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Using 
Data 
to 
Identify, 
Target, 
and 
Connect 
with 
Audiences 
Peter 
McCarthy 
McCarthy 
Digital 
| 
The 
Logical 
M...
Contents 
» Who 
am 
I 
and 
why 
am 
I 
here? 
» Audience-­‐centricity 
is 
key 
» Data 
and 
tools, 
applied 
» What’s 
...
Contents 
» Who 
am 
I 
and 
why 
am 
I 
here? 
» Audience-­‐centricity 
is 
key 
» Data 
and 
tools, 
applied 
» What’s 
...
Who 
am 
I? 
April 
30, 
2014 
Using 
Data 
to 
Identify, 
Target, 
Connect 
| 
ECPA 
4
Who 
am 
I? 
April 
30, 
2014 
Using 
Data 
to 
Identify, 
Target, 
Connect 
| 
ECPA 
5
Or 
did 
you 
mean 
this? 
» Demographics 
§ My 
gender, 
age, 
income 
level, 
education 
level, 
marital 
status, 
etc....
Why 
am 
I 
here? 
» United 
States 
Reading 
and 
eReading 
habits 
§ 76% 
of 
the 
US 
population 
18 
and 
over 
read ...
Contents 
» Who 
am 
I 
and 
why 
am 
I 
here? 
» Audience-­‐centricity 
is 
key 
» Data 
and 
tools, 
applied 
» What’s 
...
Audience-­‐centricity 
Consumer-­‐first 
1. Who 
2. What 
3. Why 
Results-­‐oriented 
§ Product 
§ Placement 
4. Where 
...
The 
universe 
of 
potential 
readers 
Aware 
& 
Will 
Buy 
Aware 
& 
Will 
Not 
Unaware 
& 
Just 
Might! 
Unaware 
& 
Jus...
So 
how 
do 
we 
find 
them? 
is 
a 
capital 
mistake 
to 
theorize 
before 
one 
has 
data. 
Insensibly 
one 
begins 
to ...
Contents 
» Who 
am 
I 
and 
why 
am 
I 
here? 
» Audience-­‐centricity 
» Data 
and 
tools, 
applied 
» What’s 
next? 
Ap...
What 
I 
talk 
about 
when 
I 
talk 
about 
data 
“Universe” 
Publishing 
Industry 
(Truly) 
Consumer 
§ Macro 
data 
§ ...
“Universe” 
All 
relevant 
and 
useful 
when 
pulling 
out 
the 
crystal 
ball 
April 
30, 
2014 
Using 
Data 
to 
Identif...
Example 
data: 
“universe” 
Global 
tablet 
market 
share 
by 
platform 
Source: 
Business 
Insider 
April 
30, 
2014 
Usi...
Example: 
“smaller 
universe” 
Time 
per 
day 
Americans 
spend 
on 
different 
digital 
activities 
April 
30, 
2014 
Usi...
Example: 
“smaller 
universe 
still” 
Demographic 
makeup 
of 
major 
social 
networks 
2013 
April 
30, 
2014 
Using 
Dat...
Example: 
“universe 
not 
so 
far 
away” 
Amazon 
share 
price 
trailing 
5 
years 
Source: 
Google 
Finance 
April 
30, 
...
Industry 
Well-­‐understood, 
(should 
be) 
easier, 
useful 
for 
running 
our 
businesses 
April 
30, 
2014 
Using 
Data ...
Example: 
industry 
6.93 
Net 
Sales 
($B) 
2011 2012 
Source: 
2013 
Bookstats 
US 
trade 
book 
sales 
Brick 
& 
Mortar ...
Example: 
industry 
US 
eBook 
sales 
2008 
-­‐ 
2012 
64 
Net 
Sales 
($M 
Logarithmic 
regression: 
R2=0.7853 
291 
869 ...
(Truly) 
Consumer 
Crazy 
and 
unbelievably 
useful 
April 
30, 
2014 
Using 
Data 
to 
Identify, 
Target, 
Connect 
| 
EC...
Some 
(really 
useful) 
sources 
of 
(US) 
consumer 
data 
The 
irrefutable 
ROI 
of 
social 
is 
in 
the 
analytics 
and ...
The 
graph 
is 
simply 
so 
rich 
and 
so 
aware 
Of 
course, 
the 
“currency” 
is 
personal 
privacy…which 
73% 
of 
us 
...
One 
can 
capture 
actual 
usage 
as 
it 
occurs 
“in 
the 
wild” 
A 
sampling 
of 
tools…mostly 
not 
huge, 
costly 
a 
l...
But 
it 
isn’t 
really 
about 
the 
tools 
Yes, 
one 
must 
have 
a 
toolkit. 
But 
it’s 
about 
triangulation 
and 
appli...
So 
let’s 
triangulate 
and 
see 
what 
happens 
I 
chose 
a 
title 
from 
the 
CBE 
Bestseller 
List, 
which 
seemed 
to ...
LibraryThing, 
GoodReads, 
Amazon… 
Establish 
true 
comp 
authors 
and 
titles 
as 
well 
as 
reader 
and 
buyer 
verbiag...
Multiple 
auto-­‐fills 
(not 
logged 
in, 
cookies 
cleared) 
Hypothesis: 
no 
one 
searches 
for 
Terri 
Blackstock? 
See...
And, 
indeed, 
it 
is 
definitely 
true 
Raven 
Tools 
(with 
assist 
from 
Moz, 
Majestic, 
SEM 
Rush 
assuage 
that 
con...
Google 
Trends: 
how 
does 
she 
stack 
up 
then? 
Waning 
but 
not 
bad. 
Interesting 
differences 
in 
regionality 
Apri...
Try 
a 
Google 
search 
(cached 
cleared, 
logged 
out) 
Tremendous 
results 
for 
author 
and 
title 
+ 
author 
queries ...
So, 
where 
are 
the 
fans? 
What 
are 
they 
saying? 
Google’s 
related 
searches, 
some 
“listening” 
in 
on 
Twitter… 
...
The 
graph 
Here 
rolled 
up 
under 
Twitter 
followers 
with 
data 
appended 
April 
30, 
2014 
Using 
Data 
to 
Identify...
Her 
Twitter 
map 
looks 
a 
lot 
like 
her 
search 
map 
Is 
she 
posting 
at 
the 
right 
time? 
April 
30, 
2014 
Using...
This 
is 
just 
fine. 
A 
deeper 
dive 
not 
worth 
it 
at 
this 
time 
Her 
Followers 
Her 
April 
30, 
2014 
Using 
Data...
So, 
Terri’s 
followers 
are 
mostly 
married 
white 
Christian 
women. 
40-­‐49 
April 
30, 
2014 
Using 
Data 
to 
Ident...
Who 
else 
do 
they 
follow? 
Some 
patterns 
emerge 
but 
really 
not 
that 
much 
April 
30, 
2014 
Using 
Data 
to 
Ide...
New 
hypothesis: 
she’s 
undersized 
for 
some 
reason 
right 
now 
And 
I 
don’t 
need 
to 
know 
why 
(I 
always 
forget...
I 
decide 
to 
ride 
the 
Francine 
Rivers 
wave 
(so 
to 
type) 
Solely 
because 
of 
Dee 
Henderson’s 
lack 
of 
real 
o...
Grab 
a 
tool 
to 
compare 
the 
two 
authors 
in 
that 
space… 
Her 
fans 
are 
there 
but 
it 
feels 
like 
there’s 
a 
...
A 
quick 
stop 
at 
her 
site 
to 
check 
that 
is 
in 
working 
order 
First 
time 
we’ve 
turned 
away 
from 
the 
audie...
Let’s 
find 
new 
fans 
on 
Facebook! 
Our 
targeting: 
Women 
(no 
age 
as 
people 
don’t 
enter 
it) 
who 
live 
in 
the...
Then 
some 
Twitter 
followers 
of 
Francine 
Rivers 
Under 
100 
to 
be 
exact. 
Strong 
ones 
with 
the 
characteristics...
Lastly 
we 
go 
looking 
for 
some 
keywords 
she 
can 
own 
Very 
dependent 
on 
her 
name. 
So, 
Christian 
suspense 
an...
A 
tool 
called 
Seorch 
gives 
us 
some 
good 
generic 
ones 
Christian 
suspense 
books 
Christian 
suspense 
fiction 
C...
Lastly, 
we’ll 
need 
to 
optimize 
for 
the 
incoming 
traffic 
Would 
have 
tightened 
the 
middle 
and 
bottom 
of 
the...
Contents 
» Who 
am 
I 
and 
why 
am 
I 
here? 
» Audience-­‐centricity 
» Data 
and 
tools, 
applied 
» What’s 
next? 
Ap...
Constant 
triangulation 
– 
before 
anything 
happens 
And, 
of 
course, 
more 
often 
than 
not 
it 
isn’t 
the 
chase 
I...
These 
guys 
predicted 
the 
future! 
Opening 
weekend 
movie 
revenues. 
With 
greater 
than 
90% 
accuracy. 
Source 
HP ...
Thank 
you 
very 
much. 
pete@mccarthy-­‐digital.com 
@petermccarthy 
April 
30, 
2014 
Using 
Data 
to 
Identify, 
Target...
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How to Identify, Target, and Connect with Audiences —ECPA 2014

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Tried something new, doing a night-before data-driven marketing dive on an author with whom I was entirely unfamiliar to form and test hypotheses on the off chance that there may remain efficient ways of keeping this book on the bestseller list. So, this deck finds me marketing a niche romance title, Parable, by Terri Blackstock. In real time. I can honestly say it was a ball.

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How to Identify, Target, and Connect with Audiences —ECPA 2014

  1. 1. Using Data to Identify, Target, and Connect with Audiences Peter McCarthy McCarthy Digital | The Logical Marketing Agency Presented April 30, 2014 Leadership Summit and ECPA Annual Member Meeting
  2. 2. Contents » Who am I and why am I here? » Audience-­‐centricity is key » Data and tools, applied » What’s next? April 30, 2014 Using Data to Identify, Target, Connect | ECPA 2
  3. 3. Contents » Who am I and why am I here? » Audience-­‐centricity is key » Data and tools, applied » What’s next? April 30, 2014 Using Data to Identify, Target, Connect | ECPA 3
  4. 4. Who am I? April 30, 2014 Using Data to Identify, Target, Connect | ECPA 4
  5. 5. Who am I? April 30, 2014 Using Data to Identify, Target, Connect | ECPA 5
  6. 6. Or did you mean this? » Demographics § My gender, age, income level, education level, marital status, etc. § Note: I include geographic region here » Psychographics § My beliefs, values, attitudes, opinions, favorite things, places, activities » Behaviors § What I have done, am doing (and, perhaps, most likely to do next) A person’s future behavior is impossible to predict. But is easier the nearer it is to happening and the more we know about the person. April 30, 2014 Using Data to Identify, Target, Connect | ECPA 6
  7. 7. Why am I here? » United States Reading and eReading habits § 76% of the US population 18 and over read a book in the past year § 28% of that group do on either an eReader or a Tablet § Demographically diverse as could be 76% of US book readers age > 18 report they have read a book in the past 12 months?! = ~183,797,307 potential consumers (that we don’t know very well) Source: Pew Center for Internet Research April 30, 2014 Using Data to Identify, Target, Connect | ECPA 7
  8. 8. Contents » Who am I and why am I here? » Audience-­‐centricity is key » Data and tools, applied » What’s next? April 30, 2014 Using Data to Identify, Target, Connect | ECPA 8
  9. 9. Audience-­‐centricity Consumer-­‐first 1. Who 2. What 3. Why Results-­‐oriented § Product § Placement 4. Where 5. When 6. How § Price § Promotion Measured Optimization § What’s working § What’s not § With nuance April 30, 2014 Using Data to Identify, Target, Connect | ECPA 9
  10. 10. The universe of potential readers Aware & Will Buy Aware & Will Not Unaware & Just Might! Unaware & Just Fine This is the gold mine of readers. It is the crossover hit. Especially true for niche and vertical publishers. April 30, 2014 Using Data to Identify, Target, Connect | ECPA 10
  11. 11. So how do we find them? is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts. –-­‐ Sherlock Holmes, A Scandal in Bohemia . It April 30, 2014 Using Data to Identify, Target, Connect | ECPA 11
  12. 12. Contents » Who am I and why am I here? » Audience-­‐centricity » Data and tools, applied » What’s next? April 30, 2014 Using Data to Identify, Target, Connect | ECPA 12
  13. 13. What I talk about when I talk about data “Universe” Publishing Industry (Truly) Consumer § Macro data § Facts, trends, projections § May be commercial in nature, may not § Likely Sources § Nonprofits like Pew Research § Trade groups § Corporate annual reports § Data Corporations (eg. ComScore, Gallup, Nielsen, etc.) § Consultancies (McKinsey, Forrester, Gartner) § News outlets § Macro and micro data § For industry as a whole § Or one publisher (our own) § Or one book/author (our own) § Facts, trends, projections § Generally commercial in nature § Likely Sources § Retailers to POS systems § Some CRM(ish) activity § Smaller set of Data Corporations (eg. Nielsen, BISG, Bowker) § Etc. § News outlets § Macro and micro data § Consumer behavior in aggregate § But also specific groups of consumers “in the wild” § Facts, trends, projections § Often not commercial in nature § Likely Sources § Bespoke surveys (Nielsen) § Real online usage § Search § Social § Commerce § Other § Analytic tools § Etc. I prefer talking about this April 30, 2014 Using Data to Identify, Target, Connect | ECPA 13
  14. 14. “Universe” All relevant and useful when pulling out the crystal ball April 30, 2014 Using Data to Identify, Target, Connect | ECPA 14
  15. 15. Example data: “universe” Global tablet market share by platform Source: Business Insider April 30, 2014 Using Data to Identify, Target, Connect | ECPA 15
  16. 16. Example: “smaller universe” Time per day Americans spend on different digital activities April 30, 2014 Using Data to Identify, Target, Connect | ECPA 16
  17. 17. Example: “smaller universe still” Demographic makeup of major social networks 2013 April 30, 2014 Using Data to Identify, Target, Connect | ECPA 17
  18. 18. Example: “universe not so far away” Amazon share price trailing 5 years Source: Google Finance April 30, 2014 Using Data to Identify, Target, Connect | ECPA 18
  19. 19. Industry Well-­‐understood, (should be) easier, useful for running our businesses April 30, 2014 Using Data to Identify, Target, Connect | ECPA 19
  20. 20. Example: industry 6.93 Net Sales ($B) 2011 2012 Source: 2013 Bookstats US trade book sales Brick & Mortar 8.03 Net Sales ($B) 7.47 2011 2012 5.72 April 30, 2014 Using Data to Identify, Target, Connect | ECPA 20
  21. 21. Example: industry US eBook sales 2008 -­‐ 2012 64 Net Sales ($M Logarithmic regression: R2=0.7853 291 869 2,109 3,042 +199% +143% +43% +355% 2008 2009 2010 2011 2012 Source: 2013 Bookstats April 30, 2014 Using Data to Identify, Target, Connect | ECPA 21
  22. 22. (Truly) Consumer Crazy and unbelievably useful April 30, 2014 Using Data to Identify, Target, Connect | ECPA 22
  23. 23. Some (really useful) sources of (US) consumer data The irrefutable ROI of social is in the analytics and BI on the back end § The Social Graph They know consumers. Now tying to offline sources. § Ad Platform Inventory, Open (APIs), demographics, beliefs, behaviors § Constant A/B testing Fail fast, fix § Result: Happy Users/Advertisers Despite incredible concerns over privacy. Relevance trumps it § Search, ads (& lots of data) Massive share of almost everything they do § Ad Platform Inventory, highly behavioral § Basically Building a Brain Yes. All products data-­‐driven § Open APIs and tools § Smaller but… Wild adoption, solid usage § Ad Platform Targeting has arrived as has the front-­‐end to allow for goals § Timely Basically “now” § Very Open (for now) Can get at the data April 30, 2014 Using Data to Identify, Target, Connect | ECPA 23
  24. 24. The graph is simply so rich and so aware Of course, the “currency” is personal privacy…which 73% of us (gladly?) pay April 30, 2014 Using Data to Identify, Target, Connect | ECPA 24
  25. 25. One can capture actual usage as it occurs “in the wild” A sampling of tools…mostly not huge, costly a la Adobe or Salesforce Social Analytics § Simply Measured § SproutSocial § Social Bakers § Followerwonk § Commmun.it § Bit.ly § Topy § Social Mention § Facebook Insights § Facebook Ad Interface § Facebook PowerEditor § EdgeRank Checker § SimplyMeasured § Twitter Ad Interface § Argyle § LinkedIn Analytics § Pinterest Analytics § Tumblr Analytics § Instagram…. Web/SEO Web/Email Analytics § Google Trends § Google AdWords § Moz § Soovle (autocompletes) § Raven § Compete § Quantcast § SEO Quake § Google universal analytics § WordTracker § WordStream § Amazon comp authors § Librarything tags/ comps § Etc. § Google Analytics § Omniture § Constant Contact § MailChimp § Optimizely – landing pages And many, many more to suit the myriad use-­‐cases, but… April 30, 2014 Using Data to Identify, Target, Connect | ECPA 25
  26. 26. But it isn’t really about the tools Yes, one must have a toolkit. But it’s about triangulation and application Goals direct research Launch Hypothesis, test, hypothesis, test, hypothesis, test, hypothesis, test Measure & Optimize Communicate, Prune, change, apply apply April 30, 2014 Using Data to Identify, Target, Connect | ECPA 26
  27. 27. So let’s triangulate and see what happens I chose a title from the CBE Bestseller List, which seemed to be doing well but maybe had a little more in it. I tried to “work it” April 30, 2014 Using Data to Identify, Target, Connect | ECPA 27
  28. 28. LibraryThing, GoodReads, Amazon… Establish true comp authors and titles as well as reader and buyer verbiage April 30, 2014 Using Data to Identify, Target, Connect | ECPA 28
  29. 29. Multiple auto-­‐fills (not logged in, cookies cleared) Hypothesis: no one searches for Terri Blackstock? Seems they do April 30, 2014 Using Data to Identify, Target, Connect | ECPA 29
  30. 30. And, indeed, it is definitely true Raven Tools (with assist from Moz, Majestic, SEM Rush assuage that concern That volume is on her name alone and only in Google. April 30, 2014 Using Data to Identify, Target, Connect | ECPA 30
  31. 31. Google Trends: how does she stack up then? Waning but not bad. Interesting differences in regionality April 30, 2014 Using Data to Identify, Target, Connect | ECPA 31
  32. 32. Try a Google search (cached cleared, logged out) Tremendous results for author and title + author queries Fine on the “branded” terms. People who Seek shall find. But what about others? April 30, 2014 Using Data to Identify, Target, Connect | ECPA 32
  33. 33. So, where are the fans? What are they saying? Google’s related searches, some “listening” in on Twitter… Again, seems okay. Now I want to know who is looking. Who are her fans? April 30, 2014 Using Data to Identify, Target, Connect | ECPA 33
  34. 34. The graph Here rolled up under Twitter followers with data appended April 30, 2014 Using Data to Identify, Target, Connect | ECPA 34
  35. 35. Her Twitter map looks a lot like her search map Is she posting at the right time? April 30, 2014 Using Data to Identify, Target, Connect | ECPA 35 Using Data to Identify, Target, Connect | ECPA
  36. 36. This is just fine. A deeper dive not worth it at this time Her Followers Her April 30, 2014 Using Data to Identify, Target, Connect | ECPA 36
  37. 37. So, Terri’s followers are mostly married white Christian women. 40-­‐49 April 30, 2014 Using Data to Identify, Target, Connect | ECPA 37
  38. 38. Who else do they follow? Some patterns emerge but really not that much April 30, 2014 Using Data to Identify, Target, Connect | ECPA 38
  39. 39. New hypothesis: she’s undersized for some reason right now And I don’t need to know why (I always forget that!). Just remedy it… topsy author comp Start to look at two comp authors who seem “larger” or “hotter” right now. April 30, 2014 Using Data to Identify, Target, Connect | ECPA 39
  40. 40. I decide to ride the Francine Rivers wave (so to type) Solely because of Dee Henderson’s lack of real online footprint People Talking About This 437 4,817 Terri Blackstock Francine Rivers A check on Facebook causes me a moment of pause April 30, 2014 Using Data to Identify, Target, Connect | ECPA 40
  41. 41. Grab a tool to compare the two authors in that space… Her fans are there but it feels like there’s a somewhat quiet echo chamber going on. § Especially with people who don’t already know her or her work. § Hypothesis: she isn’t garnering new demand from additional affinities § Hypothesis: she’s presenting as a devout Christian writer of suspenseful mysteries. Which is great for reaching the people who know and will buy. April 30, 2014 Using Data to Identify, Target, Connect | ECPA 41
  42. 42. A quick stop at her site to check that is in working order First time we’ve turned away from the audience. Ran WooRank. Site needs some touch up but it will do. So, right into some narrow-­‐casted, inexpensive Facebook ads April 30, 2014 Using Data to Identify, Target, Connect | ECPA 42
  43. 43. Let’s find new fans on Facebook! Our targeting: Women (no age as people don’t enter it) who live in the US, are married, enjoy family life, and as our first interest…Francine Rivers. Then we just take it from there April 30, 2014 Using Data to Identify, Target, Connect | ECPA 43
  44. 44. Then some Twitter followers of Francine Rivers Under 100 to be exact. Strong ones with the characteristics of Terri’s followers April 30, 2014 Using Data to Identify, Target, Connect | ECPA 44
  45. 45. Lastly we go looking for some keywords she can own Very dependent on her name. So, Christian suspense and variants jump out. We make sure she will be able to “own them.” There’s competition but she’ll get in the mix. April 30, 2014 Using Data to Identify, Target, Connect | ECPA 45
  46. 46. A tool called Seorch gives us some good generic ones Christian suspense books Christian suspense fiction Christian suspense novels Christian suspense romance Christian suspense Christian suspense romance Christian suspense writers Christian suspense romance novels Christian suspense author Christian suspense movies Christian suspense series Christian suspense thrillers Christian suspense fiction books Christian suspense book reviews Christian suspense authors list Christian suspense audiobooks Christian suspense fiction authors Top Christian suspense authors Christian suspense publishers Top Christian suspense books Good Christian suspense books Best Christian suspense novels Christian suspense authors fiction Christian suspense romance books Christian fiction suspense series Popular Christian suspense authors And we knew the brand terms well already. Should be no issue to optimize her. April 30, 2014 Using Data to Identify, Target, Connect | ECPA 46
  47. 47. Lastly, we’ll need to optimize for the incoming traffic Would have tightened the middle and bottom of the funnel first but the book is out » We’ll need to overhaul her Amazon presence to include some new terminology, without overdoing it » Speed up her site and get some of that same verbiage there » Add Google Analytics to her site so that we can set up a formal funnel to measure to our goal, which I would advocate should be a newsletter sign up… » It makes life easier when you’ve got a bigger group of people who know and will buy! April 30, 2014 Using Data to Identify, Target, Connect | ECPA 47
  48. 48. Contents » Who am I and why am I here? » Audience-­‐centricity » Data and tools, applied » What’s next? April 30, 2014 Using Data to Identify, Target, Connect | ECPA 48
  49. 49. Constant triangulation – before anything happens And, of course, more often than not it isn’t the chase I just described The smart business of the future will correlate and compute a mix of data including demographics, psychographics, web analytics, social analytics and business intelligence to create predictive scenarios that can be delivered in real time at the point of need. – Paul Simbeck-­‐Hampson Marketing Consultant April 30, 2014 Using Data to Identify, Target, Connect | ECPA 49
  50. 50. These guys predicted the future! Opening weekend movie revenues. With greater than 90% accuracy. Source HP Labs: http://www.hpl.hp.com/research/scl/papers/socialmedia/socialmedia.pdf April 30, 2014 Using Data to Identify, Target, Connect | ECPA 50
  51. 51. Thank you very much. pete@mccarthy-­‐digital.com @petermccarthy April 30, 2014 Using Data to Identify, Target, Connect | ECPA 51
  • MujidatSanni

    May. 26, 2015
  • TedHill1

    Sep. 4, 2014

Tried something new, doing a night-before data-driven marketing dive on an author with whom I was entirely unfamiliar to form and test hypotheses on the off chance that there may remain efficient ways of keeping this book on the bestseller list. So, this deck finds me marketing a niche romance title, Parable, by Terri Blackstock. In real time. I can honestly say it was a ball.

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