1. 9/30/2011
Cross Channel Strategy
How Market Mix Modeling Can Impact Cross-Channel
Budget and Business Planning
Speakers:
Dhiraj Rajaram, Mu Sigma
Craig Kronzer, UnitedHealth Group
Session Objectives
• Learn approaches to Market Mix modeling –
how it enables measurement of multi-channel
activities
• Discover the advanced framework to quantify
‘true’ cost of acquisition, netting out cross
channel effects and cannibalization
• Evaluate tools and platforms for budget scenario
planning and optimize marketing budget
allocation
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2. 9/30/2011
BACKGROUND
Organization Overview
Insurance Solutions
• Established in 1998 as a • Largest provider of pure-
AARP/UHG relationship play decision sciences
• Nation's largest and analytics services
supplemental insurance • 30 Fortune 500 Clients in
program focusing on 10 Industry Verticals
people age 50 and over • Headquartered in
• Distribution: DTC, Chicago IL with presence
Employer, Agent, Web all over the US
Business Problem
Background Business Hypotheses
• Insurance Solutions uses • The business wanted to
multiple marketing test the hypothesis that
channels to attract unattributed sales are
members driven by marketing
• Operational constraints • In particular, there was a
result in less than need to understand the
complete attribution of impact of DRTV on sales
sales to marketing efforts • The solution framework
• Several sales are not required to measure
attributable to any of the cross-channel impacts
marketing channels
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3. 9/30/2011
The Challenge of Measurement
• A major portion of sales is
Attribution by Channel
unattributed to any
Sales advertising channel
• Sales attributed to DRTV
are low compared to
proportion of investment
• Business wants to
measure the true effect of
TV advertising by
DRTV
understanding the “halo
Other Channels effect”
Unattributed
The Need for Measurement
• Due to relatively low
Cost of Acquisition
attribution of sales to
Cost per Sale DRTV, the apparent cost
of acquisition for the
Channel 1 channel is high
• There is a need for
Channel 2 improved measurement
to calculate the ‘true’ cost
DRTV of acquisition
• Cost of acquisition is a
Channel 4 key component in
marketing planning
SOLUTION APPROACH
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4. 9/30/2011
Problem Solving Framework
The What &
# Strand The Why? SCQInitial SCQFinal
How?
1 SCQInitial
 XXX  XXX
 YYY  YYY
 ZZZ  ZZZ
Factor
2 Factor
 XXX  XXX
 YYY  YYY Network
Network
 ZZZ  ZZZ
3 Hypothesis
 XXX  XXX
 YYY  YYY Hypothesis
Matrix
 ZZZ  ZZZ
Matrix
4 SCQFinal
 XXX  XXX
 YYY  YYY
 ZZZ  ZZZ
SCQFinal SCQInitial
The Mu Sigma Problem DNA ensures appropriate emphasis
on design and hypothesis leading to right representation
Solution Approach
Mapping the
exhaustive set of
factors enables
testing of all relevant
hypotheses
The Market Mix Framework
• The Market Mix Framework decomposes
total sales into contributions by advertising
vehicles and external factors
• Contributions from different channels
enable calculation of ROI
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5. 9/30/2011
MMX Modeling Approaches
Contribution
Direct Marketing  Percentage of enrollments due to
each promotional program
Promotional Activity
Direct Response TV Total and Marginal ROI for each
program
Marketing Mix Model  Cost per Sale
Print
 Lifetime Value
Sales = f(DM, DRTV, Print,
Web, Events…) Optimization
Web
 Promotional spend allocation at
aggregate program level taking
Agent into account diminishing marginal
 Portfolio level optimization for all
Unattributed Sales products
Multiplicative Additive Multi Target
Measurement of Measurement of Measurement of
diminishing returns individual cross channel
contributions effects
Ad stock – Lagged effects
DM enrollments
xx Effort adstock 0.2 adstock 0.7
Enrollments
Weeks
Adstock transformation methodology
At = Tt + λ At-1
Where:
• Tt is the value of the marketing variable at time t
• λ is the decay or lag weight parameter
• At-1 is the carryover of Advertising at time t-1
“HALO” EFFECTS AND
REATTRIBUTION
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6. 9/30/2011
Multi-target Model
DRTV Sales
Channel 1
Sales
Total Sales Each of the target
… sales modeled on all
advertising inputs as
well as external factor
Unattributed
Phone Sales
Other
unattributed
sales
Reattributed Sales
Post Modeling
Original Attribution
Reattribution
Sales Sales
DRTV DRTV
Other Channels Other Channels
Unattributed Unattributed
The Market Mix models are able to measure the contribution
of advertising to previously unattributed sales
Improved measurement
Original CPS Reattributed CPS
Cost per Sale Cost per Sale
Channel 1 Channel 1
Channel 2 DRTV
DRTV Channel 2
Channel 4 Channel 4
Due to higher level of attribution in sales, the effective
cost per sale reduces significantly
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7. 9/30/2011
Halo Effect
Contribution of Media Activities
DRTV Channel 1 Channel 2 Channel 3 Channel 4 Channel 5
Enrollments from channel
DRTV
Channel 1
Channel 2
Channel 3
Channel 4
Channel 5
Unattributed
Self Contribution
The ‘halo’ effect of advertising channels enables
quantification of cross-channel contribution Halo Effect
Impact of the initiative
Pre-MMX Modeling Post MMX Modeling
• Cost of sale calculated • The optimization process
based on direct attribution for allocating budget
used in budget planning across channels refined
• Member lifetime value by using ‘true’ cost of
calculations biased by acquisition
high cost of acquisition in • Budget allocation across
some channels marketing channels
• “Dark Test” conducted to changed significantly
verify impact of TV on • “Bright Test” conducted to
unattributed sales test additional advertising
opportunities
SPEAKER BIOS
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8. 9/30/2011
Speaker Bios
Dhiraj Rajaram Craig Kronzer
• Founder and CEO of Mu • Leads a Data Analytics team
Sigma, an analytics services for UnitedHealthcare. Team is
company that helps clients responsible for enterprise-wide
such as Microsoft and Dell analytics including building
institutionalize data-driven predictive models, designing
decision making. Prior to and analyzing marketing tests,
founding Mu Sigma, he and claim data analytics.
advised senior executives Previously, was with Carlson
across a variety of verticals as Marketing Group and Lands'
a strategy and operations End. Craig holds an MS in
consultant at Booz Statistics from the University of
Allen Hamilton and Minnesota and BS in
PricewaterhouseCoopers. Computer Science from the
University of Wisconsin.
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