is reacting instantaneously, meaning that no special feedback is necessary except to display the result.

1.0 second is about the limit for the user’s flow of thought to stay uninterrupted, even though the user will notice the delay. Normally, no special feedback is necessary during delays of more than 0.1 but less than 1.0 second, but the user does lose the feeling of operating directly on the data.

10 seconds is about the limit for keeping the user’s attention focused on the dialogue. For longer delays, users will want to perform other tasks while waiting for the computer to finish, so they should be given feedback indicating when the computer expects to be done. Feedback during the delay is especially important if the response time is likely to be highly variable, since users will then not know what to expect.

In my experience, making the right assumptions about user behavior makes all the difference between an application people enjoy and depend on and an application people dread using.

Note: We've updated the script to work on Firefox 3 as well as Firefox 2.

After the warm reception for the first version of our Enhanced Google Analytics, we decided to add some new functionality. (Nothing like a few kinds words to keep us in the giving mood.) The first script created a couple new tables in the Google Analytics interface that highlight recent changes in referral visits. It uses Greasemonkey, an add-on for Firefox that allows a user to insert javascript directly into a webpage.

Our update gives you even more ability to understand the data in Google Analytics:

• At the suggestion of Avinash Kaushik, the new script works for keyword data, helping you see how organic search traffic is changing. An increase in a keyword may indicate a general change in user interests and/or improved performance on search results.
• My coworker Pete Skomoroch also suggested that I add the ability to see declines in referrals and new keyword searches.
• With the help of Paul Irish, the script is now better able to interface with the date widget on the Google Analytics site.

Who sent me unusual traffic?

(Click the above button for a simulation.)

When you click the button, your browser will download some historical data behind the scenes, and display a nice summary of the best and worst performing keywords/referring domains.

Installation Instructions:
Firefox 2.0+
Greasemonkey
googleanalyticsdownloade.user.js

If you don't already have Firefox, install it. Install Greasemonkey, and do the required Firefox restart. You should see a handsome monkey peeking at you from the bottom right hand corner of your browser. Open the script file in your firefox browser, and Greasemonkey should give you an option to install the script.

Afterwards, log into Google Analytics, and navigate to your Referring Sources or Keywords Tab. Click the button.

Configuring the script:

We spent some time trying to find convenient default settings here at Juice Analytics, so the script should work straight out of the box. Some users, however, may find it convenient to alter some of these configurations. To do so, in Firefox, go to Tools=>Greasemonkey=>Manage User Scripts..., select Google Analytics Downloader, and then click Edit in the lower left corner of the window. This should open up the script file in a text editor. If your computer does not have a default text editor configured, you may have to choose one. 'c:\windows\notepad' is a good bet for Windows machines.

This is what you should see:

The bracket labeled 'keywords?' controls defaults for the Keywords page, and correspondingly, 'referring_sources?' controls the Referring Sources page.

To change the settings, simply change the corresponding variable to your preferred default. Make sure to refresh your Google Analytics webpage, if you have it open, so the new settings are loaded.

Now for the nitty gritty configuration details:

• display_limit: This controls the maximum entries that each table will contain. This may be useful for large, sprawling sites.
• growth_tolerance: This is the percentage growth parameter. Changing it to .10, for example, will catch everything that has grown by 10%, as opposed to the default 50% and 20%, respectively.
• minimum_number_elements: This is a significance benchmark that can be used to limit what is displayed upon the screen. By default, only keywords with at least 10 elements are displayed upon the screen. Referring Sites does not have a minimum by default, but one can be set if desired.
• limit: Limit is more of an internal parameter that determines how many entries should be downloaded from Google in order to get the results that are visible here on the page. Lower the limit to increase speed. If the limit is set to a very high number, you will get the largest result set, but you will have to sit around for a while for the results to load. Since the results are downloaded ordered by volume, raising the limit from the default numbers will not actually give more significant results. You will simply get more of the smaller results, such as keywords with only 1 hit.
• look_back: This is a very important parameter. The script uses the date displayed upon your Google Analytics page to determine the full range that you want to consider in your results, but 'look_back' determines how many of those days are used for the significance test. So, say the range you have displayed in Google is March 23 - April 22 and your look_back is 7 days. The script will compare the average referrals for a given keyword from April 16-22 to the average from March 23-April 15, and will return the keyword only if the recent average is 20% higher than the rest of the time period. Thus, if you want to increase the total range of the data, change the dates on the actual webpage. Change 'look_back' only if you want to change the period of significance.

Happy analyzing!

There is a new post with updates to the script. Find it here.

My boss Zach has a problem. Every four hours the craving strikes him. No matter where he is, he pulls out his shiny Macbook Pro and navigates to Juice's Google Analytics site. He pulls up the list of referrers to our site and meticulously searches for new domains. He has an freakish ability to pick out IP addresses that have never linked to us before. Even so, there had to be a better way.

I wondered whether Greasemonkey might be able to help. Greasemonkey is an extension for Firefox that allows users to install custom javascript when you visit a specific website. These scripts can add a delete button for Gmail, automatically display lyrics to your YouTube music video, or do pretty much anything else you would want to enhance the functionality of a website.

After poking around the subtleties of the Google Analytics interface, I came up with a little script that can identify the new referrals that Zach so desperately craves. When navigating to the "Referring Sites" section of Google Analytics, the script add the following button to the interface.

Pushing the button downloads all the referrer data for the date displayed in the Google Analytics range, as well as a similar set of data for the range up to, but not including, the last three days. The difference between the two data sources is used to calculate all of the results. The specific number of days can be changed by editing the first line of the script. Greasemonkey then displays the results in two tables above the original Referrer table. (Greasemonkey works entirely within your browser shell, so your data should be quite secure.)

The first table shows any sites that have displayed more than a 50% increase in visits over the last 3 days as compared to the rest of the time range. The second shows all new recent sites that do not appear at all more than 3 days ago. This can be quite useful to anyone, who, like Zach, absolutely needs to know about any new and exciting inbound links.

Installation Instructions:
Firefox 2.0+
Greasemonkey
googleanalyticsdownloade.user.js

If you don't already have Firefox, install it. Install Greasemonkey, and do the required Firefox restart. You should see a handsome monkey peeking at you from the bottom right hand corner of your browser. Open the script file in your firefox browser, and Greasemonkey should give you an option to install the script.

Afterwards, log into Google Analytics, and navigate to your Referring Sources Tab. Click the button.

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