Dairy of Support Engineering Manager 2015年に発表してから2年が過ぎ、トレジャーデータのサポートチームがどう成長し、どんな課題を解決してきたか https://techplay.jp/event/651515 at support engineer night

1. Diary of Support Engineering Manager
Toru Takahashi
Treasure Data, Inc.
サポートエンジニアNight vol.2
2018-02-07

2. Who AM I ?
Profile
- 髙橋 達 (Takahashi Toru)
- Treasure Data, Inc.
- Support Engineering Manager
- Twitter: @nora96o / Github: toru-takahashi
- Blog: http://blog.torut.tokyo/
Misc.
- Tech. Support Engineer from the almost beginning of my career
- 1st Tech. Support Engineer in TD
- Support Engineering Manager from 2015

3. Treasure Data Support
- Email
- Online Chat
- Support Form

4. Contents
- Past / Present / Future
- Defined Measurable KPIs for Support Performance
- Built Support Data Platform
- Improved a process for Customer Slack Channel
- Multiple Components = Huge Logs

5. Past / Present / Future
2015 / 2017 / 2018

6. 2.5 years ago...
What is Support Engineer in TreasureData

7. Treasure Data Support From 2015 to 2017/2018
Head Count from 2 to 5 to 8

8. Treasure Data Support From 2015 to 2017/2018
Head Count from 2 to 5 to 8

9. Treasure Data Support From 2015 to 2017/2018
Head Count from 2 to 5 to 8 (plan…)

10. We’re Hiring!!
https://jobs.lever.co/treasure-data/50be49f1-0194-41ac-9c99-756e02ff1dda

11. Treasure Data Support From 2015 to 2017/2018
Avg Ticket/Month from 308 to 477 to 500~600?

12. Treasure Data Support From 2015 to 2017/2018
TreasureData Components 2015

13. Treasure Data Support From 2015 to 2017/2018
TreasureData Components 2017
(Treasure
Reporting)

14. Treasure Data Support From 2015 to 2017/2018
TreasureData Components 2017
(Treasure
Reporting)

15. Treasure Data Support From 2015 to 2017/2018
TreasureData Components 2017
(Treasure
Reporting)

16. Treasure Data Support From 2015 to 2017/2018
TreasureData Components 2018 = Treasure CDP
(Treasure
Reporting)

17. Defined Measurable KPIs For Support
Performance

18. How Do You Measure Support Performance?
- Customer Satisfaction Score?
- Number of Resolved Tickets?
- Average Resolution Time?
Ref. Top 10 Key Performance Indicators for Customer Service
- But, we’re growing
- Number of customers increases
- Number of new tickets increases
- New features are released every week
- Difficult to find out “Stable/Controllable” KPI

19. Support Performance = Provide “Great Support”
- Great Support → Make a customer our fan
- Trying to measure factors of “Great Support” by the following KPIs
- Satisfy 100% SLA (1st Response Time)
- A ticket resolution rate within 7 days to 70%
- Quick Resolve helps customers
- Engineer Escalation Rate to under 15%
- Related to quick resolve
- Engineer focuses on development new features for customers
- Avg. Response Time to within 1 business days (Stretch)
- Updating a ticket frequently give customers a kindness

20. Built a KPI dashboard for Support Team

21. Built Support Data Platform

22. Problem: Data Silo = Data is not unified
= Hard to know customers quickly

23. What is Support Data Platform?
→ All data is unified inside of TreasureData

24. Support Usage for Sales / Product / Dev team in Chartio

25. Enriched User Profile in Zendesk

26. Support requires a development skill :)
embulk-output-zendesk_users with embulk & digdag

27. Improved a process for Customer Slack
Channel

28. Do you like Slack?

29. My Slack Channels...

30. For a smooth communication between Sales/SE team
and customers, but...
Problem
- Difficult to do a tagging for categorizing a question.
- For better KPI, we'd like to add a tag into all inquiry.
- Need to join Slack channel
- We can't help customers if we didn't join the slack channel.
- No SLA
- If a customer has an emergency issue for affecting their business in
holiday and ping us on slack, Nobody might knows the message.
- Lost a message where come from
- Slack is NOT todo app

31. BubbleIQ helps communications between Zendesk and Slack
- We/Customer add :key: reaction
- BubbleIQ Bot submits a ticket automatically
- We can communicate between Zendesk ticket and Slack thread

32. Multiple Components = Huge Logs

33. Many Components = Many Logs

34. Difficulty in finding out failure jobs due to data silo/SQL
Current Notification Flows:
- Ex. Presto Job Failure due to an incident, let’s check affected jobs.
1. Extract job id/account id by using a failure message in presto log on master
TD account
2. Extract executed user’s email by using job id in analytics TD account
3. Extract a contract information by using account id from salesforce info in
analytics TD account for group by “Region” (JP? US customer?)
4. Write a JP/EN Notification and send it out via Mailchimp

35. IN Progress; Trying to “Search” instead of Query

36. Future Works

37. Future Works
- Documentation / Localization
- Various Customers/Senario
- Data Engineer / Integrator / Digital Marketer
- Digital Marketing? Gaming? EC?
- Efficient Support Tool
- For Log Analytics
- For Workflow Analytics
- Global Team’s collaboration
- US / Japan / Korea
- Sales / Customer Success / Solution Architect

38. We’re Hiring!!
https://jobs.lever.co/treasure-data/50be49f1-0194-41ac-9c99-756e02ff1dda