Multi Channel Attribution - Driving Marketing Spend Planning In The Big Data Age

© AbsolutData 2013

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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


(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


Putting big data into action

Marketing attribution for a leading e-commerce company

Two other Marketing Mix modeling case studies

Ideas for future directions


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


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.


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


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)


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=


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%


Cookie Attribution involves processing a significant Volume of data coming from

Varied Sources.

Velocity in our case was not a key issue


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:





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


Phase III: Reconcile MMM & cookie attribution





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


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


Phase IV: Reporting, simulation and optimization





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




Two other marketing mix modeling case studies



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


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




Two other Marketing Mix modeling case studies



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) ?


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.

Omni-channel optimization adds another dimension : True DNA of your customers path


Action only according to the true DNA of your customers

Data Sources

CustomSegments

Targeted Message/Offer

Personalized to Individuals


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


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 √ √ √


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


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


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


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]

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Thank You

Eli Y. Kling

Director - Analytics

Phone: +44 (0)7940094976Email: [email protected]: Uk.linkedin.com/in/elikling


Uk.linkedin.com/in/elikling/

Uk.linkedin.com/in/elikling/

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Multi Channel Attribution - Driving Marketing Spend Planning In The Big Data Age