Create backend data table to monitor the health PQL actions

Summary

In order to properly understand which PQLs are effective, we need to create a product data table. This will enable us to monitor the health of these user experiences and allow us to understand if some experiences are not meeting our expectations and should either be iterated on or removed from the product. This will ensure that in time we only display feature discovery moments that are valuable to our users. The end goal is that this data is then ingested into Snowflake by the data team so we can monitor these user experiences in Sisense.

Requested data table

• user_id
• namespace_id
• pql_timestamp (date/time the event occurred)
• page_url (page URL the user took this action on)
• feature_source (unique name of the location of the feature discovery or usage PQL, example values could be security_dashboard issue weights etc)
• PQL_type (values will be hand raise or usage)
• usage value

Note these values should be stored when the data is sent to the Marketing/sales team for a usage PQL and when the in-app contact sales form (hand raise PQLs) is submitted.

Example data

user_id	namespace_id	pql_timestamp	page_url	feature_source	PQL_type	usage_value
123	456	date/time value	page URL value	security_dashboard	hand raise
123	456	date/time value	page URL value	security_dashboard	hand raise
123	456	date/time value	page URL value	security_dashboard	hand raise
789	890	date/time value	page URL value	setup for company	usage	true
01234	5678	date/time value	page URL value	group stages adopted	usage	2