The Complex Journey to Unified Marketing Analytics

Navigate the Complex Journey
Towards Unified Marketing Analytics
Joy Joseph
Practice Leader
IRI Strategic Analytics
George Musi
SVP, Head of Analytics & Insights
Blue 449

Discussion Agenda
The Convergence Imperative for Marketing Metrics
Marketing Performance Landscape
MMM &. Attribution- what's all the fuss about?
Charting a course towards unification
Ensemble Models- Vision for the future

Today’s media landscape is evolving at an
unprecedented rate
BLACK & WHITE
TV
FIRST
SATELLITE
BROADCAST
RISE OF
CABLE TV
MORE
ADVERTISER-
SUPPORTED
NETWORKS
DIGITAL
AND
INTERNET
REVOLUTION
SMARTPHONES HDTV TABLETS OVER-THE-TOP
DISTRIBUTION
1950s 1960s 1970s 1980s 1990s 2000 - 2005 2006 - 2011
2012 AND BEYOND
XBOX LIVE CAMERA IN
DEVICES/TV-
LEARNING THE
CONSUMER
TARGETED
ADS
PERSONALIZED
RECOMMEND-
ACTIONS
ENHANCED
CONTENT
SEARCH
CAPABILITIES
CLOUD-BASED
CONTENT
DELIVERY
SOCIAL TV DIGITAL RADIO
VIA MOBILE AND
TABLET APPS
…
DVR FIRST
DOWNLOAD-
ABLE
CONTENT
SOLD
MP3
PLAYERS
VIDEO
STREAMING
DIGITALTV
CONNECTED WORLD

Current State of Media: Consumers are
overwhelmed and Marketers are Confounded
Buy this
SALE!
This just
in
Did you
know?
Click here
Like this
50% off
Tweet it!
National
reach
Incremental
spend
ROAS
Cross-
channel
Our data
has scale
Purchase
data

The complex media landscape and rapidly evolving
technology resulted in a complex and often confusing
environment
Execution
Infrastructure
Channel
Segments
Life-cycle &
Metrics

Influential Advertisers have turned up the pressure on
accountability on the media industry
Heightened media scrutiny will inevitably put pressure on the performance
measurement ecosystem for greater consistency & convergence
P&G Chief Brand Officer Marc Pritchard doesn't
"want to waste time and money on a crappy
media supply chain," he said. And he urged
others in the industry to follow suit.
(AdAge Jan 29, 2017 http://bit.ly/2jmz5kw)

A rapidly evolving media landscape has
resulted in complex & multi-dimensional
consumer journeys
Smartphone
Tablet
PC
TV
11AM 12AM 1PM 2PM 3PM 4PM 5PM 6PM 7PM 8PM 9PM 10PM 11PM 12PM
BED KITCHEN WORK LUNCH WORK HOME RESTAURANT BAR BED
7:30
CNNMobile
(5m)
8:45
Instagram: Walking to
work
(2m)
10:05
Texting
(15m)
10:45
Snapchat
(1m)
11:15
Texting
(5m)
3:35
Daytrotter:
Streammusic
(1h)
4:30
Yelp:
Restaurantsearch
(10m)
5:35
Weather:
Checkweather
(2m)
7:10
Texting
(10m)
9:45
Facebook:
Watchvideo
(15m)
8:35
Snapchat
(1m)
8:10
GeneralSurfing
(20m)
6:45
Yelp:
Restaurant search(15m)
7:00
Open Table:Make
reservation(10m)
10:30
Yahoo! News (15m)
11:00
Facebook:
Post articles from Yahoo!(5m)
1:00
At work on desktop(4h)
4:45
Gchat withboyfriend
(10m)
10:35
Netflix(30m)
8:10
Watched NBC Today Show(20m)
6:45
VEEP: Watch 2 episodes(45min)
7AM 8AM 9AM 10AM

Consumers move between screens, places,
and moments…

Despite this, fundamental media objectives
have not changed
CONSUMER
MARKETER
RIGHT
PERSON
RIGHT
MESSAGE
RIGHT
TIME
RIGHT
PLACE
RIGHT
CONTEXT
ENGAGE
ASK
SHARE
RESEARCH
TRUST
DISCOVER
AWARENESS
ADVOCATE
BUY
CONSIDER
BROWSE
CONSUMER DECISION JOURNEY
C A M P A I G N L I F E C Y C L E

In today's complex atmosphere, marketers need
tools that can accurately make sense of myriad and
disparate data
Marketers need breadth & depth of insights to make the timelier
and more informed decisions at the most critical moments
Audience
(Who)
Creative
(What)
Time
(When)
Placement
(Where)
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Transform
data into
meaningful
insights that
drive actions

Reduce time to opportunities
Event
Data captured
Insights delivered
Action taken
Time
Value
Action Time
Enables us to become more responsive & adaptive to
opportunities by reducing the time form data to decisions

Support a more iterative process of
measurement, optimization and simulation
PLAN & EXECUTE MEASURE OPTIMIZE
TO
INTERATIVE & ADAPTIVE PROCESS
Marketing Campaign

Need to be an equal mix of…
SCIENCE
(STATISTICAL &
ANALYTICAL KNOW-
HOW)
ART
(MARKETING &
BUSINESS
UNDERSTANDING)

Integrated attribution modeling approach
Digital Attribution Modeling
Marketing Mix Modeling
(Top-Down)
(Bottom-Up)
Holistic
Insights
Combining Approaches
In The Right Way
Micro Decisions
Macro Decisions
To Improve
Marketing Optimization

Marketing response modeling overview
In the marketing
analytic space, there
are many types of
marketing models
The generalized goal of these approaches is to develop
an understanding of how marketing activities & non-
marketing influencers impact business outcomes,
like card applications & acquisitions
Machine
learning
(e.g., agent-
based
models)
Bottom-up
(e.g.,
attribution
models
Econometric
top-down
(e.g.,
marketing
mix models)
Three of the most common approaches to model marketing

Marketing performance management
MMM TV Attribution Attribution
FOR BREATH OF
UNDERSTANDING AND
TO GUIDE OVERALL
MARKETING STRATEGY
FOR DEPTH OF UNDERSTANDING AND TO
GUIDE TACTICAL CAMPAIGN PERFORMANCE
Three-stage Modeling Approach:
We believe in a comprehensive
marketing performance
management approach –
leveraging MMM, Attribution & TV
attribution in concert – to:
• Expand the scope & precision of measurement
• Better enable both (long-term) strategic & (near-term)
tactical decision support
• better empower both macro & micro optimizations

Evolve decision making
Decision Support
OptimizeUnderstand
Foresight
Decision Guidance
DESCRIPTIVE
What Happened?
Hindsight
DIAGNOSTIC
Why Did It Happen?
?
?
?
?
?
Insight
PREDICTIVE
What Will Happen?
PRESCRIPTIVE
What Should I Do?

Use Assortment Of Analytical Techniques
Analyzing The
Present
Reporting On
The Past
Predicting The
Future
To get the most benefit from analytics, we’ll need to have an analytic
assortment that is balanced – one that combines techniques for:

provides increased…
Brush Strokes? Color Mixture?
Composition? Full Picture?
Clarity
Confidence

ULTIMATE GOAL
BETTER &
CONNECTED DATA
BROADER & DEEPER
INSIGHT
TIMELY & SMARTER
ACTION

Why is there friction between MMM &
Attribution?
1. Wrongfully viewed as substitutes rather than complements
2. They speak different languages in terms of metrics and
interpretation
3. The same activity measured in MMM vs. Attribution can have
different ROI
1
2
3

Understand that MMM addresses different
business needs from Attribution…
1

The advent pf programmatic has further
exacerbated the great divide
1
MMM Attribution

MMM today does address tactical questions
for offline media
1

Digital Measurement in MMM is typically at
total platform level
1

Attribution operates at much more granular
levels required to optimize digital campaigns
1

MMM & Attribution speak different languages
– “contributions” = "lifts”
2
 MMMs measure marketing contributions vs. campaign “lifts” in
Attribution-
o Contribution = % of annual sales that was incrementally driven
by marketing
o Campaign lift is the incremental sales generated by consumers
exposed to the campaign during the campaign period compared
to those not exposed.
 Lifts are typically higher than contributions because of a smaller
denominator -campaigns run over weeks or months not a full year.
 For CPG and other faster moving goods, a well-run campaign
typically results in substantially higher trial rates, above normal
consumption levels, often pulling purchases forward ahead of normal
purchase cycles during campaigns (purchase acceleration)

MMM & Attribution speak different languages
– “ROI” = “ROAS”
2
 ROI measured in MMM is typically $ profits generated per $
spent of a marketing activity and is more appropriate when
making cross-platform annual budget allocations where EDIT
impact is an important consideration
 ROAS or Return on Ad Spend measured in Attributions are
usually $ Sales generated per $ spent on a marketing activity-
does not take brand profitability into account

MMM & Attribution can still yield divergent
results- they are after all inherently different
methodologies
It is however important to align the two,
primarily because MMM is still used to
manage the annual strategic budget
allocation process, including digital platforms
Reconciliation starts with recognizing
limitations
3

Recognize MMM limitations…
Digital is challenging for MMM where geo data (down to markets or trade areas) is infeasible
o Recent advances here- e.g. geo-fenced social campaigns matched to store-level mix
data sets.
Geo-targeted campaigns do not have the scale to register accurately in MMM
o Reading local Campaigns with less than 20 Million impressions in MMM is a coin toss
Traditional MMM “fits one size to all”, while marketing is increasingly targeted
Synergies are important in cross-platform campaigns and are difficult to read without either
sufficient variation in execution or single source data (down to individual audience level)
Finally, the broad and encompassing demand measurement MMM undertakes comes with
long cycle times- model updates can often take upwards of 8 weeks from data gathering to
insights/execution
o Some advances here as well with API driven data integration and “self-updating” models
3

And understand Attribution pros and cons…
Pros
o “Bottom-up” approach, measuring
total impact up from consumer
transaction level
o Ability to measure impact and
optimize spend down to publishers,
content and creative
o Rapid cycle-times (2-3 weeks)
o Supports digital-specific
requirements:
o Pay-for-performance
o Addressable platforms
o Niche- targeted campaigns and
Impression level optimization
o Exclusion of Offline Platforms
o Single source Data integration
challenges- data fusion techniques
widely used introduce biases-
o Stratified Sampling due to limitations
in consumption data
o Lookalike matching where “walled
gardens” prevent direct matching
across platforms due to PII
restrictions
o Models run on short windows are inherently
tactical, lacking any longer-term brand
impact
o Does not quantify non-marketing drivers’
impact
3
Cons

The Unification journey starts with a process of
course correction between the two approaches

Course correction ensures Ensuring “Single
Version of Truth”
4th of July
Father’s Day
Mother’s Day
Attribution Average
Without MMM input
MMM Average
Without Attribution
input
Back To School
DigitalDisplayROAS

Single Version of Truth ensures harmony across
organizational planning milestones

Meta models or “Ensemble Models” can resolve
Conflicts between models by establishing rules
and relationships to combine models
“There are a number of functional components for such a system sitting in
proprietary assets, we just need the right motivation to transcend our “walled
gardens” to allow them to work together”

Platform Lift
Campaign Lift
Brand Equity Lift
MMM
Attribution
Brand Health Metrics
Copy Tests
Engagement
Brand Affinity
Creative Affinity
Importance of an input= Its
contribution to overall system
performance and accuracy
Marketing Analytics System- Bayesian Belief Network
Latent MetricsInputs Output KPIs

A more immediate possibility are hierarchical
models that drive both micro and macro decisions
Source: IRI-PepsiCo presentation at IRI Growth Summit 2016

Immediate future will see a more cogent integration of MMM and Attribution
A greater focus on targeting will push measurement down to at least
Household-level, if not individual consumer level
Now that we have stopped being awestruck by the sheer amount of data
that is creating “insights”, it is time to address issues like data bias
Focus will shift to predictive accuracy vs. backward looking campaign
scorecard
Advanced probability based models will be explored to compensate for data
biases and for understanding non-addressable offline media
Closing thoughts….

The Complex Journey to Unified Marketing Analytics

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