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From Web Analytics to Digital IntelligenceThe Auto Club Group Shows Us How


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


Introductions


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.


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.


It Starts With A Question


That Begins The Journey...


Actions

Rules

Insights

Constraints

Analytics

Campaigns

www

@

Data Management


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


Unleashing The Secret Weapon

The Data Scientist


The Opportunity Of Digital Intelligence


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


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


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


Digital Data Capture

Digital Visitor

Digital Data Collection

<script id="ob-script-async" type="text/javascript"src="/js/ot_async.js" a=" [account-id]">


Collecting Raw Clickstream (HIT) Data

ID 1

ID 2

3

9

1

11

4

7

12


Digital Data Model


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


Addressing The Black Hole Of Data Prep

80% 20%


Summarizing Data For Visualization & Reporting


Example: Customer Journey Analysis


Summarizing Data For Predictive Analytics


Example: Analytical Decision Trees & Clustering


Auto Club Group’s Journey


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


Business Challenge

All lines of business integrated in CRM database

Data capture varies by channel:

Contact centers

Branches

Web

Mobile


Objective 1: Expand Customer profile


Objective 2: Capture Inferred Interests


Objective 3: Leverage For Predictive Modeling

Improve Model Performance with Structured Online Data


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


Considerations


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.


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


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


Model Validation

Rank Orders Sales from all Mail Campaigns across states from

Jun 2016-Dec 2016 campaigns


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


% New

Movers% Homeowner

Income under


% 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


% New

Movers% Homeowner

Income under


% 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%


Auto Quote – Funnel & Drop Off Analysis


Closing Remarks


AAA's Journey from Web Analytics to Digital Intelligence


Questions?

Documents

From Web Analytics to Digital Intelligence - SAS Science, Marketing Analytics and Marketing ... The Shift From Web Analytics To Digital Intelligence ... View Renewal Page Visiting