Google Analytics and BigQuery, by Javier Ramirez, from datawaki

Get more from
Analytics with
Google BigQuery
javier ramirez
@supercoco9

about me
19 years working on software: banking, e-commerce,
government, CMS, start-ups...
founder of
https://datawaki.com
https://teowaki.com
https://teowaki.com/services
Google Developer Expert on
the Cloud Platform
datawaki
mail: j@teowaki.com twitter: @supercoco9

BigQuery
is awes..
I use Google
Analytics
javier ramirez @supercoco9 https://teowaki.com

Isn't Google Analytics
good enough?

Google Analytics is great but...
It lets you access aggregated data and sampled
reports, not individual sessions/visits data.
Even premium accounts get sampled reports when
there are too many data (and not all the reports can
be unsampled).

If you need to manage many different segments, and
if you want to combine segments, it can get tricky.
Moreover, you can only segment or create reports
using the pre-defined filters, which might or not be
enough for you*.
*even if segments have experienced a huge
improvement with Universal Analytics

It's not easy to cross data in Analytics with data from
other sources (CRM, invoicing system...)
Now you can use Import Data from Universal
Analytics, but there are many constraints to what
you can do

Good for knowing what's happening in your
application, but difficult for:
* business intelligence/big data (data mining,
find patterns...)
* machine learning (classify information,
predict future trends...)

Google BigQuery
Designed to run analytics
over huge volumes of raw
data, and to integrate
with other data sources

Google BigQuery + GA Premium
Google Analytics Premium
users get free daily
exports from GA to
BigQuery.

Google BigQuery + GA Premium
All your raw data.
Unsampled.
Use it however you want.
BOOM!

it's just SQL

SQL is not very hard
Give me the count of visitors from our analytics who
visited yesterday using a mobile device, by
country
SELECT count(fullVisitorId)
from ga_sessions_20141203
where device.isMobile = true
GROUP BY geoNetwork.country

data
schema

basic queries (metric/dimension)
SELECT trafficSource.source, SUM( totals.transactions ) AS total_transactions
FROM playground.ga_sessions_20140621
GROUP BY trafficSource.source
ORDER BY total_transactions;
SELECT device.isMobile, SUM ( totals.pageviews ) AS total_pageviews
GROUP BY device.isMobile
ORDER BY total_pageviews;

basic queries
with a twist
SELECT
IF(DOMAIN(trafficSource.source) is null,
trafficSource.source,
DOMAIN(trafficSource.source))
AS normalized_source,
SUM ( totals.transactions ) AS total_transactions
GROUP BY normalized_source
ORDER BY total_transactions;

Average amount spent per visit
SELECT ( SUM(total_transactionrevenue_per_user) / SUM(total_visits_per_user) )
AS avg_revenue_by_user_per_visit
FROM (
SELECT SUM(totals.visits) AS total_visits_per_user,
SUM( totals.transactionRevenue ) AS total_transactionrevenue_per_user,
visitorId
WHERE totals.visits>0
AND totals.transactions>=1
AND totals.transactionRevenue IS NOT NULL
GROUP BY visitorId ) ;

Users who bought product A,
also bought product B
SELECT hits.item.productName AS other_purchased_products,
COUNT(hits.item.productName) AS quantity
WHERE fullVisitorId IN (
SELECT fullVisitorId
WHERE hits.item.productName CONTAINS 'Light Helmet'
AND totals.transactions>=1
GROUP BY fullVisitorId )
AND hits.item.productName IS NOT NULL
AND hits.item.productName !='Light Helmet'
GROUP BY other_purchased_products
ORDER BY quantity DESC;

SELECT prod_name, count(*) as transactions
FROM
(
SELECT fullVisitorId, min(date) AS date, visitId,
hits.item.productName as prod_name
FROM (
SELECT fullVisitorId, date, visitId,
totals.transactions,
hits.item.productName FROM
(TABLE_DATE_RANGE([dataset.ga_sessions_],
TIMESTAMP('2014-06-01'),
TIMESTAMP('2014-06-14')))
)
WHERE fullVisitorId IN
(
SELECT fullVisitorId
FROM (TABLE_DATE_RANGE([dataset.ga_sessions_],
TIMESTAMP('2014-06-01'),
TIMESTAMP('2014-06-14')))
GROUP BY fullVisitorId
HAVING SUM(totals.transactions) > 1
)
AND hits.item.productName IS NOT NULL
GROUP BY fullVisitorId, visitId, prod_name ORDER BY
fullVisitorId DESC
)
GROUP BY prod_name ORDER BY transactions DESC;
* example query from the lunametrics blog. Check them out for more awesomeness
Products that
are purchased
and lead to
other products
being purchased

Identify user path/user actions
SELECT fullvisitorID, visitID, visitNumber, hits.page.pagePath
where hits.type='PAGE'
order by fullvisitorID, visitID, hits.hitnumber asc

individual users data is awesome
Cross CRM data with individual users actions to see
how your response to incidents affect your users.
Use the “frequently bought together” query and find
users who didn't buy the related products. Send an
e-mail campaign with an offer for those products.

integrating with external
data sources
* Connectors/REST API
* Export into GCS
* Import into BigQuery

What if I don't have
a GA Premium
Account?

just send your own data
define a data structure that fits your needs
(or replicate the one GA provides), use a JS
snippet to send data to your server, then
to BigQuery**
..you will miss many of the GA dimensions, but
you can keep using GA and use BigQuery
only for your unsampled data
datawaki ** If you want to do this without managing
your own servers, we can help you

BigQuery pricing
$20 per stored TB
A site with 50m pageviews, would pay less
than $10 a month per every 6 months worth
of data
$5 per processed TB
*the 1st TB every month is free of charge
** GA premium get $500 free credit monthly

for GA premium users
BigQuery is effectively
for free
*unless you upload huge external data or make
huge queries

Want to know more?
https://cloud.google.com/products/bigquery/
https://datawaki.com
Need help?
https://teowaki.com/services
Thanks!
Javier Ramírez
@supercoco9

Google Analytics and BigQuery, by Javier Ramirez, from datawaki

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