module two: connected crm value creation...creating value through connected crm 3 •organizational...
TRANSCRIPT
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Module Two: Connected CRM
Value Creation How to Integrate the Connected Consumer View
Across Media and Channels
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• Know the customer across channels
• Know about service issues and needs
• Communicate consistently across channels
• Understand customer value potential
• Understand customer needs and affinity
• Optimize channel sales potential
• Differentiate cost of service
• Promote use of appropriate & efficient channels
• Get it right, the first time
Improve
Customer
Experience
Drive
Revenue
Increase
Efficiency
Creating Value Through Connected CRM
3
• Organizational alignment
• Disparate systems that don’t “communicate”
• Overwhelming amount of data, typically
captured for operational implementation
• Lack of informed offers with individual
relevancy
• Lack of measurement and understanding of
customer potential, migration, and retention
impact
Inbound
Each day, millions of customer
interactions
Relatively small number
generate incremental value
Barriers
Seizing opportunities for greater sales and better customer experience will
produce growth through retention and expansion.
Customer Service
Transactions
Problem/Issue
Capitalizing on “Moments of Truth”
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Connected Customer Interaction
Information Insights Targeting Measurement Optimization Agility
Technology Database Access Automation
Med
ia
Audience Identification
Service/Offer Prioritization
Channel Optimization
Audience Selection
Offer Selection
Contact & Media Optimization
Display
Direct mail
Search
Outbound
Web
TV
Radio
Direct
Response Ch
an
nel
Direct
response
ATM
Branch
Inbound
Phone/IVR
Social
Mobile
Site
Strategy Value Segmentation Measurement
Execution Analytics Integration Organization
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Attribution and
Measurement Cross Media and Channel Optimization
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Attribution and optimization enables value creation once
the data has been integrated
First
Party
Data
Second
Party Data
Third
Party
Data
Inte
gra
tio
n
Execution
• Direct integration to media marketplaces
(exchanges, bidding platforms, etc.)
• Real time bid management and optimization
Attribution & Optimization
• Attribution
• Mix optimization and planning
• Optimized plan by segment
• Individual level targeting
Data Management
• Integration
• Segmentation
• LTV
• Media history
The Digital Marketing Value-Chain
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Once we have the connected consumer view, integrated
analytics allows us to optimize at the segment &
individual level
Connected Customer View Segment and Individual
Level Optimization
Month 1 Month 2 Month 3
Measurement & Attribution
Optimization starts with a
connected customer view across
media and channels. This is the
only way to truly create customer
and segment level optimization.
Attribution allows us to accurately
determine the performance of
historical marketing activity.
It also must give insight into
drivers behind marketing
performance.
Optimization engine prescribes
optimal mix, cadence, and
targeting that create maximum
ROI given business constraints.
Output is a segment and
consumer-specific mix, contact
cadence, and targeting “plan”
Social
DM &EM
Display
Search
TV/Video
Mobile
Site
Product
LTV Segment
Demographics
Life Events
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3% 14% 3% 5% 5% 5% 15% 5% 5% 40%
30% 40% 30%
100%
Attribution Goal: Accurately allocate credit to every
marketing touch leading to conversion
Day 8-30 Day 1-7 Day 0-1
New
Customer:
Inspiron
purchase
Actual
experience
Customer calls the 800, so
TV gets credit
TV over-attributed
Direct
Consumer clicked on site
link. Rules: TV and DM
exposure possible in last 7
days, so assign credit
Better but still not
accurate
Rules Based
Modeled Model-adjusted interaction
Most accurate and
actionable
$
TV view Direct mail sent Newspaper view Display view Social visit Website visit Paid search click
Mass and Offline Digital
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Integration of “top-down” and “bottoms up”
National media (TV & radio)
$140
Local media (TV & radio)
$200
Direct mail
$180
Digital
$83
Calibration layer
Cost per inquiry by tactic
Top-down media mix model (Traditional media mix model: DMA by week level, 12+ months of data)
Display
$60
Video
$80
Search
$91
Lead Gen
$75
Social
$113
Agg 1 - $105
Agg 2 - $103
Agg 3 - $58
Agg 4 - $53
Agg 5 - $14
Agg 6 - $126
Agg 7 - $25
Agg 8 - $5
Agg 9 - $5
Agg 10 - $4
Search 1 - $115 LG 1 - $190 Video 1 - $121
Video 2 - $35
Video 3 - $213
Video 4 - $23
Video 5 - $8
Video 6 - $4
Search 2 - $87
Search 3 - $39
LG 2 - $163
LG 3 - $87
LG 4 - $74
LG 5 - $32
LG 6 - $29
LG 7- $11
Remarketing - $12
Guaranteed - $80
Non-guaranteed - $30
Auto / insurance - $18
Cost per inquiry by tactic
Bottom-up digital attribution model (New media mix model: zip by day level, can be built with only 1 month of data, 70+ programs estimated)
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Practical Attribution Framework
Modeling that estimates across all
media at broad level
Calibration layer integrates the
absolute accuracy from top-
down with the relative accuracy
from bottom-up. This includes in
market testing
Modeling specific to media categories
that is derived from most granular
level available
Ou
tdo
or
Mo
bile
SM
S
Na
tio
na
l TV
Dis
pla
y
So
cia
l
On
line
Vid
eo
Se
arc
h
Em
ail
On
line
Le
ad
Ge
n
Ra
dio
Prin
t
DR
TV
Ou
tbo
un
d T
ele
ma
rke
tin
g
Bottom-up modeling
Dire
ct M
ail
Top-Down Modeling
(Marketing Mix)
Calibration Layer
Digital Attribution
Anonymous (Cookie/IP)
Direct Attribution
PII (Address, Email, Phone)
Future
Extend mass media
measurement down
to user level
Digital Media Direct Media Mass Media
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Media and channel optimization analytics take a forward
looking view to generate segment and individual-level
plans
Who to target
What product and offer
How much should we
spend to target them?
When and how often to
target
How should we engage
the consumer? What is
the right message?
Customer-level optimization
Where to reach customer
(channel/media)
Agile, segment level
cross media plan
Segment and Individual
Level Optimization
Month 1 Month 2 Month 3
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Case Study: Insurer Measurement Evolution
Background
• Client had significant acquisition budget split across online/offline
channels with historical bias towards mass media brand spend
• Had used “last-view” attribution approach for all decisioning which
they knew was inaccurate
Solution
• Understand true historical performance of all media towards
generating high-value segment conversion and define go-forward
optimal budget allocation (Top Down)
• Optimize actual execution and performance of new mix within
digital and direct mail (Bottom up)
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Attribution Approach Comparison
Probabilistic (MMO) attribution approach yields dramatically
different results than direct attribution. Conclusion was made to
shift dollars from offline to online.
Top-
down
• Display has the lowest cost per
and greatest growth potential
Test into significantly more
Display
• Many media are contributing to
search lead volumes
Increase Paid Search
• Online Video is also much more
effective than previously
understood. Expand video
• Brand TV Program is performing
better than thought. Even so:
Shift from TV to Digital
350
90
550
145
210
1012
88 125 150
207
451
611
$0
$200
$400
$600
$800
$1,000
$1,200
Display Paid Search
Online Video
Direct Mail
DRTV Brand TV
Acqusition Costs by Media
Direct (Before) Mix Model (After)
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Now that, decision to shift budget to digital was made, challenge
shifted to how to effectively spend that money….
Bottom-
up
Accurately attributing performance within media
Probability • The cookie level data contains
a wide range of ad interaction
sequences
• Given any sequence of
interactions, we calculated the
probability of conversion for
that sequence
• By comparing these
conversion probabilities for
interaction sequences we
isolated the individual impact
of each of the interaction and
assign a weight to it
New Life Policy
Segment A $ D2 D3
Conversion for the sequence
Conversion for the sequence without D1
New Life Policy,
Segment A $ D1 D2 D3
Weight for D1 = [ Probability(conversion for the sequence) - Probability (conversion for the
sequence without D1)
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Display 3
Display 1
Display 2
Display 6
Video 4
Video 3
Display 4
Video 1
Display 5
Video 2
Video 5
Incremental Model Rules
Bottom-up attribution approach yielded very granular, actionable
outputs
Accurately attributing performance within media
Incremental performance report based
on attribution methodology
Compare to last click
performance report
Reporting down to granular level. Specific sites, creative, programs, etc.
Bottom-
up
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Informed Digital Media
Applying existing assets and
knowhow to new media
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Digital Media Execution
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Digital Media Opportunity
Cu
rren
t Mark
et A
pp
roach
Technology
Skill sets
Technology
Skill sets
Ad
Tech
& D
ata
Driv
en
Skills
ets
Lead
Competitive
Advantage
Opportunity
No
rmal E
vo
lutio
n L
ead
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DM List Plan Analogy
Client Goal: 200,000
Grand Totals 450,000 52.67% 237,000 491 0.21% $87.47 $23,711.34 $42.21
Keycode Use Type List Name SelectionOrder
Quantity
Merge
Retention
Net Mail
QuantityResponses
Response
%CPM Total Cost
Cost Per
Acq
MMOV-10-11 Remail 2 List 1 1 Month New Mover Age 60-79 50,000 55.00% 27,500 61 0.22% $60.18 $1,986.26 $492.89
MMOV-11-00 Continuation List 2 1 Month New Mover Age 60-79 50,000 55.00% 27,500 61 0.22% $74.91 $2,391.26 $613.51
MMOV-09-11 Remail 3 List 3 1 Month New Mover Age 60-79 50,000 55.00% 27,500 46 0.22% $60.18 $1,986.26 $648.54
MNATG-10-00 Continuation List 4 1 Month Subscribers Age 60-79 50,000 59.00% 29,500 45 0.15% $89.83 $2,981.26 $674.86
MVET-11-00 Continuation List 51 Month Members Age 60-79,
HHI $50K+ 50,000 54.00% 27,000 43 0.16% $93.89 $2,866.26 $956.00
MANIM-10-11 Remail 2 List 61 Month Members Age 60-79,
HHI $50K+ 50,000 46.00% 23,000 60 0.26% $86.74 $2,326.26 $380.57
MANIM-11-00 Continuation List 71 Month Members Age 60-79,
HHI $50K+50,000 46.00% 23,000 60 0.26% $106.09 $2,771.26 $465.46
MGRIP-11-00 Continuation List 8 1 Month Buyer Age 60-79 50,000 52.00% 26,000 57 0.22% $131.54 $3,751.26 $476.16
MGRIP-10-11 Remail 2 List 9 1 Month Buyer Age 60-79 50,000 52.00% 26,000 57 0.22% $89.23 $2,651.26 $323.01
What if your DM agency told you that from now on you couldn’t use your model
to select names from lists, that the lists sources themselves might not be visible
to you, and that you could not de-dup across lists so if you buy the same person
on 3 lists…well, too bad…
Typical DM List Plan
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Media Partner Flight Placements CPM Monthly Spend Total Spend Total Impressions
Publisher 1 Jan 1 - March 31 ROS 10.00$ 35,000.00$ 105,000.00$ 10,500,000
Publisher 2 Jan 1 - March 31 ROS 8.00$ 25,000.00$ 75,000.00$ 9,375,000
Publisher 3 Jan 1 - March 31 ROS 12.00$ 45,000.00$ 135,000.00$ 11,250,000
Ad Network 1 Jan 1 - March 31 RON 0.75$ 50,000.00$ 150,000.00$ 200,000,000
Ad Network 2 Jan 1 - March 31 RON 1.00$ 50,000.00$ 150,000.00$ 150,000,000
Ad Network 3 Jan 1 - March 31 RON 1.15$ 50,000.00$ 150,000.00$ 130,434,783
Ad Network 4 Jan 1 - March 31 RON 0.85$ 50,000.00$ 150,000.00$ 176,470,588
Ad Network 5 Jan 1 - March 31 RON 1.00$ 50,000.00$ 150,000.00$ 150,000,000
Ad Network 6 Jan 1 - March 31 RON 1.25$ 50,000.00$ 150,000.00$ 120,000,000
Ad Network 7 Jan 1 - March 31 RON 3.00$ 50,000.00$ 150,000.00$ 50,000,000
Ad Network 8 Jan 1 - March 31 RON 2.25$ 50,000.00$ 150,000.00$ 66,666,667
Ad Network 9 Jan 1 - March 31 RON 1.25$ 50,000.00$ 150,000.00$ 120,000,000
Ad Network 10 Jan 1 - March 31 RON 1.00$ 50,000.00$ 150,000.00$ 150,000,000
DSP Jan 1 - March 31 RON 0.75$ 50,000.00$ 150,000.00$ 200,000,000
Total 1,965,000.00$ 1,544,697,038
DM List Plan Analogy
Well, guess what? That is pretty much exactly what is happening in most digital
media buys today
Today’s Typical Digital Media Plan
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Current and historical model
Buying is relationship
based with targeting
and optimization done
at a very coarse grain
level – like Mad Men
“Transparency line”
ends at the network and
publisher level – what
falls below the line is
“black box”
Buying is done across
numerous platforms
without the ability to
manage frequency and
cost resulting in
significant waste
Just as bad (or worse),
targeting capability does
not allow for individual
targeting of the “greens”
“Black box” ad networks “Black box” ad networks “Black box” ad networks Direct sales force
“Remnant”
inventory
“Remnant”
inventory
“Remnant”
inventory
“Premium”
inventory
Publisher Publisher Publisher Publisher Publisher Publisher Publisher Publisher
Agency
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Trading desk / “New
agency”
Future model
Buying is done using a
data-driven targeting
skill-set and mind-set –
no more Mad Men
Consolidated buying
platforms allow for
complete transparency
and granular targeting –
no more black box
Real-time-bid
environment allows for
access to premium and
remnant inventory that
gets bid on auction-style
based on the value to
the advertiser
Control of targeting at
individual level allows
for getting more of the
greens and less of the
reds while managing
frequency and cost
Publisher Publisher Publisher Publisher Publisher Publisher Publisher Publisher
Consolidated Buying Platform (DSP)
Data &
enabling
technology
Real-time bidding auction
Data &
analytic
expertise
Trading desk/ “New” agency
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Targeting Framework
Consolid
ate
d B
uyin
g P
latform
(D
SP
)
Tra
din
g D
esk
Lookalike Modeling
Match converted consumers to anonymous ID
and create look-alike predictive
model to identify “like” cookies/
placement opportunities through RTB
Online Audience Segments
Identify high performing
online audience segments (“auto intenders”) and
target these anonymous users through the DSP
Re-Targeting
Identify users visiting site
(anonymous or authenticated)
and target customized
impressions after they leave the
site
Online-Offline Direct Match
Match offline “top deciles” to cookies through
third party match providers and target known
consumers on a 1-1 level
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Impact of the Future Model
An Advertiser working with multiple ad networks and DSPs in 2011, migrated
ad spend Merkle’s platform in 2012.
Conversions tripled while dramatically reducing spend
Spend
Conversions
CPM
Response
rate
CPA
Before=100 Month 2011
100
100
71
27
100 261
100 56
100 206
After Month 2012
Difference
-29%
-73%
+161%
-44%
+106%
Reduced
waste
Cost
efficiency
Reach
Targeted
audience
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Client Roadmap for Future Model Adoption
Focus on
Audiences
Data-driven
skillsets
Data and
enabling
technology
infrastructure
Display media plan
One
platform
Cross sell & upsell
Prospecting
Winbacks
Display media plan
Many Ad
Networks
Ad.com
Collective Media
Tribal Fusion
Ad server
Buying clout
Negotiation skills
Relationships
Manual management
Data-driven decision-making
De-averaged pricing
Impression-level buying
Automated management
cR
Offline Media
database
Online Media
database
CRM
database
Connected CRM Platform
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The Future Model for Buying
Real-time
biddable supply
sources
Publisher
direct
Mobile Social
Display Video
Client Digital Media
Optimization
Platform
Analytics &
Media buying
services
Centralized
Demand-Side
Platform
(DSP)
cR
Offline Media
database
Online Media
database
CRM
database
Connected CRM Platform
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Program Examples
Informed Digital Media
Top Three US Bank
Super Regional Bank
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Opportunity
• Financial services and insurance companies have to address
adverse selection.
• Targetable online media allow us to quickly learn what targeting and
media spend drives high-value leads and conversions vs. low-value
leads and conversions.
• Most online targeting never moves beyond anonymous targeting to
value.
• Most banks and insurance companies are not applying their data-
driven marketing assets and knowhow to online media that are
targetable and measurable at the individual consumer level.
• Media partners are targeting on clicks and not leads and conversions.
• Consumers move across media and channels in their research and
buying practices.
• We must support online engagement with offline (branch, call center)
quotes and account openings.
Current State
Case Study: Integrated online media acquisition
Consumer’s
choice of
conversion path
Finding the
wrong
customers
online
Targeting
conversion
– not clicks
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Hourly
Real-time
Anonymous data
Anonymous
Digital Data
Identifiable (CRM) data
Customers/
Accts
CDI Key Management
& Data Hygiene Lead Data
Demographic
Data
Merkle’s Digital Optimization Platform combines online
with offline data
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Integrated Digital /Offline Acquisition Program
Convert & Analyze Engage & Enable Demand Generation
1
2
3
Leads
4
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Aligning to Consumer Behavior
Write to the users cookie when an ad is
viewed
Show landing page when user
returns to the site
Online audiences don’t always click on ads – they often see an ad and go
directly to the client’s site or return through organic search
Aligning to Consumer Behaviour
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Algorithm
customization
Budget
Allocation
Remarketing
Rules
Continuous interaction and adaptation
Integration of Online Targeted Media
Integrated Search + Display Program enables incremental value
Merkle Search Program
Merkle Display Program
Connected CRM
Platform
Attribution
Customer Recognition
Marketing Rules
• Leverage keywords to inform retargeting creative
and bid strategies
• Contextual targeting based on converting keywords
• Upper funnel keyword bidding based on display
audience and contextual insights
• Keyword optimization based on offline scoring
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Campaign Tactics and Outcomes
Scale Tactics informed
by CONNECTED data Users from offline prospect or customer lists.
Market with the right offer and product
Direct Match List
Users who have identified themselves on
your site or email. Remarket with the right
offer and product
Remarketing
Leverage user online profile data t find
more users that have been identified as
high value
Lookalike
Direct Match High Value
Accuracy
Remarketing Medium Value
Accuracy
Lookalike Majority Value Accurate
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Vo
lum
e In
de
x
Mo
nth
1=
10
0) C
PA
Ind
ex
(Mo
nth
1=
10
0)
35% HV
Accounts
60% HV
Accounts
Optimization Results on Customer Outcomes
35
Population generated
higher multi-product
penetration
100
54
81
0
20
40
60
80
100
120
140
Targeted Online Pilot
Other Onlne Prospect
DM Prospects
Checking Balances
Targeted prospects
generated higher
balances compared to
other programs
100
36
70
0
20
40
60
80
100
120
140
Targeted Online Pilot
Other Onlne Prospect
DM Prospects
Household Deposit Balances
100
14
49
0
20
40
60
80
100
120
140
Targeted Online Pilot
Other Onlne Prospect
DM Prospects
Checking + Money Market
100
27
124
0
20
40
60
80
100
120
140
Targeted Online Pilot
Other Onlne Prospect
DM Prospects
Checking + Loan
Targeting the Customers You Want
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Table Discussion