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EXPERIENCE ‘18 Predict (and change) the Future with Client Experience · EXPERIENCE ‘18 Predict...
Transcript of 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
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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
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Why Act?
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Support touchpoint
So we had to think big!
RENEWALS
Accounts with promoters have
10% 9xmore support tickets more renewals
OPERATIONAL COSTS
Detractors issue
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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)
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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.
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But… I can’t change
history. I don’t want to
change history. I can only
change the future.”
Boris Becker
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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
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Until Now
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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
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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?
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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
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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
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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
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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
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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
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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!
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Change the Course of History
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Thank You!#EXP18Medallia