experience ‘18 predict (and change) the future with client experience · experience ‘18 predict...

EXPERIENCE ‘18

Predict (and change) the Future with Client Experience

Evan Reiss,

VP, Market Research & Analytics, IBM

Meet Ella

IBM © Copyright 2018. © 2017 IBM Corporation

IBM Customer Journey “Moments of Truth” instrumented with NPS

Web

Download/Webinar

Web Support

Sales Transaction

Services Delivery

Trial

Provisioning/Fulfillment

Offering Use

Renewal/Upgrade

Technical Support

Sales Relationship

3


Why Act?

4

Support touchpoint

So we had to think big!

RENEWALS

Accounts with promoters have

10% 9xmore support tickets more renewals

OPERATIONAL COSTS

Detractors issue


History is Not HardPart 1

How likely is Ella to recommend bath time to a friend?

9-10 (Promoter)

7-8 (Passive)

0-6 (Detractor)

5


History is Not HardPart 2

How likely is this client to recommend IBM to a colleague?

9-10 (Promoter)

7-8 (Passive)

0-6 (Detractor)

The IBM team is a great partner! Very easy to work with

and knowledgeable of the products.

6


But… I can’t change

history. I don’t want to

change history. I can only

change the future.”

Boris Becker


The Future is Hazy

How likely is this client to recommend IBM to a colleague?

9-10 (Promoter)

7-8 (Passive)

0-6 (Detractor)IBM Product: WebSphere

Country: Japan

Company’s average ticket duration: 2 days

# of tickets from company: 4

Severity: Medium

Calls IBM’s technical support line to get help with a product upgrade

8


Until Now


IBM’s NPS Early Warning System (N.E.W.S)

Real-time modeling on every support ticket to predict probability of being a detractor or promoter, and the reason for the prediction

Aggregate data sources into one manageable database

Embedding predictions into agents’ ticket queue

Interactive Dashboard

SPSS Modeler, SPSS Collaboration & Deployment Services, Watson Studio

MedalliaTickets Operations Performance

Renewal Revenue

Product Mapping

Account History

10


AccuracyN.E.W.S. correctly predicts detractors and promoters with a remarkable degree of accuracy

4%

88%

95%

ACCURACY RATE

Of all the predicted client experiences, how many

were accurate?

FALL-OUT RATE

Of clients with neutral/good experiences, how many

were inaccurately predicted to have a poor experience?

HIT RATE

Of clients who actually had poor experiences, how many

did we flag accurately?

11


Know What MattersN.E.W.S. also helps identify the top NPS drivers for technical support experiences

Platform

Number of ticketsby company

Severity

Product

Ticket problemcategory

Number of surveyresponses

Company's avg.ticket duration

HIGHEST IMPORTANCE

LOWEST IMPORTANCE

12


Why Predict the Future?

WITHOUT N.E.W.S WITH N.E.W.S

3% 100%of client experiences known of client experiences known

Root cause analysis and apologies feedback

received

Proactive intervention feedback is

requested

13


Next Generation Client Experience Management

WITHOUT N.E.W.S WITH N.E.W.S

Issue Identification

Ticket Prioritization

Closed Loop Feedback

Survey verbatims

Ticket severity

Clients who submit surveys

Operational drivers

Risk of negative client experience

All clients at risk of a poor experience

14


N.E.W.S In ActionJon opens a support ticket – low severity

Ticket is given lower priority Support agent (Tina) tries to resolve herself and is distracted

by other priorities

Client waits…and waits… and has time to remember its not the first

time he has waited

This could have happened…

DetractorJon is frustrated with long time to resolution

N.E.W.S flagged Jon as a potential detractor for Tina; she sees reason for

prediction: Jon’s account has a history of long ticket durations

Tina convenes a cross-functional team to swarm the case and get

immediate resolution

Manager personally reaches out and offers fix

But instead…

PromoterJon is impressed with the knowledge and personal attention provided

15


Welcome to the Future of Client Experience Analytics

Always on – not periodic or intermittent

Embedded in workflows – not sketched on a power point slide

Changes the future – doesn’t just describe the past

Built using Artificial Intelligence – not your calculator

16


You Should Absolutely Try This at Home…

Think big!

Determine where predicting client experiences has the most financial impact

It’s unlikely all required data will be in the same place; invest in aggregating data from multiple sources

You’ll need some good data scientists and lots of persistence

Make predictions available to line employees who will use it daily as part of their job

Always the hardest part!

17IBM © Copyright 2018.


Change the Course of History

experience ‘18 predict (and change) the future with client experience · experience ‘18 predict...

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