An example of modeled customer data
Below, you’ll find an example of a modeled data table derived from a large ‘atomic’ table – or single source of truth for all the different models. This data can then be used for BI and AI purposes and refreshed as often as necessary. Once we’ve collected the raw atomic data from a digital source, we can begin to create data models which describe a sample of users’ interactions. This example is from a cohort of users across the Snowplow marketing website, documentation site and user interface (Snowplow BDP Console).
This raw data sample was created using Snowplow, and spans:
5 users | 6 sessions | 3 platforms | 5 devices | 4 channels
The advantages of modeling data in a data warehouse
Similar to our out-of-the-box web model, packaged tools such as Google Analytics model your data for you behind the scenes, but the packaged approach has many inherent disadvantages.Despite the apparent convenience of packaged tools, they often make it challenging to customize data. All Snowplow’s models are fully customizable to make them perfectly relevant for your business model. You might, for example, want to redefine what counts as a session, or how time on page is calculated. Snowplow also gives you 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.
Custom modeling raw customer 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 data set with interesting dimensions around conversion and engagement.
Use Cases made possible with Data Modeling
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:
Improve time to value with out-of-the-box data models
Snowplow is the third most prolific publisher of dbt models in the world. Alongside this large collection of data models for different contexts and industries, we’ve created Data Product Accelerators, step-by-step guides which help you build advanced data products in a fraction of the time.From the modeled data table above, you can create a user level view of your data across touchpoints – aka a customer 360.Snowplow has an unparalleled number of fields which come out of the box, as well as prevalidation and a testing sandbox environment for ultimate reliability.Finally, your Snowplow pipeline can be deployed entirely in your own cloud environment, for a highly-compliant and private solution.