I remem­ber watch­ing Minor­ity Report back in 2002 with a smile on my face. Like many other techies fas­ci­nated with the future, I was so intrigued by the “ges­tural inter­face” nav­i­ga­tion tech­niques that Tom Cruise was using (mov­ing those mul­ti­ple screens around!) and when and how they would one day become a real­ity. Couldn’t wait to live in a world where diverse data sets are con­nected and aggre­gated to pro­vide a response to our inter­ests.  Twelve years later, the mat­u­ra­tion of the tablet mar­ket, sig­nif­i­cant advances in wear­able and mobile tech­nol­ogy, voice-directed search, and inno­va­tions like Google Glass por­tend a near future where answers are pro­vided to ques­tions we express by look­ing, point­ing, or merely think­ing. And though search opti­miza­tion as an indus­try has been around 20 or 30 years, back to the early days of the Web, the industry’s now matured to the point where rela­tion­ships, con­cepts, and descrip­tive object-aligned meta­data is dri­ving the next wave of infor­ma­tion retrieval. As a mar­keter, it’s impor­tant to mas­ter Seman­tic Web opti­miza­tion as the next fron­tier in the search mar­ket­ing space.

Google’s Hum­ming­bird algo­rithm ush­ered in the seman­tic search era in a more delib­er­ate and mature way. The Seman­tic Web was defined dur­ing the last cen­tury. More­over, the effort con­tin­ued through work done by the World Wide Web Con­sor­tium (W3C) and Schema​.org com­mu­ni­ties. As a result, search engines have now amassed enough data to be able to con­sis­tently deliver results through struc­tured data sets. What we see today in global search results is still a small glimpse of what’s to come when struc­tured data is rolled out more fully.

Prior to Hum­ming­bird, key­word strings typ­i­cally drove results via unstruc­tured path­ways. In turn, the struc­tured author­ship of arti­cles has pushed its way into the entity-based search algo­rithm, dri­ven by RDF and schema markup. Google’s shift to “entity search” has maneu­vered our focus to schema, snip­pets, and Open­Graph. If there’s one thing Hum­ming­bird has taught us, it’s that the age of Seman­tic Web opti­miza­tion dri­ven by struc­tured author­ing is now here.

At the Search Mar­ket­ing Expo in March, Google’s Matt Cutts con­firmed the search engine’s Author Rank (AR) is cur­rently in place. Spec­u­la­tion per­sists about whether AR is being used as a con­firm­ing sig­nal or as a rank­ing fac­tor, and what strength of author influ­ence is needed to impact results. The take­away though is that SEO, social, and Web devel­op­ment teams shouldn’t ignore struc­tured authorship.

So far, anec­do­tal research has shown a lack of even-handedness when it comes to admin­is­ter­ing com­mon author­ship stan­dards. As a result, we see a reduc­tion in “first class author­ship” in the dis­play of SERP rich snip­pets. How­ever, we’re in a nascent period, folks. There will be more refine­ment of struc­tured author­ing, and this next era will be one where we move away from strict human read­abil­ity toward pre­cise machine read­abil­ity. From an organic SEO stand­point, seman­tic search implies

  1. A depar­ture from a focus on key­word sets to intent/context/semantic tactics
  2. A move from strict rank­ing focus onto traf­fic focus
  3. Focus­ing more on the audi­ence needs and engag­ing con­tent than on spe­cific key­word usage
  4. Voice and mobile search becom­ing even more reliant on data struc­tured correctly
  5. Local­iza­tion and per­son­al­iza­tion that pushes SERP rank­ing to obsolescence

Search mar­keters have been adapt­ing their strate­gies to account for implicit sig­nals such as geolo­ca­tion and search his­tory, and with good rea­son. IP-specific search results have become the norm since the Hum­ming­bird release. We’re no longer tied to stand-alone SEO for each prop­erty. Our global search strate­gies must pro­vide con­text through an ecosys­tem that includes third-party pub­li­ca­tions, social activ­ity, and other con­tex­tual signals.

The indus­try is migrat­ing from an XML data-retrieval mode, one that is seri­al­ized, to a seman­tic mode, which is “graph” based. Linked data and con­tent has always pro­vided con­text for spi­ders to inter­pret and assem­ble. How­ever, struc­tured author­ing takes it step fur­ther, enabling engines to eas­ily index and attribute con­tent while it’s dis­trib­uted across mul­ti­ple prop­er­ties. With­out Seman­tic Web opti­miza­tion through struc­tured author­ing, con­text is miss­ing, which makes your assets harder to find. And this stan­dard and expec­ta­tion will only progress.

Today, we ask, “How do I cre­ate rel­e­vant enti­ties that pro­vide data in response to a well-refined query seek­ing nar­row results?” Seman­tic Web opti­miza­tion is the answer.

I’ll talk more about how to deploy Seman­tic Web opti­miza­tion in future posts.