More Related Content Similar to Multi Channel Attribution - Driving Marketing Spend Planning In The Big Data Age (20) More from Absolutdata Analytics (18) Multi Channel Attribution - Driving Marketing Spend Planning In The Big Data Age1. © Absolutdata 2014 Proprietary and Confidential
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April 30, 2014
Multi-Channel Attribution
Driving Marketing Spend Planning in the Big Data Age
2. © Absolutdata 2014 Proprietary and Confidential 2
Absolutdata helps forward looking organizations excel through
optimal use of data
$23MM increase in Customer
Loyalty and CRM marketing
revenue
– A major Hotel chain
Contribution of $78MM over
the last few years to their
margins
– A major Retailer
$9MM incremental revenue
as a result of focused
promotional campaigns
created
– A major Online Retail
Discounter
$50MM increase in revenue
by Market Mix Modeling
across 4 geographies
– A leading CPG Company
15% revenue growth through
Multi Channel Attribution
– A large ecommerce
company
40% increase in profits
through Conjoint based
Pricing Optimization – A top
SaaS company
3. © Absolutdata 2014 Proprietary and Confidential 3
(IBM Netizza, Hadoop, Hive, etc)
Traditional
Absolutdata
Capabilities
New Developed
Absolutdata
Capabilities
Consumer
Generated
Data
Unsolicited
customer
Feedback
Near Real-time
data feeds
Company
Generated
Data
Business
specific data
Linkage with
Financials
Analyzing Data
Data Mining Text Mining Visualization
Segmentation A/B Testing Predictive Modeling
Machine Learning Association Rules
Address
specific
business
problems
Predict,
monitor
and control
Absolutdata
provides the
manpower
and the
technology
to make Big Data
manageable
through our
in-house,
dedicated
resources Big Data Platforms
High
speed
data mining
Makes
Big Data
manageable
Absolutdata has the capabilities to help organizations leverage the
layers of big data
4. © Absolutdata 2014 Proprietary and Confidential 4
Putting big data into action
Marketing attribution for a leading
e-commerce company
Two other Marketing Mix
modeling case studies
Ideas for future directions
5. © Absolutdata 2014 Proprietary and Confidential 5
We helped an e-commercecompany change its marketing strategy by
undertaking innovative Big Data analytics on On-Line and Off-Line
channels and save20% marketing spend.
Achieve 50% operations optimization
Absolutdata is engaged in this project as a leader in market mixed
modeling with expertise in big data
45%
20%
60%
30%
Marketing
attribution @
segment level
Attribution to
person level
ON – Line
Attribution
Big Data
Bottom up
OFF – Line
Attribution
Not so Big Data
Top Down
6. © Absolutdata 2014 Proprietary and Confidential 6
The attribution challenges in the ecommerce environment are
more complex than ever
However, despite this, role of offline marketing
through different channels such as TV advertising,
Radio broadcasting, Print media cannot be ignored.
The part played by offline channel is even more
enhanced when the target customers are not
regular internet users. In this case, offline
marketing plays a key role in building brand
equity
Digital Marketing Sources Traditional Marketing Sources
Relationships
Networking
Cold Calls Referrals
Media
advertising
Trade showsSite visitors
Blog Pay-per-click
adverts
Organic
search
Social
Media
Email
Campaigns
Webinars
Online channels not only act as marketing channels
influencing customers through Search activity,
Display Ads, Emails etc. but are also gateways to
introduce customers to the offered products on the
website due to lack of physical presence. This
makes online channels very important drivers to
track for the e-commerce industry. Hence, there is
a plethora of data tracked by companies daily to
assess website traffic and to understand users‟
activities on the internet.
7. © Absolutdata 2014 Proprietary and Confidential 7
We would like to measure the direct and indirect impact of our
marketing investment at a granularity relevant to planning
Weak
Relationship
Strong
Relationship
Overall
Sales
Affiliate Clicks
Paid Search Clicks
Display Clicks
Magazine
Online
Print
Radio
TV
8. © Absolutdata 2014 Proprietary and Confidential 8
The solution arrived at combined market mix modelling, cookie attribution and a
decision support simulator => multi – channel attribution
The challenge
While impact of online channels in driving the traffic to the e-commerce website can be easily calculated
with readily available supporting data; the role of offline channels in driving day to day business and their
impact on online channels is much more complex
Methodology
Market Mix Model: to allocate sign ups at
an aggregate level to all online & offline
channels
Cookie-based Attribution Algorithm: to
attribute individual sign ups to all online
channels
Reconcile MMM & Cookie Algorithm: to
establish sign-up level attribution to all
online and offline channels
Marketing Channels in Scope
Offline Channels:
– TV
– Radio
– Print
– PR
Online Channels:
– Paid Search (Branded/Generic)
– Email
– Display
– Affiliates
– Non-Paid Search (SEO)
9. © Absolutdata 2014 Proprietary and Confidential 9
Phase I: Top down marketing mix modeling
Phase III: Reconcile
MMM & Cookie
Attribution
Phase IV: Reporting,
Simulation and
Optimization
Phase I: Marketing Mix
Modeling
Phase II: Cookie-
Based Attribution
Algorithm
Search
Clicks
Affiliates
Display
Impressions
TV Impacts
AffiliatesSecondary Relationships
Search
Signups
Email
Signups
Print
Signups
Signups
from
Other
Factors
Previous
Day’s
Baseline
Signups
+TV GI
Signups
Display
Signups+ + + + +
Daily
Signups=
10. © Absolutdata 2014 Proprietary and Confidential 10
Secondary attribution provides a refined view of the system
Paid
Search Clicks
Non
paid search
Cable
Total Impact
11.4%
9.0%
2.5%
3.8%
-1.0%
2.2%
-0.1%
2.6%-3.8%
-2.2%
Actual TV Attribution taking into
account indirect contribution of Search
Final Attribution 7.5% 5.7% 11.1%
-0.1%
11. © Absolutdata 2014 Proprietary and Confidential 11
Cookie Attribution involves processing a
significant Volume of data coming from
Varied Sources.
Velocity in our case was not a key issue
12. © Absolutdata 2014 Proprietary and Confidential 12
This approach takes into account
different
– rule-based (first click/first
touch/last click/last touch)
– and statistics- based approaches
• (linear – where each channel
gets equal weight
• and time-based – where
contribution is attributed
according to recency)
to come up with a weighted average
of contribution
This approach takes into account
primarily
– the frequency (i.e. number of
times a cookie passes through a
particular channel)
– and recency (i.e. the order in
which the cookie passes through
different channels)
In order to establish the attribution
for each online channel
Phase II: Bottom up estimating digital impacts
At Absolutdata we use different types of Cookie- Based Attribution Algorithms which help
determine the attribution for different online channels based on the path taken by each cookie:
Phase III: Reconcile
MMM & Cookie
Attribution
Phase IV: Reporting,
Simulation and
Optimization
Phase I: Marketing Mix
Modeling
Phase II: Cookie-
Based Attribution
Algorithm
Approach 1: Frequency/
Recency Approach
Approach 2:
Ensemble Approach
Bayesian Network and Markov Models
are statistical techniques used to
describe a complex system of
transitions between ‘states’. The
probability of reaching the interesting
end state (signup/visit and )is the basis
for the quantification of the channel to
contribution
Approach 3: Bayesian
Network/Markov Model
USER 1
30% Search
20% Display
15% Affiliates
USER 2
50% Search
50% Display
13. © Absolutdata 2014 Proprietary and Confidential 13
Phase III: Reconcile MMM & cookie attribution
Phase III: Reconcile
MMM & Cookie
Attribution
Phase IV: Reporting,
Simulation and
Optimization
Phase I: Marketing Mix
Modeling
Phase II: Cookie-
Based Attribution
Algorithm
Attribution’s %
impact of each
media channel
drives daily
proportions
Cookie data
captures Unique
ID activity and
measure recency
and frequency
Cookie
data
Cookie data is unique and
more detailed but only
captures a portion of
activity
Attribution
data
Benefits of a top-down/
bottoms-up Data Sources
Attribution data captures
holistic impact of media
but does not link to user
data
14. © Absolutdata 2014 Proprietary and Confidential 14
Top down model is proportioned out to people through cookie
attribution weights and then aggregated to segments
USER 1
30% Search
20% Display
15% Affiliates
USER 2
50% Search
50% Display
Segment Formed Characteristic of Segment Share of Segments
Search & Offline
Channels
~20%
SEO & Offline
Channels
~10%
All Digital
Channels
<10%
Search, Display
Impressions &
offline channels
<5%
Offline
35%
Search
65%
Offline
45%
SEO
55%
Offline
41%
Display
15%
Search
44%
Display
54%
Signup
Search
46%
Signup
Signup
Signup
15. © Absolutdata 2014 Proprietary and Confidential 15
Phase IV: Reporting, simulation and optimization
Phase III: Reconcile
MMM & Cookie
Attribution
Phase IV: Reporting,
Simulation and
Optimization
Phase I: Marketing Mix
Modeling
Phase II: Cookie-
Based Attribution
Algorithm
The management takes quarterly decisions on the marketing spend based on the results
Absolutdata helped client increase revenue by 15% while maintaining marketing spend
Q1 - Pre optimization Q1 - Post optimization
Total Cost
Q1 - Pre optimization Q1 - Post optimization
Revenue Impact
Incremental Revenue due to optimized
spend
Marketing Budget Maintained by the
Client
16. © Absolutdata 2014 Proprietary and Confidential 16
Putting big data into action
Marketing attribution for a leading
e-commerce company
Two other Marketing Mix
modeling case studies
Ideas for future directions
17. © Absolutdata 2014 Proprietary and Confidential 17
The varx is used in a big data situations looking at SKU level data in
search for key value items
Detailed transactions
Aggregate into weekly
time series
Pricing History
Promotions
Trackers
Time Series Mining
{VARx}
Impact of category or
Item price change on
shopping patterns
What if scenario
explorer tool
Co-dependencies
between categories/
items (sales)
KVI
18. © Absolutdata 2014 Proprietary and Confidential 18
We are also discussion the implantation of marketing mix modeling
in combination with brand equity trackers
Decision Support Simulator
Optimize
allocation of
media
Prioritize
contact touch points
based on quantified
effectiveness
ROI KPIs
Brand Equity KPIs
Media engagement KPIs
Media Channels
19. © Absolutdata 2014 Proprietary and Confidential 19
Putting big data into action
Marketing attribution for a leading
e-commerce company
Two other Marketing Mix
modeling case studies
Ideas for future directions
20. © Absolutdata 2014 Proprietary and Confidential 20
Social media could be harnessed in aid of marketing effectiveness
estimations
We talked about Volume and Variety
Is there a business case for real time attribution (Velocity) ?
21. © Absolutdata 2014 Proprietary and Confidential 21
Omni-channel optimization adds another dimension : True DNA of
your customers path
21
Attributing each customer to the right Place and Channel is the first step
Combining Physical, Digital, & Mobile platforms
A Google search, a
review site, a banner
ad, a billboard, a store
visit, a Facebook post, a
Tweet and a magazine
QR code scan in a
nearby coffee shop …
It’s not enough to
connect the dots, you
need to analyze the
touchpoints.
22. © Absolutdata 2014 Proprietary and Confidential 22
Action only according to the true DNA of your customers
Data
Sources
Custom
Segments
Targeted
Message/Offer
Personalized to
Individuals
23. © Absolutdata 2014 Proprietary and Confidential 23
Founded in 2001, Absolutdata is a pioneer in delivering
consulting-oriented advanced analytics services to a global
client base
We help clients understand their customers better by
statistical data analysis and delivering key analytics that help
enhance their own value
Senior management from
McKinsey, Kraft, Pfizer, Mitsubishi, Nielsen, GE, and HSBC
450+ employees across San Francisco, Los Angeles, New
York, Dubai, Singapore, London and Delhi
Mission
To help forward looking
organizations excel
through optimal use of data
Services Provided
Customer
Relationship
Management
Marketing
Effectiveness
Data
Visualization
& Reporting
Market
Research
Big
Data
Company Overview Corporate Philosophy
Absolutdata brings it all together
24. © Absolutdata 2014 Proprietary and Confidential 24
Assessing benefits of different methodologies of bottom-up cookie
attribution
ATTRIBUTION METHODOLOGIES
BENEFITS
Incorporates
Consumer
Path
Incorporates
Recency Effects
Incorporates
Frequency
Effects
Ease of
Computation
First Click √
First Touch √
Last Click √
Last Touch √
Rules-based
Model-
driven
Frequency +
Recency Approach
√ √ √
Linear √ √
Markov √ √ √
Time Decay √ √
Bayesian Network √ √ √
25. © Absolutdata 2014 Proprietary and Confidential 25
Approach 1 – Using recency and frequency - theory
For each individual User, the different online channels influencing it will be assigned a points or weights using
a frequency and recency and diminishing impact business rules
Only those channels visited within one month before the signup date are being considered as
“influencing” channels
Frequency Rule
A more recently visited channel will be given more weight than an older channelRecency Rule
Number of interactions (impressions or clicks) with a particular channel will be classified into
different stratum of pre-determined weight. e.g. frequency greater than 5 will probably get a
weight of 5 only – as more than 5 frequencies might not have additive effect
Diminishing
Impact Rule
USER 1
30% Search
20% Display
15% Affiliates
USER 2
50% Search
50% Display
26. © Absolutdata 2014 Proprietary and Confidential 26
Approach 2 – Ensemble approach - Theory
Aggregated Attribution Scores for
different channels
Last Click
Last
Touch
First Click
First
Touch
Linear
Time
Decay
Evaluation of
Cost Per
Acquisition
Estimation of
quality of
subscribers
coming through
different
channels
Simulation
Calibration
Forecasting
Calculate Aggregated
Attribution Score
Calculate Attribution
Through Rule-Based and
Model- Based techniques
Application
Display Search Email Affiliates
Rule based
Techniques
Model based
Techniques
The different attribution techniques will be prioritized based on Business
Understanding
Weighted Average based on importance of
each techniques
27. © Absolutdata 2014 Proprietary and Confidential 27
Approach 3 – Use of Markov chain and Bayesian networks - Theory
$
E1 E2 E3 E4
A user has been to 4 different events (touch/click) as shown below:
What fractional credit Wi goes to each Ei
Subject to
Markov Chain and Bayesian Networks help us to estimate Attribution weights
28. © Absolutdata 2014 Proprietary and Confidential 28
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