from web analytics to digital intelligence - sas science, marketing analytics and marketing ... the...
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From Web Analytics to Digital IntelligenceThe Auto Club Group Shows Us How
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AGENDA From Web Analytics to Digital Intelligence
Introductions
It Starts With A Question
The Opportunity Of Digital Intelligence
Auto Club Group’s Journey
Q&A
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Introductions
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Shawn TaylorDirect of Advanced Analytics, The Auto Club Group (AAA)
Shawn Taylor is the Director of Advanced Analytics for The Auto Club Group (AAA), where he is responsible for the Data Science, Marketing Analytics and Marketing Research functions. He is especially passionate about integrating these functions to provide a more comprehensive view of the member spanning across ACG’s insurance, travel, financial services and automotive lines of business.
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Suneel GroverPrincipal Solutions Architect, SASProfessorial Lecturer, GWU
Suneel Grover is a Principal Solutions Architect supporting Digital Intelligence, Marketing Analytics and Omni-Channel Marketing at SAS. In addition to his role at SAS, Grover is an professorial lecturer at The George Washington University (GWU) in Washington DC, teaching in the Masters of Science in Business Analytics graduate program within the School of Business and Decision Science.
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It Starts With A Question
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That Begins The Journey...
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Actions
Rules
Insights
Constraints
Analytics
Campaigns
www
@
Data Management
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The Challenge
Enhance approach to predictive marketing:
1. Enable ACG analysts to own, manipulate, and prepare digital data for predictive analytics and data science
2. Stitch together website visitor data with offline CRM data
3. Execute predictive analytics leveraging online and offline data to support marketing objectives
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Unleashing The Secret Weapon
The Data Scientist
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The Opportunity Of Digital Intelligence
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Although there is a tremendous amount of digital data, for the most part, the majority of digital analytic use cases remain in Reaction Mode
Digital Analytics: Level of Analytic Maturity
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The Shift From Web Analytics To Digital Intelligence
To deliver comprehensive customer insights, brands seek to merge
digital data with offline channels. Digital & customer analytic
teams are attempting to work together, but their projects struggle to
get off the ground due to a clash of approaches & culture.
1. Data Types — structured vs. unstructured, identifiable vs. anonymous
2. Skills — data scientist vs. digital ninja
3. Analysis — advanced analytics vs.
“good enough” analytics
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SAS On-premise Analytic
ComponentsMarketing Platform
SAS Cloud Raw DigitalData Collection
Tagged Website
Mobile App
UsingSDK
Normalized Data
How Did We Set AAA Up To Address This?
Data Collection Tables
On-premise Detailed Data
Store
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Digital Data Capture
Digital Visitor
Digital Data Collection
<script id="ob-script-async" type="text/javascript"src="/js/ot_async.js" a=" [account-id]">
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Collecting Raw Clickstream (HIT) Data
ID 1
ID 2
3
9
1
11
4
7
12
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Digital Data Model
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The Benefits Of The Data Model
Page load Focus change Served HTML Form field Watch video
Research online
Find a branch
Read blog post
Check on order
Customers
Visitors
Sessions
Interactions
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Addressing The Black Hole Of Data Prep
80% 20%
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Summarizing Data For Visualization & Reporting
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Example: Customer Journey Analysis
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Summarizing Data For Predictive Analytics
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Example: Analytical Decision Trees & Clustering
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Auto Club Group’s Journey
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Auto Club Group’s Background
9+ million members
Breadth of business lines
Membership
Property and Casualty insurance
Banking
Life Insurance
Travel
Automotive Services
Discounts and Loyalty
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Business Challenge
All lines of business integrated in CRM database
Data capture varies by channel:
Contact centers
Branches
Web
Mobile
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Objective 1: Expand Customer profile
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Objective 2: Capture Inferred Interests
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Objective 3: Leverage For Predictive Modeling
Improve Model Performance with Structured Online Data
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Predicting Auto Quotes - Summary and Objectives
Developed a model experimenting with a combination of online and offline data to impact business performance
1st generation model focused on Auto Insurance sales growth
Future use cases in Insurance outside of sales growth possible –need to evaluate opportunities
Objective – Brief Marketing on the sales growth model, and request support for a small test for in market validation
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Considerations
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Methodology
Predicts likelihood to complete an Auto Insurance quote across the footprint
Model build Includes website logins from Dec 2015 to Aug 2016
Model is scored on all website users monthly
Can be changed to daily, weekly, etc.
Validated on eNewsletter, Banners, and Direct Mail
Can be used to rank order a website user’s likelihood to quote for use with the eNewsletter, Direct Mail, and Website treatments.
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Considerations
Variable Name Model Impact
Online
INSURANCE Page Visit Visit an Insurance Page
COMMON Page Visit Forgot Password, Forgot User ID, etc.
Session Count Only 1 Previous Session
MYACCOUNT Page Visit Visit a My Account Page
Session Time Spent More seconds spent on the website
Organic Visit Coming to the website through Organic
Internal Campaign VisitComing to the website through an Internal
Campaign
HOME Page Visit Salvage Campaign Website Visit
Chrome Visit the website using Chrome
Chrome Mobile Visit the website using Chrome Mobile
View Renewal Page Visiting the Membership Renewal Page Only Once
Visit Membership Page Visiting the Membership Page 1 or 0 times
Offline
Estimated Income Lower Income
Estimated Current Home Value Lower home value
Year Joined AAA Newer Members
Associate Count Less Associates
MKT Attract Score Lower Market Attract Score
Length of Residence Shorter Length of Residence
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Model Validation
Rank orders clicks from all Auto Insurance eNewsletter and CWS Banner Campaigns
June 2016 - Feb 2017
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
1 2 3 4 5 6 7 8 9 10
Ra
te
Decile
eNewsletter Click Rate
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
1 2 3 4 5 6 7 8 9 10
Ra
te
Decile
CWS Banner Click Rate
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Model Validation
Rank Orders Sales from all Mail Campaigns across states from
Jun 2016-Dec 2016 campaigns
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Deciles
Estimated
Home
Value
AgeYear Joined
AAA% Female % Married % No Associates
% New
Movers% Homeowner
Income under
$50,000% 1 Adult in HH
% No Children in
HH
% With Graduate
Degree
1 74283 48 2013 50% 56% 51% 15% 44% 50% 45% 79% 11%
2 103510 50 2011 50% 65% 43% 10% 56% 39% 34% 76% 12%
3 119873 51 2009 49% 70% 39% 9% 61% 34% 30% 76% 12%
4 135027 52 2008 49% 72% 37% 7% 64% 30% 27% 75% 13%
5 150451 53 2007 49% 75% 35% 7% 67% 27% 24% 75% 14%
6 166105 53 2006 48% 77% 33% 6% 70% 23% 21% 74% 14%
7 181925 54 2004 48% 79% 30% 5% 72% 21% 19% 74% 16%
8 200326 54 2003 47% 81% 28% 4% 75% 18% 17% 73% 17%
9 223765 55 2000 46% 83% 24% 3% 78% 14% 14% 72% 18%
10 275311 56 1996 44% 85% 17% 2% 82% 10% 10% 70% 21%
Deciles
Estimated
Home
Value
AgeYear Joined
AAA% Female % Married % No Associates
% New
Movers% Homeowner
Income under
$50,000% 1 Adult in HH
% No Children in
HH
% With Graduate
Degree
1 74283 48 2013 50% 56% 51% 15% 44% 50% 45% 79% 11%
2 103510 50 2011 50% 65% 43% 10% 56% 39% 34% 76% 12%
3 119873 51 2009 49% 70% 39% 9% 61% 34% 30% 76% 12%
4 135027 52 2008 49% 72% 37% 7% 64% 30% 27% 75% 13%
5 150451 53 2007 49% 75% 35% 7% 67% 27% 24% 75% 14%
6 166105 53 2006 48% 77% 33% 6% 70% 23% 21% 74% 14%
7 181925 54 2004 48% 79% 30% 5% 72% 21% 19% 74% 16%
8 200326 54 2003 47% 81% 28% 4% 75% 18% 17% 73% 17%
9 223765 55 2000 46% 83% 24% 3% 78% 14% 14% 72% 18%
10 275311 56 1996 44% 85% 17% 2% 82% 10% 10% 70% 21%
Deciles
Estimated
Home
Value
AgeYear Joined
AAA% Female % Married % No Associates
% New
Movers% Homeowner
Income under
$50,000% 1 Adult in HH
% No Children in
HH
% With Graduate
Degree
1 74283 48 2013 50% 56% 51% 15% 44% 50% 45% 79% 11%
2 103510 50 2011 50% 65% 43% 10% 56% 39% 34% 76% 12%
3 119873 51 2009 49% 70% 39% 9% 61% 34% 30% 76% 12%
4 135027 52 2008 49% 72% 37% 7% 64% 30% 27% 75% 13%
5 150451 53 2007 49% 75% 35% 7% 67% 27% 24% 75% 14%
6 166105 53 2006 48% 77% 33% 6% 70% 23% 21% 74% 14%
7 181925 54 2004 48% 79% 30% 5% 72% 21% 19% 74% 16%
8 200326 54 2003 47% 81% 28% 4% 75% 18% 17% 73% 17%
9 223765 55 2000 46% 83% 24% 3% 78% 14% 14% 72% 18%
10 275311 56 1996 44% 85% 17% 2% 82% 10% 10% 70% 21%
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Auto Quote – Funnel & Drop Off Analysis
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Closing Remarks
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AAA's Journey from Web Analytics to Digital Intelligence
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Questions?