data strategies: integrating data science into cx …...new data is added to the models customer...

CX NYC 2018

Gina Fleming

Jon Masland

Data Strategies: Integrating Data Science into CX Measurement

Sponsored by Oracle

2© 2018 FORRESTER. REPRODUCTION PROHIBITED.

CX

3© 2018 Forrester Research, Inc. Reproduction Prohibited

Great CX drives Loyalty and Growth


Measuring CX is a critical competence required for success


CX measurement should answer three key questions:

1. How good is our customer experience?

• Across the entire customer relationship as well as within

key customer journeys and at critical touchpoints

• Benchmarked relative to competitors / peers

2. What do we need to do to improve CX

quality?

• Understand what’s most important to customers and what

drives CX quality

• Understand how to prioritize initiatives to improve CX and

increase customer loyalty

3. How are we benefiting from our efforts?

• Understand the potential financial impact of improving CX

• Link CX metrics to desired business outcomes


14% of CX pros say that their VOC is

extremely (3%) or very effective (11%) at

delivering financial results.

33% of CX pros say that their VoC is extremely

(6%) or very effective (27%) at driving action.

Source: Forrester State of Customer Experience Survey

But only....


Demonstrate value & ROI


Struggle to provide meaningful insights

Inability to drive meaningful organizational change

Inability to effectively handle organizational scale


CX Measurement Architecture: A unifying and

coherent structure of key organizational metrics


Why use a CX Measurement Architecture?

› Purpose: To understand the key drivers (interaction and perception) of customer

experience. To create linkages between customer experience and key outcomes (e.g.

account growth) to clearly show how CX contributes to the organization’s success.

› By leveraging this framework, you can adopt three key measurement traits:

Metrics that are rooted in

the customer life cycle

Metrics that inform

customer-obsessed

tradeoffs

Metrics that create a culture

of accountability and action


› Inventory and codify metrics to identify and

close gaps in the measurement framework

› Links CX to business success – helps build the

case for CX transformation

› Helps steer CX improvements by identifying the

operational levers with the greatest impact on

CX and business outcomes

› Linkage analysis can enable you to use

interaction metrics as leading indicators (or

proxies) for CX

Value of CX Measurement Architecture


Key components to a CX measurement architecture

Perception

What customers think happened

and how they feel about it (rational and

emotional)

Beacon/North Star

Where organizations point their CX

efforts because they are predictive of

Outcomes (CX Index®, NPS®, OSAT)

OutcomeWhat business result you expect from

what happened

InteractionObjective, observable events that

happened, i.e. “operational” metrics


Collaborating with data science team can help close the loop on CX measurement

What surveys

measure

Perception


and how they feel about it (rational and

emotional)

Beacon/North Star





what happened




Collaborating with data science team can help close the loop on CX measurement

What layers in

with financial

and operational

data analysis

Perception


and how they feel about it (rationaland

emotional)

Beacon/North Star





what happened




Financial Data

Finance team

Customer spending

Survey Data

Market

research or CX teams

Perception

Beacon metric

Operational Data

Sales teams or

IT teams

Customer support history

Website visitation

Customer Data

Demographics

Customer tenure

Purchase history


Techniques

Simple Complex

Machine learning

that continually

improves upon as

new data is added

to the models

Customer

segmentation:

survey and

operational

data based

Customer

segmentation:

survey based

Regression

analysis linking

interaction,

perception and

outcome metrics

Visualize CX:

revenue

relationship


Example: Visualize CX-Revenue relationship


Masked client example

Example: Use transactional data to identify detractors

Recent share of wallet score

Tota

l in

dustr

y t

ransactions

Detractors High Risk Stable Loyal


(Illustrative – masked client example)

Then model survey-based perception and beacon metrics to predict detraction

1

3

4

4

5

6

10

14

24

Product equipment working correctly

Sales rep gave good explanation of equipment operation

Dealer followed up with me

NPS

Overall satisfaction

Country

Sales rep understands equipment needs

Knowledge of sales representative

Brand

Predictor importance


Challenge

Getting access to data from

different parts of the organization.

Finding a common identifier, issues

with data quality and missing data.


Shortcut

Instead of building a large and

complete model with all possible

data pieces, generate a profile of

profile promoters and detractors.


Challenge

Difficult to get all data sources for

all customers integrated perfectly.


Shortcut

Just get a sample—or start with

one customer segment.

Don’t integrate all of your data

points—just focus on what’s

important.


Challenge

Have incomplete data to design the

best CX for every segment.


Shortcut

Focus on creating the best

experiences for the most valuable

customers.

CX NYC 2018

Amy ShiojiVice President, Customer Experience & Insights, Gannett / USA TODAY NETWORK

CX NYC 2018

Sara Perelli-Minetti Director, Customer Intelligence, Capital One Bank

1. Moments that Matter

2. Customer Segmentation

3. Call Center KPIs & NPS

DRIVING CX IMPACTHow data science powers customer-centricity.

29

Confidential

Moments that Matter

1. Are there certain experiences that have an

outsize impact on a customer’s perception of

their bank?

2. How are we doing on those experiences?

3. For each moment, what makes for a good or

bad customer experience?

30Confidential

Feature importance ranking

Confidential

1. Who are our most financially valuable

customers?

2. Who are our most behaviorally desirable

customers?

3. What happens at the intersection of these two

things?

Customer Segmentation

31

K-means clustering

Confidential

Call Center KPIs & NPS

1. What is the relationship between operational

KPIs at the call center and NPS?

2. Which elements of the call center customer

journey best predict likelihood to recommend?

3. Can we model call center NPS?

32Confidential

Predictive modeling

Decision trees


Questions

Thank youGina Fleming

[email protected]

Jonathan Masland

[email protected]

data strategies: integrating data science into cx …...new data is added to the models customer...

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