1. Steve Susina
Marketing Director, LYONSCG

2. Page
2
@ssusina #MKTGNATION
Our Journey
• The
Evolving
Martech
stack
• What
is
Predic7ve
analy7cs/marke7ng/scoring
• Making
A
Decision
• Internal
Business
Case
/
Selling
the
Execs
• How
we
Evaluated
• Apply
to
prior
7
months
data
(July
2015
–
January
2016)
• Review
Mee7ngs,
Opportuni7es,
Pipeline,
Closed
Business
• Analysis
of
Prospec7ng
• Results
• Lessons
Learned
/
Work
to
do

3. Page
3
@ssusina #MKTGNATION
The Evolving MARTECH Stack
MARKETING AUTOMATION
MARKETO
SOCIAL MEDIA &
CURATION
FEEDLY & BUFFER
DATA
DATA.COM, ETAIL INSIGHTS,
LINKEDIN, HOOVERS,
BUILTWITH
CONTENT WORKFLOW
DIVVYHQ
WEBSITE/CONTENT MGT
WORDPRESS
WEBINARS
GO-TO-WEBINAR
CONTENT GENERATION
GRAMMARLY
SPEECHPAD
MEDIA RELATIONS
PR WEB/CISION
ANALYTICS
GOOGLE ANALYTICS, MARKETO
QUILL BY NARRATIVE SCIENCECRM
SALESFORCE.COM

4. Page
4
@ssusina #MKTGNATION
It Starts . . .
Marketing Nation 2015
• Recognized
the
buzz
about
Predic7ve
• Research
&
Educa7on
• Conclusion
.
.
.
We
know
our
prospects
.
.
.
We
have
a
defined
ICP
.
.
.
We
have
a
good
lead
scoring
model
.
.
.
We
Don’t
Need
This!

5. Page
5
@ssusina #MKTGNATION
Mountains of Data
Known
Engaged
MQL
SAL

6. Page
6
@ssusina #MKTGNATION
Mountains of Data
Known
Engaged
MQL
SALWARNING
Falling
Conversion
Rates

7. Page
7
@ssusina #MKTGNATION
Problem: Too Much and Too Little Data

8. Page
8
@ssusina #MKTGNATION
Chet Holmes 3% Rule

9. Page
9
@ssusina #MKTGNATION
Moments of Clarity
• TOPO
B2B
Predic7ve
Technology
Report
• Forrester
Report
“New
Technologies
Emerge
To
Help
Unearth
insight
From
Mountains
of
B2B
Data

11. Using
these
tools
.
.
.
.
.
.
considering
these
.
.
.
.
.
.
PA
is
next
step
on
the
con7nuum.

12. I should look at Predictive Analytics
again!

13. Page
13
@ssusina #MKTGNATION
Predictive Analytics

14. Page
14
@ssusina #MKTGNATION
Interesting Market Dynamics
• Number
of
strong,
venture-­‐funded
firms
with
seemingly
similar
models
• Labce
Engines
• 6
Sense
• Min7go
• Infer
• Leadspace
• Everstring
• FlipTop
exited
w/
LinkedIn
acquisi7on
in
late
2015
• Strong
desire
by
industry
players
to
build
client
base
ahead
of
consolida7on,
posi7on
for
addi7onal
funding,
acquisi7on

15. Page
15
@ssusina #MKTGNATION
Interesting Market Dynamics
• Number
of
strong,
venture-­‐funded
firms
with
seemingly
similar
models
• Labce
Engines
• 6
Sense
• Min7go
• Infer
• Leadspace
• Everstring
• FlipTop
exited
w/
LinkedIn
acquisi7on
in
late
2015
• Strong
desire
by
industry
players
to
build
client
base
ahead
of
consolida7on,
posi7on
for
addi7onal
funding,
acquisi7on

16. Page
16
@ssusina #MKTGNATION
Interesting Market Dynamics
• Number
of
strong,
venture-­‐funded
firms
with
seemingly
similar
models
• Labce
Engines
• 6
Sense
• Min7go
• Infer
• Leadspace
• Everstring
• FlipTop
exited
w/
LinkedIn
acquisi7on
in
late
2015
• Strong
desire
by
industry
players
to
build
client
base
ahead
of
consolida7on,
posi7on
for
addi7onal
funding,
acquisi7on

17. What IS Predictive Analytics?
Statistical Model based on our
Closed-Won and Closed-Lost data
Integrates with our Salesforce
and Marketo databases
Scoring
model
applied
to
our
exis7ng
data
New
Lead
Acquisi7on
External
Buying
Triggers

18. Page
18
@ssusina #MKTGNATION
ABOUT OUR TRIAL
• Ini7ated
Trial
with
Everstring
12/2015
• Analysis
of
our
exis7ng
Closed-­‐Won
and
Closed-­‐Lost
• Crea7on
of
data
model
using
buying
triggers
• Built
model
to
create
predic7ve
score
of
our
exis7ng
database
and
real-­‐7me
scoring
on
all
newly
created
leads
• Lead
genera7on
component

19. Page
19
@ssusina #MKTGNATION
Our Database Model

20. Page
20
@ssusina #MKTGNATION
Baseline performance of our data

21. No
way
to
validate
costs
based
on
the
incremental
lead
genera7on
/
cost
per
lead.
Evaluating Predictive Analytics
Two Month Trial Six Month Sales Cycle

22. Page
22
@ssusina #MKTGNATION
Analysis of 167 SCHEDULED MEETINGS
(Inbound and Prospected) from US ISRs
July 2015 to February 2016
50
48
33
36
Prospec(ng
Mee(ngs
-­‐
Overall
A
B
C
D

23. Page
23
@ssusina #MKTGNATION
Inbound vs. prospecting-driven
meetings
40
41
16
19
10
7
17
17
0
10
20
30
40
50
60
As
Bs
Cs
Ds
Inbound
Prospected

24. Page
24
@ssusina #MKTGNATION
32 Opportunities Created
15
12
2
3
Prospec(ng
Mee(ngs
–
Non-­‐Inbound/Event
A
B
C
D

25. Page
25
@ssusina #MKTGNATION
Prospecting Activity
2468 new contacts with prospecting activity
533
608
768
559
0
200
400
600
800
1000
A
B
C
D
55% of ISR Prospecting against C and D Rated Leads!
More D-rated Leads prospected than A-Rated!

26. Page
26
@ssusina #MKTGNATION
Most of our Opportunities from Prospecting
are from A- and B-rated leads
0
20
40
60
80
100
120
140
Mee7ngs
Opportuni7es
A
B
C
D
85% of Opportunities
were based on A & B
rated leads!

27. Page
27
@ssusina #MKTGNATION
So, the only thing left to do . . .

28. Page
28
@ssusina #MKTGNATION
Not Quite . . .
• Pride
of
ownership:
“We
know
enough
to
call
the
right
prospects!”
• Fear
of
missing
out
–
some
of
those
Cs
and
Ds
might
s9ll
convert!
• There’s
no
way
we
can
afford
this.

29. Page
29
@ssusina #MKTGNATION
Avoid FOMO via Fast Track For Inbound C & D

30. Page
30
@ssusina #MKTGNATION
Overcome Expense Concerns: Use Math
• If
prospec(ng
(me
on
Cs/Ds
was
shiBed
to
As/Bs,
and
rate
of
mee+ng
&
opportunity
crea+on
is
consistent:
• 28
incremental
opportuni7es
over
the
past
7
months
• 48
incremental
opportuni7es
for
a
full
12
months
• Assuming
$350K
average
deal
size,
that’s
$9.8
to
$16.8
million
addi7onal
pipeline
• Based
on
33%
close
rate,
$5.5
million
in
addi(onal
sales

31. Page
31
@ssusina #MKTGNATION
2016 Sales Activity YTD
0
10
20
30
40
50
60
Closed
Won
Lost
-­‐
Compe7tor
Lost
-­‐
No
Decision
D
C
B
A

32. Page
32
@ssusina #MKTGNATION
Recommendations
• Approve
full-­‐year
Everstring
contract
• Set
new
rules
of
engagement
for
ISRs:
• Reassign
all
Cs
and
Ds
to
Drip
Programs
• ISR
general
prospec7ng
to
be
restricted
to
As
and
Bs
• When
building
out
lists,
score
account
first,
only
pursue
contacts
if
account
is
rated
A
and
B
• Any
inbound
or
event
follow-­‐up
requests
will
be
immediately
changed
MQL,
regardless
of
score
• Marke7ng
to
build
engagement
campaigns
for
Cs
and
Ds,
qualify
and
pass
at
TBD
minimum
engagement
threshold

33. Page
33
@ssusina #MKTGNATION
Two Month Post-Implementation
Prospecting
21.60%
24.60%
31.10%
22.60%
27.50%
28.00%
27.80%
16.70%
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
A
B
C
D

34. Page
34
@ssusina #MKTGNATION
Two-Month Post-Implementation
Meetings Set
34.00%
32.50%
13.70%
16.40%
48.70%
25.60%
5.10%
17.90%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
A
B
C
D

35. Page
35
@ssusina #MKTGNATION
Post-Recommendation Pipeline
Generated
34.00%
32.50%
13.70%
16.40%
48.70%
25.60%
5.10%
17.90%
0.00%
10.00%
20.00%
30.00%
40.00%
50.00%
60.00%
A
B
C
D
$1.25
million
in
opportunity
pipeline
$0
in
pipeline
$20,000
in
pipeline
$0
in
pipeline

36. Page
36
@ssusina #MKTGNATION
Not losing opportunistic C and D Leads
$1,250,000
$20,000
$0
$0
$25,000
$0
$772,000
$250,000
$0
$200,000
$400,000
$600,000
$800,000
$1,000,000
$1,200,000
$1,400,000
A
B
C
D

37. Page
37
@ssusina #MKTGNATION
Conclusions
• Look
for
trial
opportuni7es
• A
longer
paid
trial
is
bemer
than
a
short
free
trail
• Make
sure
you
get
your
en7re
database
scored
• You’ll
need
it
to
determine
how
your
sales
team
is
spending
their
prospec7ng
7me.
• Take
advantage
of
market
condi7ons
when
nego7a7ng
• Separate
Inbound
from
Outbound
for
your
analysis
• Commit
to
fast-­‐track
high-­‐quality
inbound
leads