Library Analytics with Char Booth and Paul Signorelli, Session 1 Part 1
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Library Analytics with Char Booth and Paul Signorelli, Session 1 Part 1
- 4. TELL US WHO YOU ARE. TELL US HOW MUCH YOU KNOW. TELL US WHY YOU’RE HERE.
- 5. “ The process of obtaining an optimal or realistic decision based on existing data. ” -Wikipedia “ Web Analytics is the measurement, collection, analysis and reporting of Internet data for the purposes of understanding and optimizing Web usage. ” -WAA Official Definition
- 6. “ Web Analytics is the science and the art of improving websites … by improving the customer’s website experience. It is a science because it uses statistics, data mining techniques, and a methodological process. It is an art because… the analyst or marketer has to draw from a diverse pallet of colors (data sources) to find the perfect mix that will yield actionable insights.” -Weisberg and Kaushik
- 22. OR… “ LOGFILE ANALYSIS ” “ PAGE TAGGING”
Editor's Notes
- Welcome, and thanks for joining us for the first of two ALA TechSource sessions designed to help demystify the topic of web analytics. We intend to make these sessions as interactive as possible so that we highlight and explain key concepts and draw from the wisdom and experience you bring to the sessions. Today, we ’ll be looking at basics: defining the subject and looking at what makes this of use to you and those you serve. We’re also going to look at ways to translate what web analytics provides into something that leads to positive action for you, your colleagues, the libraries you serve, and those who rely on your libraries to provide meaningful services. At the heart of all we do is one basic question: so what?
- Paul and Char introduce each other beyond what Dan says in his intro.
- Paul will be the session lead for #1 and Char for #2, due to our differing perspectives on the topic (mine more data-oriented and techie, his more public service/big picture oriented). We are both going to be active in the sessions and will help each other field questions. Those with questions after the sessions can direct those questions about session 1 to Paul, and those with questions about session 2 can direct them to me.
- [Three-part poll will run in WebEx while this slide is up.]
- Let ’s take a look at two very basic definitions of web analytics, one from Wikipedia, and the other from the Web Analytics Association.
- Here ’s another definition that gets more at the heart of how you can use analytics in your library – as a decision making tool that translates data into action.
- Let ’s translate this into real-world terms. We’ll start with an example that’s on a lot of librarians’ minds at the moment. Many of us are all wondering about mobile use – which devices are accessing our sites, catalogs, and content, how many users are out there, etcetera. The problem is, there are assumptions behind these worries, but no real data. Say your library is considering a mobile redesign or app. How do you translate assumptions into knowledge?
- Here ’s where web analytics comes in.
- What you ’re looking at here is a typical representation of data in Google Analytics. Boring and crazy looking, right? Well, maybe. But there’s a treasure trove of knowledge buried in all of this information. What we’re going to do today is a bit radical. We’re going to help you understand how analytics provides you with this information, and we’re going to remind you – REPEATEDLY – that if you don’t translate this information into understandable insights, this is what you get. You lose the treasure.
- Here we ’re getting closer to what we need. In this confusing mess are some very important things if we’re considering a mobile site or app. Who are our users? How much traffic are we likely to get? Well, here’s where we find the answers. The top line explains the number of total visits to a particular library’s website over two years using mobile devices, and the total operating systems their devices are running. Thirteen is a lot of systems to consider when you’re testing and designing,
- So, Google Analytics helps you see what the most popular devices and operating systems are, challenging some of your assumptions and affirming others. We can see here that Apple products are dominant, but that Android products are catching up fast. We ’re just skating the surface of analytics in general and Google Analytics specifically here, but we’ll go deeper into the specifics in a moment. Any basic questions so far?
- A quick review: at its most basic (and sometimes least significant level), web analytics is about collecting and using information. It ’s really just fancy talk for measuring how visitors use your website. We’re attempting to use Google Analytics or any of the other tools out there that help us see how many people visit those websites; how much time they spend on the sites, and how many pages they visit within the sites. But, of course, if that’s all there was to it, we could close the session now; extend our hope that you have a wonderful day, week, month, and year; and send you on your way. As you might have guessed by the schedules for this session and the one we’re presenting next week, this provokes one response: fat chance!
- Let ’s take a quick breather here and see if we’re on target. Using the live chat box on your screen, please let us know what you’ve been doing either with web analytics or with any other evaluation tool to bring you closer to your users via your website and your overall online presence. We’re also open to any questions you have on the basics before we move more specifically into Google Analytics and what that tool might mean for you and your library.
- Let ’s dive a little deeper. For each bit of information we collect, analyze, and attempt to use, we are faced with as many questions as answers. Here’s a simple example: *Given the way that information is collected through web analytics tools, we need to know whether “visits” and “visitors” reflect a unique session during which someone enters, uses, and then leaves our website, or whether a “visit” can include a start-to-finish session which includes the visitor jumping to other websites and then returning to ours. This could be as simple as someone looking for a specific item within our library collection, then jumping to a site that provides a review of that item, then coming back to our library website to place a reserve on that item. It might also include a job-seeker using our website to locate an employment possibility within our library or library system, going to another website like a city or county human resources department to fill out the application, then returning to our library website to print out a copy of our library’s annual report or another page that will help the applicant learn more about our organization in anticipation of being called for an interview. *A further twist on the “visits” and “visitor” question is whether our web analytics tool can, with any degree of accuracy, tell us how many different visitors actually used our site within the period or periods we’re tracking. If the same person uses the website five different times in one day, does our analytics tool record each of those sessions as five different visits from five different visitors, or is it accurate enough to show that the same person was on our site five times that day? A reality check: Avinash Kaushik, in his book “Web Analytics: An Hour a Day,” confirms what anyone moderately familiar with web analytics already knows—there is no tool which is 100 percent accurate in providing this information. You go into web analytics knowing that if the information is even within 10 percent of the actual figure, you’re doing well. Which means, of course, that you’re not going to take the information you receive and run with it as if it’s the final word in what you’re trying to ascertain. A key concept here: the information gathered through web analytics can be very helpful as long as we combine it with other sources of information: surveys, focus groups, discussions with colleagues who through their own work have a sense of what is working and what is not.
- While we were preparing for these two web analytics sessions, we were lucky enough to be able to talk with colleagues about what they are doing—or not doing. It became clear pretty early in the process that we ’re all over the map in terms of usage and ideas about usage—which is great since it means we have plenty of ideas to explore. Sarah Houghton-Jan, our colleague from San Jose Public Library and author of the Librarian in Black blog, said quite a few things that we’ll review in greater depth next week, but one tidbit that stood out and confirmed what we’ve seen from our own interviews and experience was the following: “You don't hear or read about libraries using web analytics often. Maybe we're all doing it in the closet, secretly, but I doubt it. My guess would be that maybe 10% of libraries actually use that data.” And we didn’t even try to entice her into an exploration of how many are using the data effectively.
- We found ourselves in agreement on an important point we want to be sure isn ’t lost in all of this. Again, quoting Sarah: “I'd suggest starting with web analytics, and then talking with real live users to get answers to the questions that those stats raise for you. I don't think they're an end-all, but just a place to begin.” For those with an interest in really getting to the heart of what web analytics information offers, the process would include checking the data we collect against information from follow-up surveys face to face and online, through focus groups which include library members and guests as well as staff, and other sources. Here’s the key point: web analytics provides raw data; follow-ups help us understand what is driving the behavior the raw data documents. Once we understand the data and the behavior, we’re ready to take actions that make things better for everyone.
- One very important and basic element which is going to guide much of what we discuss with you is the need to focus on users. It ’s all too common in web analytics to focus on those clicks, counting the numbers, and thinking we’ve discovered something worth knowing. The problem with that approach is that it can lead to focusing on our organizations rather than on those we serve. The bottom line for all of us is what the information tells us—or fails to tell us—about the services we provide. The very term we ’re using should imply the obvious: we need to spend much more time analyzing and responding to the question “so what?” than we have to spend in pumping out reports that bury us under meaningless—or meaningful—minutiae. If the information we are using isn’t leading to some sort of productive action, the larger question remains: what are we doing here?
- First question to ask when addressing web analytics is: why does this webpage exist?
- When we start diving into the numbers to see how many people visited our sites, what pages they used, how much time they spent there, and what they accomplished, we can do a lot to stay focused by trying to determine whether users accomplished what they set out to accomplish. There may be some rudimentary answers provided through web analytics: are smaller or larger numbers of people placing reserves now than they were a month or year ago? is the number of people visiting a library events page increasing at the same time that attendance at events is increasing? is the number of people volunteering increasing or decreasing while visits to a library volunteer page is increasing or decreasing? is increased or decreased use of library job postings matching an increase or decrease in job applications received? We may not be able to draw conclusions through what we ’re seeing, but we may be able to spot and act upon useful trends, and learn even more by engaging in follow-up surveys, onsite usability studies with groups including as few as 10 or 15 people, or in any number of other ways that help us establish contact and nurture productive relationships with those whose needs we hope to meet.
- If we take seriously the idea that the heart of web analytics is analysis leading to results, we need to be creatively explosive in how we face the challenges of sifting through and using data. We need to engage in creative pyrotechnics: ask—and keep asking—the right questions that help us parse the information. We need to approach our challenge with the sense of excitement that comes from seeking gems instead of drowning in a murky pond of sleep-inducing reports that are full of pseudo facts and figures. If it sounds as if we ’re suggesting that it all comes down to how you use your web analytics tools, we’re on the right track here.
- The data produced by these programs allows assists you in the process of making iterative design changes to web interfaces and tools. This flow chart is taken from the Waisberg and Kaushik reading we recommended that attendees take a look at. They describe the analytics process as defining goals for a site or web-based service, identifying “key performance indicators” of users – that’s the second box – such as where they are entering a site from, how long they linger on a page, how deeply they dive into the site itself, etcetera. The next two steps are where your analytics programs come in – collecting and analyzing data. Finally, once you’ve come to understand the implications of your traffic and user behavior with other types of data input, you are able to make minor or major adjustments to a site, from making a mobile redesign to moving the location of a “contact us” graphic.
- Logfile analysis from server logs: most basic analytics strategy. Or, page tagging with javascript . Limitations and benefits of each. Refer to the Elizabeth Black reading for the use case at Ohio State University in the readings. Explain what is actually tracked and how by analytics: visitors, unique visitors, time on page, entry sources, “bounce rate” (aka whether they leave without diving any deeper).
- Explain “code snippet” of Google Analytics – an example of page tagging using Javascript. Tracking code that captures traffic and information about users. Inserted directly before closing body tag. Can create client-side call that streamlines changes.
- Before we jump back into another quick bit of q & a with you, let ’s step beyond the usual scope of an introductory web analytics discussion to talk about this in a larger playing field. There are many alternatives to Google Analytics, such as logfile analyzers Analog , the most popular tool, AWStats , an older product, and Deep Log Analyzer . On the page tagging side, direct Google Analytics competitors are Clicky , FoxMetrics , and GoStats -- just search “ google analytics alternatives ” and you ’ ll find all of these and more. Many are free, some are “ freemium ” or basic features for free and more advanced features for a fee. While we were at the ALA midwinter meeting in San Diego last week, we had a chance to talk with colleagues from LogiXML, a company that is doing some pretty interesting things with collecting and using information collected online by three academic libraries. Boston College Libraries, for example, pays to use the “LogiInsight” product to combine information from its integrated library system to spot user trends at incredibly detailed levels, to monitor budgets in real time, and accomplish a variety of other tasks. Many of us currently rely on our IT staff to collect that information, process it, and prepare reports we need. LogiInsight puts all of that squarely into the hands of staff who need the information in real time. That means we have access to it when we need it rather than when our colleagues have time to provide it to us. The product is only a year old at this point, but it hints of things that are at the “horizon”—the roll-out—stage, and makes us think we’re going to seeing more and more ways to quickly and effectively obtain what we need as the field of web analytics continues to develop and evolve.
- In this first of two screenshots provided by LogiXML, we get a hint of where something like their LogiInsight product can take us. The combination of what Google Analytics or any other web analytics tool can tell us about how many people are visiting our sites and what they ’re doing at a big picture level, LogiInsight starts drilling down into specific information such as “circulation event history.” A simple example: if we follow the links LogiInsight provides, anyone proficient in the system can explore and run reports on something as basic as which collections or items tend to be have the highest overdue rates—which might be useful in helping staff more quickly identify the need for purchasing additional copies of whatever is most heavily used or even starting a conversation about extending loan periods for materials where users need more time with those materials than they normally have.
- When the time comes to report that information to colleagues, LogiInsight again expedites the process by leading individual staff members through the process of creating their own reports on a variety of topics following a series of prompts so that custom-designed reports can be easily compiled and shared. The screenshot here gives only the smallest hint of the wide variety of reports to be generated and the relative simplicity of compiling and producing those reports through a series of prompts. Again, our point here is not to promote a specific product or to take us down a confusing number of paths today: it ’s to remind ourselves that it’s one thing to gather information, and another thing to quickly put it to use in ways that inspire action rather than leaving us buried under information overload.
- Let ’s take our second break here for your questions and your comments. Again, using the live chat box on your screen, please let us know what you’ve been doing with Google Analytics or any other web analytics tools you’ve used before we move on to our final topic today: presentation tools for conveying what you’ve gained through web analytics data. And if you have posted a question we haven’t answered, please bring it back to our attention now.