turbo-charge your search traffic with structured data

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Post on 09-May-2015

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In this presentation, given at On The Edge Manchester 2013, we present structured data mark-up and how using it can deliver semantic meaning to your search results; plus additional, attractive decision-criteria that seriously boosts your search traffic!

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  • 1.Supercharge SearchH o w U sin g S tru c tu re d D ata C a n B o o st Yo u rTra ffic w ith R ich S n ip p e tsb y N ich o la S to tt

2. Its a sim p le ch a n g e to th e d isp lay o fse a rch re su lts, y e t o u r ex p e rim e n tsh av e sh o w n th a t u se rs fin d th e n e wd a ta v a lu a b le - O th a r H a n sso n e t a l- W e b m a ste r C e n tra l B lo g 2 0 0 9 3. C o v e rin gW h at is stru c tu re d d ata ?H o w d o e s it lo o k in se a rch ?H o w d o I im p le m e n t it?A n y to o ls to h e lp ? 4. StructuredDATA TYPES 5. How does thattranslateto search? 6. RichSnippets 7. re a lly ? y o u c a n a d d -u p ? 8. ThatsAwesome! 9. prepTimeco o k T im e 10. M ic ro d a ta is a nH T M L sp e cific a tio n u se d to n e stse m a n tics w ith in e x istin g co n te n to n w e b p a g e s. 11. a m icro fo rm a t is aw e b -b a se d a p p ro a ch to se m a n tic m a rk -u p w h ich se e k s to re -u se e x istin gH T M L /X H T M L ta g s to co n v e y m e ta d a taa n d o th e r a ttrib u te s 12. R D Fa is a W 3 C re co m m e n d a tio nth a t a d d s a se t o f a ttrib u te -le v e le x te n sio n s to H T M L , X H T M L a n d v a rio u sX M L-b a se d d o cu m e n t ty p e s fo re m b e d d in g rich m e ta d a ta w ith in W e bd o cu m e n ts. 13. One ProtocolT O R U L E T H E M A L L ! 14. recipe Schema 15. co o k in g M e th o dco o k T im ein g re d ie n tsn u tritio np re pT im ere cip e C a te g o r yre cip e C u isin ere cip e In stru c tio nre cip e Y ie ldto ta lT im e 16. prepTimeco o k T im e 17. M u sic 18. To o ls 19. C lick tose le c td a ta ite m 20. R e su lts 21. IncreasedClickThroughRate 22. S ch e m a >A g g re g a te R a tin gu p to3 0 0 % 23. S ch e m a >R e cip eu p to1 9 5 % 24. 1 9 %C T R 25. 2 4 %C T R 26. M icro d a ta V o c a b u la ryS u p p o r te d b y M a jo r S e a rchE n g in e s G u id a n ce, To o ls a n dR e so u rce s S ig n ific a n t In c re a se to Tra ffic 27. F u r th e r R e a d in gT h in k in g o n th e W e b B e rn e rs L e e , G o d e l a n d Tu rin gh ttp ://sch e m a .o rg /h ttp ://m icro fo rm a ts.o rg /h ttp s://d e v e lo p e rs.g o o g le .co m /w e b m a ste rs/rich sn ip p e ts/h ttp ://w w w.g o o g le .co m /w e b m a ste rs/to o ls/rich sn ip p e tsh ttp ://sch e m a -cre a to r.o rg /h ttp ://w w w.m icro d a ta g e n e ra to r.co m / 28. THANKSF O R L IS T E N IN G 29. Te l: +4 4 (0 )1 4 2 0 5 4 0 2 2 7E -m a il: n ic h o la .sto tt@ th e m e d ia flo w.co mTw itte r: @ N ic h o la S to ttW e b : w w w.th e m e d ia flo w.co mD o w n lo a d N o w :w w w.th e m e d ia flo w.co m