We are excited to announce the publication of a new Looker Block developed by our founder, Aron Clymer: Dynamic Cohort Analysis. Looker Blocks are building blocks — pre-built pieces of code that you can leverage to accelerate your analytics in Looker.

Block Description:

Anyone doing a deep dive analysis on customer behavior will want to easily look at a cohort and see what kinds of interesting insights can be discovered about that cohort. For instance, one simple but powerful question like “Of the customers that purchased Product A, what other products did they purchase?” can help sales target upsell or cross-sell opportunities.

In the world of SQL, this kind of question requires a sub-query to define the cohort (customers who purchased product A) and a main query to answer a question (what other products did they purchase). In the spirit of creating a friction-free, self-service analytics environment, the question is: how can we give end users the capability of dynamically creating ad-hoc cohorts at run-time and then asking other questions about the behavior of those cohorts? All without having to develop any LookML or write custom queries!

Why This Is Important:

Looker is an amazing data platform, but because it’s generic it doesn’t come prepackaged with advanced analytical patterns. So, we developed this Looker block to give end users a powerful pattern for dynamic cohort analysis.

Anyone doing a deep dive analysis on customer behavior will want to easily look at a cohort and see what kinds of interesting insights can be discovered about that cohort. For instance, one simple but powerful question like “Of the customers that purchased Product A, what other products did they purchase?” can help sales target upsell or cross-sell opportunities.

In the world of SQL, this kind of question requires a sub-query to define the cohort (customers who purchased product A) and a main query to answer a question (what other products did they purchase). In the spirit of creating a friction-free, self-service analytics environment, the question is: how can we give end users the capability of dynamically creating ad-hoc cohorts at run-time and then asking other questions about the behavior of those cohorts? All without having to develop any LookML or write custom queries!

At a Glance:

 


Data Clymer is a premier consulting firm specializing in full-stack analytics and data culture transformation. Our proven methodology includes full data stack implementation, data democratization, custom training, and analytics to enable data-driven decisions across the organization. We have curated a set of best practices from our deep expertise in LookerTableauSnowflakeRedshiftBigQueryPanoplyMatillonDBT, Sigma, and Fivetran.

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