Customer segmentation is the process of dividing our customers into groups based on common characteristics so that we can understand who our customers are and provide them with a great customer experience. There are many characteristics that identify our customers including industry, product category, sales segment, delivery, and territory to name a few. The Customer Segmentation Analysis page will provide the information and tools that GitLab team members can use to explore customer data and develop customer insights.
This data solution delivers three Self-Service Data capabilities:
From a Data Platform perspective, the solution delivers:
Much of the data within and supporting the Customer Segmentation Dashboard is Orange or Yellow. This includes ORANGE customer metadata from the account, contact data from Salesforce and Zuora and GitLab's Non public financial information, all of which shouldn't be publicly available. Care should be taken when sharing data from this dashboard to ensure that the detail stays within GitLab as an organization and that appropriate approvals are given for any external sharing. In addition, when working with row or record level customer metadata care should always be taken to avoid saving any data on personal devices or laptops. This data should remain in Snowflake and Sisense and should ideally be shared only through those applications unless otherwise approved.
|Customer Segmentation||Provide insights into Quarterly ARR and Customer Count metrics by various customer dimensions|
A great way to get started building charts in Sisense is to watch this 10 minute Data Onboarding Video from Sisense. After you have built your dashboard, you will want to be able to easily find it again. Topics are a great way to organize dashboards in one place and find them easily. You can add a topic by clicking the
add to topics icon in the top right of the dashboard. A dashboard can be added to more than one topic that it is relevant for. Some topics include Finance, Marketing, Sales, Product, Engineering, and Growth to name a few.
ARRAYand do a
COUNT DISTINCTof customers or do a
COUNT DISTINCTof customers without the product category or delivery dimensions included.
effective_end_monthof the charge is the first day or month of the renewal respectively.
effective start month= 2020-07-01 and
effective end month= 2021-07-01 would have its ARR summed from 2020-07-01 through 2021-06-01 for 12 months of ARR.
| Diagram/Entity | Grain | Purpose | Keywords | | ————————————————————————————————————————– | —– | ——- | ——– | | Lead to Cash Overview ERD | All of the below | General overview of all processes for lead to cash | Parent Customer, Product Category, Delivery, Industry, Account Owner Team, Territory, Sales Segment, CRM, Persons, Accounts | | ARR and Customer Count Analytics ERD | Month, Subscription, Product Category | Provide insights into ARR and Customer Count by various customer dimensions | Parent Customer, Product Category, Delivery, Industry, Account Owner Team, Territory, and Sales Segment |
|Customer Segmentation SQL Script||Query to slice ARR and Customer Count by Product Category, Delivery, Industry, Account Owner Team, Territory, and Sales Segment|
|Customer Segmentation TY Quarter vs. LY Quarter SQL Script||Query to pull TY versus versus LY ARR and Customer Count by Quarter and slice by Product Category, Delivery, Industry, Account Owner Team, Territory, and Sales Segment|
The dbt solution generates a dimensional model from RAW source data. The exceptions are the following fields that are calculated based on business logic implemented within specific dbt models:
|product_category||Calculated based of Zuora product_rate_plan_name|
|delivery||Calculated based of product_category|
|service_type||Calculated based of product_rate_plan_name|
See overview at Trusted Data Framework
dbt guide examples for details and examples on implementing further tests