analytics – ACRL TechConnect

Learning Web Analytics from the LITA 2012 National Forum Pre-conference

Note: The 2012 LITA Forum pre-conference on Web Analytics was taught by Tabatha (Tabby) Farney and Nina McHale. Our guest authors, Joel Richard and Kelly Sattler were two of the people who attended the pre-conference and they wrote a summary of the pre-conference to share with the ACRL TechConnect readers.

In advance of the conference, Tabby and Nina reached out to the participants ahead of time with a survey on what we the participants were interested in learning and solicited questions to be answered in the class. Twenty-one participants responded and of them seventeen were already using Google Analytics (GA). About half those using GA check their reports 1-2 times per month and the rest less often. The conference opened with introductions and a brief description of what we were doing with analytics on our website and what we hoped to learn.

Web Analytics Strategy

The overall theme of the pre-conference was the following:

A web analytics strategy is the structured process of identifying and evaluating your key performance indicators on the basis of an organization’s objectives and website goals – the desired outcomes, or what you want people to do on the website.

We learned that beyond the tool we use measure our analytics, we need to identify what we want our website to do. We do this by using pre-existing documentation our institutions have on their mission and purpose as well as the mission and purpose of the website and who it is to serve. Additionally, we need a privacy statement so our patrons understand that we will be tracking their movements on the site and what we will be collecting. We learned that there are challenges when using only IP addresses (versus cookies) for tracking purposes. For example, does our institution’s network architecture allow for you to identify patrons versus staff using IP address or are cookies a necessity?

Tool Options for Website Statistics

To start things off, we discussed the types of web analytics tools that are available and which we were using. Many of the participants were already using Google Analytics (GA) and thus most of the activities were demonstrated in GA as we could log into our own accounts. We were reminded that though it is free, GA keeps our data and does not allow us to delete it. GA has us place a bit of Javascript code on the pages we want tracked. It is easier to set up GA within a content management system but it may not work as well for mobile devices. Piwik is an open-source alternative to Google Analytics that uses a similar Javascript tagging method. Additionally we were reminded that if we use any Javascript tagging method, we should review our code snippets least every two years as they do change.

We learned about other, less common systems for tracking user activity. AWStats is installed locally and reads the website log files and processes them into reports. It offers the user more control and may be more useful for sites not in a content management system. Sometimes it provides more information than desired and will be unable to clearly differentiate between users based on IP. Other similar tools are Webalizer, FireStats, and Webtrends.

A third option is to use Web Beacons which are small, invisible transparent GIFs embedded on every page. This is useful for when Javascript won’t work, but they probably aren’t as applicable today as they once were.

Finally, we took a brief look at the heat mapping tool, Crazy Egg. It focuses on visual analytics and uses Javascript tagging to provide heat maps of exactly where visitors clicked on our site offering insights as to what areas of a page receive the most attention. Crazy Egg has a 30 day free trial and then it costs per page tracked, but there are subscriptions for under $100/month if you find the information worth the cost. The images can really give webmasters an understanding of what the users are doing on their site and are persuasive tools when redesigning a page or analyzing specific kinds of user behavior.

Core Concepts and Metrics of Web Analytics

Next, Tabby and Nina presented a basic list of terminology used within web analytics. Of course, different tools refer to the same concept by different names, but these were the terms we used throughout our session.

Visits – A visit is when someone comes to the site. A visit ends when a user has not seen a new page in 30 minutes (or when they have left the site.)
Visitor Types: New & Returning – A cookie is used to determine whether a visitor has been to the site in the past. If a user disables cookies or clears them regularly, they will show up as a new user each time they visit.
Unique Visitors – To distinguish visits by the same person, the cookie is used to track when the same person returns to the site in a given period of time (hours, days, weeks or more).
Page Views – More specific than “hits,” a page view is recorded when a page is loaded in a visitor’s browser.
User Technology – This includes information about the visitor’s operating system, browser version, mobile device or desktop computer, etc.
Geographic Data – A visitor’s location in the world can often be determined to which city they are in.
Entry and Exit Pages – These refer to the page the visitor sees first during their visit (Entry) and the last page they see before leaving or their session expires (Exit).
Referral Sources – Did the visitor come from another site? If so, this will tell who is sending traffic to us.
Bounce Rate – A bounce is when someone comes to the site and views only one page before leaving.
Engagement Metrics – This indicates how much visitors are on our site measured by time they spent on the site or number of pages viewed.

Goals/Conversion

Considering how often the terms “goals” and “conversions” are used, we learned that it is important to realize that in web analytics lingo, a goal is a metric, also referred to as a conversion, and measures whether a desired action has occurred on your site. There are four primary types of conversions:

URL Destination – A visitor has reached a targeted end page. For commercial sites, this would be the “Thank you for your purchase” page. For a library site, this is a little more challenging to classify and will include several different pages or types of pages.
Visit Duration – How much time a visitor spends on our site. This is often an unclear concept. If a user is on the site for a long time, we don’t know if they were interrupted while on our site, if they had a hard time finding what they were looking for, or if they were enthralled with all the amazing information we provide and read every word twice.
Pages per Visit – Indicates site engagement. Similar to Visit Duration, many pages may mean the user was interested in our content, or that they were unable to find what they were looking for. We distinguish this by looking at the “paths” of page the visitor saw. As an example, we might want to know if someone finds the page they were looking for in three pages or less.
Events – Targets an action on the site. This can be anything and is often used to track outbound pages or links to a downloadable PDF.

Conversion rate is an equation that shows the percentage of how often the desired action occurs.

Conversion rate = Desired action / Total or Unique visits

Goal Reports also known as Conversion Reports are sometimes provided by the tool and include the total number of conversions and the conversion rate. We learned that we can also assign a monetary value to take advantage of the more commerce-focused tools often used in analytics software, but the results can be challenging to interpret. Conversion reports also show an Abandonment Rate as people leave our site. However, we can counter this by creating a “funnel” that identifies the steps needed to complete the goal. The funnel report shows us where in the steps visitors drop off and how many make it through the complete conversion.

Key Performance Indicators (KPIs) were a focus of much of the conference. They measure the outcome based on our site’s objectives/goals and are implemented via conversion rates. KPIs are unique to each site. Through examples, we learned that each organization’s web presence may be made up of multiple sites. For instance, an organization may have its main library pages, Libguides, the catalog, a branch site, a set of sites for digitized collections, etc. A KPI may span activities on more than one of these sites.

Segment or Filter

We then discussed the similarities and differences between Segments and Filters, both of which offer methods to narrow the data enabling us to focus on a particular point of interest. The difference between the two is that (i) filtering will remove the data from the collection process thereby resulting in lost data; whereas (ii) segmentation hides data from the reports leaving it available for other reports. Generally, we felt that the use of Segments was preferable over Filters in Google Analytics given that it is impossible to recover data that is lost during GA’s real-time data collection.

We talked about the different kinds of segments that some of us are using. For example, is Joel’s organization, he is using a technique to segment the staff computers in their offices from computers in the library branches by adding a query string to the homepage URL of the branch computers’ browsers. Using this, he can create a segment in Google Analytics to view the activity of either group of users by segmenting on the different Entry pages (with and without this special query string). Segmenting on IP Address also further segregates his users between researchers and the general public.

Benchmarking

As a step towards measuring success for our sites, we discussed benchmarking, which is used to look at the performance of our sites before and after a change. Having performance data before making changes is essential to knowing whether those changes are successful, as defined by our goals and KPIs.

Comparing a site to itself either in a prior iteration or before making a change is called Internal Benchmarking. Comparing a site to other similar sites on the Internet is known as External Benchmarking. Since external benchmarking requires data to make a comparison, we need to request of another website their data or reports. Another alternative is to use service sites such as Alexa, Quantcast, Hitwise and others, which will do the comparison for you. Keep in mind that these may use e-commerce or commercial indicators which may not make for a good comparison to humanities-oriented sites.

Event Tracking

Page views and visitor statistics are important for tracking how our site is doing, but sometimes we need to know about events that aren’t tracked through the normal means. We learned that an Event, both in the conceptual sense and in the analytics world, can be used to track actions that don’t naturally result in a page view. Events are used to track access to resources that aren’t a web page, such as videos, PDFs, dynamic page elements, and outbound links.

Tracking events doesn’t always come naturally and require some effort to set up. Content management systems (CMS) like Drupal help make event tracking easy either via a module or plugin or simply by editing a template or function that produces the HTML pages. If a website is not using a CMS the webmaster will need to add event tracking code to each link or action that they wish to record in Google Analytics. Fortunately, as we saw, the event tracking code is simple and easy to add to a site and there is good documentation describing this in Google’s Event Tracking Guide documentation.

Finally, we learned that tracking events is preferable to creating “fake” pageviews as it does not inflate the statistics generated by regular pageviews due to the visitors’ usual browsing activities.

Success for our websites

Much of the second half of the conference was focused on learning about and performing some exercises to define and measure success for our sites. We started by understanding our site in terms of our Users, our Content and our Goals. These all point to the site’s purpose and circle back around to the content delivered by our site to the users in order to meet our goals. It’s all interconnected. The following questions and steps helped us to clarify the components that we need to have in hand to develop a successful website.

Content Audit – Perform an inventory that lists every page on the site. This are likely to be tedious and time-consuming. It includes finding abandoned pages, lost images, etc. The web server is a great place to start identifying files. Sometimes we can use automated web crawling tools to find the pages on our site. Then we need to evaluate that content. Beyond the basic use of a page, consider recording last updated date, bounce rate, time on page, whether it is a landing page or not, and who is responsible for the content.

Identifying Related Sites – Create a list of sites that our site links to and sites that link back to our site. Examples: parent site (e.g. our organization’s overall homepage), databases, journals, library catalog site, blog site, flickr, Twitter, Facebook, Internet Archive, etc.

Who are our users? – What is our site’s intended audience or audiences? For us at the conference, this was a variety of people: students, staff, the general public, collectors, adults, teens, parents, etc. Some of us may need to use a survey to determine this. Some populations of users (e.g. staff) might be identified via IP Addresses. We were reminded that most sites serve one major set of users with other smaller groups of users served. For example, students might be the primary users whereas faculty and staff are secondary users.

Related Goals and plans – Use existing planning documents, strategic goals, a library’s mission statement to set a mission statement and/or goals for the website. Who are we going to help? Who is our audience? We must define why our site exists and it’s purpose on the web. Generally we’ll have one primary purpose per site. Secondary purposes also help define what the site does and fall under the “nice to have” category, but are also very useful to our users. (For example, Amazon.com’s primary purpose is to sell products, but secondary purposes include reviews, wishlists, ratings, etc.)

When we have a new service to promote, we can use analytics and goals to track how well that goal is being met. This is an ongoing expansion of the website and the web analytics strategy. We were reminded to make goals that are practical, simple and achievable. Priorities can change from year to year in what we will monitor and promote.

Things to do right away

Nearing the end of our conference, we discussed things that we can do improve our analytics in the near term. These are not necessarily quick to implement, but doing these things will put us in a good place for starting our web analytics strategy. It was mentioned that if we aren’t tracking our website’s usage at all, we should install something today to at least begin collecting data!

Share what we are doing with our colleagues. Educate them at a high level, so they know more about our decision making process. Be proactive and share information; don’t wait to be asked what’s going on. This will offer a sense of inclusion and transparency. What we do is not magic in any sense. We may also consider granting read-only access to some people who are interested in seeing and playing with the statistics on their own.
Set a schedule for pulling and analyzing your data and statistics. On a quarterly basis, report to staff on things that we found that were interesting: important metrics, fun things, anecdotes about what is happening on our site. Also check our goals that we are tracking in analytics on a quarterly basis; do not “set and forget” our goals. On monthly basis, we should report to IT staff on topics of concern, 404 pages, important values, and things that need attention.
Test, Analyze, Edit, and Repeat. This is an ongoing, long-term effort to keep improving our sites. During a site redesign, we compare analytics data before and after we make changes. Use analytics to make certain the changes we are implementing have a positive effect. Use analytics to drive the changes in our site, not because it would be cool/fun/neat to do things a certain way. Remember that our site is meant to serve our users.
Measure all content. Get tracking code installed across all of our sites. Google Analytics cross-domain tracking is tricky to set up, but once installed will track users as they move between different servers. Examples for this are our website, blog, OPAC, and other servers. For things not under our control, be sure to at least track outbound to know when people leave our site.
Measure all users. When we are reporting, segment the users into groups as much as possible to understand their different habits.
Look at top mobile content. Use that information to divide the site and focus on things that mobile users are going to most often.

Summary

Spending eight hours learning about a topic and how to practically apply it to our site is a great way to get excited about taking on more responsibilities in our daily work. There is still a good deal of learning to be done since much of the expertise in web analytics comes from taking the time to experiment with the data and settings.

We, Kelly and Joel, are looking forward to working with analytics from the ground-up, so to speak. We are both are in an early stage of redeploying our website under new software which allows us to take into account the most up-to-date analytics tools and techniques available to us. Additionally, our organizations, though different in their specific missions and goals, are entering into a new round of long-term planning with the result being a new set of goals for the next three to five years. It becomes clear that the website is an important part of this planning and that the goals of our websites directly translate into actions that we take when configuring and using Google Analytics.

We both expect that we will experience a learning curve in understanding and applying web analytics and there will be a set of long-term, ongoing tasks for us. However, after this session, we are more confident about how to effectively apply and understand analytics towards tracking and achieving the goals of our organization and create an effective and useful set of websites.

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About our Guest Authors:

Kelly Sattler is a Digital Project Librarian and Head of Web Services at Michigan State University. She and her team are involved with migrating the Libraries’ website into Drupal 7 and are analyzing our Google Analytics data, search terms, and chat logs to identify places where we can improve our site through usability studies. Kelly spent 12 years in Information Technology at a large electrical company before becoming a librarian and has a bachelor’s degree in Computer Engineering. She can be found on twitter at @ksattler.

Joel Richard is the lead Web Developer for the Smithsonian Libraries in Washington, DC and is currently in the process of rebuilding and migrating 15 years’ worth of content to Drupal 7. He has 18 years of experience in software development and internet technology and is a confirmed internet junkie. In his spare time, he is an enthusiastic proponent of Linked Open Data and believes it will change the way the internet works. One day. He can be found on twitter at @cajunjoel.

Personal Data Monitoring: Gamifying Yourself

The academic world has been talking about gamification of learning for some time now. The 2012 Horizon Report says gamification of learning will become mainstream in 2-3 years. Gamification taps into the innate human love of narrative and displaying accomplishments. Anyone working through Code Year is personally familiar with the lure of the green bar that tells you how far you are to your next badge. In this post I want to address a related but slightly different topic: personal data capture and analytics.

Where does the library fit into this? One of the roles of the academic library is to help educate and facilitate the work of researchers. Effective research requires collecting a wide variety of relevant sources, reading them, and saving the relevant information for the future. The 2010 book Too Much to Know by Ann Blair describes the note taking and indexing habits taught to scholars in early modern Europe. Keeping a list of topics and sources was a major focus of scholars, and the resulting notes and indexes were published in their own right. Nowadays maintaining a list of sources is easier than ever with the many tools to collect and store references–but challenges remain due to the abundance of sources and pressure to publish, among others.

New Approaches and Tools in Personal Data Monitoring

Tracking one’s daily habits, reading lists and any other personal information is a very old human habit. Understanding what you are currently doing is the first step in creating better habits, and technology makes it easier to collect this data. Stephen Wolfram has been using technology to collect data about himself for nearly 25 years, and he posted some visual examples of this a few weeks ago. This includes items such as how many emails he’s sent and received, keystrokes made, and file types created. The Felton report, produced by Nick Felton, is a gorgeously designed book with personal data about himself and his family. But you don’t have to be a data or design whiz to collect and display personal information. For instance, to display your data in a visually compelling way you can use a service such as Daytum to create a personal data dashboard.

In the realm of fitness and health, there are many products that will help capture, store, and analyze personal data. Devices like the Fitbit now clip or strap to your body and count steps taken, floors climbed, and hours slept. Pedometers and GPS enabled sport watches help those trying to get in shape, but the new field of personal genetic monitoring and behavior analytics promise to make it possible to know very specific information about your health and understand potential future choices to make. 23andMe will map your personal genome and provide a portal for analyzing and understanding your genetic profile, allowing unprecedented ability to understand health. (Though there is doubt about whether this can accurately predict disease). For the behavioral and lifestyle aspects of health a new service called Ginger.io will help collect daily data for health professionals.

mendeley — Number of readers recorded by Mendeley

Visual cues of graphs of accomplishments and green progress bars can be as helpful in keeping up research and monitoring one’s personal research habits just as much as they help in learning to code or training for a marathon. One such feature is the personal reading challenge on Goodreads,which lets you set a goal of how many books to read in the year, tracks what you’ve read, and lets you know how far behind or ahead you are at your current reading pace. Each book listed as in progress has a progress bar indicating how far along in the book you are. This is a simple but effective visual cue. Another popular tool, Mendeley, provides a convenient way to store PDFs and track references of all kinds. Built into this is a small green icon that indicates a reference is unread. You can sort references by read/unread–by marking a reference as “read”, the article appears as read in the Mendeley research database. Academia.eduprovides another way for scholars to share research papers and see how many readers they have.

Libraries and Personal Data

How can libraries facilitate this type of personal data monitoring and make it easy for researchers to keep track of what they have done and help them set goals for the future? Last November the Academic Book Writing Month (#acbowrimo) Twitter hashtag community spun off of National Novel Writing Month and challenged participants to complete the first draft of an academic book or other lengthy work. Participants tracked daily word counts and research goals and encouraged each other to complete the work. Librarians could work with researchers at their institutions, both faculty and students, on this type of peer encouragement. We already do this type of activity, but tools like Twitter make it easier to share with a community who might not come to the library often.

The recent furor over the change in Google’s privacy settings prompted many people to delete their Google search histories. Considered another way, this is a treasure trove of past interests to mine for a researcher trying to remember a book he or she was searching for some years ago—information that may not be available anywhere else. Librarians have certain professional ethics that make collecting and analyzing that type of personal data extremely complex. While we collect all types of data and avidly analyze it, we are careful to not keep track of what individuals read, borrowed, or asked of a librarian. This keeps individual researchers’ privacy safe; the major disadvantage is that it puts the onus on the individual to collect his own data. For people who might read hundreds or thousands of books and articles it can be a challenge to track all those individual items. Library catalogs are not great at facilitating this type of recordkeeping. Some next generation catalogs provide better listing and sharing features, but the user has to know how to add each item. Even if we can’t provide users a historical list of all items they’ve ever borrowed, we can help to educate them on how to create such lists. And in fact, unless we do help researchers create lists like this we lose out on an important piece of the historical record, such as the library borrowing history in Dissenting Academies Online.

Conclusion

What are some types of data we can ethically and legally share to help our researchers track personal data? We could share statistics on the average numbers of books checked out by students and faculty, articles downloaded, articles ordered, and other numbers that will help people understand where they fall along a continuum of research. Of course all libraries already collect this information–it’s just a matter of sharing it in a way that makes it easy to use. People want to collect and analyze data about what they do to help them reach their goals. Now that this is so easy we must consider how we can help them.

Works Cited

Blair, Ann. Too Much to Know : Managing Scholarly Information Before the Modern Age. New Haven: Yale University Press, 2010.

Glimpses into user behavior

Heat map of clicks on the library home page

Between static analytics and a usability lab

Would you like an even more intimate glimpse into what users are actually doing on your site, instead of what you (or the library web committee) think they are doing? There are several easy-to-use web-based analytics services like ClickTale , userfly, Loop11, Crazy Egg, Inspectlet, or Optimalworkshop. These online usability services offer various ways to track what users are doing as they actually navigate your pages — all without setting up a usability lab, recruiting participants, or introducing the artificiality and anxiety of an observed user session. ClickTale and userfly record user actions that you can view later as a video; most services offer heatmaps of where users actually click on your site; some offer “eye tracking” maps based on mouse movement.

Most services allow you to sign up for one free account for a limited amount of data or time.
Most allow you to specify which pages or sections of your site that you want to test at a time.
Many have monthly pricing plans that would allow for snapshots of user activity in various months of the year without having to pay for an entire year’s service.

We’re testing Inspectlet at the moment. I like it because the free account offers the two services I’m most interested in: periodic video captures of the designated site and heat maps of actual clicks. The code is a snippet added to the web pages of interest. The screen captures are fascinating — watch below as an off-campus user searches the library home page for the correct place to do an author search in the library catalog. I view it as a bit of a cautionary illustration about providing a lot of options. Follow the yellow “spotlight” to track the user’s mouse movements. As a contrast, I watched video after video of clearly experienced users taking less than two seconds to hit the “Ebsco Academic Search” link. Be prepared; watching a series of videos of unassisted users can dismantle your or your web committee’s cherished notions about how users navigate your site.

This is a Jing video of a screen capture — the actual screen captures are much sharper, and I have zoomed out for illustrative purposes. The free Inspectlet account does not support downloads of capture videos, but Rachit Gupta, the founder, wrote me that in the coming few weeks, Inspectlet is releasing a feature to allow downloads for paid accounts. Paid accounts also have access to real time analytics, so libraries would be able to get a montage of what’s happening in the lobby as it is happening. Imagine being able to walk out and announce a “pop-up library workshop” on using the library catalog effectively after seeing the twentieth person fumble through the OPAC.

Another thing I like about Inspectlet is the ability to anonymize the IP addresses in the individual screen captures to protect an individual patron’s privacy.

The chart below compares the features of a few of the most widely used web-based analytics tools.

Vendor	Video Captures	Heat Maps	Mouse & Click Tracking	Real Time mode	Other	Privacy Policy	Pricing plan
ClickTale	√	√	√	√	Scroll maps, form analytics, conversion funnels, campaigns	Privacy Policy	Basic $99/month; limited free plan; month to month pricing; higher education discounts available (call)
Crazy Egg		√	√		Scroll maps, click overlays, confetti overlay	Privacy Policy	Basic $9/month (annual)
Inspectlet	√	√	√	√	Scroll maps, Custom API, anonymized IP addresses	Privacy Policy	Starter $7.99/month; limited free account.Can cancel subscription at any time.
mouseflow	√	√	√		Movement heatmaps, link analytics	Privacy Policy	Small: aprox $13 US/month; free plan.Can cancel subscription at any time.
seevolution		√	√	√	Scroll maps, visual tool set for real time	Privacy Policy	Light: $29/month. Free plan, but very limited details.
userfly	√					Terms (with a brief privacy explanation)	Basic $10/month; free 10 captures a monthCan cancel subscription at any time.

If you are using one of these services, or a similar service, what have you learned about your users?

Testing new designs or alternative designs – widely used web-based usability tools

After you’ve watched your users and determined where there are problems or where you would like to try an alternative design, these services offer easy ways to test new designs and gather feedback from users without setting up a local usability lab.

Loop11	Create test scenarios and analyze results (see demo)	Privacy Policy	First project free; $350 per project
Optimalworkshop	Card sorting, Tree Testing, Click Testing	Privacy Policy	Free plan small project; $109 for each separate plan; 50% discount for education providers
OpenHallway	Create test scenarios and analyze results	Terms of Service	Basic: $49/month; limited free account, Can cancel subscription at any time.
Usabilla	Create test scenarios and analyze results; mobile UX testing	Terms of Service	Starter: $19/month. Can cancel subscription at any time.

Action Analytics

What is Action Analytics?

If you say “analytics” to most technology-savvy librarians, they think of Google Analytics or similar web analytics services. Many libraries are using such sophisticated data collection and analyses to improve the user experience on library-controlled sites. But the standard library analytics are retrospective: what have users done in the past? Have we designed our web platforms and pages successfully, and where do we need to change them?

Technology is enabling a different kind of future-oriented analytics. Action Analytics is evidence-based, combines data sets from different silos, and uses actions, performance, and data from the past to provide recommendations and actionable intelligence meant to influence future actions at both the institutional and the individual level. We’re familiar with these services in library-like contexts such as Amazon’s “customers who bought this item also bought” book recommendations and Netflix’s “other movies you might enjoy”.

BookSeer β by Apt

Action Analytics in the Academic Library Landscape

It was a presentation by Mark David Milliron at Educause 2011 on “Analytics Today: Getting Smarter About Emerging Technology, Diverse Students, and the Completion Challenge” that made me think about the possibilities of the interventionist aspect of analytics for libraries. He described the complex dependencies between inter-generational poverty transmission, education as a disrupter, drop-out rates for first generation college students, and other factors such international competition and the job market. Then he moved on to the role of sophisticated analytics and data platforms and spoke about how it can help individual students succeed by using technology to deliver the right resource at the right time to the right student. Where do these sorts of analytics fit into the academic library landscape?

If your library is like my library, the pressure to prove your value to strategic campus initiatives such student success and retention is increasing. But assessing services with most analytics is past-oriented; how do we add the kind of library analytics that provide a useful intervention or recommendation? These analytics could be designed to help an individual student choose a database, or trigger a recommendation to dive deeper into reference services like chat reference or individual appointments. We need to design platforms and technology that can integrate data from various campus sources, do some predictive modeling, and deliver a timely text message to an English 101 student that recommends using these databases for the first writing assignment, or suggests an individual research appointment with the appropriate subject specialist (and a link to the appointment scheduler) to every honors students a month into their thesis year.

Privacy Implications

But should we? Are these sorts of interventions creepy and stalker-ish?* Would this be seen as an invasion of privacy? Does the use of data in this way collide with the profession’s ethical obligation and historical commitment to keep individual patron’s reading, browsing, or viewing habits private?

Every librarian I’ve discussed this with felt the same unease. I’m left with a series of questions: Have technology and online data gathering changed the context and meaning of privacy in such fundamental ways that we need to take a long hard look at our assumptions, especially in the academic environment? (Short answer — yes.) Are there ways to manage opt-in and opt-out preferences for these sorts of services so these services are only offered to those who want them? And does that miss the point? Aren’t we trying to influence the students who are unaware of library services and how the library could help them succeed?

Furthermore, are we modeling our ideas of “creepiness” and our adamant rejection of any “intervention” on the face-to-face model of the past that involved a feeling of personal surveillance and possible social judgment by live flesh persons? The phone app Mobilyze helps those with clinical depression avoid known triggers by suggesting preventative measures. The software is highly personalized and combines all kinds of data collected by the phone with self-reported mood diaries. Researcher Colin Depp observes that participants felt that the impersonal advice delivered via technology was easier to act on than “say, getting advice from their mother.”**

While I am not suggesting in any way that libraries move away from face-to-face, personalized encounters at public service desks, is there room for another model for delivering assistance? A model that some students might find less intrusive, less invasive, and more effective — precisely because it is technological and impersonal? And given the struggle that some students have to succeed in school, and the staggering debt that most of them incur, where exactly are our moral imperatives in delivering academic services in an increasingly personalized, technology-infused, data-dependent environment?

Increasingly, health services, commercial entities, and technologies such as browsers and social networking environments that are deeply embedded in most people’s lives, use these sorts of action analytics to allow the remote monitoring of our aging parents, sell us things, and match us with potential dates. Some of these uses are for the benefit of the user; some are for the benefit of the data gatherer. The moment from the Milliron presentation that really stayed with me was the poignant question that a student in a focus group asked him: “Can you use information about me…to help me?”

Can we? What do you think?

* For a recent article on academic libraries and Facebook that addresses some of these issues, see Nancy Kim Phillips, Academic Library Use of Facebook: Building Relationships with Students, The Journal of Academic Librarianship, Volume 37, Issue 6, December 2011, Pages 512-522, ISSN 0099-1333, 10.1016/j.acalib.2011.07.008 . See also a very recent New York Times article on use of analytics by companies which discusses the creepiness factor.