Integrated Marketing Analytics & Data-Driven Intelligence

• Bruce Swann • Manager, CI / Integrated Marketing, SAS

• Scott Briggs • Principal Solutions Architect, Customer Intelligence, SAS

• Suneel Grover • Sr. Solutions Architect, Integrated Marketing Analytics, SAS• Adjunct Professor, The George Washington University (GWU)

Module 4: Emerging Analytical Approaches for Integrated Marketing

AgendaI. High-performance marketing optimizationII. Social media analytics and real-time actionsIII. Social network analytics and community influence

HIGH-PERFORMANCE MARKETING OPTIMIZATION

Marketer’s Have Been Searching for the Holy Grail…

The Right Offer…To The Right Customer…

In the Right Channel…At The Right Time…

The Marketing Assignment Problem

Customers & Prospects

Offers, Services, and Pricing

ChannelsWeb Email Mail Mobile Phone Branch ATM Advisor

Acquisition

Which customer gets what offer?Through what channel?

At what time?

Awareness RetentionSpecial Offers Win Back

Social

The Core of the Problem is a Big Data Challenge

High Performance Marketing Optimization Helps Solve this

Problem

Let’s Break it Down…

High Performance

Vertica

Teradata

Greenplum

Oracle

Microsoft PDW

Hadoop

$- $20,000 $40,000 $60,000 $80,000 $100,000

Today 2009

Cost of Storage, Memory, Computing • In 2000 a GB of Disk $17 today < $0.07• In 2000 a GB of Ram $1800 today < $1• In 2009 a TB of RDBMS was $70K today < $ 20K

2011 – 2012 “Big Data” Technology Advances • Greenplum MapR (May ‘11) • IBM Big Insights (May ‘11) • Microsoft and Hadoop (Oct ‘11) • SAP Sybase IQ & Hadoop (November ‘11) • Oracle & Cloudera Appliance (Jan ‘12) • Teradata Partners w. Hortonworks (Feb ‘12) • SAS LASR Server on Hadoop (Mar‘12)

In-Memory Technology • SAS HP Solutions Announced (Nov 2010)• SAP HANA (December 2010) = $160M in 2011 • Oracle Exalytics (October 2011) • SAS LASR In-memory Server (March 2012)

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 $- $2 $4 $6 $8

$10 $12 $14 $16 $18 $20

Cost per Gigabyte

Marketing Optimization

Customer Offer A Offer B Offer C1 100 120 902 50 70 753 60 75 654 55 80 755 75 60 506 75 65 607 80 70 758 65 60 609 80 110 75

Objective Maximize projected profit

Constraints1 offer per customer3 customers per offer

Campaign Prioritization

Campaign Prioritization = $655

A Simple Example

Customer Offer A Offer B Offer C1 100 120 902 50 70 753 60 75 654 55 80 755 75 60 506 75 65 607 80 70 758 65 60 609 80 110 75

Objective Maximize projected profit

Constraints1 offer per customer3 customers per offer

Campaign Prioritization = $655

Customer Prioritization = $715

Customer Prioritization

A Simple Example

Customer Offer A Offer B Offer C1 100 120 902 50 70 753 60 75 654 55 80 755 75 60 506 75 65 607 80 70 758 65 60 609 80 110 75

Objective Maximize projected profit

Constraints1 offer per customer3 customers per offer

Campaign Prioritization = $655

Customer Prioritization = $715Campaign Optimization = $745

Campaign Optimization

A Simple Example

High Performance Marketing Optimization Solves Previously Unsolvable Problems

Example

• 26 million customers• 910 potential offers• 21 business constraints • 52 million rows of contact history

1 captain1 thread

128 captains 4 threads Improvement

Load data 1hr 24min 1hr 24min

Prepare input data 1hr 1min 30sec 122x

Execute Optimization 5hr 29min 6min 15sec 53x

Total optimization time 6hr 30min 6min 45sec 58x

A Previously Unsolvable Problem

1 captain1 thread

10 captains4 threads

124 captains 4 threads

Improvement

Load data 7hr 7hr 7hr

Prepare input data 9hr 35min 38min 3min 10x

Execute Optimization Can’t be solved 2hr 5min 17min 7x

Total optimization time

3hr 53min 20min 8x

• 50 million customers• 1000 potential offers• 100 business constraints

• Financial Services– Cross-sell and up-sell in retail banking: savings accounts, home equity

loans, credit cards, lines of credit, etc. – Insurance policy offers– Deciding credit line increases– Deciding what APR to offer on balance transfer offers

• Telecom– Complex cell phone or calling plan offers– Bundled service offers– Cross channel offers with different costs of execution

• Others – Loyalty offers (Hotels, Casinos) – Personalized coupons (Retail)

Use Cases

How much $$ are you leaving on the table without optimization?

SOCIAL MEDIA ANALYTICS AND REAL-TIME ACTIONS

http://www.youtube.com/watch?v=TXD-Uqx6_Wk

The Ask…

1. What’s working?2. Are we responding adequately?3. Are we growing our reach and driving

revenue?

The Opportunity 1. Know how many visits, leads, and customers each individual

social channel is generating…2. Improve the customer experience…3. Leverage social networks and communities…

Social Intelligence

Listening

Data Mining

Correlation & Forecasting

Text Mining Natural Language Processing

TaxonomiesInfluence & Engagement

Sentiment AnalysisCategorization

Portals CRM

CollectClean

IntegrateOrganize

Accessible by All

Analytics, Classify, Segment, Sentiment,

Natural Language Processing

iPad apps

Dataset Export

WebData

SurveyData

CallLogs

Text Analytics

Social Media Analytics

• WHAT are consumers saying about your brand? About the competition?

• WHO is creating content about your brand…Journalists? Bloggers? Forum members?

• WHO among these authors is a threat to reputation? An opportunity for advocacy?

• WHERE are consumers talking?• Is volume trending up or down?

• WHICH sites matter most?• WHICH sites are more positive?

Negative?

• WHAT aspects of your business drive satisfaction and loyalty?

• WHAT questions and unmet needs emerge?

• HOW do perceptions differ across the various channels through which customers give you feedback?

The Business Need

How can I ensure it’s accurate and relevant to my

business?

How do I cut out the noise and get to the true insights

and action? How can I customize it to understand my business,

my brands and competitors?

How does social media fit with my other business

intelligence?

How can social data augment what I already know?

How can it help me get a clearer picture of my business as it changes?

How do I use social media to drive my business forward?

Where does it fit within my

business strategy?Where can I focus for the

best returns? How can I use it to get a

competitive edge? How do I monetize it?

Engage

1. CRM2. Outbound/Inbound Marketing3. Integrated Marketing

Engage

Engage

Increase Engagement Across Email, Then All Channels

Website Visitor Analysis

Targeted Email Collection Content

1 2 3

Sign-up Incentive

Loyalty Program Email and/or Mobile Capture Call-to-Action

Engage

Optimized Performance

Return-trip Propensity Model

Targeted Cross-sell Messaging

1 2 3

2x Email Open Rate

72% Increase in Conversion Rate

Targeted Email OfferPush to

Conversion

Post to Social

Targeted

Message• Email & Mobile activity• Online behavioral data• Survey results• Social profile• Customer service events

Engage

0%

10%

20%

30%

0 1 2 3 4 5 6 7+

Per

cen

t o

f T

ota

l F

ile

# of Social Networks

Social Participation

Seg 1

Seg 2

Social Network Engages

Social | Email Influencers

Create Friend-centric Message

1 2 3

0%

10%

20%

30%

40%

0 1 2-3 4-10 11-19 20+

Per

cen

t o

f T

ota

l F

ile

# of Friends

Social Reach

Seg 1

Seg 2

32%28%

40%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

Inactive Opener Clicker

Email Activity Segments - 20+ Friends

Engage

Enable customer care agents monitoring social media to communicate with

consumers in order to…

Address the consumer’s service issues and questions

Mitigate or respond to negative comments or threats

Reinforce a customer’s positive sentiment• Broadcast comments to

other customers• Reward with offers

Facilitate customer consideration process • e.g. consumer comparing

hotel options for a vacation

Engage

SOCIAL NETWORK ANALYTICS AND COMMUNITY INFLUENCE

Social Network Analysis

‘A social network is a social structure made up of individuals, which are

connected by one or more specific types of interdependency, such as

friendship, kinship, common interest, financial exchange‘ etc.

Social Network Analysis

Social Network Analysis

Data feeds: ETL to data environment

1 Data Management: Cleanse, parse, categorize, and standardize social media data around “Vail Customers”

2

HUBEpic Mix

Social Media Chatter(e.g. Twitter, Facebook)

3

4

Executive Insights: Explore results in Visual Analytics

5

Social Network Analysis

Social Media Analytics Data

Social Media Analytics Data

Batch ETL

HUB

Outbound

Inbound

Customer DB (or EDW)

Non-matched/Possible matchedSocial/Customer data

Mastered data with Social Info Appended

Matched customer Info (is this possible?)

Community Influence

Social Media Analytics Extract

Social Media Analytics Extract

Data Available

Fuzzy Match Processing

Facebook Twitter

MDM

Community Influence

SarahCasinoVisitor

Sarah’sSocial Network

• Semi-frequent Visitor

• High Value

• Large Friend Network• Content Creator and

Contributor

• Active Social Elements• Encourages Sharing• Friend-centric

Targeted Email Campaign to Sarah

SarahEngages

• Engages with Email• Forwards to Friends• Posts Content

• Network Engages and Converts

• Individuals begin to contribute content (blogs, reviews, etc.)

Sarah’s Network Engages

Community Influence

Virality is the effect of influencers on followers.

In particular, what is the increased likelihood of churn within a

community once an influencer churns.

Virality churn lift is the churn rate delta of followers.

Influencer churn

Follower churn

Social Profile

Social Audience Profile

•Number of Friends•Social Membership•Number of Profiles•Last Activity Date•Social Tenure

Map your constituent audience to social profiles

Use email address as match key

Match constituent to social behavior

Access publicly available social data

Build Social Audience Profile

Assess social engagement levels

Social Profile

0%

10%

20%

30%

0 1 2 3 4 5 6 7+

Per

cen

t o

f T

ota

l F

ile

# of Social Networks

Social Participation

Seg 1

Seg 2

Social Network Engages

Social | Email Engagers

Create Friend-centric Message

1 2 3

0%

10%

20%

30%

40%

0 1 2-3 4-10 11-19 20+

Per

cen

t o

f T

ota

l F

ile

# of Friends

Social Reach

Seg 1

Seg 2

32%28%

40%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

Inactive Opener Clicker

Email Activity Segments - 20+ Friends

Mobile PurchaseWebsiteEmail

Search SocialDisplay Ad

M

D

PE

S

W

O

Case Study

• Major US-based wireless carrier with 30+ million customers.• 89 million individuals within the overall population.• Average community size is roughly 18.• 5% of all subscribers are influencers.

• Followers’ churn rate increased by 25% when influencers churned.

• 30% model lift when SNA was used.• Campaign take rate among followers doubled when

influencers took.

Questions?