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 Coll… |
https://snowplowanalytic… | / | https://www.google.com/ | google.com | / | search | 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 Coll… |
https://snowplowanalytic… | / | direct | direct | Snowplow Docs – Document… |
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 Produc… |
https://snowplowanalytic… | /terms-of-service/snowpl… | https://www.google.com/ | google.com | / | search | organic search | Snowplow BDP Produc… |
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 | example-user@company.com | 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… | https://www.google.com/u… | google.com | /url | sa=t&rct=j&q=&esrc=s&sou… | search | paid-search | 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… | snowplowanalytics.com | / | 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_… | https://www.linkedin.com… | linkedin.com | / | social | paid-social | mobile | paid social | Snowplow Customers | Her… |
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… | snowplowanalytics.com | /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 Coll… |
https://snowplowanalytic… | / | direct | direct | Introduction to marketin… |
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… | https://www.google.com/ | google.com | / | search | search | data-modeling | paid search | Snowplow BDP Pricin… |
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 |
| Introducing 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.