2. 10 point of views. You agree or not?
______________________________________________________________________________
1. Distrust
of
adver0sing
is
the
most
important
reason
why
consumers
rely
more
and
more
on
recommenda0ons.
2. Recommenders
have
a
narcissis0c
personality
disorder.
Helping
others
is
the
least
of
their
business.
3. Brands
should
invest
more
in
their
product
and
service
delivery
instead
of
raising
their
marke0ng
budget.
4. When
brands
measure
sa0sfac0on
and
re-‐purchase
inten0on
among
your
own
customers,
you
know
everything
you
have
to
know.
5. Don’t
waste
your
0me
neutralizing
the
detractors
of
a
brand
and
try
to
wake
up
the
passives.
Focus
on
the
promoters.
6. Recommenders
win
the
baJle
on
facts/features
based
argumenta0on.
Never
on
emo0ons.
It’s
a
“ Test-‐Aankoop”
world
we
live
in.
7. Unless
you
day-‐aTer-‐day
search
for
recommenders,
make
them
happy
end
influence
them
you
will
never
succeed
in
the
new
marketplace.
8. RFM
will
remain
thé
parameter
for
segmenta0on
of
your
marke0ng
ac0vi0es.
9. Media
that
have
the
highest
%
of
brand
recommenders
should
be
the
prime
medium
in
which
brands
have
to
buy
space/0me.
10. Social
media
like
TwiJer,
Facebook,
Instagram,
Pinterest
are
the
best
performing
media
for
recommenders
to
influence
their
followers.
3. I am a recommender. One of the 15% recommenders worldwide.
______________________________________________________________________________
4. Recently, on my Facebook-page I recommended Europe … while I made
clear I didn’t recommend Shanghai.
______________________________________________________________________________
5. I even was an active multimedia recommender. I took pictures and
screenshots. That’s probably only 1% of all citizens doing that.
______________________________________________________________________________
6. I published my recommendations on Buzzfeed to make my case
stronger and get more reach. (I did it once☺)
______________________________________________________________________________
7. There we go! That’s how it looked like when it was
published.
______________________________________________________________________________
8. I could follow closely the (meager) results of my recommendations.
______________________________________________________________________________
9. I am more successful to attract readers for my recommendations on
Tripadvisor though.
______________________________________________________________________________
10. Are all of these other snippets also recommendations? Like mine?
Not really. But some people think they are…
______________________________________________________________________________
And nobody
mentions TV?
11. Q&A’s in the next 15 slides.
______________________________________________________________________________
Why do brands and media get more and more
interested in recommenders?
Why do some consumers write recommendations?
Why do all the others read their recommendations?
Why are they willing to be influenced?
12. Recommenders directly influence 20-50% of all purchase
decisions.
______________________________________________________________________________
13. The motives of recommenders?
Zocalo Research
______________________________________________________________________________
14. The
latest
data
on
influencing
power
from
…
China
(Epsilon
2013).
______________________________________________________________________________
“Please indicate which, if any, of the following influence your decisions when
deciding whether or not to purchase or sign up for a product or a service”
15. “Friends & family” are the trusted source. Where is advertising …?
______________________________________________________________________________
21. Trust?
Watch out. Some reviews can be fake. Consumers are fully aware.
______________________________________________________________________________
22. The big issue: “Influencing power” and “action generating power” of
paid, owned and earned media (Nielsen 2013)
______________________________________________________________________________
Earned
Paid
Owned
23. Hence: brands permanently remix Paid, Owned and Earned Media
based on their respective ROI (Zenith 2013)
______________________________________________________________________________
Earned media
have most
influence, are
more trusted
and generate
more action
Paid media generate
more touch points,
hence they are strong in
building awareness.
Their “Opportunity To
Be Seen” is bigger.
Paid media cost more than earned
and owned media. Brands plan to
invest less in paid media in the
coming years.
24. ______________________________________________________________________________
Prof.
Dr.
Gerard
Tellis
1980-‐1990:Adver.sing
+10%
=
+2.2%
marketshare
2008:Adver.sing
+20%
=
+2.2%
marketshare
“
….
the
authors
conduct
a
meta-‐analysis
of
751
short-‐term
and
402
long-‐term
direct-‐to-‐
consumer
brand
adverCsing
elasCciCes
esCmated
in
56
studies
published
between
1960
and
2008.
the
study
finds
several
new
empirical
generalizaCons
about
adverCsing
elasCcity.
the
most
important
are
as
follows:
the
average
short-‐term
adverCsing
elasCcity
is
.12,
which
is
substanCally
lower
than
the
prior
meta-‐analyCc
mean
of
.22;
there
has
been
a
decline
in
the
adverCsing
elasCcity
over
Cme.”
Gerard
Tellis,
PhD
Michigan,
is
Professor
of
Marke.ng,
Management,
and
Organiza.on,
Neely
Chair
of
American
Enterprise,
and
Director
of
the
Center
for
Global
Innova.on,
at
the
USC
Marshall
School
of
Business.
He
is
Dis.nguished
Visitor
of
Marke.ng
Research,
Erasmus
University,
RoVerdam
and
has
been
Visi.ng
Chair
of
Marke.ng,
Strategy,
and
Innova.on
at
the
Judge
Business
School,
Cambridge
University,
UK.
Tellis
specializes
in
the
areas
of
innova.on,
adver.sing,
global
strategy,
market
entry,
new
product
growth,
promo.on,
and
pricing.
25. One of many brands’ issues: “The ROI of advertising is not what it
was before”.
______________________________________________________________________________
YOY-growth
+7%
+3,8%
+3,8%
+4,6%
+5,2%
26. And when people talk advertising. They still talk TV.
Let’s have a look.
______________________________________________________________________________
27. 18-24 Year Old in the US watching less TV. Doing what instead?
______________________________________________________________________________
28. They generate content. “User generated content”. Or they watch that
UGC. (Recommendations are part of it).
______________________________________________________________________________
29. User generated content grows. Mainly on mobile.
But of course, not all UGC are recommendations.
______________________________________________________________________________
32. “50% of the internet traffic today is video. Looks like 80% to 90% of internet
traffic will be video in the next few years. Video for us is a place that
consumers really like.” (Tim Armstrong AOL)
______________________________________________________________________________
“Google's biggest revenue driver in the future. Mark Suster of Upfront Ventures, which invests in a large YouTube
content partner, suggested to the Wall Street Journal that the video platform could soon be generating $20bn in
revenue.”
33. VRT & Video & Instagram. Vers nieuws. Fijngesneden (1)
______________________________________________________________________________
34. VRT & Video & Instagram. Vers nieuws. Fijngesneden (2)
______________________________________________________________________________
35. VRT & Video & Instagram. Vers nieuws. Fijngesneden (3)
______________________________________________________________________________
36. Next to TV there are also media like Twitter. Let’s have a look here too…
______________________________________________________________________________
37. Tweeted online recommendations are “massive”
broadcast + engagement.
______________________________________________________________________________
38. Only 3.6% of tweets are about brands.
Majority of recommendations are offline!
______________________________________________________________________________
39. Offline! Still the method of recommendation.
______________________________________________________________________________
41. Industry sectors in which brands are mentioned on Twitter
______________________________________________________________________________
42. The social consumer. Buying a lot based on recommendations from
other social consumers
______________________________________________________________________________
44. Some buy a lot online and tell it to a lot of people online too.
______________________________________________________________________________
45. What do we suggest you to do @ this ever changing marketplace?
______________________________________________________________________________
1. Measure
your
own
recommenda0on
power
–
use
NPS
–
read
Reichheld’s
books
–
or
hire
us
for
a
while.
2. Measure
your
compe0tors’
recommenda0on
–
use
“public,
real
0me,
con0nuous”
benchmarkers
like
Holaba.
3. Never
stop
the
search
for
recommenders.
Also
in
your
own
database.
They
are
so
important.
4. Iden0fy
recommenders
(Socio
–
Demo).
Don’t
rely
on
samples.
5. Gather
insights
(why
do
they
say
what
they
say)
of
real
recommenders.
6. Select
the
media
to
influence
these
influencers
7. Don’t
push
too
much.
Let
them
find
you.
8. Be
nice
and
trustworthy.
Be
human.
They
are
human
media
too.
9. Do
all
the
above
points
permanently,
wherever
you
can.
10. Add
the
recommenda0on
data
to
data-‐streams
about
traffic,
conversion,
sales,
sa0sfac0on,
re-‐purchase
intent,
rfm
models
….
11.
Adjust
your
tac0cs
everyday.
Never
give
up.
Measure
everything.
12. DIY!
46. Moments of truth. All 3 are crucial.
______________________________________________________________________________
1st
moment
of
truth
2nd
moment
of
truth
3rd
moment
of
truth
Buying
Consuming
Evalua.ng
Consumer
(Neutral)
Promoter><Detractor
85%
are
being
influenced
Selec.ng
shop
Selec.ng
brand
15%
are
“influencing”
others
47. Why is “recommendation measurement” so crucial?
______________________________________________________________________________
14
7
49. From
RFM
to
RRFM
to
decide
about
what
to
invest
where.
______________________________________________________________________________
Recency,
frequency,
monetary
value
(RFM)
of
clients
are
decisive
for
investment
in
marke.ng
communica.on.
Therefore
lots
of
money
spent
(wasted)
in
this
group
of
heavy
and
recent
buyers
Light
and
non
frequent
buyers
are
oden
“neglected”
RFM
-‐
axis
50. The recommendation power of clients becomes the decisive tool to
decide on marcom-investments
______________________________________________________________________________
RFM
-‐
axis
+RFM
&
-‐
REC
+RFM
&
+
REC
Does
not
mean
they
all
give
posiCve
recommendaCons
Recommenda0on
axis
Frequency
and
intensity
of
recommenda2on.
-‐
RFM
&
+
REC
-‐
RFM
&
-‐
REC
52. “If you know the enemy and know yourself, you need not
fear the result of a hundred battles.”
______________________________________________________________________________
“If
you
know
the
enemy
and
know
yourself,
you
need
not
fear
the
result
of
a
hundred
baWles.
If
you
know
yourself
but
not
the
enemy,
for
every
victory
gained
you
will
also
suffer
a
defeat.
If
you
know
neither
the
enemy
nor
yourself,
you
will
succumb
in
every
baWle”
Sun
Tzu,
The
Art
of
War,
hVp://www.youtube.com/watch?v=erZ2YidTZp4.
Minute
06:45
Marke2ng
is
War.
It
s2ll
is.
53. The Holaba data. Produced by our benchmarking-tool based on the
“Net Promoter System”.
______________________________________________________________________________
What
is
NPS*?
“Net
Promoter
System”
was
launched
in
2003
by
Mr.
Reichheld
of
Bain.
It
not
only
measures
the
recommendaCon
power
of
brands
but
also
of
consumers.
Consumers
can
either
be
promoters,
detractors
or
just
fence
siWers.
The
One
Ques2on
Holaba
asks
always
and
everywhere.
How
likely
is
it
that
you
will
recommend
this
brand?
Very likely
The
calcula2on
of
the
recommenda2on
score
based
on
1000’s
of
consumer
scores.
10
9
Not likely
8
7
SUM
OF
9
&
10
SCORES
58,1%
of
the
scores
are
9
or
10
5
4
3
2
1
0
SUM
OF
ALL
OF
ALL
0
TO
6
SCORES
38,1%
of
all
scores
are
in
between
0
and
6
included.
-‐
58,1
38,1
=
+20
NPS-‐score
&
Holaba-‐score
are
indicators
of
recommenda2on
power.
In
this
case
the
score
of
the
brand
is
+20
Benchmarking
to
find
out
where
all
major
compe2tors
are
on
this
scale.
6
-‐100
+100
A
NPS-‐score
is
a
number
in
between
-‐100
and
+100
* Net Promoter, NPS, and Net Promoter Score are trademarks of Satmetrix Systems, Inc., Bain & Company, and Fred Reichheld.
54. The
flow
of
the
ques0ons
that
produce
the
data.
Easy
and
fast
for
consumers.
S0ll
delivering
plenty
of
informa0on.
______________________________________________________________________________
1. How likely is it that you would recommend this brand?
0
1
2
3
4
5
6
7
8
9
10
Scores
in
between
0
and
10.
2. What is your experience with this brand?
None
Have
it
Had
it
Will
have
it
Could
have
it
5
experience
levels
3. Why do you give the score you give?
This
is
what
I
like:
“….”
Easy
for
consumers
to
give
posi2ve
and
nega2ve
comments
This
is
what
I
don’t
like:
“…”
This
is
what
they
should
improve:
“…”
55. Day after day Holaba-users can share opinions.
That way they gradually build their brand profile.
______________________________________________________________________________
Scores Scores
9-‐10
9-‐10
Scores
9
-‐10
Scores
9-‐10
Scores
9-‐10
Scores
9-‐10
Scores
9-‐10
The brands these boys also
recommend
Scores
9-‐10
Scores
9-‐10
Scores
9-‐10
Scores
9-‐10
Scores
9-‐10
Brand Profile of boys 18-25 yrs old,
who all give a very high 9 & 10
recommendation score to brand X.
Scores
0-‐6
Scores
0-‐6
Scores
Scores
Scores
0-‐6
Scores
0-‐6
0-‐6
Scores
0-‐6
0-‐6
Scores
Scores
0-‐6
Scores
0-‐6
0-‐6
Scores
Scores
0-‐6
0-‐6
Scores
0-‐6
The brands they don’t
recommend at all.
56. Holaba-‐data
are
data
our
registered
users
share
about
the
brands
they
recommend.
______________________________________________________________________________
1.
We
know
the
brands
they
do
&
don’t
recommend
Our
users
build
profiles
brand
aYer
brand.
-‐
all
users
build
their
personal
brand
profile.
2.
We
know
(since
they
tell
us)
their
status
with
the
brands
they
review
-‐
actual
user
-‐
past
user
-‐
future/potenCal
user
3.
ATer
a
while
we
also
know
who
is
a
recommender
We
learn
who
the
recommenders
are.
or
not.
-‐
Why
is
that
important?
•
•
The
more
recommenders
we
iden.fy,
the
higher
the
return
on
marke.ng
investment
will
be:
only
then
brands
can
start
influencing
these
influencers
first.
Someone
who
mainly
gives
6_7_8
scores
is
probably
not
a
recommender,
but
a
fence
siVer.
°
And
of
course
we
know:
1.
2.
3.
4.
Gender
&
Age
Loca.on
Educa.on
(income?)
Network
1.
2.
3.
Size
of
their
network
Degree
of
interac.vity
–
Klout
score
SNS-‐member
ship
5. Phone
number
&
Email
address
(physical
address
if
needed)
57. Brands
indeed
win
more
baJles
when
they
monitor
their
own
recommenda0on
score
…
and
compare
it
to
their
compe0tor’s.
______________________________________________________________________________
Holaba
is
the
ideal
tool
for
companies
…
– Who
already
monitor
their
own
recommenda.on
power.
– Who
want
to
partly
base
their
strategy
and
tac.cs
on
the
score
of
their
compe.tors.
Are
you
in
a
good
posi2on
for
the
coming
months?
EXAMPLE
Your
own
research
points
out
that
your
recommenda.on
score
is
+20
Now
you
want
to
know
how
good
or
bad
that
is,
since
an
isolated
score
shows
only
your
part
of
the
story.
Very bad ranking
for “You “
1
2
3
4
Competitor
Competitor
Competitor
Competitor
5
6
7
8
9
Competitor
Competitor
Competitor
Competitor
Competitor
10.You (+20)
Excellent
ranking for “You”
1.You (+20)
2 Competitor
3 Competitor
4 Competitor
5
6
7
8
9
Competitor
Competitor
Competitor
Competitor
Competitor
10 Competitor
More or less ok
ranking for “You”
1
2
3
4
Competitor
Competitor
Competitor
Competitor
2.You (+20)
6
7
8
9
Competitor
Competitor
Competitor
Competitor
10 Competitor
When
you
know
the
score
your
clients
give,
you
also
need
to
know
the
ranking
of
you
and
your
compe2tors.
A
high
score
and
be^er
ranking
is
a
prelude
of
a
growing
market
share.
58. Two
reasons
why
the
Holaba-‐data
are
important
for
‘paid
media’
too.
______________________________________________________________________________
1.
Media
that
know
which
brands
their
audience
recommend
or
not
recommend
(i.e.
brand
profiling)
have
more
arguments
to
sell
space
&
0me
to
brands.
Selling
to
readers/viewers/listeners
who
recommend
a
brand
is
different
from
selling
to
non-‐
recommenders.
2.
The
more
“recommenders”
a
medium
has,
the
higher
its
value:
they
can
sell
access
to
them
at
a
higher
price.
Selling
to
the
15%
brand
recommenders
gives
brands
more
potenCal
to
generate
free
word
of
mouth.
Recommenders have a
vast network and talk
about brands.
Fence sitters have a
smaller network and talk
less about brands.
59. For brands that buy time & space in media, the added value of the
15% (or more) recommenders among media-audience is huge.
______________________________________________________________________________
Brands
Media
“Just
humans”
“Human
media”
“Just
humans”
Extra-contacts generated : brands reach consumers through
media, but since not all consumers
are created equal, some become
“human media” and some not.
When
brands
influence
the
influencers
in
your
audience,
they
influence
more
than
just
the
influencers.
60. Do ask the total market. Do ask “non-clients” too. They too judge, talk
and influence. That’s why we ask them.
______________________________________________________________________________
NPS
61. Those who “want, but cannot yet” are strong recommenders too.
In 2006 I recommended even Jaguar. Now …Tesla.
______________________________________________________________________________
62. 1.Comparison based on turnover/M2
______________________________________________________________________________
63. 2. Comparison based on sold items/ticket
______________________________________________________________________________
64. 3. Comparison based on ticket price.
______________________________________________________________________________
65. 4. Comparison with objectives in annual budget
______________________________________________________________________________
66. 5. Comparision with sales in previous year
______________________________________________________________________________
67. Brandprofiles built on the Holaba-platform
______________________________________________________________________________
68. 15%
of
all
consumers
are
recommenders.
We
find
them
where
they
are
ac0ve.
______________________________________________________________________________
“Recruitment”
will
be
most
oTen
indirect.
Through
brands’
‘owned
media’
and
‘paid
media’
like
yours.
•
•
•
•
On
your
newssite
(widget
on
home
page,
pop-‐up
to
survey)
In
your
email
to
clients,
prospects,
former
clients
On
the
package
Off
-‐
&
online
shop.
(The
best
recruitment
moment
is
immediately
ader
a
purchase)
•
•
During
phone
survey’s
through
your
call
center.
In
an
SMS.
We
target
recommenders.
They
search
and
check
review
sites
more
oTen.
– They
are
indeed
very
ac.ve
when
they
are
in
a
pre-‐purchasing
phase
and
search
for
second
opinions.
• Like
they
use
Tripadvisor
or
any
other
review-‐site
…
when
they
feel
the
need.
– We
indeed
want
those
respondents
(15%
of
all
consumers)
who
are
pro-‐ac.ve
and
eager
to
voice
their
opinion.
Pushing
someone
into
giving
his
opinion
doesn’t
work.
69. Paid media that support the Holaba-tool & drive traffic to it in their
medium will get access to the data. Which data will they get?
______________________________________________________________________________
Which
real
users
give
high/low
scores?
On
which
brands?
Real
consumers
are
beWer
than
any
anonymous
survey
Why
do
they
give
the
scores
they
give?
How
does
your
audience
compare
-‐on
a
brand
profile
level-‐
with
other
users
in
our
plaworm?
What
is
the
%
of
recommenders
in
your
audience?
He gave a 10
She
gave
a
6
He gave a 10
He
gave
a
5
He
gave
a
7
He
gave
a
8
She gave a 9
She
gave
a
3
He
gave
a
10
He gave a 9
70. We don’t need a lot of “samples” to have trustworthy results,
representative for “the recommenders”.
______________________________________________________________________________
We
concentrate
on
those
consumers
–recommenders-‐
who
make
the
difference.
They
influence
the
others:
recommenders
influence
directly
20-‐50%
of
purchases.
We
don’t
survey
a
couple
of
1000
average
consumers
(representa2ve
for
10.000.000
average
consumers)
We
iden2fy
the
“born
recommenders”
(15%
of
all
consumers)
who
are
representa2ve
for
other
recommenders.
What
is
a
valuable
survey
within
a
given
period?
• 1.
Each
survey
should
have
1000-‐1500
parCcipaCng
registered
users.
• 2.
Brands
reviewed
by
less
than
100
consumers
spontaneously,
will
not
be
included
in
the
overall
result.
Confidence level
15%
recommenders
Confidence interval
Sample size needed
Confidence interval
All
consumers
Sample size needed
Confidence interval
Sample size needed
Confidence interval
Sample size needed
Hypothetical population 100.000.000
hVp://marketresearch.about.com/od/market.research.surveys/a/Surveys-‐Research-‐Confidence-‐Intervals.htm
95%
99%
4
4
600
1.040
3
3
1.067
1.849
2
2
2.401
4.160
1
1
9.306
16.638
71. Brands/Media
will
get
plenty
of
data
to
improve
their
business
on
several
aspects.
Even
at
dealer-‐level.
______________________________________________________________________________
1.
2.
3.
4.
5.
6.
7.
Evalua0on
of
exis0ng
products
and
services
New
product
development-‐ideas
Price
elas0city
calcula0on
Distribu0on
analysis
Marke0ng
posi0oning
Communica0on
audit
ATer-‐sales
performance
–
8.
Dealer
performance
data
BeJer
segmenta0on
–
Up
to
the
level
of
age,
loca.on
(country,
region,
city,)
gender,
…
and
plenty
of
other
data
you
want
to
gather
in
the
private
part
72. Even
more
ac0onable
data:
discovery
and
Iden0fica0on
of
new,
interes0ng
consumers.
______________________________________________________________________________
1.
2.
3.
4.
Discovery
of
recommenders
on
as
many
external
plaworms
as
possible
You
will
find
them
anywhere.
You
only
have
to
be
ac.ve.
Iden0fy
them.
Name,
gender,
loca.on,
brand-‐scores
&
reviews,
brand
profile
and
many
more.
Understand
them.
Why
do
they
give
the
score
they
give.
They
intui.vely
update
your
SWOT-‐analysis.
Based
on
individual
consumer
insights,
brands
can
target
and
personalize
their
communica.on
on
several
plasorms.
Influence
them.
“Influence
the
influencers”
because
influencers
are
media
-‐
human
media
74. The difference between a “recommended” beer and a marketing beer.
______________________________________________________________________________
http://www.ratebeer.com/
75. The difference in family fortune between a “recommended” beer
and a marketing beer.
______________________________________________________________________________
$11 billion
€ 270 million