This document analyzes data from marketing blogs to determine what types of posts receive the most engagement in terms of tweets and shares. It examines data from Econsultancy, Hubspot, and SEOMoz blogs. Some key findings include: - Lists, how-to posts, and posts on popular topics like search and social media tend to receive above average engagement. - Posts by individual authors with a track record of highly shared "outlier" posts bring the average up for that author. - Guest posts on average perform similarly to staff posts in terms of shares. Certain guest blogger roles like consultants see higher than average engagement. - Introductory posts targeting broader audiences tend to outperform more advanced or niche posts

1. The big marketing
blog popularity
study
22 graphs comparing how different kinds of
posts lead to more, or fewer, shares/tweets, at
Econsultany.com, the Hubspot blog and the
SEOMoz blog
Conceived & designed by
Ryan Skinner (@rskin11)

2. BACKGROUND
• Data was collected from publicly available tweet
counts on-site, inApril/May 2013
• Sample sizes range from 7 to 40. Each site’s
baseline was established from a sample size of 40
posts. Most common sample size = 10 posts.
• Circle sizes and position on Y-axis correspond to
average number of tweets per post in sample.
• Bar = first quartile to third quartile of sample
Whiskers = entire range of sample
• Open data: Link to Google Docs Spreadsheet

3. “First ze
graph, zen
ze analysis”

4. FIRSTUP,ECONSULTANCY’S
EXCELLENTMARKETINGBLOG

5. AnoteontheEconsultancydata:
Econsultancy’sblogposttweetcounts
displayonlytweetsmadebyUS
readers.ThankstoG.Charltonat
Econsultancy,Iwasabletogetthe
tweetcountsfromUKvisitors,aswell.
(Thanks,Graham!)

6. Question#1:
DoEconsultancy’sleadingstaff
bloggersgetsignificantlydifferent
amountsoftweetsperpost?

7. Econsultancy.comtweetsperpostbypostauthor
200
400
600
800
1000
1188 1702
Ryan
Sommer
David
Moth
Graham
Charlton
Ashley
Friedlein
Chris
Lake

8. Therearenolosershere.Sommer
averages100+tweetsperpost
(unattainableforusmeremortals).
Sommer’sandMoth’saveragesare
broughtdownduetotheircontributionsto
serieslikeQ&Aandstart-upposts,which
garnerfewtweets.
AsCEO,Friedlein’sinfrequentpostings
getalotoftweets(*butnotuniversally)
Lake’stalentatmakingresearchpostson
recenttopicsthatareeasytodigestget
hugetweetcounts(watchtrend:
Accessible,research-heavycontentwins!)
Friedlein’sandLake’sbigoutlierposts
(1000+)tweetslifttheiraverageintoa
differentleague

9. Theoryoftheslugger:
Allbloggershavetheir“batting
average”butsluggersareabletoreach
wellabovethisfairlyfrequently–outlier
postsofveryhighpopularity.
Inthisinstance,Lake’sthe slugger.

10. Question#2:
DoEconsultancy’sguest
bloggersgetmoreorfewertweets
thanthestaffbloggers?

11. Econsultancy.comguestsvs.baselinetweets
200
300
400
500
600
648
80
Baseline Guestposts
50

12. Interestingly,guestpoststrackthe
Econsultancybaseline(averagetweet
countoverasampleof40posts)almost
perfectly.
Inotherwords,guestbloggersdono
worseatdrawingtweetsthanstaff
bloggers,onaverage.However,thereare
variationsfromguestbloggertoguest
blogger.Howdotheyvary,dependingon
thetitleoftheguestblogger,forexample?
We’llsee…

13. Question#3:
Howdodifferentjobroles/titles
amongEconsultancyguest
bloggersrelatetotweetsperpost?

14. Econsultancy.comguestposttweetsbyauthortitle
100
200
300
400
500
CEO/Founder
Headof
<Discipline>
Guests
Marketingtitles
Consultants

15. OnemightexpectCEOstodrawmore
attention,butthedatashowedthem
trackingabitundertheaverageamong
guestbloggers.Presumably,theyfindit
hardertoresistwritingsales-heavy,me-
centricposts.
“Consultants”isavague title,butthesedid
noticeablybetterthanothertitles(though,
itmustbenotedfromasmallsample).
Marketers(CMOs,VPMarketing,
MarketingManager)did betterthantheir
peersonaverage.Wemightinferthey
knowtheiraudiencewell,andmighthave
moreexperiencewithbloggingthan
average(thusgettingmoretweets).

16. Question#4:
Howdodifferentblogtopics
(search,social,mobile,etc.)relate
totweetsperpost?

17. Econsultancy.composttweetsbytopic
100
200
300
400
500
Start-Up
Posts
Post
Baseline
“Content”
Posts
“Mobile”
Posts
648 881
Q&A
Posts
“Social”
Posts
“Search”
Posts

18. Firstofall,thepostsprofilingtechstart-ups
andQ&Asgetfarfewertweetsperpost
thantheblog’saverage.Clearly,forthis
blog’saudience,theseareofniche
interest.Fewreadershaveamotiveto
sharetheseposts.
Attheotherendofthescale,searchand
socialwerewellabovetheblog’saverage.
Searchbeatssocialonaverage,butis
heavilyinfluencedbyoneoutlier.Postson
socialmediavariedconsiderably(broad
range).
Postsoncontentmarketingandmobile
marketingtrackedjustabove theblog’s
averagetweets-per-post.

19. Question#5:
Dopostsaboutspecifictech
brands/vendorsgetmorelovein
termsoftweetsperpost?

20. Econsultancy.comguestposttweetsbybrandtopic
50
100
150
200
250
Microsoft
Posts Apple
Posts
Facebook
Posts
Post
Baseline
287 366 648

21. It’sworthnotingthatsamplepostsfromall
threebrandsgeneratedfewertweetson
averagethantheEconsultancyblog
baseline.
PostsaboutFacebook,however,
generatedalotmorebuzzthanmostposts
aboutAppleorMicrosoft(eachhadone
outlier,aboutApple’sunwillingnessto
engageinsocialmediaandMicrosoft’s
useofsocialmedia,respectively).
MostpostsaboutFacebook,Microsoft,
Appleoranyoneparticulartechorbrand
arereportingbyEconsultancy,notbest
practiceorstrategy.Ingeneral,these
reportingpostsseemtogetfewertweets.

22. Theoryofthehumanfilter:
Peopleprefertoshare/tweetpoststhat
havealreadybeenanalyzedandpre-
digested(comparedtofirst-hand
reporting).

23. Question#6:
Dopoststhataimforcontroversy
getmoreloveintermsoftweetsper
post?

24. Econsultancy.comcontroversial postsvs.baseline
100
200
300
400
500
691648
Post
Baseline
Controversial
Posts
Controversial
Posts
(sansoutlier)

25. Forreference,postscourtingcontroversy
included“IstheRFPBroken?”and“Isthis
socialmarketingframeworkaccurate(or
evennecessary)?”
Thecontroversialpostsgotonaverage
slightlymoretweets,butdiscountingone
outlier(“Coca-Cola:TheSinsocialmedia
doesn’tstandforsales”)theyactually
averagedwellbelowthebaselineoftweets
forEconsultancyposts.
[It’sworthnotingthatthebaselineincludes
anoutlierortwoaswell,buttheseare
averageddownbythelargesamplesize].
Youcan’tdrawhardandfastconclusions
fromasmallsample,butcourting
controversydoesnotseemto
automaticallyequal“moreshares”.

26. Question#7:
Dopostswithanegativepremise
getmoreloveintermsoftweetsper
post?

27. Econsultancy.comnegativepostsvs.baseline
100
200
300
400
500
1704648
Post
Baseline
Negative
Posts
Negative
Posts
(sansoutlier)

28. Forreference,postswithanegative
premiseincluded“Fivepointless
discussionposts”and“WhyaChief
DigitalOfficerisabadidea”.
Negativepostsgotonaveragefarmore
tweets–almostdouble,butdiscounting
oneoutlier(“14lousywebdesigntrends
thataremakingacomeback”)they
averagedonlyslightlyabovethebaseline
oftweetsforEconsultancyposts.
[It’sworthnotingthatthebaselineincludes
anoutlierortwoaswell,buttheseare
averageddownbythelargesamplesize].
Conclusion:
Despiteaclearboost,itisheavily
influencedbyonebigoutlier.Thegreater
averageoftweetsfornegativepostssans
outlierprobablyisn’tsignificantenoughto
warranta“howcanwegivetheposta
negativespin?”approach.

29. Question#8:
Dopostswithanambiguoustitle
(doesn’texplicitlystatecontentofpost)
getmoreloveintermsoftweetsper
post?

30. Econsultancy.comtweets:ambiguoustitlevs.baseline
100
200
300
400
500
648
Baseline
Ambiguous
Titles

31. Apersonaltheory:Manypeopleliketo
writeclever,slightlypoetictitlesinthe
beliefthatsomewitwillwinmoreshares.
Thiswasmeanttotestthat.
Conclusion:
Whenapost’stitledoesn’tveryspecifically
statewhatthepostwillbeabout(ex.“Why
youneedrealoptions”and“Areyou
gettingpersonalwithyourcustomers”),it
seemstogetfewershares.Inotherwords,
ifpeoplearewritingpoetictitlestoget
moreshares,theywouldmoreoftenthan
notbe…wrong.
(forclarity’ssakeANDfortwitter’ssake,
giveyourpostaclearanddescriptive title.)

32. Question#9:
Dopostswithlistsgetmorelovein
termsoftweetsperpost?

33. Econsultancy.comtweets:listpostsvs.baseline
100
200
300
400
500
648
Baseline
Listposts
535

34. Thedata’sprettyclearonthisone.Posts
like“SeventipsforcharitiesusingTwitter”
and“11greatwaystousesocialproofin
ecommerce”aresharedmoreoftenon
average.
Econsultancy’sowndataalsoshows
they’reviewedmoreoften.Sameforguest
postswithlists.Peoplelovetheirdamn
lists.
Generally,youneedtodosomeamountof
originalresearchtocreatealistof
examplesorapproaches.Andpeoplejust
lovetoshareoriginalresearch.
Conclusion:
Ifyoudon’tlikelists,getoverit.They’re
useful,peoplelikethemandpeopleshare
them.

35. Question#10:
Dopostswithatitleasquestionget
moreloveintermsoftweetsper
post?

36. Econsultancy.comtweets:questiontitlepostsvs.baseline
100
200
300
400
500
648
Baseline
Questiontitle
posts

37. Dopostswithtitlesasquestionsgetmore
shares,onaverage?Theanswerisno(at
leastnotinasignificant-enoughwayto
movethedials).
Intermsofpopularity,postsposinga
questiondon’tgetsharedmoreoftenthan
thosethatdon’t.

38. Question#11:
Dopostswithlotsofimages(8or
moreperpost)getmorelovein
termsoftweetsperpost?

39. Econsultancy.comtweets:imageheavypostsvs.baseline
100
200
300
400
500
648
Baseline
Imageheavy
posts

40. We’reoftenadmonishedto“show,don’t
tell”,andtoldofthepoweroftheimage.
Thismaybetrue,but–basedonthis
sampleanyway–there’snocorrelation
betweenproducinganimage-richpost
andgettingmoresharesontwitter.
Thisdoesn’tmeantosayputtingimages
inapostisabadthing(onthecontrary)
buttheydonotautomaticallymakeyour
postmoreshareable.

41. NEXTUP,HUBSPOT’SGREATIN-
HOUSEMARKETINGBLOG

42. AnoteontheHubspotdata:
Hubspot’sblogseemstohaveseena
markedincreaseintrafficandtweets
recently,whichcouldskewresults.For
thatreason,thebaseline(takenfrom
morerecentposts)couldbeartificially
inflated.Otherwise,comparisonsare
likeforlike(oldtoold,newtonew).

43. Question#1:
Dopostsfortheintroductory,
intermediateoradvanceddigital
marketergetmoretweetsthan
Hubspot’sbaseline?

44. Hubspotblogtweetsbypostdifficulty level
500
1000
1500
2000
2500
Intermediate
Posts
Baseline
Advanced
Posts
Introductory
Posts
2976 4182

45. Hubspotclassifiestheirownposts
accordingtodifficultylevel:Introductory,
intermediateoradvanced.Whichtendto
getmoretweets?
Theonlysignificantupliftseemstocome
fromthepoststheylabelintroductory.You
candrawanyconclusionfromthatyou
like.Mine:Introductorypostsaremore
readilyavailabletomoremarketers,and
thusgetmoretweets.(Asmaller
proportionoftheirreaderswillbe
interestedinthemostadvancedtopics).

46. Theoryofthemassmass:
Poststhattargetthebroadestswathe
possibleofagivenblog’stypical
readershipwill,unsurprisingly,often
garnermoretweetsthannarrowposts.
However,earningtweetsbeyond
“typicalreaders”isanotherstory(and
maybemoreimportanttoreadership
growth).

47. Question#2:
Dopostswithtriggerwordslike
“awesome”,“love”or“simple”get
moretweetsthanaverage?

48. Hubspotblogtweetsbytrigger words
300
600
900
1200
1500
“Fantastic”
Posts
“Love”
Posts “Simple”
Posts
4183
“Awesome”
Posts
Baseline “You”
Posts
4182

49. Drawingaconnectionbetweenonetrigger
wordinaposttitleandthenumberof
tweetsthatpostgets,istenuousatbest,
admittedly.Sotheresultsshouldbetaken
withagrainofsalt.
Interestingly,however,self-described
“awesome”,“fantastic”and“love”posts
gotsignificantlyfewertweetsonaverage
(overcompensation,anyone?).
Poststhataddressthereaderspecifically
(“Whenyoushouldandshouldn’t
outsourceyourmarketing”,forexample)
seemtodobetteratwinningtweetsfrom
readers.Note,however,thatthisresultis
stronglyinfluencedbyoneoutlier.Sans
outlier,itwouldprobablylieevenwiththe
baseline.

50. Question#3:
Docertain“types”ofposts(lists,
negativesentimentorhow-to’s)get
moretweetsthanaverage?

51. Hubspotblogtweetsbyposttype
300
600
900
1200
1500
Negative
Posts
(sansoutlier)
How-To
Posts
List
Posts
Baseline Negative
Posts
4182 4193

52. Somesimilaritiesanddifferencesfromthe
Econsultancydatahere.Negativeposts
seemtodobetter,but–sansoneoutlier–
theydosignificantlyworse.
It’snotclearwhylistpostswoulddo,on
average,worsethantheblog’sbaselinefor
tweetsperpost,unlessit’sbecausethe
baselineisinflatedtowardstherecent
upwardsbumpintrafficandshares.
Discountingthebaselineandthesingle
negativepostoutlier,weseethatlistposts
getmarginallymoretweetsthanhow-to’s,
andthesegetmarginallymoretweetsthan
negativeposts.Thisprobably
correspondstotheirrelativeutilityseenin
theeyesofHubspot’sblogvisitors.

53. Question#4:
Whichgetmoretweets:Marketing
postsabouthowtooptimize
LinkedIn,TwitterorFacebook?

54. Hubspotblogtweetsbysocialnetworkastopic
500
1000
1500
2000
2500
“Twitter”
Posts
“Facebook”
Posts
“LinkedIn”
Posts
4252

55. Hubspotlovestodopostshelping
marketersoptimizetheiractivitiesonthe
majorsocialnetworks,andparticularly
“thethreebiggies”–LinkedIn,Twitterand
Facebook.
Whichgarnerthemosttweets?
Surprisingly,nottwitter.Thedifferences
aren’thuge,buttheyaresignificant.Posts
aboutFacebookgetsharedthemost,and
thoseaboutLinkedIngetsharedleast.
(Itwouldbeinterestingtostudythesame
sampleonLinkedInsharesandFacebook
likesorshares,butthat’sanotherstudy).

56. Question#5:
WhichHubspotbloggergetsthe
mosttweetsonaverage?

57. HubspotblogtweetsbyHubspotblogger
300
600
900
1200
1500
Brittany
Leaning
Pamela
Vaughan
Mike
Volpe
Corey
Eridon
Dan
Zarrella
1654 2216

58. LikewithEconsultancy’sAshleyFriedlein,
onemightexpectHubspotCMOMike
Volpetogetthebiggesttweetcull.
However,he’sedgedoutinthesampleby
CoreyEridon,andblownawaybyDan
Zarella.
It’sparticularlyworthwhileignoring
averagesonthisoneandlookingatthe
bars(firsttothirdquartiles),asVolpe’s
averageistweakedupwardsbyahefty
outlier.
ForZarella,eventhougheverypost
doesn’tgeneratehugenumbersoftweets
(onegotonly193–gasp!),hemakesvery
shareableposts,veryconsistently.

59. Theoryofthesluggerreconfirmed:
LikeEconsultancy’sLake,Zarrella’sgot
aresearchanddatafocus,combined
withaknackforstorytelling.
Asaresult,heconsistentlynailshighly
shareableposts.

60. FINALLY,SEOMOZ’SHUGELY
POPULARDIGITALMARKETING
BLOG

61. AnoteontheSEOMozdata:
Nothingparticulartomentionhere.
TweetswereculledfromSEOMoz’s
owntweetcounts,andpostsamples
weredrawnasrandomlyaspossible
overthepastsixmonths,
approximately.

62. Question#1:
Whichgetsmoretweetsonthe
SEOMozblog:TechnicalSEO
postsorsocialmediaposts?

63. SEOMozblogtweets:TechnicalSEOvs.SocialMedia
300
600
900
1200
1500
Baseline
SocialMedia
PostsTechnicalSEO
Posts
2577 1664

64. Atfirstpass,thiscameasasurprise.A
communityofSEO’smoreeagertoshare
postsaboutsocialmediathantechnical
SEO–what?!?!
However,it’sworthnotingthatasocial
mediapostonSEOMozisoftenabouthow
socialmediaworksinanSEOcontext.
AlmostanySEOwillbeinterestedinthat.
AndatechnicalSEOpostonSEOMozis
fortheSEOwithadeeeeepinterestin
technicalSEO–asmallergroup,evenat
SEOMoz.

65. Theoryofthemassmassreconfirmed:
Again,poststhatappealtothebroadest
swatheofyourtypicalaudiencewill,
becausea(almost)fixedproportionof
interestedreaderstweetanygivenpost,
getthemosttweets.(Thesepostsmay
notbethosethatwinyouthenew
readers,though.Infact,theyoften
don’t.)

66. Question#2:
Whichgetsmoretweetsonthe
SEOMozblog:Content&blogging
postsorVerticalSEOposts?

67. SEOMozblogtweets:VerticalSEOvs.Content/Blogging
300
600
900
1200
1500
Baseline
Content&Blogging
Posts
VerticalSEO
Posts
2577 20801635

68. Similarresulttothefirsthead-to-head.
Again,the“non-SEOtopic”walkedoffwith
farmoretweetsthantheSEOtopic.
Likethefirststudy,however,Ithinkvertical
SEOinterestsanarrowersub-setofSEOs
thanthebroadercontenttopic,givingthe
lattermoretweets.(Iwouldalsoexpectthat
here,asintheprevious study,thenon-
SEOtopicsdrewinmoretweetsfrom
peoplewhodon’ttypicallyreadSEOMoz’s
blog).

69. Question#3:
Whichgetsmoretweetsonthe
SEOMozblog:Analyticspostsor
LinkBuildingposts?

70. SEOMozblogtweets:Analyticsvs.LinkBuilding
500
1000
1500
2000
2500
Baseline
LinkBuilding
Posts
Analytics
Posts
2577 4022

71. LinkBuildingpostspulledin,onaverage,
farmoretweetsthananalytics posts.I’d
loveforanySEOstoweighinhere,butI
suspectthatlinkbuilding’sabitlikefishing
–they’realwaysthinkingabouthowtodo
itbetter,tryingtogetthebigoneand
feelingabitfrustratedbyit.
Analyticsposts,likeverticalSEO,appealto
anarrowsubsetoftheblog’sreaders,
leadingtofewertweets.

72. Question#4:
Whichgetmoretweets–alltopics?

73. SEOMozblogtweetsbytopic
500
1000
1500
2000
2500
Technical
SEO
Posts
Analytics
Posts
Social
Media
Posts
4022
Vertical
SEO
Posts
Content
Posts
LinkBuilding
Posts

74. Seenonaline,weseethedivision
betweenposttypesforanicheaudience
(verticalSEO,technicalSEOandanalytics)
andthoseforSEOMoz’sbroadaudience
(socialmedia,contentandlinkbuilding).
Thisisparticularlyrevealingwhenyou
lookonlyatthebars–firsttothirdquartile
–andignoretheaveragesandoutliers
(whiskers).

75. Question#5:
Whichgetmoretweets–postswith
fewcomments(<50)orpostswith
morecomments(>50)?

76. SEOMozblogtweets:Lesscommentsvs.more
300
600
900
1200
1500
Postswith
>50comments
Postswith
<50comments

77. Commentsareoftenaproxyforapost’s
credibilityorsimple“interesting-ness”,
andtheassumptionwasthatthiswould
correlatetoaveragetweetcounts.
Theresultslooselycorroboratethis
finding.Allinall,notasurprising
conclusion.

78. Question#6:
Whogetsmoretweets–Rand
Fishkin,Dr.PeteorYouMoz-
promotedposts?

79. SEOMozblogtweets:Bywriter
500
1000
1500
2000
2500
Dr.Pete’s
Posts YouMoz-
Promoted
Posts
Rand’s
Posts

80. Again,theheadhonchoisedgedoutbya
data/research-drivenlieutenant–inthis
case,Dr.Pete.LikeLakeandZarrella,he’s
focusedonoriginalresearch,and
communicatingthatclearlytothe
community.
YouMoz-promotedpostsbeatboth,
however,intermsofaveragetweetsper
post.Thisisnosurprise,astheSEOMoz
teamiscuratingthosepoststhatgetthe
mostengagementfromtheYouMozblog.
Whiletweetsarenottheircoremetricfor
promotion,poststhatgettractionarelikely
togettractionontwittertoo.

81. Theoryofthesluggerreconfirmed,
again:
Clearlythere’sagenusofblogger
representedbyLake,ZarrellaandDr.
Pete,whobringnewknowledgetotheir
communitiesineasy-to-digestways.
Thismeanstheygethighengagement
oneverypost(asanaverage),and
tremendousengagementonsome
posts(grandslams).

82. Somefinalobservationsontheoutliers
-poststhatarekindaviral(>500tweets
onEconsultancyor>1500tweetson
HubspotorSEOMoz)

83. Econsultancyoutliers=
Poststhatspecificallyaimtohelp
marketersnavigatethecontextof
somethingthey’retryingtofigureout
rightnow,withoriginalresearchor
highlyrelevantfirst-handexperience.

84. Hubspotoutliers=
Poststhatconsistofshort-cutsorrich
andclearinstructions(thataren’t
explicitly“simple”).Triggerwords:
“Cheatsheet”and“Disaster”or
“Catastrophe”

85. SEOMozoutliers=
Poststhatcontainanauthoritativetake
forSEOsonanichetopic,suchas
contentmarketingstrategy,social
sharing,responsivedesign,facebook
orvideo.

86. Conclusions
Thereareclearlypatternsworthfindingand
actingon,buttheinsightsarenotobviousor
immediate,northeactionsworthtaking.
Thismethodologycanbeusedtodiscover
whatworksonyourownsite,orcompetitors.
Actonhunches.Thenmeasure.Andadjust.

87. Making-ofthisstudy:
~30hours(2hoursconcept,10hoursdata
collection,8hoursgraphs,8hoursanalysis,
2hoursediting)
Builtonmethodologyfirstproposedhere:
http://www.velocitypartners.co.uk/our-
blog/content-marketing-measurement-one-
metric/
(Pleaseshareifyouenjoyedit:)