Driving marketing roi across on line and off-line channels

© AbsolutData 2013 Proprietary & Confidential

DRIVING MARKETING ROI

ACROSS ON-LINE

AND OFF-LINE CHANNELSAn approach to maximize ROI and generate

incremental business in a multi device, multi

channel environment

Presenter: Guha AthreyaSr. Manager, Customer Analytics

Predictive Analytics World, ChicagoJune 10-13, 2013

Venue:


AbsolutData Snapshot

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Company Overview

• Founded in 2001, AbsolutData is a pioneer in delivering consulting-oriented advanced analytics services to a global client base

• It helps 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

• 400+ employees across San Francisco, Los Angeles, New York, Dubai, Singapore, London and Delhi

Corporate Philosophy

MissionTo help forward looking

organizationsexcel through optimal use of

data

Services Provided

CRM MEData

Visualization & Reporting

Market Research

&

Business Research

Big Data


THE ATTRIBUTION PROBLEM

In a Multi Channel, Multi Device Ecosystem, adding the reported impact of all on-line and off-line channels gives a number higher than company’s revenue

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Find the True Value

30% 45% 60% 20%

100% ???


The Root Causes….

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Revenue is impacted by many drivers beyond just Media Investments

Media channels work together, but each channel reports all of the synergies

Media• On-line• Offline

Other Marketing• CRM

• Direct Marketing• Pricing

• Distribution

External• Competition • Technology• Demographic shits• Macro economics

Operations• Supply• CSAT

Revenue

Prediction

Sometimes Modeling efforts result in over-fitting History and don’t focus on Prediction Accuracy

History


AbsolutData’s Approach to Revenue Attribution

Phase 1- Holistic Base Model, Market Mix

Holistic approach incorporates all drivers

Appropriate level of modelling sophistication - OLS, HB etc

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)) ((DailySignups

Baselinesales

Regression Coefficients

Level of Media Effort

Regression Coefficients Other Factors

Phase 2- Refinements, e.g. Structural Equation Models

Assess Synergies, Refine Attribution

Phase 3 – Triangulation e.g. Cookie Data, Discrete Choice Models

Search

Clicks

TV Impac

ts

Display

Impressions

TV GISearch Clicks

Email Campaig

nPrint

Affiliates

DisplayOver all Signups+ + + + +

TV Impacts

TV ImpactsSecondary Relationships

Attribution’s % impact of each media channel drives

daily proportions

Cookie data captures Unique ID activity and measure recency and frequency


Building A Global Knowledge Base Across Verticals

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Russia Ukraine Brazil China Philippines

Poland Serbia Argentina Australia Malaysia

Turkey Spain USA India UAE

Romania Puerto Rico UK Indonesia Saudi Arabia

South Africa Denmark Germany France Thailand

Egypt Belgium Mexico Japan Vietnam

…Consumer Durables…Telecom…Electronics….Retail…CPG…Banking…Online Services…


Case Study in Hospitality – Phase 1 Results

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Bringing disparate data sources together Building a Master Model

Model built on Historical Data

Model robustness checked by predicting Recent Past

DATA TYPES DESCRIPTION

Bookings Individual, Group, Corporate

MediaOnline & Offline Spends

Competition

EmailsDelivered, Clicked

Competition

Promotions

Loyalty Promotions

Rate Sales

Competition

Other Drivers

Pricing, Inventory, Locations, Guest Satisfaction

Competition

Macro Economics GDP, CPI, Sentiment, Unemployment etc


Case Study in Hospitality – Phase 2 Results, Phase 3 in Progress

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Phase 2: Model used to understand Historical Impact

Phase 3: Insights inform planning and ROI Optimization

Media Impact

7%

Impact of Other

Drivers

12%

Competition

Impact

-1%

Base

Line

82%

Cumulative

Effect of All

Past

Marketing

Efforts

Incremental

18%

Search TV Affiliates Display PR E-mail Print 0%

10%

20%

30%

40%

50%

60%

53.1%

20.5%

14.1%

6.0%4.1% 2.9%

0%

23.00%

45.6%

12.3%8.8%

5.5%4.5%

0.1%

Primary Attribution

After Secondary Attribution (Actual Contribution from Model)

Impact of Secondary

Relationship on Search= - 30%

Impact of Secondary

Relationship on TV= +25%

© AbsolutData 2013 Proprietary & Confidential 9

If you need help with Analytics or Research, please write to us:[email protected]@[email protected]

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For all other queries [email protected]

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