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© Absolutdata 2014 Proprietary and Confidential
Chicago New York London Dubai New Delhi Bangalore SingaporeSan Francisco
www.absolutdata.com
April 30, 2014
Multi-Channel Attribution
Driving Marketing Spend Planning in the Big Data Age
© 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
© 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
© 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
© 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
© 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.
© 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
© 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)
© 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=
© 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%
© 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
© 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
© 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
© 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
© 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
© 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
© 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
© 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
© 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
© 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) ?
© 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.
© 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
© 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
© 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 √ √ √
© 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
© 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
© 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
© Absolutdata 2014 Proprietary and Confidential 28
If you need help with Analytics or Research, please write to us:
americas.sales@absolutdata.com
europe.sales@absolutdata.com
asia.sales@absolutdata.com
For Media related queries -media.relations@absolutdata.com
For all other queries -info@absolutdata.com
HEAD OFFICE
314 Marble Arch Tower,
55 Bryanston Street,
London W1H 7AA
Phone : + 44 207 868 2240
UK OFFICE
DLF Cyber City SEZ,
Building#14, 4th Floor, Tower B,
DLF Phase-III, Sector 24 &
25A, Gurgaon-122002,
Phone: +91.124.4953.400
INDIA OFFICE
Absolutdata Analytics
Middle East JLT
Office 1604, Tower BB1
Mazaya Business Avenue
Jumeirah Lake Towers
Phone: +97150-1577257
DUBAI OFFICE
1851 Harbor Bay Parkway,
Suite 125, Alameda,
California, USA – 94502
Phone: +1 510 748 9922
Fax: +1 510 217 2387
Eli Y. Kling
Director - Analytics
Phone: +44 (0)7940094976
Email: Eli.Kling@absolutdata.com
LinkedIn: Uk.linkedin.com/in/elikling
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Multi Channel Attribution - Driving Marketing Spend Planning In The Big Data Age

  • 1. © Absolutdata 2014 Proprietary and Confidential Chicago New York London Dubai New Delhi Bangalore SingaporeSan Francisco www.absolutdata.com 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 If you need help with Analytics or Research, please write to us: americas.sales@absolutdata.com europe.sales@absolutdata.com asia.sales@absolutdata.com For Media related queries -media.relations@absolutdata.com For all other queries -info@absolutdata.com HEAD OFFICE 314 Marble Arch Tower, 55 Bryanston Street, London W1H 7AA Phone : + 44 207 868 2240 UK OFFICE DLF Cyber City SEZ, Building#14, 4th Floor, Tower B, DLF Phase-III, Sector 24 & 25A, Gurgaon-122002, Phone: +91.124.4953.400 INDIA OFFICE Absolutdata Analytics Middle East JLT Office 1604, Tower BB1 Mazaya Business Avenue Jumeirah Lake Towers Phone: +97150-1577257 DUBAI OFFICE 1851 Harbor Bay Parkway, Suite 125, Alameda, California, USA – 94502 Phone: +1 510 748 9922 Fax: +1 510 217 2387
  • 29. Eli Y. Kling Director - Analytics Phone: +44 (0)7940094976 Email: Eli.Kling@absolutdata.com LinkedIn: Uk.linkedin.com/in/elikling Follow us on: