Transcript Just Giving - Insight SIG

Integrating Web
Analytics
Agenda
1. Agree on Terminology – clarify the common
misconceptions of web analytics
2. The fundamental rules of making online
data as effective as offline
3. Cognitive match a case study
Common misconceptions of web analytics
• Common understanding of web analytics as
–
–
–
–
Bounce rates
Conversions
Site optimisation
Marketing
• Web analytics must be treated like off line data and
includes
–
–
–
–
–
–
IP
Location
Gender
Recency/frequency/value
Screen resolution
Platform
Making online data as effective as offline
• Ensure that there is clear context and purpose
– Acquire customers
• Those that give money
• Those that will fundraise
• Those that will be strong advocates
– Engage customers
• Get them to read content that we think is important
• Get them to buy a product frequently
• Get them to share their story regular
• Remove the concept of lonely metric
– Top referring sites
– Top referring sites by visit
– Top referring sites by new visits
Making online data as effective as offline
• Append useful data
– Add region to customer data
– Add mosaic code to customer
– Age and gender
• Use historical data to predict the future
– Historical sales by regions
– Product sales by age
– Product sales by gender
Cognitive Match: a working example
1. Context
2. No lonely metrics
3. Append useful data
4. Making use of historical data
Cognitive Match: a working example
• Context
• Optimise the engagement of sponsors and
donors
• Based on the historical analysis of pain points
• Ensuring that fundraisers and charities raise
more on JG
Cognitive Match: a working example
• Use analytics to determine
what types of forms different
people will react to
• Use analytics to determine
which form to serve up to
which individual to optimise
the donation
Standard Donation Form
Cognitive Match: Following the Rules
Basic Analytics Data
•
•
•
•
•
Browser type
Operating system
Device
Referrer
Search Keywords
Useful Web data to
append
•
•
•
•
Time of day
Day of week
Location
Local weather
Historical data
• Recency/frequency
• Pages viewed
• Previous activity
Cognitive Match: Putting the data to work
Cognitive Match: The results
Cognitive Match: The results
Example 2
Cognitive Match: a working example
• Context: Targeted relevant content to attract
more fundraisers – “Supporter acquisition”
• Use web analytics to understand our donors
• Use web analytics to determine what content
to show them
• In real-time match donors the right donors to
the right content
Cognitive Match: a working example
• Different images will perform
attract different people
• Different images will result in
a different conversion for
different people
1. Identify
Matching
content
3. Present
the matching
option
2. Use the
analytics to
Determine
the matching
option
Cognitive Match: How does it work
Cognitive Match: The Results