quantifying customer experience - presented at customer experience design 2013

1

Quantifying

greg Stewart SMS Management & Technology www.smsmt.com @clarityrules #CX13 6 may 2013

of customer

analytics

questions

A CX LEADER’S CHALLENGE

and

to

try and make a difference?

If I do, will it be

CFO

They count . They want .

customer analytics practices

1.0 2.0 3.0 4.0

four eras

Thurston Howell

Crystal Ball

Trip Fall

1.0 2.0 3.0

4.0

1.0 2.0 3.0 4.0

to get from your

you are using

What Questions

things that you can

ask

you can ask questions

and answers are returned

How you explore data

1.0

out inside

1.0

era 1.0 – inside out

1.0

main symptom of 1.0:

A is just the

of each business unit.

he isn’t.

to get from your

What Questions

How you explore data

2.0

in outside

era 2.0 – outside in

:

Customer’s Point of View

entity identification

top process owners

Guiding Principles

Customer Journey

Opportunities

SMS Services

RIGHT VISION

STAGES

ACTIVITIES

DOING

THINKING

FEELING

Trip Initiation Enrolment Finalise Trip Post-Trip

College e-Enabled Field Trip Experience Map

• Where should I organise the trip?

• When is a good time to go?

• What processes do I need to follow?

• I’m excited about this trip!

• I’m worried it won’t get approval from the Principal

Obtain approval Schedule resources Give permission View itinerary

Go paperless &

move towards

online processes

Adopt automated

workflow where

possible

Introduce “cloud”

spaces for remote

collaboration

Develop web forms,

portals, and apps

Build platforms to

push notifications to

SMSs & emails

• Do we have enough information about this trip?

• How much will this cost?

• Who else is going?

• Can we pay online?

• Happy that Wesley College allowed us to nominate the preferred

communications channel

• Wish we are able to see other parents’ responses

• I need to have an up-to-date view of the responses to

help me finalise arrangements

• What do I do if there is not enough responses?

• Stressed that this is taking longer than expected

• Worried about getting things wrong

RIGHT INVESTMENT RIGHT INFORMATION RIGHT INTEGRATION RIGHT OUTCOMES

Submit internal

trip request form

Mr. Smith

(History

Teacher)

Receive trip

approval

Organise volunteers,

buses, etc.

Mr & Mrs

Lincoln

(Parents)

View trip

itinerary

Receive trip

details

Submit permission slips

Make payment

Mr. Smith

(History

Teacher)

Monitor

responses

Answer

questions

Confirm

arrangements

Notify parents

Receive

confirmation

For queries / difficulties encountered

Consolidate responses Schedule resources Gather feedback Share experience

Mr. Smith

(History

Teacher)

Mr & Mrs

Lincoln

(Parents)

Sarah

(Student)

Share photos

• I need to know if the students enjoyed themselves

• Next time, I will need to plan more carefully

• Excited to share photos with students and parents

Organise payment

Discuss

experience

Get

trip

ratings

Allow knowledge

re-use and

discovery

Encourage sharing

& participations

through online

communities

Build-in intelligence

to merge & report

information from

disparate sources

Trip

Trip

occurs

Participate

For unexpected

changes

Continuous, non linear Non linear, but time based Linear Processes

Business Performance Improvement (BPI)

Information & Data Management (IDM)

Systems Integration (SI)

Program & Project Services (PPS) Project Management

System Architecture Social Media Integration

Business Intelligence

Customer Experience Improvement

Workflow Automation

Transformation Business Case

Experiences have a life cycle

19

Experiences have a life cycle

20

Want Consider Evaluate Buy Experience Advocate Bond

Social media monitoring

IMAGE – TWITTER, FACEBOOK Sentiment analysis

23

BI Reports

top process owners entity identification customer journey mapping Customer decision journey Social media monitoring Chief Customer Officer Customer satisfaction

:

Customer’s Point of View

2.0 analytics activities Measuring

Tick

Generating showing

performance and stats by

business unit/product etc…

Tick

oh dear…

a little knowledge is a dangerous thing

good idea, bad execution

all the kit, but still pretty s#&t

some

some

some

some

some

Why Q

A Answers not correlated with revenue

customer

delighting

exceeding expectations

Excellence

Recall

Question: Strong correlation to growth?

4%

Source: Satmetrix: The Power behind a single number,

chasing

funding

he isn’t.

he is asking questions

answers are and in fixed structures

What Questions

How you explore data

to get from your

something

3.0

in – but better

outside

no insights false insights explored insights predicted insights insights you didn’t know to ask for

era 3.0 – business outcomes

no insights false insights explored insights predicted insights insights you didn’t know to ask for

Better Question

to get from your

you are using

What Questions

A CX LEADER’S CHALLENGE

and

to

try and make a difference?

If I do, will it be

of CX measure no insights false insights explored insights predicted insights insights you didn’t know to ask for To Get actionable insight: 1.0 You’re not even asking 2.0 you can ask questions of the wrong data but you’re asking bad questions and the answers are slow in coming 3.0 You can ask much better questions about what happened, and the answers are instant, and you can ask follow up questions 3.5 You can ask good questions about what might happen next? 4.0 you don’t have to ask questions -

Quantitative

Qualitative

Outcome

(what happened to the customer?)

(how did they feel about it?

(What will they do as a result?)

The high priest of Loyalty research

Net Promoter Score

Probably the most measure of customer intention

Open Source Simple Surprisingly Robust and versatile

Legitimising investment in customer experience

Question: Strong correlation to growth?

80% 4%

Source: Satmetrix: The Power behind a single number,

what’s it

what’s the of loyalty?

from a to a ?

by 10 points?

Value estimate the of the

average customer in each segment 1.

Look at the

between , and 2.

Hypotheses – find in your

experience design that affect NPS 3.

Work on those 4.

NPS Gives TEETH to customer metrics

1.0 1.0 1.0

NPS Gives TEETH to customer metrics

1.0 1.0 1.0

wallet share

NPS Gives TEETH to customer metrics

1.0 1.0 1.0

retention

wallet share

NPS Gives TEETH to customer metrics

1.0 1.0 1.0

referrals

retention

wallet share

NPS Gives TEETH to customer metrics

bad-mouthing

cost to serve

wallet share

1.0 1.0 1.0

referrals

retention

wallet share

Your industry’s & YOURS

7.6x

referrals

cost to serve

wallet share

bad-mouthing

cost to serve

wallet share

1.0 1.0 1.0

1.0

1.9

0.65

NPS Gives TEETH to customer metrics

Some examples

Cautionary tales no insights false insights explored insights predicted insights insights you didn’t know to ask for

“NPS is , but it’s .

There are lots of things you have to do to and make it .

cautionary tale - sampling

Promoters 45%

Neutrals 22%

Detractors 33%

cautionary tale - sampling

Promoters 45%

Neutrals 22%

Detractors 33%

Promoters 20%

Neutrals 29%

Detractors 51%

64

“ ” Our NPS is 20 A guy from (a well-known Australian Brand), two months ago

cautionary tale – oversimplifying

What’s good?

Segment – with a two tier system no insights false insights explored insights predicted insights insights you didn’t know to ask for

Tier one

Overall

Tier two

Discrete details

Associate

Actionable insight

0.56499

lorem

0.56499

lorem

0.56499

ipsum

0.56499

ipsum

Related to a

Related to

Related to

Related to a

Related to a

Use NPS to prototype service

it creates a

for leaders

to get from your

you are using

What Questions

to get from your

you are using

What Questions

you can ask questions

and answers are returned

How you explore data

data disco- very

73

Analytic

Extensive data modeling to

respond

Works within

Issues with traditional BI

reporting isn’t good enough no insights false insights explored insights predicted insights insights you didn’t know to ask for

“ is

so last year.”

exploring beats reporting no insights false insights explored insights predicted insights insights you didn’t know to ask for

“link .”

76

BI Reports

BI Reports Business discovery

Business DISCOVERY over Business Intelligence

NPS Data

CRM Data

Segmentation

Data

Services Owned

Data

Billing info

Churn

IVR Data - #calls,

route, scripts

Financial Data

Usage data

Service outages

Provisioning info

ASSOCIATED IN INSIGHT ENGINE

YOUR DATA EXPLORE & DISCOVER

THAT POSES A

QUESTION

LEADS TO A

THOUGHT

IDEA

LEADING TO

INSIGHT

79

Let me show you

Source: our technology partner: Qlikview

Why is NPS low?

ask a question of your

dear data:

What Questions

dear data:

predictive analytics

to see

patterns

enough

data

Take Business DISCOVERY…

NPS Data

CRM Data

Segmentation

Data

Services Owned

Data

Billing info

Churn

IVR Data - #calls,

route, scripts

Financial Data

Usage data

Service outages

Provisioning info

ASSOCIATED IN INSIGHT ENGINE

YOUR DATA EXPLORE & DISCOVER

THAT POSES A

QUESTION

LEADS TO A

THOUGHT

IDEA

LEADING TO

INSIGHT

...and add in a layer of analytics

NPS Data

CRM Data

Segmentation

Data

Services Owned

Data

Billing info

Churn

IVR Data - #calls,

route, scripts

Financial Data

Usage data

Service outages

Provisioning info

ASSOCIATED IN INSIGHT ENGINE

YOUR DATA EXPLORE & DISCOVER

THAT POSES A

QUESTION

LEADS TO A

THOUGHT

IDEA

LEADING TO

INSIGHT

it’s about likelihood no insights false insights explored insights predicted insights insights you didn’t know to ask for

Based on

risk of

risk of

likely to

likely to likely to

likely to

likelihood of

what do you want to find out?

likely to

Source: Getty Images

it’s about

better decisions

NBO

NBA

NBA and NBO better customer decisions, in real time

Source: our technology partner: ibm spss

what’s it

better

8.4x Simon Taranto - Amex

marketing campaign increase

100% ibm

to target profitable

customers 33%

ibm

on predictive analytics 250% independent

he isn’t.

he is asking questions

answers are and in fixed structures

she is asking questions

she can get answers about

she can ask questions about what’s

What Questions

How you explore data

to get from your

it’s about likelihood Based on

is quite a lot

segmentation

no more sampling

Insure the box

A CMO will have

available to filter for insights than will be produced by the

array

era 4.0 – rocket surgery

era 4.0 – pre hypothesis analysis

what if you

where to look?

with data..

what if you don’t have to?

4.0 topological analysis

abduction and

topological data analysis

Explore

without an hypothesis

It finds similar nodes

It folds the data set together

Shapes reveal the relationships

You explore for meaning and action image: ayasdi.com

create

using shape and colour image: ayasdi.com

Basketball

Image: HD Wallpapers

image: ayasdi.com

image: ayasdi.com

image: ayasdi.com

image: ayasdi.com

just think what could you do with

analytics everything we listed in

segmentation unexpected discoveries in

refinement customer-specific or staff specific

marketing uber

he isn’t.

he is asking questions

answers are and in fixed structures

she is asking questions

she can get answers about

she can ask questions about what’s

he

What Questions

How you explore data

to get from your

1.0 insights

2.0 insights

3.0 insights insights

4.0 insights you

What Questions

How you explore data

A CX LEADER’S CHALLENGE

and

to

try and make a difference?

If I do, will it be

113

thank you

greg Stewart SMS management & Technology www.smsmt.com greg.stewart@smsmt.com @clarityrules #CX13