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SEO State of the Union 2015
- 1. The SEO State of the Union Anirvan Chatterjee • Brian Turner • Leslie Yuan Clinical & Translational Science Institute University of California, San Francisco
- 7. Agenda 1. Why SEO works 2. How we tested SEO for 52 websites 3. The SEO leaderboard 4. Three things we learned from the results 5. How to boost your rankings
- 8. 1. WHY SEO WORKS
- 10. Back in 2010…
- 11. Back in 2010…
- 12. Back in 2010…
- 13. Back in 2010…
- 14. UCSF Profiles launch promotion • Email every single member of faculty • Postcards to faculty • Free iPad contest
- 15. UCSF Profiles traffic October 2010 • 5,185 visits July 2015 • 97,864 visits
- 16. UCSF Profiles visits, 2010-2015 - 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 Daily visits to UCSF Profiles
- 17. UCSF Profiles visits, 2010-2015 - 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 Daily visits to UCSF Profiles search vs. everything else
- 18. The dream Latina breast cancer
- 19. The dream
- 20. The dream
- 21. The dream !
- 22. The theory Photo: Joseph Wu
- 23. The reality Photo: Jim Deane
- 24. How search-driven users navigate People search for names, e.g.: • kristine yaffe • lawrence fong ucsf • eric vittinghoff • aaron fields ucsf • jack taunton • aimee kao ucsf • joe derisi • abul abbas
- 25. How search-driven users navigate
- 26. How search-driven users navigate
- 27. Homepage vs. search engine Visits that start off on our home page… Visits that go from search engine to a profile…
- 28. Homepage vs. search engine Visits that start off on our home page… • 68% look at 2+ pages per visit • n = 3,222 in July 2015 • 2,190 visits of 2+ pages in July 2015 Visits that go from search engine to a profile… • 18% look at 2+ pages in a visit • n = 92,743 in July 2015 • 16,245 visits of 2+ pages in July 2015
- 29. Why does SEO matter? • Usage • Usage • Usage
- 30. UCSF Profiles search traffic, 2010-15 - 500 1,000 1,500 2,000 2,500 3,000 Visits per day from search engines
- 31. We think we’re doing great
- 32. But we’re not alone
- 33. 2. HOW WE RANKED 52 WEBSITES ON THEIR SEO PERFORMANCE
- 34. Inclusion criteria We picked 52 research networking sites that are… • Associated with a single institution – exclude trade groups, collaborations, etc. • Based in a majority English-language locale – exclude France, Germany, etc. • Accessible to the public and search engines – exclude systems behind a firewall • Running on a host on port 80 – exclude 54.213.177.247 or hostname.edu:8080
- 35. Institutions Albert Einstein College of Medicine Arizona State University Boston University Case Western Reserve University Clinical Translational Science Institute at Children's National Cornell Duke University Georgia Regents University Harvard University Indiana University Johns Hopkins University Michigan State University Montana State University Northern Arizona University Northwestern Northwestern University Ohio State University Oregon Health & Science University Penn State Scripps Research Institute Stanford University Temple University Texas A&M Thomas Jefferson University University of Arizona University of California, Davis University of California, San Diego University of California, San Francisco University of Colorado Boulder University of Colorado Denver University of Florida University of Hawai‘i University of Illinois - Chicago University of Iowa University of Maryland-Baltimore University of Massachusetts University of Melbourne University of Miami University of Minnesota University of Montana University of Nebraska University of Nevada, Las Vegas University of Nevada, Reno University of Pennsylvania University of Rochester University of South Africa University of Southern California University of Utah Wake Forest Washington State University Wayne State University Western Michigan University
- 36. Platforms • 21 SciVal Experts • 13 VIVO • 11 Profiles RNS • 4 Elsevier Pure • 3 Custom front-ends
- 37. Methodology, step 1 of 4 Get a list of ~every profile
- 38. Methodology, step 2 of 4 Pick 500 random users per site
- 39. Methodology, step 3 of 4 Search Google for “name institution”
- 40. Methodology, step 3 of 4 How we picked institution names 1. First, look at the domain name of the homepage for the institution – profiles.ucsf.edu → www.ucsf.edu → ucsf – vivo.experts.scival.com/indiana → www.iu.edu → iu 2. If the site’s not on the main domain, and the RNS URL includes a common variation of the name, use that – vivo.experts.scival.com/indiana → indiana 3. If #1 and #2 give different results, use both (with OR) – indiana OR iu
- 41. Methodology, step 4 of 4: Record the search ranking position #1
- 42. Methodology, step 4 of 4: Why only consider the top 3 results? The first 3 organic search results made up 71% of all non-mobile Google organic clicks. Source: “Google Organic Click-Through Rates in 2014” Philip Petrescu, Advanced Web Ranking https://moz.com/blog/google-organic-click-through-rates-in-2014
- 43. Methodology • Looked at search rankings for 24,583 profile pages across 52 sites • Used the RankTank keyword rank checker tool to automate the process
- 44. 3. THE RESULTS
- 45. How we measured What % of a site’s profiles appear in the top 3 Google results for “First Last Institution”? (e.g. “brian turner ucsf”
- 46. The winners! 1. findanexpert.unimelb.edu.au (95%) 2. profiles.umassmed.edu (91%) 3. profiles.ucsf.edu (88%) 4. vivo.med.cornell.edu (81%) 5. profiles.ucdenver.edu (76%) 6. profiles.bu.edu (74%) 7. connects.catalyst.harvard.edu (72%) 8. gru.pure.elsevier.com (67%) 9. experts.umn.edu (64%) 10. profiles.psu.edu (63%)
- 47. Between 25-60% 11. scholars.opb.msu.edu (59%) 12. vivo.scholars.northwestern.edu (55%) 13. scholars.northwestern.edu (55%) 14. ohiostate.pure.elsevier.com (54%) 15. experts.scival.com/unisa (45%) 16. experts.scival.com/ctsicn (44%) 17. profiles.ucsd.edu (42%) 18. vivo.colorado.edu (42%) 19. profiles.stanford.edu (38%) 20. profiles.tsi.wakehealth.edu (38%) 21. umaryland.pure.elsevier.com (37%) 22. www.icts.uiowa.edu (35%) 23. profiles.sc-ctsi.org (35%) 24. experts.scival.com/uic (30%) 25. hawaii.vivo.ctr-in.org (27%)
- 48. 26. experts.scival.com/cwru (23%) 27. profiles.jefferson.edu (23%) 28. experts.scival.com/jhu (22%) 29. vivo.scripps.edu (20%) 30. unlv.vivo.ctr-in.org (20%) 31. experts.scival.com/wayneresearchconnect (19%) 32. experts.scival.com/wsu (18%) 33. vivo.upenn.edu (17%) 34. umt.vivo.ctr-in.org (16%) 35. experts.scival.com/wmich (15%) 36. scholars.duke.edu (15%) 37. www.urmc.rochester.edu (15%) Between 15-25%
- 49. Under 15% 38. vivo.ufl.edu (14%) 39. msu.vivo.ctr-in.org (13%) 40. experts.scival.com/nebraska (13%) 41. unr.vivo.ctr-in.org (12%) 42. temple.pure.elsevier.com (11%) 43. experts.scival.com/nau (11%) 44. experts.scival.com/arizona (10%) 45. experts.scival.com/asu (9%) 46. experts.scival.com/ucdavis (9%) 47. experts.scival.com/miami (7%) 48. experts.scival.com/einstein (4%) 49. experts.scival.com/utah (2%) 50. vivo.library.tamu.edu (1%) 51. experts.scival.com/indiana (1%) 52. experts.scival.com/ohsu (1%)
- 51. #1. Use your own domain Institutional domain? (e.g. vivo.cornell.edu) • average score = 49% Unrelated domain? (e.g. experts.scival.com/asu) • average score = 21%
- 52. #2: Pick the right software 1. findanexpert.unimelb.edu.au Custom 2. profiles.umassmed.edu Profiles 3. profiles.ucsf.edu Profiles 4. vivo.med.cornell.edu Vivo 5. profiles.ucdenver.edu Profiles 6. profiles.bu.edu Profiles 7. connects.catalyst.harvard.edu Profiles 8. gru.pure.elsevier.com Elsevier Pure 9. experts.umn.edu SciVal Experts 10. profiles.psu.edu Profiles
- 53. #2. Pick the right software • Custom software average score = 56% • Profiles average score = 56% • Elsevier Pure average score = 42% • Vivo average score = 26% • SciVal Experts average score = 23%
- 54. #1+2: Software and domain • SciVal Experts + institutional domain average score = 59% • Profiles + institutional domain average score = 58% • Custom + institutional domain average score = 56% • Elsevier Pure + unrelated domain average score = 42% • Profiles + unrelated domain average score = 35% • Vivo + institutional domain average score = 31% • Vivo + unrelated domain average score = 18% • SciVal Experts + unrelated domain average score = 14%
- 55. #3. Get incoming links <a href="http://your.site.here/">
- 56. #3. Get incoming links: inside pagerank
- 57. #3. Get incoming links: search engine ranking factors
- 58. #3. Get incoming links: search engine ranking factors Links to the page Links to the subdomain or domain
- 59. #3. Get incoming links: linking root domains A linking root domain is a domain under a public suffix that includes links to your sites. • *.cnn.com CNN • *.ox.ac.uk Oxford University • *.anoka.k12.ca.us Anoka School District, Minn.
- 60. #3. Get incoming links: the top 3 sites 1. findanexpert.unimelb.edu.au (95%) 2. profiles.umassmed.edu (91%) 3. profiles.ucsf.edu (88%)
- 61. #3. Get incoming links findanexpert.unimelb.edu.au has 488 linking root domains: • newscientist.com • f1000.com • anl.gov • duraspace.org • electionwatch.edu.au • and 483 more root domains…
- 62. #3. Get incoming links profiles.umassmed.edu has 249 linking root domains: • en.wikipedia.org • grants.nih.gov • theguardian.com • bloomberg.com • nih.gov • and 244 more root domains…
- 63. #3. Get incoming links profiles.ucsf.edu has 858 linking root domains: • sourceforge.net • harvard.edu • ucsf.edu • universityofcalifornia.edu • ucsfhealth.org • and 853 more root domains…
- 64. #3. Get incoming links: why (diverse) incoming links matters 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 1 10 100 1000 %of"Name[School]"intop3Googlesearchresults # Linking Root Domains (log) Linking Root Domain Count vs. Google Top 3 results %
- 65. 5. HOW TO MAKE PEOPLE LINK TO YOUR SITE Photo: Ryan McFarland
- 66. 1. Be worthy of love • Most people care about people, not generic information-finding site • Make profile pages beautiful and chock-full of information, so people will want to link to them
- 67. 2. Establish benchmarks • Install Google Analytics on every page • Learn how to use it – read Web Analytics 2.0 by Avinash Kaushik
- 68. 3. Get good with Google • Add a sitemap.xml (sitemaps.org) • Register on Google Webmaster Tools to: • register your sitemap • catch indexing errors early • link to your Google Analytics account
- 69. 4. Look good with Google <title> tag <meta name="description"> Schema.org people metadata URL
- 70. Virtuous cycle 1. Some people link to you 2. You show up on Google 3. More people see you, and link to you 4. You do even better on Google
- 71. Show the world you’re worthy of love • Get campus sites to link to your homepage as a trusted campus resource • Get campus sites to link to individual profiles from departmental profiles, news stories, directory, etc. • Encourage reuse of your data via APIs, and ask for a link back as attribution
- 72. If that works… • Some researchers will link to their profile pages on their own sites • Some blogs and social media will link to your profile pages as authoritative sources • Some departments may link to your profiles because your data is more current than theirs
- 73. The state of the union is strong.
- 74. The state of the union is weak.
- 75. The state of the union is mixed.
- 77. Thank you. Anirvan Chatterjee· @anirvan – profiles.ucsf.edu/anirvan.chatterjee – anirvan.chatterjee@ucsf.edu Brian Turner – profiles.ucsf.edu/brian.turner – brian.turner@ucsf.edu Leslie Yuan· @leslieyuan – profiles.ucsf.edu/leslie.yuan – leslie.yuan@ucsf.edu
Hinweis der Redaktion
- Welcome… [Public domain image : https://commons.wikimedia.org/wiki/File:2011_State_of_the_Union_fisheye.jpg]
- My fellow RNS implementors — Some of us are part of the VIVO community, others from the Profiles community, yet others are Elsevier customers. Some of us even built our own homegrown systems. But over half a decade into our work, we’ve come together, putting aside our differences, to help advance the state of research networking for all. [Public domain image : https://commons.wikimedia.org/wiki/File:2011_State_of_the_Union_fisheye.jpg]
- When we began our journey together, many of our leaders believed that “if you build it, they will come.” But time has shown that our users are fickle, that they will ignore our repeated pleas to visit our website, leaving our sites empty, and sadly bereft of users. [Public domain image: http://pixabay.com/en/grandstand-audience-sit-chairs-334476/]
- And what’s up with users, anyway? We built them such powerful tools, which they keep choosing to ignore… [image: NIH Project Reporter screenshot]
- Instead, they turn to Google — leaving us a choice: will we go where our users are, or will we wait for them to someday abandon Google and return to the fold?
- Because we know they’re going to come back to interfaces that look like this, right? [image: NIH Project Reporter screenshot]
- Over the next half hour, we’re going to talk about three things. First, we’re going to dive into search engine optimization, or SEO, how it works, and how it helped us grow usage of your system by an order of magnitude Second, we’re going to describe how we put together the first systematic survey of search engine optimization in the research networking space. Third, we’re going to release the results of our survey looking at 52 different universities. We’re going to name names — and if you stick around, you’ll see how your institution ranks compared to everybody else. Fourth, we’re going to look at three things we learned from these results — what separates the winners and the losers? And finally, we’re going to share 5 things you can do today to help improve your sites search engine rankings Any questions? Let’s begin!
- First, what is search engine optimization, how does it work for research network sites, and why it matters [Public domain image: https://pixabay.com/en/trombone-day-ulm-human-group-51221/]
- I want you to think back to 2010… [Photo: Duracell promotional image, via http://inhabitat.com/leds-light-up-new-years-eve-2010-in-times-square-nyc/
- Barack Obama was President…
- Katy Perry was on the charts…
- A new gadget called the iPad hit the stores…
- And a little university in San Francisco was about to launch its first research network system
- When the site was launched, our team went all in. We had a high muckety-muck email every single member of the faculty. We sent postcards. We were giving away iPads. (We tried to book Katy Perry for the launch, but she was busy that day.) It was the biggest on-campus promotion we had ever done, and it was all coordinated to strike in September 2010
- So in October 2010, a month after this hard-core promotion effort, we had 5,000 visits from on and off campus sources. [Next] By July 2015, that number had grown about 19-fold, to 98,000 visits per month [Next] So where did all this traffic come from?
- So here’s that growth in visits, plotted out over the course of 5 years
- And here it is, split up. That’s a stacked area graph. On the bottom is traffic from other websites, from bookmarks, from marketing campaigns, etc. And on the top, is search engine traffic. [Pause and let folks take that in]
- When we launched our website back in 2010, we had this idea that… [next step] Users would look at the search options [next step] Type in a keyword [next step] And then hit the search button
- And then the search results come up, and you see a list of people …you see a list of people… [next] And you click on one of them
- …and then you land on that researcher’s profile page. And as you look through their profile, you go, “hmm, OK, this is someone I’d love to collaborate with!”
- And then you talk. And soon, there’s a beautiful research collaboration forming, just because you knew how to search for people using a research networking system [Public domain mages by ClkerFreeVectorImages https://pixabay.com/en/stick-male-figure-looking-right-29937/ https://pixabay.com/en/stick-figure-face-look-right-30109/]
- So that was our original expectation —that we can educate our users to come to our site, to run good queries, evaluate options, and make the perfect connection [Creative Commons noncommercial no-derivatives photo: https://www.flickr.com/photos/josephwuorigami/4198854549/
- But that was just theory. In reality, at UCSF, most of our traffic comes through search engines. And that SEO traffic is kind of dumb and messy, but it’s also awesome, and totally blows away our original expectations. [Creative Commons photo: https://flic.kr/p/6cRASC]
- The way SEO works, at least at UCSF, is that searchers will typically search Google for the name of a specific researcher
- Because of our search engine optimization work, UCSF Profiles pages often rank high in the results, so users click that link
- The user arrives on the page, looks around — but more often than not, they leave immediately thereafter, without doing any of the deep interaction or reading we had in mind.
- So again, we have two groups. The first group is smart and thoughtful, they come to our homepage, they run smart searches, they evaluate the data, etc. The second group is a drive-by user. They randomly pop in via Google, take a look around, and usually leave right afterwards
- And you see the differences when we look at the numbers. For the first group, 68%, 2 out of 3 of them, look at more than one page of the site While for the second group, only 18% get beyond the first page that they look at. Which kind of makes sense. They’ve come from a site like Google, they just wanted to know about someone, maybe they glance at the page, and then leave. The first group is obviously way more hard core. But things look different when we consider the volume of traffic from the two sources. In July, there were only three thousand people in the first group, so we had about 2,000 of those users who went two or more pages into the site. But in the second group, n is close to 100,000 visits a month, so 18% of that is huge, 16,000 people a month. Even when it comes to engaged usersmost of our engaged users come out of the pool of drive-by SEO users.
- And that’s why SEO matters
- SEO has really help drive UCSF Profiles traffic over the years…
- And we think we’re doing pretty great…
- …there’s also a whole community of RNS implementors who have been investing in search engine optimization, and seeing the rewards
- So we decided that we needed to evaluate how we—and the rest of the research networking community—is actually doing on search engine optimization [Public domain image: https://pixabay.com/en/ruler-dimension-measure-684005/]
- We looked at 52 public English-language research networking sites run by a single university or organization, featuring profiles of it’s own people.
- Here are the 52 institutions we looked at. If you’re from one of these institutions, could you raise your hands?
- These 52 institutions represent a wide range of platforms: VIVO, Profiles, SciVal Experts, Elsevier Pure, and even some homegrown systems
- First, for every single site, we started off by getting a list of every profile on the system, or as many as we can get. In some cases, we got the list via R2R, which is based on Dave Eichmann’s CTSAsearch at the University of Iowa. In other cases, we were able to look at a sitemap.xml file In other cases, we ran a search on the site, to get back a list of every person in a system, and just scraped that list No matter the mechanism, we ended up with a list of names and URLs for pretty much every single profile in the system
- Second, We then went through and picked out 500 random users from every single system. In cases where there were fewer than 500 total profiles in the system, we just selected all of them.
- Third, for each of the names, we basically searched Google for the name of the person, and then the name of that person’s institution We include the institution to be extra-clear about which person we’re looking for. Including the institution name is a common search behavior, according to our web analytics data for UCSF Profiles.
- When we include the name of the institution, we include a common short form. So we’d say “John Doe UCSF,” not “John Doe University of California San Francisco” If you’re interested, here are the gory details for how we actually determined the short form of the name of each institution. But this is boring, so I’m going to skip ahead…
- And finally, for every single name we’re testing, we look at the Google results to see if the profile shows up among the first 3 Google search results
- There are lots of ways to measure SEO success. We picked whether or not a profile page shows up among the first 3 organic — or non-advertising — search results. According to a 2014 analysis by Advanced Web Ranking, if a desktop Google user was going to click on a normal organic (on unpaid) link, 71% of the time they’ll do so on one of the first 3 results. (And according to their latest numbers, that’s even more pronounced on mobile devices.)
- We checked the search rankings of almost 25,000 profile pages across 52 different institutions. We could have done that by hand, but itwould have been incredibly slow and difficult, and Google may have blocked us, so we used a tool called RankTank keyword rank checker tool do do the bulk checking.
- And after doing all that work, we finally have some results to share…
- We’re ranking sites by what percent of a site’s profiles show up in the top 3 results on Google when you search for a person’s name and their institution
- And here are the winners… In first place is the University of Melbourne, then UMass, and UCSF, Cornell, Denver, BU, Harvard, Georgia Regent’s University, University of Minnesota, and Penn State Each one of these schools had their profile pages come up in the top 3 results over 60% of the time. [Hoot and holler! Encourage the audience to clap. If anyone from the institution’s here, ask them to stand up. This is a moment for over-the-top silliness.]
- Next up, we have the folks doing a pretty good job on the SEO front. Is anyone here from these schools? Give them a round of applause!
- Is anyone here from the third group? [Make a joke about them being brave, or chicken, as the case may be]
- And in last place for Google discoverability… Does anyone notice a pattern here? [wait for someone to mention SciVal Experts] Scival Experts seems to have this category almost locked up — but it’s more complicated than that, and I’m going to explain why in a second
- OK, so we have the rankings, but what can we learn from them? [Public domain photo by HamiJeezy @ Pixabay: https://pixabay.com/en/school-chalkboard-pear-hand-colored-172345/]
- First, the single most important takeaway from this exercise is that you if you’re implementing an RNS for an institution, you should always put it under your own domain name, not the domain name of your vendor. Systems that used their own domain name had scores over double that of those who used some other name. Using your institution’s domain is a strong signal to Google that the contents are closely related to that institution.
- Second, we can look at the top software. When we look at the top 10 sites, each of the platforms we looked at shows up at least once. Profiles shows up 6 times, and then 1 slot each for Custom, Vivo, Pure, and SciVal Experts. So while Profiles is obviously a really strong option, it’s actually possible to do pretty well with any one of the packages.
- And here are each of the platforms, broke out by average score. Folks who used either Profiles RNS or a custom system did way better than everyone else, on average. Look at the bottom of the list. SciVal Experts looks pretty bad, huh?
- But here’s what happens when we look at the average score, based on your software and the kind of domain name you use. I see three things here. First, institutions that use SciVal, Profiles, or a custom system on their own domain name did really well on average. But then I look at the top and bottom of the list — and SciVal’s listed in both places. If you’re using SciVal with your own domain name, that’s about as good as it gets. But if you’re using their domain name, it’s about as bad as it gets. And finally, I have to admit, I’m a little confused by VIVO. I don’t understand why, on average, VIVO installations do worse than every other platform. If you have an idea, maybe we can talk about it during the Q&A.
- The third big takeaway from the data is the importance of incoming links. A lot of us here are software people, and this is veering a little bit toward the realm of user engagement and product marketing, but this is incredibly important, and I’m going to show you why.
- Have any of you heard of PageRank? That’s the name for the original algorithm powering Google. Basically, the more links you get from other sites — and particularly other “important” sites — the more “important” you are. PageRank was critical to the original Google algorithm. [Public domain image: https://en.wikipedia.org/wiki/File:PageRanks-Example.svg]
- The Google algorithm has gotten way more complex over the years. But one of the best guides to reverse-engineer how Google’s algorithm works is the Moz search engine ranking factors document, which describes how 122 different factors seem to correlate with Google search rankings.
- Moz looked at 122 different factors, and here are the top factors most predictive of high search rankings, broken out by category. When we look at the top factors, two categories stand out. All those factors labeled in light yellow are all about the volume and diversity of links you’re getting to a given page. And the factors labeled in blue are about the volume and diversity of links you’re getting to other web content on your whole website, or across the rest of your domain.
- Out of the 122 factors, we’re going to look at one in specific —linking root domains. A linking root domain is a domain under a public suffix, like .edu for educational sites, or .ma.us for sites in Massachusetts, etc., which includes ones one or more links to your site. So if my blog has 100 links from cnn.com, and one link from a harvard.edu site, that’s two linking root domains. Does that make sense?
- Here are our top 3 sites. We started digging into how many linking root domains each of the top sites got…
- The University of Melbourne’s reserch profiling site has links from 488 different domains
- The University of Massachusetts Medical School’s Profiles installation has incoming links from 249 sites
- And UCSF’s installation of Profiles has links from 858 different sites
- Here’s what it looks like, plotted out. On the x axis is the number of linking root domains, or basically, the number of distinct sites linking to a website On the y axis is the likelihood that it’s profiles are showing up in the top 3 search results when people search for them We see a very clear correlation between how many diverse people are linking to you, and how well you do in search rankings
- So with all that in mind, we’re dive into how we can get people to link to our sites and click our links on Google [Photo used under Creative Commons Attribution 2.0: https://www.flickr.com/photos/zieak/3360293485]
- First, make sure your content is worthy of love. Your home page is important, but for most sites, people will be way more interested in the individual people profile pages. If your content isn’t useful, then nobody will want to link to it. At UCSF, we include publications, photos, grants, bios, Twitter handles, etc. for researchers. And we work hard to make it as easy to read and browse as possible.
- Next you have to have some kind of analytics package set up, so you know how you’re doing. We recommend Google Analytics.
- Third, make sure your site is getting properly indexed by Google. Use sitemaps, and set up Google Webmaster Tools to see if there are any hiccups in how your site’s seen by Google.
- Fourth, make sure your results look good on Google. And you can control almost every part of this just by tweaking your HTML. [next] The main title should be clean, and include the person’s name [next] if you have a clean, authoritative-looking URL, more people are likely to trust it and link to it [next] you can add extra job title metadata with Schema.org [next] and you can add a short description of the page. In this case, we always say “Name’s profile, publications, research topic, and co-authors”
- There’s a virtuous cycle here. You need to start getting people to link to you. If you do that, more people will see your site, add links of their own, and you’ll do better on Google. And if you keep doing this, your SEO performance just keeps increasing. But you need a way to kickstart this process. And you do that by priming the pump on stage 1.
- Start off by maximizing links to your site from your own campus — because if your friends won’t link to you, nobody else will. And being linked to by folks on your own campus is a signal to Google that you’re legit. The way you do this will vary at each institution, but at UCSF, we worked with a variety of departments and campus-wide websites to make sure they had a link to our RNS on their departmental websites. But we went deeper, building in links to individual profile pages. For example, our campus-wide people directory now always includes links back to that person’s profile page. And every time UCSF.edu publishes a news story about someone, they also link back to their profile page. There’s no silver bullet, but the more of this you do, the stronger signal it is to Google that this is valid and relevant content — otherwise they wouldn’t be linking to you.
- If that works, you may start seeing signs of early traction… All of these are further signals to Google that your pages are relevant, that they’re interesting, that they’re worthy of love
- The research networking community has been at this for years, and I’m pleased to report that the state of RNS SEO union is strong. Our research shows that a range of research networking sites have figured out how to make SEO work for them, often successfully pivoting away from their original plans to make it succeed.
- But the state of the union is also weak. Looking across 52 sites, far too many of them are being run on the assumption that just because you build it, they will come. Some of us are paying good money for sites that fundamentally don’t work, that fundamentally aren’t meeting people’s needs because they’re simply not discoverable, and that’s not OK.
- And in the end, what matters right now isn’t just what we’ve done, but how we help each other as a community. We hope you’ll congratulate our winners, and share best practices, so we can come back next year with better scores for everyone.
- We’re sharing the results of this talk online, and we’d really appreciate it if you’d share it with your teams. So can everyone pull out your phone, and go to bit.ly/rnsseo
- Thank you.