Improving the customer experience using big data customer-centric measurement and analytics

How may we [email protected] 2013

Improving the Customer Experience Using Big Data, Customer-Centric

Measurement and AnalyticsBob E. Hayes, PhD

mailto:[email protected]

TCE: Total Customer Experience

Copyright 2013 TCELab

1. Customer Experience Management

2. Customer Loyalty

3. Optimal Customer Survey

4. Value of Analytics

5. Big Data Customer-Centric Approach

For more info on book:http://bit.ly/tcebook

http://bit.ly/tcebook


Customer Experience,Customer Experience Management

and Customer Loyalty

Customer Experience Management (CEM)

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



Optimal Customer

Relationship Survey

Customer Relationship Surveys


• Solicited feedback from customers about their experience with company/brand

• Assess health of the customer relationship• Conducted periodically (non-trivial time period)• Common in CEM Programs

– Guide company strategy– Identify causes of customer loyalty– Improve customer experience– Prioritize improvement efforts to maximize ROI

Four Parts to Customer Surveys


1. Customer Loyalty – likelihood of customers engaging in positive behaviors

2. Customer Experience – satisfaction with important touch points

3. Relative Performance – your competitive advantage

4. Additional Questions – Extra value-added questions

Customer Loyalty Types

The degree to which customers experience positive feelings for

and engage in positive behaviors toward a company/brand

Emotional(Advocacy)

Behavioral(Retention, Purchasing)

Love, Consider, Forgive, Trust

Stay, Renew, Buy, Buy more often, Expand usage


Customer Loyalty Measurement FrameworkLoyalty Types

Emotional Behavioral

Measuremen

t Approach

Objective

ADVOCACY• Number/Percent of new

customers

RETENTION• Churn rates• Service contract renewal rates

PURCHASING• Usage Metrics – Frequency of

use/ visit, Page views• Sales Records - Number of

products purchased

Subjective(Survey Questio

ns)

ADVOCACY• Overall satisfaction• Likelihood to recommend• Likelihood to buy same product• Level of trust• Willing to forgive• Willing to consider

RETENTION• Likelihood to renew service contract• Likelihood to leave

PURCHASING• Likelihood to buy different/ additional

products• Likelihood to expand usage

1 Using RAPID Loyalty Approach - Overall satisfaction rated on a scale from 0 (Extremely Dissatisfied) to 10 (Extremely Satisfied). Other questions are rated on a scale from 0 (Not at all likely) to 10 (Extremely likely). * Reverse coded so lower rates of these behaviors indicates higher levels of Retention Loyalty. Copyright 2013 TCELab

Customer Experience


• Two types of customer experience questions• Overall, how satisfied

are you with…

Area General CX Questions Specific CX Questions

Product 1. Product Quality

1. Reliability of product2. Features of product3. Ease of using the product4. Availability of product

Account Management

2. Sales / Account Management

1. Knowledge of your industry2. Ability to coordinate resources3. Understanding of your business issues4. Responds quickly to my needs

Technical Support 3. Technical Support

1. Timeliness of solution provided2. Knowledge and skills of personnel3. Effectiveness of solution provided4. Online tools and services

0 1051 2 3 4 6 7 8 9

ExtremelyDissatisfied

ExtremelySatisfied

Neither SatisfiedNor Dissatisfied

Customer Experience


• Overall, how satisfied are you with each area?

1. Ease of doing business

2. Sales / Account Management

3. Product Quality

4. Service Quality

5. Technical Support

6. Communications from the Company

7. Future Product/Company Direction

0 1051 2 3 4 6 7 8 9

ExtremelyDissatisfied

ExtremelySatisfied

Neither SatisfiedNor Dissatisfied

CX Predicting Customer Loyalty


Company A Company B Company C Company D0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

74%

42%60%

85%

0%

4%

2%

4%Specific CX QuestionsGeneral CX Questions

Perc

ent o

f Var

iabi

lity

(R2)

in C

us-

tom

er

Loya

lty E

xpla

ined

by

CX Q

uesti

ons

General CX items reflected areas (e.g., product quality, ease of doing business, tech support) and additional specific CX items reflected specific aspects of the general items (product reliability, tech support knowledge, account management’s ability to respond quickly).R2 reflects percent of variance of customer loyalty that is explained when using general items in regression analysis . ∆R2 reflects the additional percent of variance explained above what is explained by general items when using general items and specific items in a stepwise regression analysis.

1. General CX questions explain customer loyalty differences well.

2. Specific CX questions do not add much to our prediction of customer loyalty differences.

3. On average, each Specific CX question explains < .5% of variability in customer loyalty.7 General CX 5 General CX 6 General CX 7 General CX

0 Specific CX 14 Specific CX 27 Specific CX 34 Specific CX

• Customer experience questions may not be enough to improve business growth– You need to understand your relative performance

• HBR study (2011)1: Top-ranked companies receive greater share of wallet compared to bottom-ranked companies

• Focus on increasing purchasing loyalty (e.g., customers buy more from you)

Competitive Analytics


Relative Performance Assessment (RPA)

• Ask customers to rank you relative to the competitors in their usage set

• What best describes our performance compared tothe competitors you use?


RPA Predicting Customer Loyalty


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

69% 72%

18% 16% 14%

1% 2%

8% 7%1%

1 RPA Question

7 General CX Questions

Loyalty Questions

Perc

ent

of V

aria

bilit

y (R

2) i

n Cu

stom

er

Loya

lty

Expl

aine

d by

Gen

eral

CX

Que

stion

s an

d Re

lativ

e Pe

rfor

man

ce A

sses

smen

t (R

PA)

What best describes our performance compared to the competitors you use?

1. General CX questions explain purchasing loyalty differences well.

2. Relative Performance Assessment improved the predictability of purchasing loyalty by almost 50%

3. Improving company’s ranking against the competition will improve purchasing loyalty and share of wallet

Understanding your Ranking


1. Correlate RPA score with customer experience measures

2. Analyze customer comments about the reasons behind their ranking– Why did you think we are better/worse than the

competition?– Which competitors are better than us and why?

• What to improve?– Product Quality was top driver of Relative Performance

Assessment– Open-ended comments by customers who gave low RPA

rankings were primarily focused on making the product easier to use while adding more customizability.

Additional Questions


• Out of necessity or driven by specific business need• Segmentation Questions

– How long have you been a customer?– What is your role in purchasing decisions?– What is your job level?

• Specific topics of interest to senior management– Perceived benefits of solution (What is the % improvement in

efficiency / productivity / customer satisfaction)– Perceived value (How satisfied are you with the value

received?)• Open-ended questions for improvement areas

– If you were in charge of our company, what improvements, if any, would you make?

Summary: Your Relationship Survey


1. Measure different types of customer loyalty (N = 4-6)

2. Consider the number of customer experience questions in your survey (N = 7)– General CX questions point you in the right direction.

3. Measure your relative performance (N = 3)– Understand and Improve/Maintain your competitive advantage

4. Consider additional questions (N = 5)– How will you use the data?


Big Data, Analytics and Integration

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 large, quickly-expanding, diverse data


Big Data Landscape – bigdatalandscape.com


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


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




Value from Analytics: Accenture 2012 Study


1. Measure Right Customer Metrics - only 20% were very satisfied with the business outcomes of their existing analytics programs

2. Focus on Strategic Issues - only 39% said that the data they generate is "relevant to the business strategy"

3. Integrate Business Metrics - Half of the executives indicated that data integration remains a key challenge to them.

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 satisfaction4. Sentiment

1. Employee Loyalty2. Satisfaction with

business areas

Operational

Partner Feedback

1. Partner Loyalty2. Satisfaction with

partnering relationship

CustomerFeedback

EmployeeFeedback

Financial


Data Integration is Key to Extracting Value


Linkage Analysis

Operational Metrics

Transactional Satisfaction

Relationship Satisfaction/

Loyalty

Financial Business Metrics

Constituency Satisfaction/

Loyalty


Customer Feedback Data Sources

Relationship Survey

(satisfaction/loyalty to company)

Transactional Survey

(satisfaction with specific transaction/interaction)

Social Media/ Communities

(sentiment / shares / likes)

Business Data Sources

Financial(revenue, number of sales)

• Link data at customer level

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

N/A

• Link data at customer level

• Quality of relationship (sentiment / likes / shares) impacts financial metrics

Operational(call handling, response time)

N/A

• Link data at transaction level

• Operational metrics impact quality of the transaction

• Link data at transaction level

• Operational metrics impact sentiment / likes/ shares

Constituency(employee / partner feedback)

• Link data at constituency level

• Constituency satisfaction impacts customer satisfaction with overall relationship


• Constituency satisfaction impacts customer satisfaction with interaction


• Constituency satisfaction impacts customer sentiment / likes / shares

Integrating your Business Data


Customer Feedback / 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.

.

.

.

.

.

.

.

.

.


Determine ROI of Increasing 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 / Customer Feedback 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 of Satisfaction


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


3 Implications of Big Data in CEM

1. Ask/Answer bigger questions

2. Build company around the customer

3. Predict real customer loyalty behaviors


[email protected]@bobehayesbusinessoverbroadway.com/blog

How may we [email protected] 2013

Improving the Customer Experience Using Big Data, Customer-Centric

Measurement and AnalyticsBob E. Hayes, PhD

For more info on book:http://bit.ly/tcebook

mailto:[email protected]

http://bit.ly/tcebook

RAPID Loyalty Measurement

Index Definition Survey Questions

Retention Loyalty

Index (RLI)

The degree to which customers will remain as a customer/not leave to competitor (0 – low loyalty to 10 – high loyalty)

Likelihood to switch to another company*

Likelihood to purchase from competitor*

Likelihood to stop purchasing*

AdvocacyLoyalty

Index (ALI)

The degree to which customers feel positively toward/will advocate your product/service/brand (0 – low loyalty to 10 – high loyalty)

Overall satisfaction

Likelihood to choose again for first time

Likelihood to recommend (NPS)

Likelihood to purchase same product/service

Purchasing Loyalty

Index (PLI)

The degree to which customers will increase their purchasing behavior (0 – low loyalty to 10 – high loyalty)

Likelihood to purchase different products/services

Likelihood to expand usage throughout company

Likelihood to upgrade

1 Overall satisfaction rated on a scale from 0 (Extremely Dissatisfied) to 10 (Extremely Satisfied). Other questions are rated on a scale from 0 (Not at all likely) to 10 (Extremely likely). * Reverse coded so lower rates of these behaviors indicates higher levels of Retention Loyalty.

• Assesses three components of customer loyalty


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


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

Improving the customer experience using big data customer-centric measurement and analytics

Business

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