big data has big implications for customer experience management

How may we [email protected] 2012

Using Big Data to Improve Customer Loyalty

Bob E. Hayes, PhD

mailto:[email protected]

Overview

1. Big Data

2. Analytics

3. Data Integration

4. Customer Loyalty

5. Customer Experience Management

6. Linkage Analysis

7. Implications

Copyright 2012 TCELab

Big Interest in Big Data: Google Trends

Jan

2006

Apr 2

006

July

2006

Oct

200

6

Jan

2007

Apr 2

007

July

2007

Oct

200

7

Jan

2008

Apr 2

008

July

2008

Oct

200

8

Jan

2009

Apr 2

009

July

2009

Oct

200

9

Jan

2010

Apr 2

010

July

2010

Oct

201

0

Jan

2011

Apr 2

011

July

2011

Oct

201

1

Jan

2012

Apr 2

012

0

1

2

3

4

5

6

Customer ExperienceBig Data

Sea

rch

Vo

lum

e In

dex

Scale is based on the average worldwide traffic of customer experience in all years.


Big Data

• Big Data refers to the tools and processes of managing and utilizing large datasets.

• An amalgamation of different areas that help us try to get a handle on, insight from and use out of data


Three Vs of Big Data

• Volume – amount of data is massive

• Velocity – speed at which data are being generated is very fast

• Variety – different types of data like structured and unstructured


Disparate Sources of Business Data

1. Call handling time2. Number of calls until

resolution3. Response time

1. Revenue2. Number of products

purchased3. Customer tenure4. Service contract

renewal5. Number of sales

transactions6. Frequency of

purchases

1. Customer Loyalty2. Relationship satisfaction3. Transaction satisfaction

1. Employee Loyalty2. Satisfaction with

business areas

Operational

Partner Feedback

1. Partner Loyalty2. Satisfaction with

partnering relationship

CustomerFeedback

EmployeeFeedback

Financial


Value from Analytics: MIT / IBM 2010 Study

Top-performing organizations use analytics five times more than lower performers


http://sloanreview.mit.edu/the-magazine/2011-winter/52205/big-data-analytics-and-the-path-from-insights-to-value/

Number one obstacle to the adoption of analytics in their organizations was a lack of understanding of how to use analytics to improve the business




Three Big Data Approaches

1. Interactive Exploration - good for discovering real-time patterns from your data as they emerge

2. Direct Batch Reporting - good for summarizing data into pre-built, scheduled (e.g., daily, weekly) reports

3. Batch ETL (extract-transform-load) - good for analyzing historical trends or linking disparate data


Data Integration is Key to Extracting Value


CEM and Customer Loyalty

• Customer loyalty is key to business growth

• The process of understanding and managing your customers’ interactions with and perceptions of your brand / company


Linkage Analysis

• Linkage analysis answers the questions:– What is the $ value of improving customer

satisfaction/loyalty?– Which operational metrics have the biggest impact on

customer satisfaction/loyalty?– Which employee/partner factors have the biggest impact on

customer satisfaction/loyalty?

Operational Metrics

Transactional Satisfaction

Relationship Satisfaction/

Loyalty

Financial Business Metrics

Constituency Satisfaction/

Loyalty


Customer Feedback Data Sources

Relationship(satisfaction/loyalty to company)

Transaction(satisfaction with specific transaction/interaction)

Business Data Sources

Financial(revenue, number of sales)

•Link data at customer level

•Quality of the relationship (sat, loyalty) impacts financial metrics N/A

Operational(call handling, response time)

N/A

•Link data at transaction level

•Operational metrics impact quality of the transaction

Constituency(employee / partner feedback)

•Link data at constituency level

•Constituency satisfaction impacts customer satisfaction with overall relationship

•Link data at constituency level

•Constituency satisfaction impacts customer satisfaction with interaction

Integrating your Business Data


Financial Metrics / Real Loyalty Behaviors

• Linkage analysis helps us determine if our customer feedback metrics predict real and measurable business outcomes

• Retention– Customer tenure– Customer defection rate– Service contract renewal

• Advocacy– Number of new customers– Revenue

• Purchasing• Number of products

purchased• Number of sales

transactions• Frequency of purchases

Relationship Satisfaction/

Loyalty

Financial Business Metrics

VoC / Financial Linkage

Customer(Account) 1

Customer(Account) 2

Customer (Account) 3

Customer(Account) 4

Customer(Account) n

Customer Feedbackfor a specific

customer (account)

Financial Metricfor a specific

customer (account)

x1

x3

x2

xn

x4

y1

y3

y2

yn

y4

yn represents the financial metric for customer n.

xn represents customer feedback for customer n.

.

.

.

.

.

.

.

.

.


ROI of Customer Loyalty

Disloyal (0-5) Loyal ( 6-8) Very Loyal (9-10)

Customer Loyalty

Pe

rce

nt

Pu

rch

as

ing

Ad

dit

ion

al

So

ftw

are 55%

increase


Operational Metrics

• Linkage analysis helps us determine/identify the operational factors that influence customer satisfaction/loyalty

• Support Metrics– First Call Resolution (FCR)– Number of calls until resolution– Call handling time– Response time– Abandon rate– Average talk time– Adherence & Shrinkage– Average speed of answer (ASA)


Operational Metrics

Transactional Satisfaction

Operational / VoC Linkage

Customer 1Interaction




Customer nInteraction

Operational Metricfor a specific

customer’s interaction

Customer Feedback for a specific

customer’s interaction

x1

x3

x2

xn

x4

y1

y3

y2

yn

y4

yn represents the customer feedback for customer interaction n.

xn represents the operational metric for customer interaction n.

.

.

.

.

.

.

.

.

.


Identify Operational Drivers


Identify Operational Standards

1 call 2-3 calls 4-5 calls 6-7 calls 8 or more calls

Number of Calls to Resolve SRSa

t with

SR

1 change 2 changes 3 changes 4 changes 5+ changes

Number of SR Ownership Changes

Sat

wit

h S

R


Constituency Metrics

• Linkage analysis helps us understand how constituency variables impact customer satisfaction/loyalty

• Satisfaction Metrics– Employee satisfaction with

business areas– Partner satisfaction with

partner relationship

• Loyalty Metrics– Employee/Partner loyalty

Employee Metrics

Customer Satisfaction/

Loyalty

Partner Metrics

• Other Metrics• Employee training metrics• Partner certification status


Constituency / VoC Linkage

Employee 1

Employee 2

Employee 3

Employee 4

Employee n

Employee Satisfactionwith the Company

CustomerSatisfaction / Loyalty

x1

x3

x2

xn

x4

y1

y3

y2

yn

y4

.

.

.

.

.

.

.

.

.

yn represents average customer satisfaction scores across survey respondents for Employee n. _ xn represents employee satisfaction score for Employee n.

1 Analysis typically conducted for B2B customers where a given employee (sales representative, technical account manager, sales manager) is associated with a given customer (account)

_

_

_

_

_


Identify Employee Drivers


Validate Training Standards


Implications

• Ask and answer bigger questions about your customers– Explore all business data to understand their

impact on customer experience / loyalty

• Build your company around your customers– Greenplum social network platform– Cross-functional teams working together to

solve customer problems

• Use objective, “real,” loyalty metrics


How may we [email protected] 2012

Using Big Data to Improve Customer Loyalty

Bob E. Hayes, PhD

[email protected]@bobehayesbusinessoverbroadway.com/blog

mailto:[email protected]

big data has big implications for customer experience management

Business

financial

infotcelab

customer interaction

link data

customer feedback

specific customer

customer satisfactionloyalty

big data