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Explore Snowplow data – Modeled Data

Model raw data into actionable insights for your teams

The scenario

we’re exploring

In part one of this example scenario we explored the raw, granular data that Snowplow might collect for a typical website and we saw events describing a sample of users’ interactions across the Snowplow marketing website, documentation site and Snowplow BDP Console.

Snowplow BDP gives you the tools to transform, aggregate and model your raw data into a number of interesting data sets from fairly standard sessions to more complex analyses around attribution and engagement.

To get you started quickly we provide out-of-the box SQL models, but the true power of Snowplow lies in you being equipped to model your raw data into tables specific to your teams and use cases. Take a look at examples of both out of the box and custom tables below!

Out-of-the-box modeling with Snowplow

Snowplow comes with an out-of-the-box web model which models the raw data into typical data sets you might use to create reports for the business; for example tables for page views, sessions and users. The table below shows a sample of data modeled into sessions.

session timestamps user app engagement first page referer marketing last page location ip useragent
session_id session_index session_start_date session_start_time session_end_time user_id domain_userid network_userid app_id page_views conversions session_length time_engaged_in_s first_page_title first_page_url first_page_urlpath first_page_urlquery page_referrer refr_urlhost refr_urlpath refr_urlquery refr_medium refr_source mkt_medium mkt_source mkt_campaign mkt_clickid channel last_page_title last_page_url last_page_urlpath geo_country geo_region geo_city geo_zipcode geo_timezone user_ipaddress br_name br_version br_lang os_name os_version device_family device_class device_name
289d6851-77ca-45ad-a611-… 4 2020-04-01 2020-04-01 10:00:03 2020-04-01 10:00:18 be5016ad-c6ef-442d-aa18-… 28172b1a-96aa-4fe2-a4e3-… website 2 0 15 10 Snowplow | The Data
https://snowplowanalytic… / / search Google organic search Snowplow
BDP | A Da…
https://snowplowanalytic… /snowplow-bdp/ US CA San Francisco 94102 America/Los_Angeles 192.171.81.XX Chrome 80 en-US Mac OS X 10 Other Desktop Apple Macintosh
01e67be0-600b-4e48-a57e-… 22 2020-04-01 2020-04-01 10:00:15 2020-04-01 10:01:42 292a1aaa-89f9-4441-b001-… 93252393-6d33-4dab-90b7-… website 2 0 87 20 Snowplow | The Data
https://snowplowanalytic… / direct direct Snowplow Docs –
https://docs.snowplowana… /docs/setup-snowplow-on-… GB MA Manchester M1 Europe/London 185.242.7.XX Chrome 80 en-GB Windows 10 Other Desktop Desktop
501d77d3-6e2a-4a6e-b66e-… 1 2020-04-01 2020-04-01 10:04:33 2020-04-01 10:04:38 5f1c3cd1-046d-4af3-a8f6-… 7128ff2f-781f-416c-99e2-… website 1 0 5 0 Snowplow BDP
https://snowplowanalytic… /terms-of-service/snowpl… / search Google organic search Snowplow BDP
https://snowplowanalytic… /terms-of-service/snowpl… GB EN London EC3 Europe/London 196.245.163.XX Chrome 80 en-GB Mac OS X 10 Other Desktop Apple Macintosh
7f0a23a5-e392-48d2-b37b-… 67 2020-04-01 2020-04-01 10:12:07 2020-04-01 10:30:00 e39fd884-8992-480f-8e37-… 4b82d683-813b-4969-a4ae-… console 2 0 1073 10 Snowplow BDP https://console.snowplow… / direct direct Snowplow BDP https://console.snowplow… /4d9aac25-a9f7-46d2-8b4f… US NY New York 10021 America/New_York 138.68.41.XX Chrome 76 en-US Linux Other Desktop Linux Desktop
b23a6994-9520-4347-b1c7-… 3 2020-04-01 2020-04-01 10:14:55 2020-04-01 10:15:26 eb8c2f9b-8357-4e01-84e9-… f4605d71-c3cd-4dc3-962c-… website 2 0 31 20 Building a data
quality …
https://snowplowanalytic… /blog/2020/02/04/buildin… utm_campaign=data-qualit…… /url sa=t&rct=j&q=&esrc=s&sou… search Google paid-search google data-quality EAIaIQobChMIwPu5t4qs1AIV… paid search Snowplow
BDP | A Da…
https://snowplowanalytic… /snowplow-bdp/ DE BE Berlin 10365 Europe/Berlin 2.205.35.XX Chrome 80 de-DE Mac OS X 10 Other Desktop Apple Macintosh
694baa7b-67d8-470a-9dc7-… 14 2020-04-01 2020-04-01 10:18:21 2020-04-01 10:24:06 example-user2@company2.c… 463cc6f3-c4e2-4aee-b946-… 192f5fc0-2f41-4fcb-93d9-… console 4 0 345 110 Snowplow BDP https://console.snowplow… / https://snowplowanalytic… / internal internal Snowplow BDP https://console.snowplow… /45d4104d-4f5d-4134-8588… US CA Mountain View 94040 America/Los_Angeles 74.125.151.XX Edge 81 en-US Windows 10 Other Desktop Desktop
c9a528c2-72ae-458a-9e05-… 1 2020-04-01 2020-04-01 10:29:59 2020-04-01 10:30:10 f729daad-549e-4a0a-ae1c-… c27d4731-1a2f-495b-9615-… website 2 0 11 0 Snowplow |
Collect rich
https://snowplowanalytic… /mobile/ utm_campaign=mobile&utm_…… / social Linkedin paid-social linkedin mobile paid social Snowplow Customers |
https://snowplowanalytic… /customers/ US TX Austin 78702 America/Chicago 207.91.133.XX Mobile Safari UI/WKWebVi… 13 en-US iOS 13 iPhone iPhone Apple iPhone
86e9281f-8a12-4fa1-b761-… 5 2020-04-01 2020-04-01 10:42:28 2020-04-01 10:44:32 be5016ad-c6ef-442d-aa18-… 28172b1a-96aa-4fe2-a4e3-… website 1 1 124 70 Snowplow BDP
| Get …
https://snowplowanalytic… /get-started/ https://snowplowanalytic… /snowplow-bdp/ internal internal Snowplow BDP
| Get …
https://snowplowanalytic… /get-started/ US CA San Francisco 94102 America/Los_Angeles 192.171.81.XX Chrome 80 en-US Mac OS X 10 Other Desktop Apple Macintosh
db33e52e-9dfd-4d18-b804-… 6 2020-04-01 2020-04-02 10:22:04 2020-04-02 10:25:44 3d16fcf1-9b9d-404e-b014-… aed54c2d-dbec-4328-86b6-… website 3 0 220 60 Snowplow | The Data
https://snowplowanalytic… / direct direct Introduction to
https://snowplowanalytic… /blog/2020/04/06/introdu… NL NH Amsterdam 1011 Europe/Amsterdam 192.81.220.XX Chrome Mobile 80 nl-NL Android 10 Samsung SM-G975W Phone Samsung SM-G975W
042d0e66-f3ea-4d55-b7b4-… 3 2020-04-01 2020-04-03 10:35:26 2020-04-03 10:39:49 91e669dd-7ab2-472b-9557-… 535179d3-1d4b-4506-b5b6-… website 7 1 263 140 Data modeling 101 https://snowplowanalytic… /lp/data-modeling/ utm_source=google&utm_me… / search Google search google data-modeling paid search Snowplow BDP
https://snowplowanalytic… /pricing/ US MA Boston 02129 America/New_York 73.238.158.XX Safari 13 en-US Mac OS X 10 Other Desktop Apple Macintosh

Similar to our out-of-the-box web model, tools such as Google Analytics model your data for you behind the scenes. The advantage of Snowplow is that you can customise and extend this model to make it perfectly relevant for your business model. For example, you can see and change the assumptions that we’ve made about what the definition of a session is.

Secondly, because you have access to the raw data you can run custom analyses for multiple scenarios and use cases. Below we share an example of some custom modeling.

Want to collect and model your own raw data? Try Snowplow!

Custom modeling
on raw data

Imagine our marketing team have requested a report about content performance. From the raw data collected through Snowplow BDP, we can build them a custom set of data with interesting dimensions around conversion and engagement.

content total engagement avg monthly engagement
title page_urlpath author date_published month_published year_published unique_users sessions page_views avg_time_engaged_in_s avg_scroll_depth subsequent_demo_requests conversion_rate avg_unique_users avg_sessions avg_page_views avg_subsequent_demo_requests
Snowplow Mic…
/blog/2019/07/17/introdu… Joshua Brady 2019-07-17 07-2019 2019 543 727 6758 148 77% 26 3.6% 60 81 751 2.9
Time spent is the
most i…
blog/2019/08/07/time-spe… Lewis Newman 2019-08-07 08-2019 2019 389 430 2552 162 74% 28 6.5% 49 54 319 3.5
Re-thinking the structur… /blog/2020/01/24/re-thin… Marie Hertzog 2020-01-24 01-2020 2020 383 461 2610 121 82% 23 5.0% 128 154 870 7.7
Building a data
quality …
/blog/2020/02/04/buildin… Anna Soininen 2020-02-04 02-2020 2020 147 183 649 157 45% 8 4.4% 74 92 325 4.0
Understanding the SameSi… /blog/2020/02/17/underst… Sean Hutchinson 2020-02-17 02-2020 2020 276 302 1466 189 63% 7 2.3% 138 151 733 3.5

This is a simple example of how you can harness the raw, granular data provided by Snowplow. Imagine some other scenarios you may want to model from the same data:

  • The Product team want to understand feature engagement – so we build them a model which provides insight into granular use of specific features.
  • The Lead Generation team want to understand which of their channels is performing best – so we build them a model that tracks conversions across time per channel.
  • The Customer Success team want to have a 360 degree view on each customer – so we build them a model that stitches the behavioural data from Snowplow with data coming from our ticketing platform and CRM system.

Interested in harnessing one set of raw
data to drive all of your critical use cases?