Product Usage Data

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On this page

• On this page
• Product Usage Data
  • Business Use Cases for Customer Product Usage Data:
  • Sources of Product Usage Data
• Self-Service Capabilities
  • Data Platform Components
  • Quick Links
  • Data Security Classification
  • Solution Ownership
  • Key Terms
  • North Star Metrics and Leading Indicators
    • North Star Metrics
    • Leading Indicators
  • Key Metrics, KPIs, and PIs
  • Metric Formats
• Self-Service Data Solution
  • Self-Service Dashboard Developer
  • Self-Service SQL Developer
    • Key Fields and Business Logic
    • Entity Relationship Diagrams
• Data Platform Solution
  • Gainsight Data Pump
    • EDW to Gainsight Data Pump:
    • Gainsight to Snowflake Data Pipeline:
  • Data Lineage
• Trusted Data Solution
  • Product Usage Trusted Data Dashboard
  • RAW Source Data Pipeline validations

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Product Usage Data

Product Usage Data provides quantitative measurement of how, when, and where the Customer is using GitLab as a product and is used by GitLab teams to build better products, accelerate customer adoption, and improve customer retention. The Product Usage Data page will provide the information and tools that GitLab team members can use to explore Product Usage Data and develop customer insights.

Business Use Cases for Customer Product Usage Data:

• Insight into user adoption/onboarding for a customer instance of GitLab
• Monitor trends for increase/decrease in utilization of capabilities within a particular DevOps Stage
• Monitor for drops in active users
• Identify accounts that are far behind on the latest version
• Identify the type of installation used in their deployment
• Visibility into increase/decrease in # of GitLab Runners
• Visibility into GitLab implementation architecture, scaling up/down of infrastructure
• Discussing utilization and adoption patterns with our internal champions to strategize on where to apply energy in terms of new user onboarding and overall value realization for the account

In the future, we plan on adding support for the following use cases:

• Tracking of stage adoption by a customer and eventually teams for ROI and maturity. This will also act as insight to progress against the customer success plan objectives.
• Driving digital engagement (i.e., actions and content) based on utilization, customer lifecycle phase and engagement (i.e., time-to-value).

Sources of Product Usage Data

Source	Key Use Cases	Data Flow From Source To EDW
Service Ping	Gainsight Product Usage, xMAU, Estimated MAU	JSON payload sent from Self-Managed instances → version.gitlab.com → Version Postgres Database ← pgp → snowflake.raw.version_db
Seat Link	Gainsight Product Usage	Customers Portal -> Customers Postgres Database ← pgp → snowflake.raw.tap_postgres.customers_db_license_seat_links
Version Check	None	version → Version Postgres Database ← pgp → snowflake.raw.version_db.version_checks
GitLab.com	Product Adoption Dashboard, Gainsight Product Usage (coming soon)	gitlab.com -> replicas/clones ← pgp → snowflake.raw.tap_postgres
Snowplow	Snowplow Summary, Product Adoption Dashboard	Snowpipe

Self-Service Capabilities

The data solution delivers three Self-Service Data capabilities:

1. Gainsight Users: Self-Managed product usage data is now available within Gainsight, enabling Gainsight users to create specific workflows, visualize trends, build customer health scorecards, and review use case adoption strategies. The Using Product Usage Data in Gainsight a full guide.
2. Dashboard Developer: A new Sisense data model containing the complete dimensional model components to build new dashboards and link existing dashboards to Customer Product Adoption Data.
3. SQL Developer: A Enterprise Dimensional Model subject area. Refer to the R2A Objects tab.

Data Platform Components

From a Data Platform technology perspective, the solution delivers:

1. Gainsight Data Pump - EDW to Gainsight and Gainsight to EDW
2. An extension to the Enterprise Dimensional Model for Product Usage data
3. Testing and data validation extensions to the Data Pipeline Health dashboard
4. ERDs, dbt models, and related platform components

Quick Links

Data Security Classification

Much of the data within and supporting the Product Usage Data 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.

ORANGE

• Description: Customer and Personal data at the row or record level.
• Objects:
  • dim_crm_account
  • dim_billing_account
  • dim_ip_to_geo
  • dim_location

YELLOW

• Description: GitLab Financial data, which includes aggregations or totals.
• Objects:
  • dim_subscriptions
  • prep_recurring_charge

Solution Ownership

• Source System Owner:
  • Service Ping: @jfarris
  • Salesforce: @jbrennan1
• Source System Subject Matter Expert:
  • Service Ping: @jfarris
  • Gainsight: @jbeaumont
  • Salesforce: @jbrennan1
• Data Team Subject Matter Expert: @iweeks @snalamaru @mdrussell

Key Terms

1. Customer
2. Service Ping
3. GitLab Self-Managed Subscription
4. GitLab SaaS subscription
5. Seat Link
6. Product Category, Product Tier, Delivery
7. Version Check
8. Billable Members: API, Definition, EDM Field Name: billable_user_count
9. Active Users: Customer Docs, Metric Dictionary, EDM Field Name: active_user_count

North Star Metrics and Leading Indicators

Partnering with cross-functional teams, the Data Team is defining metrics indicative of product adoption. These metrics are categorized as North Star Metrics and Leading Indicators.

North Star Metrics

A North Star Metric is a single value that gives a high-level summary of product adoption. Each Use Case has one North Star Metric. A North Star Metric must meet three criteria:

1. The metric must directly connect to the value that customers realize from a Use Case. Ideally, it measures a customer's breadth and/or depth of Use Case adoption.
2. The metric must be available for a high proportion of customers and ARR.
3. The metric must be easy to understand and explain. All else being equal, we prefer a simple metric to an aggregate or composite metric.

Leading Indicators

A Leading Indicator is a measure that impacts Use Case adoption, but is not comprehensive enough to be a North Star Metric. For example, a Leading Indicator might give insight into adoption of a single feature within a Use Case. Alternatively, a Leading Indicator can track any prerequisite activities that are required to unlock the primary value of a Use Case.

Key Metrics, KPIs, and PIs

1. Gainsight Metric Definitions
2. Stage and Group Performance Indicators
3. ARR
4. Use Case Health Scoring

Metric Formats

1. User-based metrics: # of users who performed an action/event
   • Last 28 days
   • Last 7 days
     • Example: "# of users who completed a merge request in the last 28 days"
2. Event-based metrics: # of [actions performed]
3. Total counts # of [events/attributes/users]
   1. Example: "# of runners" or "# of auto_devops_enabled projects"
4. Indicator Metrics: Whether an attribute is true or false
   1. Example: "Whether shared runners are enabled or not"
5. Power user metrics: # of users who did this action, and this action, and that action

Self-Service Data Solution

Self-Service Dashboard Developer

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.

Self-Service SQL Developer

Key Fields and Business Logic

• The Product Usage data sourced from GitLab SaaS and GitLab Self-Managed customer deployments is fed into the Enterprise Data Warehouse on a regular basis to be consumed by Gainsight and Salesforce.
• We utilize Service Ping to derive self-managed customer usage data. Self-Managed customer product usage data is largely contained in the self-contained Service Ping packets.
• The SaaS Customer Product Usage Data is rebuilt using source database tables.
• The Seat Link data encompasses license utilization data for all customers, regardless of type (self-managed or SaaS).
• The Aggregated metrics are collected in 7 and 28 day time frames are added into Service Ping payload under the aggregated_metrics sub-key in the counts_weekly and counts_monthly top level keys.
• Aggregated metrics for all time frame are present in the count top level key, with the aggregate_ prefix added to their name.
• The underlying tables for Gainsight's consumption are built on the set of all Zuora subscriptions that are associated with a Self-Managed rate plans. Seat Link data from Customers DB is combined with high priority Service Ping metrics to build out the set of facts included in this table. The most recently received and the latest Service Ping (by created_at date for a given subscription_id) and Seat Link (by dim_subscription_id) payload from each month are reported.

Entity Relationship Diagrams

Diagram/Entity	Grain	Purpose	Keywords
Product Usage Data ERD	All of the below	Shows all table structures, including column name, column data type, column constraints, primary key, foreign key, and relationships between tables.	Customer, Service Ping, Subscription, Seat Link, Self- Managed, SaaS, Product, Delivery, Accounts

Data Platform Solution

Gainsight Data Pump

Data is sourced from GitLab SaaS and GitLab Self-Managed customer deployments. For information on how to add additional Service Ping metrics to the Gainsight Data Pump, please see the Data Guide for Adding Service Ping Metrics to Gainsight.

EDW to Gainsight Data Pump:

The Data Team has leveraged the native capabilities in Gainsight to read data from the Snowflake Enterprise Data Warehouse. The Data Team has build a read-only mart-level table for Gainsight to access and it will contain all of the data currently available. Over time as the Data Team adds more metrics or customer segments, this table will automatically be refreshed with the additional data. This “interface” is called the Gainsight Data Pump.

Gainsight to Snowflake Data Pipeline:

The Data Team to develop a new source data pipeline from Gainsight into Snowflake to include new custom objects and data created in Gainsight to increase the Service Ping match rate, among other improvements.

The diagram Product Usage data developmental Streams illustrates our development approach for managing the delivery of Self-Managed and SaaS Product Usage to Gainsight.

Data Lineage

The dbt solution generates a dimensional model that represents the flow of data through each of the tables from Source Models to Gainsight.

• A complete data lineage of the dbt models can be found at Data Flow Diagram
• Details of the mappings from raw service ping data to our product usage marts can be found in this document.

Trusted Data Solution

See overview at Trusted Data Framework

Kindly refer the dbt guide examples for details and examples on implementing further tests.

Product Usage Trusted Data Dashboard

A detailed Dashboard showing dbt tests, Source Model freshness, Record Counts, Last Run Dates, Golden records Validation etc.. This reports on latest Enterprise Dimensional model test and runs.

(WIP) Product Usage Trusted Data Dashboard

RAW Source Data Pipeline validations

Data Pipeline Health Validations