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!

1. Supercharge Search
H o w U sin g S tru c tu re d D ata C a n B o o st Yo u r
Tra ffic w ith R ich S n ip p e ts
b y N ich o la S to tt

3. “It's a sim p le ch a n g e to th e d isp lay o f
se a rch re su lts, y e t o u r ex p e rim e n ts
h av e sh o w n th a t u se rs fin d th e n e w
d 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

4. C o v e rin g
W 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 ?

5. Structured
DATA TYPES

8. How does that
translate…
to search?

9. Rich
Snippets

11. re a lly ? y o u c a n a d d -u p ?

12. That’s
Awesome!

13. prepTime
co o k T im e

15. M ic ro d a ta is a n
H T M L sp e cific a tio n u se d to n e st
se m a n tics w ith in e x istin g co n te n t
o n w e b p a g e s.

16. a m icro fo rm a t is a
w 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 g
H T M L /X H T M L ta g s to co n v e y m e ta d a ta
a n d o th e r a ttrib u te s…

17. R D Fa is a W 3 C re co m m e n d a tio n
th a t a d d s a se t o f a ttrib u te -le v e l
e x te n sio n s to H T M L , X H T M L a n d v a rio u s
X M L-b a se d d o cu m e n t ty p e s fo r
e m b e d d in g rich m e ta d a ta w ith in W e b
d o cu m e n ts.

19. One Protocol
T O R U L E T H E M A L L !

21. recipe Schema

22. co o k in g M e th o d
co o k T im e
in g re d ie n ts
n u tritio n
p re pT im e
re cip e C a te g o r y
re cip e C u isin e
re cip e In stru c tio n
re cip e Y ie ld
to ta lT im e

23. prepTime
co o k T im e

26. M u sic

27. To o ls

31. C lick to
se le c t
d a ta ite m

35. R e su lts

36. Increased…
Click
Through
Rate

37. S ch e m a >A g g re g a te R a tin g
u p to
3 0 0 %

38. S ch e m a >R e cip e
u p to
1 9 5 %

39. 1 9 %
C T R

40. 2 4 %
C T R

41. • M icro d a ta V o c a b u la ry
S u p p o r te d b y M a jo r S e a rch
E n g in e s
• G u id a n ce, To o ls a n d
R e so u rce s
• S ig n ific a n t In c re a se to Tra ffic

42. F u r th e r R e a d in g
T 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 g
h 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 ts
h 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 /

43. THANKS
F O R L IS T E N IN G

44. Te l: +4 4 (0 )1 4 2 0 5 4 0 2 2 7
E -m a il: n ic h o la .sto tt@ th e m e d ia flo w.co m
Tw itte r: @ N ic h o la S to tt
W e b : w w w.th e m e d ia flo w.co m
D o w n lo a d N o w :
w w w.th e m e d ia flo w.co m