Presentation given at PR News Measurement Conference in Chicago on November 18, 2015. Covers key measurement concepts including Share of Voice (SOV), Competitive Benchmarking & Correlations

1. FEAR
FACTOR
METRICS:
A
LOOK
AT
THE
METRICS
COMMUNICATORS
FEAR
THE
MOST
Sandra
Fathi
President
Affect
@sandrafathi
web:
affect.com
blog:
techaffect.com
email:
sfathi@affect.com
PR
News
Measurement
Conference
Chicago,
November
18,
2015
Slides:
www.slideshare.net/sfathi

2. ABOUT
ME
• Sandra
Fathi
• President,
Affect
• Public
RelaKons,
Social
Media,
MarkeKng
• Board
Council
of
PR
Firms
• PRSA
Past
PosiKons:
– Tri-­‐State
Chair
– NY
Chapter
President
– Technology
SecKon
Chair
• Board
PRSA-­‐NY
2
@sandrafathi

3. Technology
Healthcare
Professional Services:
SAMPLE
PAST
&
PRESENT
CLIENTS
@sandrafathi

4. MEASUREMENT
&
METRICS
Measurement Objectives
1. Proving value of public relations activities
2. Proving ongoing improvement in performance
3. Securing headcount/budget for programs
4. Demonstrating ROI compared with true business metrics
Holy Grail:
PR = Sales
@sandrafathi

5. 5
PR
MEASUREMENT
Sample
Business
Metrics
• Market
PenetraKon
• Market
Share
• Lead
GeneraKon
• Revenue
• Cost-­‐Savings
Sample
MarkeKng
Metrics
• Traffic
to
Website
• Downloads
• RegistraKons
• Lead
GeneraKon
Measure
what
maYers
to
the
C-­‐Suite
In
a
language
they
understand
–
and
value
@sandrafathi

6. MEASUREMENT
&
METRICS
Sample
PR
Key
Performance
Indicators
(KPIs):
1. Scores: Indices/scoring mechanisms to track valuable outcomes/results
• Quantity: sheer volume of media hits
• Quality: score for Tier 1,2,3, score for feature, prominent, mention
2. Correlations: Between outputs, outcomes and business results.
• Track events with lead generation (online, email, phone, events)
• Track PR/social events with Web traffic
3. Check Boxes: Meeting specific, finite objectives
• # of articles/month
• # of articles in target industries/vertical markets
• # of press releases per year
• # of members/attendees/downloads/registrations (hard numbers)
@sandrafathi

7. PR
MEASUREMENT
Three
Concepts
for
Discussions:
• Share
of
Voice
• CompeKKve
Benchmarking
• CorrelaKons
@sandrafathi
7

8. PART
I:
SHARE
OF
VOICE

9. DEFINITION
Share
of
Voice:
Comparing
your
crucial
performance
metrics
against
those
of
compeKtors
or
the
market.
• You
have
to
measure
something
• What
you
measure
needs
to
be
analyzed
proporKonately
against
compeKtor
data
(or
market
data)
to
establish
market
share
@sandrafathi
9

10. THE
FORMULA
Number
of
ConversaKons
That
Include
Your
Company
=
X
*
100
=
%
SOV
Total
ConversaKons
on
a
Topic
@sandrafathi
10

11. ADVERTISING
CONCEPT
25%
SOV
75%
SOV
@sandrafathi
11

12. SHARE
OF
VOICE
I
72%
28
%
Total
ConversaKons
Talk
About
Me
@sandrafathi
12

13. SHARE
OF
VOICE
II
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Q1
Q2
Q3
Q4
CompeKtor
C
CompeKtor
B
CompeKtor
A
Our
Company
@sandrafathi
13

14. KEEP
IN
MIND
• Share
of
voice
should
be
defined
for
a
period
of
Kme
(finite
start
and
end).
• Share
of
voice
is
omen
most
useful
when
limited
to
a
single
planorm
or
medium.
For
example,
business
press
coverage
or
TwiYer.
• Share
of
voice
can
be
overwhelming
if
trying
to
look
at
too
large
a
segment
or
industry.
Try
choosing
SOV
among
top
compeKtors
or
in
key
interest
areas.
@sandrafathi
14

15. SOV:
SOCIAL
MEDIA
ANALYTICS
PLATFORMS
@sandrafathi
15

16. SOCIAL
MENTION
@sandrafathi
16

17. SIMPLE
EXCEL
FORMULA
@sandrafathi
17

18. ONLY
PART
OF
THE
STORY
• Doesn’t
consider
senKment
• Doesn’t
consider
sources
(exclude
self
produced/owned
media)
• Doesn’t
consider
quality,
only
quanKty
(Is
NYT
blog
same
as
obscure
geek’s
tweet?)
• Don’t
accept
the
data
blindly
–
human
verificaKon
is
required
with
any
tool
@sandrafathi
18

19. OTHER
APPLICATIONS
&
CONSIDERATIONS
ConsideraKons:
• Apply
senKment
or
tonal
filters
(posiKve/negaKve)
• Apply
qualitaKve
measures
(by
Ker
or
by
type)
ApplicaKons:
• Industry
trends/hot
topics
(i.e.
SOV
on
cloud
security)
• Specific
products
or
services
• Broken
down
by
geographic
or
demographic
parameters
(i.e.
SOV
in
18-­‐25
market)
@sandrafathi
19

20. PART
II:
COMPETITIVE
BENCHMARKING

21. DEFINITION
CompeKKve
Benchmarking:
The
conKnuous
pracKce
of
comparing
a
company’s
pracKces
and
performance
metrics
against
the
most
successful
compeKtors
in
the
industry.
• You
measure
processes
and
results
• You
must
idenKfy
a
‘benchmark’
or
indicator
that
will
be
a
unit
of
measure
to
compare
• The
desired
outcome
is
to
understand
which
processes
lead
to
greater
success
(best
pracKces)
in
order
to
improve
your
company’s
performance
@sandrafathi
21

22. COMPETITIVE
BENCHMARKING
• IdenKfy
my
compeKKve
set
for
comparison
• Choose
my
units
of
measure:
press
coverage
• Set
parameters:
top
20
business
and
trade
• Define
a
Kme
period:
6
months
• Choose
a
tool
(news
monitoring
service)
or
begin
manual
research
@sandrafathi
22

23. EXAMPLE:
RADWARE
ObjecKve:
• Build
&
Maintain
Radware’s
PosiKon
as
a
Thought
Leader
on
Security
• Maximize
Radware’s
Overall
Public
RelaKons
Results
Strategy:
• Compare
and
Contrast
Radware’s
Press
Release
Output
with
Top
3
Security
CompeKtors
• Analyze
Results
• Apply
Best
PracKces
and
Lessons
Learned
to
Radware
to
Improve
Overall
Performance
@sandrafathi
23

24. EXAMPLE:
RADWARE
Network
Security
CompeKtors
@sandrafathi
24

25. • Analysis
of
press
release
strategy
and
resulKng
coverage
over
6
month
period
• Specifically
as
it
relates
to
relevant
products
or
business
units
• Only
in
top
20
business
and
industry/sector
publicaKons
METHODOLOGY
@sandrafathi
25

26. RADWARE
PRESS
RELEASES
Security
43%
ADC
27%
Both*
12%
Other*
18%
Press
Releases
*
‘Both’
includes
releases
related
to
both
security
and
ADC,
‘Other’
includes
non-­‐product
releases
(e.g.
company
news,
financial
announcements
etc.)
Press
Releases
Security
14
ADC
9
Both
2
Other
6
@sandrafathi
26

27. SECURITY
COMPETITORS
14
23
28
10
84
164
68
68
0
20
40
60
80
100
120
140
160
180
Radware
Arbor
Imperva
Prolexic
Press
Releases
ArKcles
PRESS
RELEASES
VS.
NUMBER
OF
ARTICLES
@sandrafathi
27

28. SECURITY
COVERAGE
BY
TYPE
0
50
100
150
200
Radware
Arbor
Imperva
Prolexic
Other
Report
Commentary
AYack
AcquisiKon
Partner
Customer
Product
Customer
Partner
AcquisiKon
AYack
Commentary
Report
Other
Radware
8
9
6
0
28
22
11
2
Arbor
19
1
1
18
29
44
52
0
Imperva
2
1
1
0
8
18
19
19
Prolexic
0
0
0
0
43
2
14
9
@sandrafathi
28

29. COVERAGE
BY
QUALITY
0
20
40
60
80
100
120
140
160
180
Radware
Arbor
Imperva
Prolexic
MenKons
Features
35%
65%
31%
69%
34%
66%
54%
46%
FEATURE
VS.
MENTION
@sandrafathi
29

30. SECURITY
CONCLUSIONS
• Radware
is
#2
in
overall
SOV
but
the
quality
is
not
as
strong
(more
menKons
vs.
features)
• Leading
customer
and
partner
conversaKons
(ValidaKon)
• Good
job
at
Story
Hijacking
(responding
to
security
hacks)
but
room
for
improvement
(ValidaKon)
• CompeKtors
winning
at
report
coverage
and
commentary
(Opportunity!)
@sandrafathi
30

31. CONSIDERATIONS
• Good
for
understanding
what
worked
but
not
necessarily
‘how’
it
worked
• Costs
for
research
may
outweigh
benefits
of
insights
• Once
you’ve
idenKfied
the
‘best
pracKces’
you
may
or
may
not
be
able
to
replicate
them
• Consider
non-­‐compeKtor
companies
to
benchmark
• Do
you
want
to
‘emulate’
or
‘innovate’?
@sandrafathi
31

32. PART
III:
CORRELATIONS

33. DEFINITION
CorrelaKon:
A
mutual
relaKonship,
or
interdependence,
between
two
or
more
things.
• In
the
absence
of
being
able
to
prove
‘causality’
you
may
be
able
to
demonstrate
a
‘correlaKon’
to
demonstrate
the
impact
of
a
PR
or
markeKng
program
• A
correlaKon
is
posiKve
when
the
values
of
both
variables
increase
together
• A
correlaKon
is
negaKve
when
the
value
of
one
variable
increases
while
the
value
of
the
other
variable
decreases
@sandrafathi
33

34. TYPES
OF
CORRELATION
Source:
MathisFun.com
34

35. THE
FORMULA
35
Pearson’s
CorrelaKon:
@sandrafathi

36. FUNCTION
IN
EXCEL
36
@sandrafathi

37. CORRELATION
IN
EXCEL
37
@sandrafathi

38. FUNCTION
IN
EXCEL
38
@sandrafathi

39. SCATTER
CHART
39
@sandrafathi

40. LINE
CHART
40
@sandrafathi
AcquisiKon

41. MULTIPLE
DATA
SETS
41
@sandrafathi
0
500
1000
1500
2000
2500
3000
3500
4000
4500
Q1
Q2
Q3
Q4
Sales
Web
Traffic
Press
Coverage

42. SPURIOUS
CORRELATION
42
@sandrafathi
Source:
TylerVigen.com

43. 43
@sandrafathi
Source:
TylerVigen.com
SPURIOUS
CORRELATION

44. CONSIDERATIONS
• User
correlaKons
cauKously
and
don’t
trust
the
math
blindly
• The
visuals
omen
tell
a
story
as
well
• Remember
that
correlaKon
is
not
causality,
it
can
only
help
as
an
indicator
or
potenKally
predict
probability
• Data
is
sKll
beYer
that
your
opinion
44
@sandrafathi

45. FINAL
THOUGHTS
• In
measurement,
speak
the
language
of
the
C-­‐Suite
• Excel
is
sKll
the
best
dashboard
for
data
visualizaKon
• Don’t
be
afraid
to
learn
that
you
are
wrong
• Don’t
be
afraid
to
change
direcKon
• Use
the
data
to
gain
execuKve
support
– Strategy
– Resources
– Headcount
– Budget
45
@sandrafathi

46. THANK
YOU
CONTACT:
Sandra
Fathi
President
Affect
@sandrafathi
web:
affect.com
blog:
techaffect.com
email:
sfathi@affect.com
Slides:
www.slideshare.net/sfathi