Customer Experience and Predictive Analytics

30
1 1 Marketing Automation Direct Marketing Social Media Email Marketing Search & PPC Demand Generation Analytics CRM Content Marketing Database Services Mobile Marketing Business Intelligence CX & Predicti ve Analytic s

Transcript of Customer Experience and Predictive Analytics

Page 1: Customer Experience and Predictive Analytics

11

Marketing Automation

Direct Marketing

SocialMedia

Email Marketing

Search & PPC

Demand Generation

Analytics

CRM

ContentMarketing

Database Services

MobileMarketing

BusinessIntelligence

CX & Predictive Analytics

Page 2: Customer Experience and Predictive Analytics

2

Customerfocused

Business unit / media

engagement

Campaignfocused

The Digital Evolution…

Level 1

Level 2

Level 3

Level 4

Level 5

Enterprise engagement

Infrastructure focus, basic capabilities

Single campaign, simple data, little offerand customer customization

Basic multi-channel, model integration,and campaign automation

Contact Optimization, multi-touch campaigns, integrated measurement platform

Customer Value Optimizationfully integrated programs & campaigns

Low value

High value

Moving from

Level 3 to Level 4 is hard.

Page 3: Customer Experience and Predictive Analytics

3

Search

Display

Site

Social

Email

MobileDat

a M

an

agem

ent

Dat

a In

teg

rati

on

An

aly

tic

s:

Targ

eti

ng

, M

ea

su

rem

en

t, I

ns

igh

t

Be

ha

vio

ral

Se

gm

en

tati

on

, L

ife

tim

e V

alu

e, A

ttri

bu

tio

n,

CR

M m

ix

First Party Data

(CRM)

Second Party Data

(Partner)

Third Party Data

Cookies

Open Graph date

Real-time Bidding

Text

Key word bidding by

segment

IndividualPersonalization

Anonymous & Individual

Personalization

Our evolution has improved how businesses analyze, segment, target and sell to customers through digital media, sales and marketing channel, RIGHT?

Page 4: Customer Experience and Predictive Analytics

4

NOT. As far as we have evolved, the reality is that optimizing buyer behavior and customer experience has become increasingly complicated as the number of channels and media options grow.

TV & Radio

Print

Mail

Email

Display

Social

Search

Web

Store

Care

Phone

Partner

Position Expectation ProductPrice

ServiceSales

Purchase Promotion Experience Customization

Me

dia

Ch

ann

els

Brand Value proposition

Customer Centric

Question: How do we operationalize our business around customer experience given this level of complexity?

Page 5: Customer Experience and Predictive Analytics

5

Are we at risk of losing business from any of our top 50 accounts?

Answer tough questions…

What technology service or product will my clients likely buy?

WHAT ARE MY COMPETITORS DOING?

What can I do to prevent churn?

What is the best channel and time to connect with my customers?

Page 6: Customer Experience and Predictive Analytics

6

Strategies in CX set into motion preemptive business tactics...

Developing a 360º view of the customer

Ex-customerNon-buyer

ProspectSuspect

UserBuyer

Likely attrition customer

Sales and Marketing are responding by…

Developing advanced insight, targeting and

measurement capabilities

Social networks as a purchase advisor

Changing consumer buying process

Shift in media consumption patterns

Engaging consumers in more effective and productive ways

Putting the customer at the center of business strategy

The digitization

of media and channels

Socially enabled

mass engagement

Challenging economic

climate

Macro-level trends are changing the landscape

Evolving consumer expectations

Customer Experience is

constantly changing

Buyer

Page 7: Customer Experience and Predictive Analytics

7

There are six domains that must be mastered in order to create sustainable competitive advantage…

Ability to identify,

segment and manage high

value customers

Ability to allocate

resources that optimize ROI and long-term

customer value

Ability to micro target,

customize and personalize media and

channel experience

Ability to create metrics as

currencies and measure the incremental

impact of each marketing

activity

Ability to organize in a fashion that

allows you to respond to changes in customer,

competitor or marketplace

behaviors faster than the

competition

Ability to create and manage a 360º view of the customer

Information Insights Targeting MeasurementOptimization Agility

Page 8: Customer Experience and Predictive Analytics

8

BIG DATA, CX AND Predictive Analytics: What’s different?

Predictive

Analytics

Smarter

Decisions

Page 9: Customer Experience and Predictive Analytics

9Listen PredictLearn

Website

Call

Display

Search

Email

Social

Mobile

PIE solves the BIG DATA challenges brought on in today’s multi-channel, multi-tactic, multi-sourced CUSTOMER EXPERIENCE environment

BI

BI

BIBI

BI

BI

PIE channels all sources of customer interactions and intelligence into one

environment

PIE

Optimized

Page 10: Customer Experience and Predictive Analytics

10

Predictive indicators (PIE) will improve every stage of customer engagement and enhances employee and CUSTOMER EXPERIENCE

10

Ex-customerNon-

buyer ProspectSuspect UserBuyer

Shopping

Retain

Renew

Up-sell

Deselect

Cross-sell

Activate

Entangle

Stimulate

Qualify

Win back

Acquire

Qualify

Strategy & Direction Create & Customize Manage & Perform

• Business strategy• Brand strategy• Customer strategy• Segment strategy• Offer/treatment strategy• Media strategy• Multi-channel strategy• Customer experience strategy

• Insight management• Campaign management• MRM• CRM/media mix mgmt• Segment management• Lead management• Content management• Experimentation management• Performance management

• Program development• Offer creation• Content creation• Message development• Treatment• Personalization• Customization

PIE: Analytics, Data, Applications, Tools, Infrastructure, TALENT

CRM Management Systems • Metrics and incentivesOptimization and business rules

• Organizational structure• Roles and responsibilities

• Decision authority• Governance

Buyer

Page 11: Customer Experience and Predictive Analytics

11

What is PI3E? –PI3 is LSC’s proprietary technology and methodology that builds customer experience roadmaps for companies operating in both B-to-B and B-to-C environments. It is in part a predictive intelligence program that integrates customer insights, business intelligence, information from a clients’ CRM program that improves marketing effectiveness, sales effectiveness and business performance at every stage of engagement with clients and prospects.

Predictive Intelligence

Engine

PIEAcross Buying

Stages

Awareness

Advocacy - Loyalty -Retention

(Relationship Value Based)

Knowledge & Research

Buying(Less Selling)

Selection(Rational

& Emotional)

Satisfaction(Experience)

Evaluation &Consideration

PersonasProfilesCI

TrendingBehaviorMonitoring

Big Data, Social, Mobile,Digital

Multi-Channel

EngagementEngine

Behavioral Insights

Page 12: Customer Experience and Predictive Analytics

1212

Marketing Automation

Direct Marketing

SocialMedia

Email Marketing

Search & PPC

Demand Generation

Analytics

CRM

ContentMarketing

Database Services

MobileMarketing

BusinessIntelligence

APPENDIX

Draft do not use

Page 13: Customer Experience and Predictive Analytics

13

LSC Digital’s Executive Leadership Team

Ed MallinEd’s career spans over 27 years of experience and executive leadership in the Direct Marketing, Information Technology and Database Services industry providing sales and marketing solutions to businesses. He has been a tremendous asset to Huron with its investment in LeadingResponse..

Ed entered the industry in 1984 as a Partner and President of Compilers Plus, a leading direct marketing firm in New York. In 1990, Infogroup (then, Infousa acquired Compilers Plus; Ed served as a corporate officer, Executive VP and Group President for Infogroup for 20 years. During Ed’s tenure at Infogroup, it grew from a $30 million boutique business to an $800 million public company. Ed managed 7 Infogroup Divisions, was involved in over 15 acquisitions, and was responsible for over $200 million in annual revenue. Ed also led Infogroup’s transition into the digital space, building Infogroup Interactive into a $100 million online division.

Joe McCluskey Joe is a senior level marketing/business management expert who helps clients navigate the ever changing customer experience and how newer digital channels impact customer's loyalty. Joe works closely with the product teams on solutions that "weaponize" social, mobile, web, CRM and big data / analytics channels. Joe is recognized for his ability to align digital strategies into operational systems that can deploy business information, data and analytical solutions that increase each client's ROI.

Charlie TarzianCharlie’s career spans over 25 years of experience and executive leadership in the Agency, Direct Marketing, Information Technology and Database Services industry providing sales, technology and marketing solutions to businesses. Charlie has served as President of Barry Blau & Partners, Brann Worldwide (a division of Dan Schneider Communications. In 1998, Euro RSCG acquired Brann Worldwide; Charlie served as a CEO for Euro Circle / Circle and eventually MKTG until 2009. While Charlie served in these posts as well as a partner with HealthED and Consultant at Satori, he also developed many patented technologies that serve as the basis for the Big Willow project indication engine. He has been with LSC for the last three years where he is responsible for building out product, service and data innovation.

Page 14: Customer Experience and Predictive Analytics

1414

1980

1984

2000

2005

2011

Present

List Services Corporation (LSC) is Founded

State-of-the-art Data Center goes live, Data Brokerage division established

In-house compilation group launched, first hosted marketing database goes live

LSC adds digital services with email marketing group

Acquisition of Accession Media, merger with Last Mile Networks

LSC Digital consolidates all acquired digital brands. Launches brand new website…

Our History

Page 15: Customer Experience and Predictive Analytics

15

According to Sirius Decisions, 66% of the buying process is complete before sales reps are engaged

That research happens in a LOT of different locations

Who else is involved in the decision?Buying teams average 6 people for complex B2B projects

Teams swell to 20+ in large enterprise buys

Individual actions trigger resource intensive follow up to qualify the opportunity

Is there a real project at all or is this just an individual request?

The Realities in Technology Services - B2B Marketing

Page 16: Customer Experience and Predictive Analytics

16

Executive SummaryReport Background Key Findings & Results

Data sources used to generate this report: OOW 2011 Event Attendee Lists:

Speaking Session Attendees Booth Attendees BICG Customer Appreciation

Reception Revenue and marketing analysis Capgemini Pipeline Analysis

Based on post event funnel progression

Following OOW 2011, Capgemini booked $24.5MM with 7 accounts that were in attendance at OOW.

3 of those accounts were on Capgemini’s Top 40 List Monitored by LSC: Johnson & Johnson, Cisco Systems, Loews

Following OOW 2011, Capgemini generated $128MM in pipeline at 15 accounts that were in attendance at OOW (Pipeline defined as opportunities progressing between Stages 3 – 7 from Capgemini supplied pipeline files

7 out of Capgemini’s Top 40 accounts were in attendance at OOW 2011 AT&T, Johnson & Johnson, Lowe's, Cisco Systems, Farmers Insurance, Home

Box Office, Inc., US Department of the Treasury 2 of the 7 were on Capgemini’s Country Managed Account list (CMA) in NA

Home Box Office (part of Time Warner), Johnson & Johnson 366 attendees from 251 companies engaged with us via our:

19 speaking sessions Visitors to our booth BICG Customer Appreciation Reception

18 “Hot Leads” were generated for subsequent nurturing programs. As of Q1 2012 these leads show the following status in Capgemini’s SPADE system:

Accepted - 7 Converted – 1 Rejected - 3 Retired - 1 Unqualified – 6

Comments

LSC Digital’s CX engagement was the impetus behind driving Capgemini’s transformation into the Social CRM and digital marketing space behind Rick Vargas’s leadership.

Page 17: Customer Experience and Predictive Analytics

17

Impact on Bookings and Pipeline within Existing Accounts

Capgemini booked $24.5MM at 7 accounts In attendance at OOW

Cisco Systems Inc, $1.98MM

Citrix Sys-tems Inc,

$11K

Johnson & Johnson Inc,

$777K

Lowes Companies

Inc, $10.8M

Manheim Auctions, $8.3MM

McDonalds Corporation,

$40K

Royal Caribbean Cruises$2.5MM

AT&T, $200K

Cabot $ 330K

Celgene $4.2MM

Cisco Systems Inc, $945K

Citrix Systems $2MM

D&H Distributing$230K

Fidelity Invest-ments Company

Inc, $290K

Land O'Lakes Inc, $571K Lowes Com-

panies Inc, $100MM

Manheim Auc-tions, $4MM PNC $600K

Royal Caribbean $2.6MM

SAVVIS, Inc., $45K

Schreiber Foods $100K

Starbucks $12MM

$128MM in pipeline across 15 accounts advanced through the

funnel following OOW

Page 18: Customer Experience and Predictive Analytics

18

Event Impact on Capgemini’s bookings and pipeline by Sector

General Services, $12K

MALS, $24.5MM

EUC, $331K

FS, $890K

General Ser-

vices, $2MM

MALS, $125M

M TME, $200K

Most of the bookings and pipeline generated following OOW happened in Life Sciences

$128MM in pipeline between Stage 3 and

7

$24.5MM in bookings

Page 19: Customer Experience and Predictive Analytics

19

Capgemini engaged with attendees from 251 companies from mostly large multi national conglomerates in Capgemini’s target account list

MALS62

25%

General Services

5321%

FS35

14%

Public33

13%

CPRD32

13%

EUC187%

TME114%

Health7

3%

A majority of our attendees work for companies in Life Sciences,

Financial Services, CPRD, and Public sectors

Greater than $1B11245%

Between $500M and $1B

2610%

Less than

$500M88

35%

Annual Revenue Not Available

2510%

Almost half of the companies we engaged with have annual revenue

greater than $1B

Page 20: Customer Experience and Predictive Analytics

20

Capgemini’s Speaking Sessions drawed the most qualified target audience…

Between $500MM and

$1B10

16%

Greater than $1B32

50%

Less than $500MM20

31% Not Available, 2

3%

Between $500MM and $1B

37%

Greater than $1B23

53%

Less than $500MM

1228%

Not Available5

12%

Between $500MM and $1B159%

Greater than $1B66

42%

Less than $500MM57

36%

Not Available21

13%#1 Speaking Sessions:

66 companies within our target audience had attendees attend one of our speaking sessions

#2 BICG Customer Appreciation Reception:

32 companies within our target audience attended the BICG reception

#3 Booth Visitors

23 companies within our target audience visited our booth

Page 21: Customer Experience and Predictive Analytics

21

Speaking Session Results Details (ranked by # of attendees)

Speaking Session # of Attendees# of

Attendees Top 40

Company Size: Greater than

$1B

Company Size:

Between $500M and

$1B

Company Size:

Less than $500M

Operational Reporting with Oracle Business Intelligence Publisher and JD Edwards: Success Story 39 5 1 18

Oracle Application Express Within Oracle SOA Suite 32 14 5 8

Merits of being a project-oriented organization: How Oracle helps Achieve it 22 11 1 8

Implementing Oracle Application Integration Architecture 11g: A Customer Case Study 21 6 3 3

Oracle Business Intelligence 11g Action Framework: Basic to Advanced Integration 17 5 0 7

"A Single Version of the Truth": Diebold Shares Its Global Vision for BI 12 9 1 1

Oracle Business Intelligence Action Framework: Actionable Intelligence Study 12 1 7 0 3

Using Disruptive Technology and BI Analytics to Automate Business Services 12 3 2 2

Accelerated Interface Development Approach: Integration Framework 11 2 4 1 6

Enabling Social Sign-in with OpenID and ATG Applications 10 1 2 1 3

Simplifying Work Order Management in the Utilities market with Oracle BPM Solutions 10 4 1 1

MDM in the Public Sector 8 2 0 1

Page 22: Customer Experience and Predictive Analytics

22

Speaking Session Results Details (ranked by # of attendees) Continued

Speaking Session # of Attendees# of

Attendees Top 40

Company Size: Greater than

$1B

Company Size:

Between $500M and

$1B

Company Size:

Less than $500M

Oracle E-Business Suite 12 Implementation Success Story 7 1 4 0 1

Innovation and Rationalization: The definitive Guide to Oracle Applivations Landscaping 5 1 3 0 1

TDC Denmark: A Case Study of Order Management's Role in Business Transformation 4 2 0 1

Moving Prisoners and Illegal Aliens with Oracle – JPATS “The Real ConAir” 3 2 0 0

Capgemini's Buyers Workbench: Purchasing Optimization Tool for Wholesale Distribution 2 0 0 2

Rising Costs and Falling Prices: What's a Distributor to do? 2 0 0 1

Increased Bottom-Line Cost Reductions and Top-Line Growth in Multichannel Distribution Support 1 1 0 0

Grand Total 230 6 84 16 67

Page 23: Customer Experience and Predictive Analytics

23

We have created a way for technology services, consulting and outsourcing companies to decipher and interpret the digital behavior of their client and prospective client’s buying process

Finance

UK

IT Procure

USA

Finance IT Procure

Our data is broad and deep, and we can find professionals by department.

Competitive intelligence has not been centrally organized which in turn limits our ability to react to conditions happening across the family tree of our accounts

In this example, a competitor just won business at a family member of one of our accountsOur solutions move away from

depending on the actions of a single individual as an indicator of a sales opportunity, to using the analysis of all the web activities of an entire organization.

CX Roadmaps –

We build Customer Experience Roadmaps

SubsidiaryFrance

Page 24: Customer Experience and Predictive Analytics

24

We use advanced IP targeting and account based marketing with contextual alignment to reach your exact targets at the right point in time

Our technology accesses, analyzes, and reports on a data stream that by itself is significant but as part of a larger integration of data points – creates a 360° view of a company of interest – while it is happening

Capturing the Data You Care About Most:

Page 24

Page 25: Customer Experience and Predictive Analytics

25

• IP Address - What is it?      - Every device on the Internet has a unique ID number, called an IP Address       - A typical IP address looks like this: 64.149.181.87      - IP addresses are routable locations on the Internet.         - There are a theoretical maximum of 4.3 Billion IP addresses on the Internet. (IPv4)      - 3 Billion IP addresses are currently in use.        • Host name - What is it?      - Every website and most Internet servers and devices have a host name.

      - Host names are easier for humans to read than IP addresses.      - A host name looks like this: adsl-64-149-181-87.dsl.austtx.sbcglobal.net 

      - Nearly 1 Billion host names have been collected and organized so far.

IP Addresses and Host Names

Page 26: Customer Experience and Predictive Analytics

26

How It Works

We map each IP address to their URL

That URL gets map to a business entity in one of the multiple B-to-B databases we host:

– We take the URL’s of interest and use our B-to-B contact databases to do a lookup against all known, current contacts in that organization and present those as qualified leads that can be integrated directly into your marketing/sales automation systems or as a prospect list inclusive of email and postal addresses

Those leads can be integrated directly into your marketing/sales automation process flow or handed off to you as a more traditional contact list

These leads will:

– Accelerate and compress your lead generation process by delivering high value leads with specific data behind them

– Give you context – you now have something relevant to talk about/message to key decision makers/influencers

While giving you:

– A leg up on your competition – you are getting in market buy signals before the traditional methods of intelligence pick them up

– A dynamic way to market on the fly – you can now tie together critical business intelligence with near real time messaging

Page 27: Customer Experience and Predictive Analytics

27

Organizational Criteria:

– Revenue

– Employee Size

– Industry/Vertical

– Named Accounts

• Customer Lists

• Sales Prospects

• Public Lists – Fortune 500, etc

– Location

• Country

• City and/or State

– Competitive InstallsIBM Storwize, XIV, & DS5000 & EMC Data Domain

Choose Your Target

Page 27

Page 28: Customer Experience and Predictive Analytics

28

By Keyword:

– Give us your SEM keywords aligned against your site, a particular solution, or even a specific product

By Topic:

– Give us the topics that most closely align with your product or service, we’ll create the list of keywords and phrases matching these topics

– Think like your customer

Choose Your Topic

Page 28

Page 29: Customer Experience and Predictive Analytics

29

Contextually aligned ads served against topical content pages mapped to customer keywords

Ads enable data collection: Organizations are identified and targeted via IP Targeting across all relevant pages

Data Analytics normalizes activity to see an entire organization's activity and the buying team across the web

How It Works

Page 29

Page 30: Customer Experience and Predictive Analytics

30

We are technology agnostic domain experts

30