9. WHAT DID WE ACHIEVE?
• Fewer emails, less resources in support
• Community focus on community projects
• Improved prioritization within team
• Happier community
11. THE FRAMEWORK
RETURN
TO THE RETURN
PRODUCT TO THE
PRODUCT
INTERNAL
TRAFFIC ON HELP EMAIL
SOURCE BEHAVIOR ACTIVITY
ANALYSIS
CHURNED
USERS
CHURNED
USERS
13. INTERNAL TRAFFIC SOURCES
Where are they coming from?
•importance of page grouping
•identify group of pages that send traffic to help
Who are they?
•segmentation
•custom variables
Web analytics or APIs for reporting.
14. ON HELP BEHAVIOR
What are they looking for?
•which articles they consume?
•what are they searching for?
Who will go back to the product?
•more segmentation
Create report with keyword and article
efficiency.
15. EMAIL ACTIVITY
What are they talking about?
•labeling and education
•using API’s to cross-reference data
Who are they?
•user profiling and segmentation
•custom variables
16. 7 STEPS
Identify 3 biggest traffic sources to help
Segmentation, segmentation, segmentation
On help consumption behavior
Performance report for articles and search terms
Labeling and analysis
Improvements to help pages and your product
Regressions and education emails
21. Online Intelligence Solutions
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Editor's Notes
When I started about 3 years ago growth from 600k to 30m focus on community
CEO at Global meetup day, 80 cities
Community team, 6 months time, now 3x
redesign based on concept of negative conversions 1st -> contact community now enter search Startup way, iterating
visits to help -> down!!! search is main navigation email down!!! API: ati, desk (support)
email down, NPS stable at 59
churn: leave sc.com from support
Needs tool that can do this^^ when coming from upload page, segment by those and find out which articles they consumed custom variables tell us more about these users (prefilled with # of tracks, premium level, etc.) page grouping: level 2 -> better path analysis (70m+ pages) flow between level 2s, coming from track, user etc pages api for nice aggregation of data
spanish visitors from premium pages
segment on traffic sources eg premium look at keyword and article efficiency -> boom, bad perfoming articles nice to have: search exits
where from and what looking for: checked! Next email: cross ref article read and email label again: spanish?
logistic regression of likelihood that people encounter problems
And here’s where we’ve gotten so far.
And here’s where we’ve gotten so far.
And here’s where we’ve gotten so far.
With that, I’ve come to the end of my presentation. I’d like to conclude by saying just this - Let’s give one of our five senses the attention it truly deserves. Thank you for listening.