12. What’s your job?
Webops (it’s up, and it’s fast)
User experience (it’s easy to use)
Community management and monitoring
Market research (what people think and why)
Support
Other
13. What we’ll cover
Analytics, interaction, UX, Voice of the Customer
EUEM, synthetic tests, RUM
Online communities, internal communities
Competitive analysis
Integrating data sources
28. Possible viable
offering
You are
Trial startup
t
here
vo
Pi
Possible Possible Possible
viable Trial startup problem Trial startup viable
offering space offering
Trial startup
Possible viable
offering
29. As we become more agile,
we need to be more aware.
30. Startups 101: as seen by Eric Ries & Sean Ellis
ps: the concepts in the next two slides are full of awesome. Look Sean, Eric and Dave up.
IDEAS
Learn
Faster Code
Faster
LEARN Growth
BUILD Unit
Tests
Split
Tests
Customer
Interviews Transition to Usability
Tests
Customer
Development Growth Con7nuous
Integra7on
Five
Whys
Root
Cause
Analysis Incremental
Deployment
Customer
Advisory
Board Free
&
Open-‐Source
Components
Falsifiable
Hypotheses Product/Market Fit Cloud
Compu7ng
by: Sean Ellis Cluster
Immune
System
Product
Owner
Accountability
Customer
Archetypes Just-‐in-‐7me
Scalability
DATA CODE Refactoring
Cross-‐func7onal
Teams
Semi-‐autonomous
Teams Developer
Sandbox
Smoke
Tests
Measure
Faster
MEASURE
Split
Tests Funnel
Analysis
Clear
Product
Owner Cohort
Analysis
Con7nuous
Deployment Net
Promoter
Score
Usability
Tests Search
Engine
Marke7ng
Real-‐7me
Monitoring Real-‐Time
Aler7ng
Customer
Liaison Predic7ve
Monitoring
31. !"# !"#$"%&'()*!+',-(,(
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5.
R
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AARRR! by Dave McClure
33. Users do what we wanted
Enrolment: They sign up
Purchases: They buy stuff
Invitations: They tell their friends
Stickiness: They stay for longer
Loyalty: They come back
Contribution: They add content
34. What could we watch?
What we’d like to know Tool set
How much did visitors benefit my business? Internal analytics
Where is my traffic coming from? External analytics
What’s working best (and worst?) Usability testing
How good’s my relationship with my market? Customer surveys, community
How healthy is my infrastructure? Performance monitoring
How am I doing against my competitors? Search, external testing
Where are my risks? Search, alerting
What are people saying about me? Search, community monitoring
How is my content being used elsewhere? Search, external analytics
35. How much did visitors
benefit my business?
Internal analytics
Conversion and Billing and account use
abandonment
Click-throughs
Offline activity
User-generated content
Subscriptions
36. Where’s my traffic coming
from?
External analytics
Referring websites
Inbound links from social networks
Visitor motivation
37. What’s working best (and
worst)?
Usability testing, A/B testing
Site effectiveness Trouble ticketing and
escalation
Upselling effectiveness
Content popularity
Ad and campaign
effectiveness Usability
Findability and search User productivity
effectiveness
Community ranking and
rewards
38. How good is my relationship
with my market?
Customer surveys, community monitoring
Loyalty
Enrollment
Reach and rewards
39.
40. How healthy is my
infrastructure?
Performance monitoring
Availability and Impact of performance
performance on outcomes
SLA compliance
Content delivery
Capacity and flash traffic
41. How am I doing against my
competitors?
Performance monitoring
Site popularity and ranking
How are people finding my competitors?
Relative site performance
Competitor activity
42. Where are my risks?
Search, alerting
Trolling and spamming
Copyright and legal liability
Fraud, privacy, and account sharing
43. What are people saying
about me?
Search, community monitoring
Site reputation
Trends
Social network activity
44. How is my content being
used elsewhere?
Search, external analytics
API access and usage
Mashups, stolen content, and illegal syndication
Integration with legacy systems
61. Organic Ad
Campaigns
search network $
1 1 1
Advertiser site
Visitor 2 O er 3 $
8 Upselling 4
Abandonment
Reach
5 Purchase step $
Mailing,
alerts, Purchase step $
9 promotions
$
Conversion $
Disengagement 7
Enrolment 6
Impact on site
$ Positive $ Negative
62.
63.
64. Bad
$
4 content
Social Search
Invitation
network link results
4 Good
content
1 $
1 1
Collaboration site
2
Visitor Content creation Moderation
$
3 Spam & trolls
$
Engagement 5
Viral
6 Social graph
spread
7
Disengagement $
Impact on site
$ Positive $ Negative
65.
66. Enterprise subscriber $
1
End user (employee) $
Refund $
2
Renewal, upsell, SLA
reference SaaS site violation
Performance
Good Bad 3
Helpdesk Support
5 $
Usability escalation costs
7
4
Good Bad
Productivity
Good Bad
6
Churn $
Impact on site
$ Positive $ Negative
67.
68. $
Media site
Enrolment Targeted
2 embedded ad 5
$
6 1
Ad
Visitor
network
4
3 5
Advertiser $
Departure $ site
Impact on site
$ Positive $ Negative
69. Analytics is the measurement of
movement towards those goals.
http://www.flickr.com/photos/itsgreg/446061432/
70. ATTENTION ENGAGEMENT CONVERSION
NEW
VISITORS
SEARCHES GROWTH CONVERSION
PAGES TIME RATE
TWEETS NUMBER
OF VISITS
PER ON x
MENTIONS VISIT SITE
GOAL
ADS SEEN LOSS VALUE
BOUNCE
RATE
72. “Hard” data
Analytics Usability Performability
(what did they (how did they (could they do
do on the interact with what they
site?) it?) wanted to?)
Complete Web Monitoring
VoC Communilytics Competition
(what were (what were (what are they
their they saying?) up to?)
motivations?)
“Soft” data
73. “Hard” data
Analytics Usability Performability
(what did they (how did they (could they do
do on the interact with what they
site?) it?) wanted to?)
Complete Web Monitoring
VoC Communilytics Competition
(what were (what were (what are they
their they saying?) up to?)
motivations?)
“Soft” data
75. These people drive nicer cars
than us. :/
Source: http://www.webanalyticsdemystified.com/sample/Web_Analytics_Demystified_RESEARCH_-_March_2007_-_Salary_Survey.pdf
96. Old analytics: New analytics:
report the news optimize goals
http://www.flickr.com/photos/thomasclaveirole/538819881/ http://www.flickr.com/photos/sanchom/2963072255/
97. blah blah blah ...
A unique visitor arrives at your website, possibly after following a link that
referred them. They land on a web page, and either bounce (leave
immediately) or request additional pages.
In time, they may complete a transaction that’s good for your business,
converting them from a mere buyer into something more—a customer, a
user, a member, or a contributor—depending on the kind of site you’re
running. On the other hand, they may abandon that transaction and
ultimately exit the website.
That visitor has many external attributes—such as the browser they’re
using, or where they’re surfing from—that let you group them into
segments. They may also see different offers or pages during their visit,
which are the basis for further segmentation.
The goal of analytics, then, is to maximize conversions by optimizing your
website, often by experimenting with different content, layout, and
campaigns, and analyzing the results of those experiments on various
internal and external segments.
98. Find the site
The three
stages of a Use the site
unique visit
Leave the site
110. Landing page:
Task: View one story
Create account
Task: Log in
Pick name Place: View stories
Check if free Enter credentials
Vote up Next 25
Set Password Verify
Vote down Last 25
CAPTCHA Recovery
Send mail
Place: Read
Get confirm
poster comments
Vote up Next 25
Task:
Vote down Last 25
Forward a story
Task: Submit Enter recipients
a new story Place: My Enter message
Enter URL account Send
Describe Change My
address comments
Deduplicate
Change PW See karma
Post it
111. Landing page:
Create acct.
Create acct. View one story
Form uptime Place: View stories
Task: Log in
# started Place: View stories
Bad form
Stories/visit # up/down
Place: Read
# CAPTCHA poster comments
Time/story
Mail uptime Top stories
Task:
Forward a story
Task: Submit Refresh time
Mail bounced Views/page
a new story Place: My
Confirm & return account
Return 3x
112. Places
Efficiency matters
How quickly, how many,
productivity
Learning curve OK
Leave when they’re bored
Collect “aha” feedback
A/B test content for
pages/session, exits
113. Tasks
Effectiveness matters
Completion, abandonment
Intuitiveness rules
Leave when they change their
mind or it breaks
Collect “motivation” feedback
A/B test layouts for conversion
114.
115.
116. Now suppose that you have a specific goal, such as a visitor filling out a survey on your website. You can analyze how many people completed that goal over time and measure the success of your business in a report like the one in
122. Pages per visit Time on site
:-D :-)
16 2,1
15 1,6
Minutes
14 1,1
13 0,5
12 0
September October September October
Email opt-outs Days between visits
:-| O_o
26.000 5
19.500 3,75
13.000 2,5
6.500 1,25
0 0
September October September October
123.
124. “Hard” data
Analytics Usability Performability
(what did they (how did they (could they do
do on the interact with what they
site?) it?) wanted to?)
Complete Web Monitoring
VoC Communilytics Competition
(what were (what were (what are they
their they saying?) up to?)
motivations?)
“Soft” data
127. Yes
Seen
False
(perceptible)
Perceptual information
affordance
affordance
(did I see it?)
Unseen
Correct
(hidden)
rejection
affordance
No
No Affordance Yes
(was I supposed to interact with it?) Adapted from Gaver (1991)
140. “Hard” data
Analytics Usability Performability
(what did they (how did they (could they do
do on the interact with what they
site?) it?) wanted to?)
Complete Web Monitoring
VoC Communilytics Competition
(what were (what were (what are they
their they saying?) up to?)
motivations?)
“Soft” data
153. “Hard” data
Analytics Usability Performability
(what did they (how did they (could they
do on the interact with do what they
site?) it?) wanted to?)
Complete Web Monitoring
VoC Communilytics Competition
(what were (what were (what are they
their they saying?) up to?)
motivations?)
“Soft” data
