What marketing tactics drive the most traffic to my website?
Which pages on my website are the most popular?
How many visitors have I converted into leads or customers?
Where did my converting visitors come from and go on my website?
How can I improve my website’s speed?
What blog content do my visitors like the most?
a Google Analytics account. If you have a primary Google account that you use for other services like Gmail, Google Drive, Google Calendar, Google+, or YouTube, then you should set up your Google Analytics using that Google account. Or you will need to create a new one.
Big tip: don’t let your anyone (your web designer, web developer, web host, SEO person, etc.) create your website’s Google Analytics account under their own Google account so they can “manage” it for you. If you and this person part ways, they will take your Google Analytics data with them, and you will have to start all over.
go to Google Analytics and click the Sign into Google Analytics button.
Google Analytics offers hierarchies to organize your account. You can have up to 100 Google Analytics accounts under one Google account. You can have up to 50 website properties under one Google Analytics account. You can have up to 25 views under one website property.
Data Architecture: I was an active member of the RBMS Bibliographic Standards Committee, the ARLIS/NA Artists’ Books Thesaurus project, and an OCLC initiative on Web archiving metadata. I used to contribute to development of international schemas, controlled vocabularies, and content standards for free, as a service activity. Meanwhile, I could have earned $134,677 as a data architect.
Web Development: I developed applications and customized discovery layers to help library patrons find resources. I learned several markup and scripting languages in order to take on this extra work for the library, in the hot-hot pursuit of grant funding to list on my CV. I could have earned $88,285 as a front-end developer (the folks who use HTML, CSS, and JavaScript to build the parts of a website that you see), or $101,021 as a back-end developer (the folks who work with APIs, and transfer data to/from databases).
Data Engineering: Libraries are constantly integrating data from publishers, digitization projects, legacy catalogs, union catalogs, and more. I became a whizz at data wrangling and transformation. I developed countless data pipelines and ETL processes to combine disparate data streams. I should have been earning $112,935 as a data engineer.
User Experience Research: To inform cataloging guidelines, and to better design catalogs and finding aids to meet user needs, I spent a lot of time in libraries researching information-seeking behaviors. I became intimately familiar with Google Analytics and Google Tag Manager. I ran focus groups, conducted usability tests, and led card-sorting exercises in order to gather insights on how to improve our discovery interfaces and their navigation. As a user experience researcher outside of libraries, I could have earned $140,985.
Fundraising: As a special collections professional, I was routinely asked to give tours and host events, with the goal of building relationships with donors. I cultivated skills in storytelling, and learned to quickly craft narratives about my projects’ efficacy and impact. As an academic and a gig worker, I helped develop numerous grant applications, and served as a principal investigator on several large-sum projects. Overall, I honed techniques that are crucial to fundraising and philanthropy. In the nonprofit sector, I could have earned between $98,765 as a development managerand $102,546 as a director of development.
Project Management: In libraries, I never had less than five major projects going at once. I oversaw several large-scale database and website migrations, making sure that each of my team members’ contributions were completed in sequence and on time, while I myself served as a project contributor. In the tech sector, I could have been working as a project manager — someone whose sole job is to hold others accountable to the development timeline — and earned $87,086.
Technology is a branch of moral philosophy, not of science
The process of making technology is design
Design is a branch of moral philosophy, not of a science
System design reflects the designer’s values and the cultural content
Andreas Orphanides
Fulbright BOYD
Byzantine history professor Bulgarian – all that is 200 years old is politics, not history
Access, privacy, equity, values for the prof organization ARLD.
Mike Monteiro
This is how bad design makes it out into the world, not due to mailcioius intent, but whith nbo intent at all
Cody Hanson
Our expertise, our service ethic, and our values remain our greatest strengths. But for us to have the impat we seek into the lives of our users, we must encode our services and our values in to the software
Ethical design.
Design interprets the world to crate useful objects. Ethical design closes the loop, imaging how those object will affect the world.
A good science fiction story should be able to predict not the automobile, ut the traffics jam. Frederic Pohl
Victor Papanek The designer’s social and moral judgement must be brought into play long before she begins to design.
We need to fear the consequences of our work more than we love the cleverness of our ideas Mike Monteiro
Analytics
Qual and quan data – lirarainas love data, usage, ILL, course reserves, data – QQLM.
IDEO – the goal of design research isn’t to collect data, I tis to synthesize information and provide insight and guidance that leads to action.
Google Analytics: the trade off. besides privacy concners. sometimes data and analytics is the only thing we can see.
Frank CHimero – remove a person;s humanity and she is just a curiosity, a pinpoint on a map, a line in a list, an entry in a dbase. a person turns into a granular but of information.
by designing for yourself or your team, you are potentially building discrimination right into your product Erica Hall.
Search algorithms.
what is relevance. the relevance of the ranking algorithm. for whom (what patron). crummy searches.
reckless associsations – made by humans or computers – can do very real harm especially when they appear in supposedly neutral environments.
Donna Lanclos and Andrew Asher Ethonography should be core to the business of the library.
technology as information ecology. co-evolve. prepare to start asking questions to see the effect of our design choices.
ethnography of library: touch point tours – a student to give a tour to the librarians or draw a map of the library , give a sense what spaces they use, what is important. ethnographish
Q from the audience: if instructors warn against Google and Wikipedia and steer students to library and dbases, how do you now warn about the perils of the dbases bias? A: put fires down, and systematically, try to build into existing initiatives: bi-annual magazine, as many places as can
++++++++++++++
On behalf of the 2018 LITA Library Technology Forum Committee, I am pleased to notify you that your proposal, “Virtual Reality (VR) and Augmented Reality (AR) for Library Orientation: A Scalable Approach to Implementing VR/AR/MR in Education”, has been accepted for presentation at the 2018 LITA Library Technology Forum in Minneapolis, Minnesota (November 8-10).
++++++++++++++
Mark Gill and Plamen Miltenoff will participate in a round table discussion Friday. November 9, 3:30PM at Haytt Regency, Minneapolis, MN. We will stream live on Facebook: https://www.facebook.com/InforMediaServices/
U of MN has a person, whose entire job is to read and negotiate contracts with vendors. No resources, not comfortable to negotiate contracts and vendors use this.
If you can’t open it, you don’t own it. if it is not ours… we don’t get what we don’t ask for.
libraries are now developing plenty, but if something is brought in, so stop analytics over people. Google Analytics collects data, which is very valuable for students. bring coherent rink of services around students and show money saving. it is not possible to make a number of copyright savings. collecting such data must be in the library, not outside. Data that is collected, will be put to use. Data that is collected, will be put to uses that challenge library values. Data puts people at risk. anonymized data is not anonymous. rethink our relationship to data. data sensitivity is contextual.
stop requiring MLSs for a lot of position. not PhDs in English, but people with specific skills.
perspective taking does not help you understand what others want. connection to tech. user testing – personas (imagining one’s perspective). we need to ask, better employ the people we want to understand. in regard of this, our profession is worse then other professions.
pay more is important to restore value of the profession.
Stanford – Folio, Cornell, Duke and several others. https://www.folio.org/ Alma too locked up for Stanford.
Easy Proxy for Alma Primo
Voyager to OCLC. Archive space from in-house to vendor. Migration
Polaris, payments, scheduling, PC sign up. Symphony, but discussing migration to Polaris to share ILS. COntent Diem. EasyProxy, from Millenium no Discovery Layer to Koha and EDS. ILL.
WMS to Alma. Illinois State – CARLY – from Voyager to Alma Primo. COntent Diem, Dynex to Koha.
Princeton: Voyager, migrating Alma and FOlio. Ex Libris. Finances migrate to PeopleSoft. SFX. Intota
RFPs – Request for Proposals stage. cloud and self-hosted bid.
Data Preparation. all data is standard, consistent. divorce package for vendors (preparing data to be exported (~10K). the less to migrate, the better, so prioritize chunks of data (clean up the data)
Data. overwhelming for the non-tech services. so a story is welcome. Design and Admin background, not librarian background, big picture, being not a librarian helps not stuck with the manusha (particular records)
teams and committees – how to compile a great team. who makes the decision. ORCHID integration. Blog or OneNote place to share information. touch base with everyone before they come to the meeting. the preplanning makes large meetings more productive.
+++++++++++++
Using Design Thinking — Do we really want a makerspace?
Anne Shelley, Illinois State University, Milner Library
Julie Murphy, Illinois State University, Milner Library
Paul Unsbee, Illinois State University, Milner Library
makerbot replicator 3d printer
one touch studio 4 ready record studio. data analytics + several rooms to schedule.
social media – call centers in Iowa, where agriculture is expected. not an awesome job. http://sched.co/D7pQ
Caleris as featured in New York Times.
Sarah Roberts talk about psychological effects of working at Caleris; it resembles the effect of air strikes on the drone pilots
Drupal based. Google Analytics like. Bookmarks. objects list can be shared through social media, email, etc. Pachyderm used to have timeline like Islandora. still images, audio, video
how data is produced, collected and analyzed. make accessible all kind of data and info
ask good q/s and find good answers, share finding in meaningful ways. this is where digital literacy overshadows information literacy and this the fact that SCSU library does not understand; besides teaching students how to find and evaluate data, I also teach them how to communicate effectively using electronic tools.
connecting people tools and resources and making it easier for everybody. building collaborative, open and interdisciplinary
robust data computational literates. developing workshops, project and events to practice new skills. to position the library as the interdisciplinary nexus
what are data: definition. items of information, facts, traces of content and form. higher level, conception discussion about data in terms of social effects: matadata capturing information about the world, social political and economic changes. move away the mystic conceptions about data. nothing objective about data.
the emergence of IoT – digital meets physical. cyber physical systems. smart objects driven by industry. . proliferation of sensor and device – smart devices.
what does privacy looks like ? what is netneutrality when IoT? library must restructure : collaborate across institutions about collections of data in opien and participatory ways. put IoT in the hands of make and break things (she is maker space aficionado)
make and break things hackathons – use cheap devices such as Arduino and Pi.
data literacy programs with higher level conception exploration; libraries empower the campus in data collection. data science norms, store and share data to existing repositories and even catalogs. commercial services to store and connect data, but very restrictive and this is why libraries must be involved.
linked data and dark data
linked data – draw connections around online data most of the data are locked. linked data uses metadata to link related information in ways computers can understand.
libraries take advantage of link data. link data opportunity for semantics, natural language processing etc. if hidden data is relative to our communities, it is a library responsibility to provide it. community data practitioners
dark data
massive data, which cannot be analyzed by relational processing. data not yield significant findings. might be valuable for researchers: one persons trash is another persons’ treasure. preserving data and providing access to info. collaborate with researchers across disciplines and assist decide what is worth keeping and what discarding and how to study.
rich learning experience working with lined and dark data enable fresh perspective and learning how to work with data architecture. data literacy programming.
in context of data is different from open source and open projects. the social side of data science . advising researchers on navigation data, ethical compilations.
open science movement .https://cos.io/ pushing beyond licences and reframe, position ourselves as collaborators
analysis and publishing ; use tools that can be shared and include data, code and executable files.
reproducibility and contestability https://www.lib.ncsu.edu/events/series/summer-of-open-science
In the age of Big Data, there is an abundance of free or cheap data sources available to libraries about their users’ behavior across the many components that make up their web presence. Data from vendors, data from Google Analytics or other third-party tracking software, and data from user testing are all things libraries have access to at little or no cost. However, just like many students can become overloaded when they do not know how to navigate the many information sources available to them, many libraries can become overloaded by the continuous stream of data pouring in from these sources. This session will aim to help librarians understand 1) what sorts of data their library already has (or easily could have) access to about how their users use their various web tools, 2) what that data can and cannot tell them, and 3) how to use the datasets they are collecting in a holistic manner to help them make design decisions. The presentation will feature examples from the presenters’ own experience of incorporating user data in decisions related to design the Bethel University Libraries’ web presence.
silos, IT barrier, focusing on student success, retention, server space is cheap, if
promotion and tenure for faculty can include incentive to work with the librarian
lack of fear, changing the mindset.
deep collaboration both within and cross-consortia
don’t rely on vendor solutions. changing mindset
development = oppty (versus development as “work”)
private higher education is PALNI
3d virtual picture of disastrous areas. unlock the digital information to be digitally accessible to all people who might be interested.
they opened the maps of Katmandu for the local community and they were coming up with the strategies to recover. democracy in action
i can’t stop thinking that the keynote speaker efforts are mere follow up of what Naomi Klein explains in her Shock Doctrine: http://www.naomiklein.org/shock-doctrine: a government country seeks reasons to destroy another country or area and then NGOs from the same country go to remedy the disasters
A question from a librarian from the U about the use of drones. My note: why did the SCSU library have to give up its drone?
Douglas County Library model. too resource intensive to continue
Marmot Library Network
ILS integrated library system – shared with other counties, same sever for the entire consortium. they have a programmer, viewfind, open source, discovery player, he customized viewfind community to viewfind plus. instead of using the ILS public access catalogue, they are using the Vufind interface
Caiifa Enki. public library – single access collection. they purchase ebooks from the publisher and they are using also the viewfind interface. but not integrated with the library catalogs. Kansas public library went from OverDrive to Viewfind. CA State library is funding for the time being this effort.
types of content – publisher will not understand issue, which clear for librarians
PDF and epub formats
purchase content –
title by title selection – academia is tired of selections. although it is intended to buy also collections
library – owned ( and shared collections)
host content from libraries – papers in academic lib, genealogy in pub lib.
options in license models .
e resource content. not only ebooks, after it is taken care of, add other types of digital objects.
instead of replicate, replacement of the commercial aggregators,
Amigos Shelf interface is the product of the presenter
instead of having a young reader collection as SCSU has on the third floor, an academic library is outsourcing through AMigos shelf ebooks for young readers
Harper Collins is too cumbersome and the reason to avoid working with them.
security issues. some of the material sent over ftp and immediately moved to sftp
decisions – use of internal resources only, if now – amazon
programmer used for the pilot. contracted programmers. lack of the ability to see the large picture. eventually hired a full time person, instead of outsourcing. RDA compliant MARC.
ONIX, spreadsheet MARC.
Decision about who to start with : public or academic.
attempt to keep pricing down –
own agreement with the customers, separate from the agreement with the Publisher
current development: web-based online reading, shared-consortial collections and SIP2 authentication
social media has a strong return on investment (ROI) – how to
Social media data is the collected information from social networks that show how users share, view or engage with your content or profiles. These numbers, percentages and statistics provide better insights into your social media strategy.
social media analytics to make sense of the raw information.
media data as the ingredients to your meal and the analysis as your recipe. Without the recipe, you wouldn’t know what to make or how to cook it.
Some of the raw social media data can include:
Shares
Likes
Mentions
Impressions
Hashtag usage
URL clicks
Keyword analysis
New followers
Comments
Key performance indicators (KPIs) are the various business metrics used to measure and analyze certain aspects of your business. Social media KPIs are the metrics that likely factor into your social media ROI.
Facebook business page, you can analyze some KPIs within the social network. The most essential Facebook metrics include (see entire article).
Twitter Analytics
Engagement Rate: Total link clicks, Retweets, favorites and replies on your Tweet divided by total impressions.
Followers: Total number of Twitter followers.
Link Clicks: Total number of URL and hashtag links clicked.
Mentions: How many times your @username was mentioned by others.
Profile Visits: Total Twitter profile visits.
Replies: How many times people replied to your Tweets.
Retweets: Total Retweets received by others.
Tweet Impressions: Total of times your Tweet has been viewed whether it was clicked or not.
Tweets: How many Tweets you’ve posted.
LinkedIn Analytics
Here are the top LinkedIn metrics:
Clicks: Total clicks on a post, company name or logo.
Engagement: Total interactions divided by number of impressions.
Followers: Total number of new followers through a sponsored update.
Impressions: Total times your update was visible to other users.
Interactions: Total number of comments, likes, comments and shares.
Google Analytics
Average Session Duration: Average session times users spend on your site.
Bounce Rate: Percentage of users leaving your site after one page view.
New Users: Total number of new users coming to your site for the first time.
Pages / Session: Average number of pages a user views each session.
Pageviews: Number of pages loaded or reloaded in a browser.
Sessions: Total times when users are active on your site.
need to decipher what’s most important.
If you wanted to track audience growth on Facebook, consider engagement rates, new followers, Post reach and organic Likes.
For example, if you launched a social media campaign, track data that highlights your ROI. According to Mashable, your ROI cycle for a social media campaign should be set up in three stages:
Launch
Management
Optimization
41% of companies and agencies no clue about their social media financial impact. It’s nearly impossible to figure out data overnight. Instead, it takes months of tracking to ensure your future business decisions are valuable.
