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Modern Relationships
Machines + the pursuit of meaningful customer relationships
Jason Maynard
Zendesk
Director, Data and Dating Analytics
@channelthetiger
Adrian McDermott
Zendesk
SVP, Product Developme...
In the past, people
married out of
need.
Marrying for love
was a luxury.
Customer Service
was personal,
direct and local
Today, singles and consumers have more choices.
More choices means more complication.
v
v
i <3 d8ta p0rn
The Data Science of Relationships
km
Prob ( )
0 1 3 7 15 63 255 1023 4095
Slide Title
Subtitle text
Slide Title
Subtitle text
Four Dating Tips for Business
“Hi”
It’s Not What You Do,
It’s What You Say
Just Pop the
Question Already
Playing Hard to
G...
Nine days later
Six days later
Hey - want to come with me to see the new exhibit at the
Getty Saturday?
4 days later
Oh hi...
68%
70%
72%
74%
76%
78%
80%
82%
84%
0 5 10
Interactionsthatendedwithahappycustomer
Number of agent replies
“Hi”
“Hi”
Hi Rachel. Since I never got a chance to ask
you to dance at Marissa and Chris’s wedding
(I’m Chris’s old roommate fr...
Companies Customers
”
“
“Hi”
“
”
Sixteen minute gap
Don’t think so. Gotta get ready for show and having a
quick glass of wine with Zach.
Sent at 6:34
You c...
65%
70%
75%
80%
85%
90%
0 10 20 30 40 50
Interactionsthatendedwithahappycustomer
Hours between agent responses
“Hi”
“Hi”
Satisfaction Prediction
Time Metrics
• first reply time
• full resolution time
• requester wait time
Ticket Text
• comments
• channel
• priority
“...
“Bill’s a hard
dog to keep on
the porch.”
The Guardian 1st Aug 1999
Boomerang
Support agents get a lot of thank you’s from customers,
but each “thank you” reopens a ticket
Decreased performance
Re-ope...
No “Thank You”
•Thanks, but I am still confused. = 64%
•Thank you, can I ask another question? =
26%
•Thank you, but this ...
Four Dating Tips for Business
It’s Not What You Do,
It’s What You Say
Actions may speak louder
than words, but words are
s...
Thanks :]
#RelateLive
Modern Relationships: Machines and the pursuit of meaningful customer relationships (Relate Live London)
Modern Relationships: Machines and the pursuit of meaningful customer relationships (Relate Live London)
Modern Relationships: Machines and the pursuit of meaningful customer relationships (Relate Live London)
Modern Relationships: Machines and the pursuit of meaningful customer relationships (Relate Live London)
Modern Relationships: Machines and the pursuit of meaningful customer relationships (Relate Live London)
Modern Relationships: Machines and the pursuit of meaningful customer relationships (Relate Live London)
Modern Relationships: Machines and the pursuit of meaningful customer relationships (Relate Live London)
Modern Relationships: Machines and the pursuit of meaningful customer relationships (Relate Live London)
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Modern Relationships: Machines and the pursuit of meaningful customer relationships (Relate Live London)

Adrian McDermott, SVP of Technology and Jason Maynard, Director of Data and Analytics at Zendesk
Predictive analytics and machine learning models are the next steps in managing customer satisfaction. In this talk, you’ll learn how these innovative technologies will help businesses predict customer happiness and improve relationships with their customers.

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Modern Relationships: Machines and the pursuit of meaningful customer relationships (Relate Live London)

  1. 1. #RelateLive
  2. 2. #RelateLive Modern Relationships Machines + the pursuit of meaningful customer relationships
  3. 3. Jason Maynard Zendesk Director, Data and Dating Analytics @channelthetiger Adrian McDermott Zendesk SVP, Product Development @amcdermo
  4. 4. In the past, people married out of need. Marrying for love was a luxury.
  5. 5. Customer Service was personal, direct and local
  6. 6. Today, singles and consumers have more choices. More choices means more complication.
  7. 7. v v
  8. 8. i <3 d8ta p0rn
  9. 9. The Data Science of Relationships
  10. 10. km
  11. 11. Prob ( ) 0 1 3 7 15 63 255 1023 4095
  12. 12. Slide Title Subtitle text
  13. 13. Slide Title Subtitle text
  14. 14. Four Dating Tips for Business “Hi” It’s Not What You Do, It’s What You Say Just Pop the Question Already Playing Hard to Get is for Bozos We’re All Special Snowflakes
  15. 15. Nine days later Six days later Hey - want to come with me to see the new exhibit at the Getty Saturday? 4 days later Oh hiiii Oh hi. I was just getting a christmas tree now. I would but have to go to Disneyland with my family :( Hey, well tell Donald Duck I say hello Will do! Nah. I can’t do Friday. Lets talk next week Shit. I can’t Thursday, Friday? Sushi on Thursday? Hey! You out tonight? “Hi”
  16. 16. 68% 70% 72% 74% 76% 78% 80% 82% 84% 0 5 10 Interactionsthatendedwithahappycustomer Number of agent replies “Hi”
  17. 17. “Hi” Hi Rachel. Since I never got a chance to ask you to dance at Marissa and Chris’s wedding (I’m Chris’s old roommate from Purdue…), he gave me your number. I wanted to say hi and sort of “texty” introduce myself. Haha :-) Hope you had a great weekend… hope to chat with you soon!
  18. 18. Companies Customers ” “ “Hi” “ ”
  19. 19. Sixteen minute gap Don’t think so. Gotta get ready for show and having a quick glass of wine with Zach. Sent at 6:34 You coming back to the hotel before going to the comedy club? Want to meet us? Nah Ok just checkin :) Not a grump text at all Is that a grump text or not Sent at 6:36 Note the twenty minute gap here See you at the comedy club Sent at 6:56 Sent at 7:17 Sent at 7:01 Sent at 7:17 “Hi”
  20. 20. 65% 70% 75% 80% 85% 90% 0 10 20 30 40 50 Interactionsthatendedwithahappycustomer Hours between agent responses “Hi”
  21. 21. “Hi”
  22. 22. Satisfaction Prediction
  23. 23. Time Metrics • first reply time • full resolution time • requester wait time Ticket Text • comments • channel • priority “Hi” Effort Metrics • replies • reopens • reassignments % Do Something! • prioritize tickets • drive biz rules • trigger integrations
  24. 24. “Bill’s a hard dog to keep on the porch.” The Guardian 1st Aug 1999
  25. 25. Boomerang
  26. 26. Support agents get a lot of thank you’s from customers, but each “thank you” reopens a ticket Decreased performance Re-opened tickets increase total resolution time for an agent Wasted time On average, an agent spends 5 seconds
 re-solving a ticket
  27. 27. No “Thank You” •Thanks, but I am still confused. = 64% •Thank you, can I ask another question? = 26% •Thank you, but this is not what I was asking. = 25% •I still need your help = 23% •Hello, any update on this? = 4% •Thanks! = 98% •Thank you = 92% •Thank you for your help! = 92% •Cheers mate. Really appreciate your help. = 81% •You’re super helpful. Thanks! = 75% Will Solve Ticket 100% - 75% Will Re-Open Ticket 74.999% - 0%
  28. 28. Four Dating Tips for Business It’s Not What You Do, It’s What You Say Actions may speak louder than words, but words are still really important Just Pop the Question Already Nothing causes the spark to fizzle faster than too much blather Playing Hard to 
 Get is for Bozos Timing is important, let her know what is going on We’re All Special Snowflakes Personalize service for your customers“Hi”
  29. 29. Thanks :]
  30. 30. #RelateLive

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