20. The Gatherer
Landing section: Homepage
Least time
per page
Most number of
pages viewed
Highest number of
conversions per
session
Most likely to
download a
brochure
Description:
The Gatherer comes directly to the website to the homepage,
visits multiple car models to download a brochure for each to look
at offline later.
Example CRO Test:
Link to a model comparison table from
the homepage with option to download
a brochure for each model
Likely onsite journey
Example segment from
Car manufacturer
Second section: Car Models
Exit section: Car Models
@richlawre
21. The Skipper
Example segment from
Train operator
Description:
The Skipper has likely already done their travel research (around
when to travel & where) multiple times without buying and are
simply returning - likely at the last minute - to finally finish task.
Example CRO Test:
Use a cookie to add a banner to the
homepage that takes a returning user
back to where they left off in the
transaction process.
Slightly more time
per page then average
More likely to buy in
the evening or at night
Fewest days since last
session
Fewest pages per
visit
Over index for
visiting via tablet
Over index for
visiting via email
@richlawre
29. Using Google Analytics API
Extract by Session ID
or Client ID
@richlawre
https://www.jcchouinard.com/google-analytics-api-using-python/
30. Using Google Analytics API
Useful dimensions:
landingPagePath
secondPagePath
exitPagePath
@richlawre
https://www.jcchouinard.com/google-analytics-api-using-python/
31. Using Google Analytics API
Useful metrics:
pageviewsPerSession
revenuePerTransaction
goalXXCompletions
https://www.jcchouinard.com/google-analytics-api-using-python/
@richlawre
32. Using Google Analytics API
There is a limit on the
number of
metrics/dimensions
10
@richlawre
https://www.jcchouinard.com/google-analytics-api-using-python/
33. Using Google Analytics API
There is also a limit
on the number of
rows per call
25,000
@richlawre
https://www.jcchouinard.com/google-analytics-api-using-python/
34. Using Google Analytics API
The answer is to
loop over days,
metrics, dimensions
& merge!
@richlawre
https://www.jcchouinard.com/google-analytics-api-using-python/
35. Using BigQuery
Data is nested -
I’ve found it makes
things more
difficult at the
session level
https://adswerve.com/blog/google-analytics-queries-in-bigquery-part-two-users-sessions-unnesting-hits/
@richlawre
36. Using BigQuery
However it is
possible to do and
there is some great
information around
https://adswerve.com/blog/google-analytics-queries-in-bigquery-part-two-users-sessions-unnesting-hits/
@richlawre
37. Using BigQuery
Can also run the
unsupervised
machine learning
algorithm directly
in SQL
https://adswerve.com/blog/google-analytics-queries-in-bigquery-part-two-users-sessions-unnesting-hits/
@richlawre
38. Using BigQuery
Previously used 1M
sessions with
Python & Google
Colab - BigQuery
wasn’t necessary
https://adswerve.com/blog/google-analytics-queries-in-bigquery-part-two-users-sessions-unnesting-hits/
@richlawre
39. Using BigQuery
Choose days at
random to ensure
variation
https://adswerve.com/blog/google-analytics-queries-in-bigquery-part-two-users-sessions-unnesting-hits/
@richlawre
41. Useful data transformations
Change hours of
the day to
morning,
afternoon,
evening,night
SESSION ID DAY DAY TYPE
Session 1 Monday Weekday
Session 2 Tuesday Weekday
Session 3 Saturday Weekend
Session 4 Wednesday Weekday
@richlawre
42. Change days to
weekday &
weekend
@richlawre
SESSION ID DAY DAY TYPE
Session 1 Monday Weekday
Session 2 Tuesday Weekday
Session 3 Saturday Weekend
Session 4 Wednesday Weekday
Useful data transformations
43. Change pages to
sections
@richlawre
SESSION ID DAY DAY TYPE
Session 1 Monday Weekday
Session 2 Tuesday Weekday
Session 3 Saturday Weekend
Session 4 Wednesday Weekday
Useful data transformations
44. Useful data transformations
Combine certain
conversion points
@richlawre
SESSION ID DAY DAY TYPE
Session 1 Monday Weekday
Session 2 Tuesday Weekday
Session 3 Saturday Weekend
Session 4 Wednesday Weekday
45. Here is a useful link
to do find and
replace it in Python
& Pandas
@richlawre
SESSION ID DAY DAY TYPE
Session 1 Monday Weekday
Session 2 Tuesday Weekday
Session 3 Saturday Weekend
Session 4 Wednesday Weekday
Useful data transformations
46. You could use
Google DataPrep
instead
@richlawre
SESSION ID DAY DAY TYPE
Session 1 Monday Weekday
Session 2 Tuesday Weekday
Session 3 Saturday Weekend
Session 4 Wednesday Weekday
Useful data transformations
47. One hot encoding
Converts categories
to 1s & 0s.
SESSION
ID
CHANNEL
Session 1 Organic Search
Session 2 Paid Search
Session 3 Direct
Session 4 Direct
SESSION
ID
ORGANIC
SEARCH
PAID
SEARCH
DIRECT
Session 1 1 0 0
Session 2 0 1 0
Session 3 0 0 1
Session 4 0 0 1
@richlawre
48. Values aren’t
increasing so doesn’t
skew the clustering
algorithm
SESSION
ID
CHANNEL
Session 1 Organic Search
Session 2 Paid Search
Session 3 Direct
Session 4 Direct
SESSION
ID
ORGANIC
SEARCH
PAID
SEARCH
DIRECT
Session 1 1 0 0
Session 2 0 1 0
Session 3 0 0 1
Session 4 0 0 1
@richlawre
One hot encoding
49. Use for numerical as
well as categorical
data
SESSION
ID
CHANNEL
Session 1 Organic Search
Session 2 Paid Search
Session 3 Direct
Session 4 Direct
SESSION
ID
ORGANIC
SEARCH
PAID
SEARCH
DIRECT
Session 1 1 0 0
Session 2 0 1 0
Session 3 0 0 1
Session 4 0 0 1
@richlawre
One hot encoding
50. See here for how to
do it with Python
SESSION
ID
CHANNEL
Session 1 Organic Search
Session 2 Paid Search
Session 3 Direct
Session 4 Direct
SESSION
ID
ORGANIC
SEARCH
PAID
SEARCH
DIRECT
Session 1 1 0 0
Session 2 0 1 0
Session 3 0 0 1
Session 4 0 0 1
@richlawre
One hot encoding