Multi Channel Attribution - Driving Marketing Spend Planning In The Big Data Age
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Transcript of Multi Channel Attribution - Driving Marketing Spend Planning In The Big Data Age
© AbsolutData 2013
Chicago New York London Dubai New Delhi Bangalore SingaporeSan Francisco
www.absolutdata.com
Multi-Channel Attribution
Driving Marketing Spend Planning in the Big Data Age
© AbsolutData 2013 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 2013 3
(IBM Netizza, Hadoop, Hive, etc)
Traditional AbsolutdataCapabilities
New Developed AbsolutdataCapabilities
Consumer Generated DataUnsolicited
customer Feedback
Near Real-time data feeds
Company Generated DataBusiness
specific data
Linkage with Financials
Analyzing DataData Mining Text Mining Visualization
Segmentation A/B Testing Predictive Modeling
Machine Learning Association Rules
Address specific business problems
Predict,monitorand control
Absolutdataprovides the manpowerand the technologyto make Big Data manageable through ourin-house, dedicated resources Big Data Platforms
Highspeeddata mining
MakesBig Data manageable
Absolutdata has the capabilities to help organizations leverage the layers of big data
© AbsolutData 2013 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 2013 5
We helped an e-commerce company 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 toperson level
ON – LineAttribution
Big Data
Bottom up
OFF – LineAttribution
Not so Big Data
Top Down
© AbsolutData 2013 6
The attribution challenges in the ecommerce environment are more complex than ever
However, despite this, role of offline marketingthrough different channels such as TV advertising,Radio broadcasting, Print media cannot be ignored.The part played by offline channel is even moreenhanced when the target customers are notregular internet users. In this case, offline marketingplays a key role in building brand equity
Digital Marketing Sources Traditional Marketing Sources
Blog Pay-per-clickadverts
Organicsearch
RelationshipsNetworking
Cold Calls Referrals
Media advertising
Trade showsSite visitorsSocialMedia
EmailCampaigns
Webinars
Online channels not only act as marketing channelsinfluencing customers through Search activity,Display Ads, Emails etc. but are also gateways tointroduce customers to the offered products on thewebsite due to lack of physical presence. This makesonline channels very important drivers to track forthe e-commerce industry. Hence, there is a plethoraof data tracked by companies daily to assess websitetraffic and to understand users‟ activities on theinternet.
© AbsolutData 2013 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
Radio
TV
© AbsolutData 2013 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 2013 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
AffiliatesDisplay
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 2013 10
Secondary attribution provides a refined view of the system
PaidSearch Clicks
Nonpaid search
CableTotal 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%
SAM
PLE O
UTP
UT
-0.1%
© AbsolutData 2013 11
Cookie Attribution involves processing a significant Volume of data coming from
Varied Sources.
Velocity in our case was not a key issue
© AbsolutData 2013 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
USER 1
30% Search
20% Display
15% Affiliates
USER 2
50% Search
50% Display
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
© AbsolutData 2013 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
Cookiedata
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 2013 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%
Offline35%
Search65%
Offline45%
SEO55%
Offline41%
Display15%
Search44%
Display54%
Signup
Search46%
Signup
Signup
Signup
© AbsolutData 2013 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 2013 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 2013 17
The varx is used in a big data situations looking at SKU level data in search for key value items
Detailed transactionsAggregate 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 2013 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 2013 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 2013 20
Social media could be harnessed in aid of marketing effectiveness estimations
We talked about Volume and VarietyIs there a business case for real time attribution (Velocity) ?
© AbsolutData 2013 21
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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.
Omni-channel optimization adds another dimension : True DNA of your customers path
© AbsolutData 2013 22
Action only according to the true DNA of your customers
Data Sources
CustomSegments
Targeted Message/Offer
Personalized to Individuals
© AbsolutData 2013 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
MissionTo help forward lookingorganizations excelthrough optimal use of data
Absolutdata brings it all together
Services Provided
CustomerRelationshipManagement
MarketingEffectiveness
Data Visualization& Reporting
Market Research
Big Data
Company Overview Corporate Philosophy
© AbsolutData 2013 24
Assessing benefits of different methodologies of bottom-up cookie attribution
ATTRIBUTION METHODOLOGIES
BENEFITSIncorporatesConsumer Path
Incorporates Recency Effects
Incorporates Frequency Effects
Ease of Computation
First Click √
First Touch √
Last Click √
Last Touch √
Ru
les-
bas
ed
Mo
de
l-d
rive
n
Frequency +
Recency Approach√ √ √
Linear √ √
Markov √ √ √
Time Decay √ √
Bayesian Network √ √ √
© AbsolutData 2013 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
USER 1
30% Search
20% Display
15% Affiliates
USER 2
50% Search
50% Display
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
DiminishingImpact Rule
© AbsolutData 2013 26
Approach 2 – Ensemble approach - Theory
Aggregated Attribution Scores for different channels
Last ClickLast
TouchFirst Click
First Touch
LinearTime 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 2013 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 2013 2828
If you need help with Analytics or Research, please write to us:[email protected]@[email protected]
For Media related queries [email protected]
For all other queries [email protected]
28
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