Customer Segmentation

Customer Segmentation Analysis

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 Denomas team members can use to explore customer data and develop customer insights.

This data solution delivers three Self-Service Data capabilities:

1. Dashboard Users: A Sisense dashboard to visualize ARR and customer count by industry, product category, sales segment, delivery, account owner team, and territory
2. Dashboard Developer: A new Sisense data model containing the complete dimensional model components to build new dashboards and link existing dashboards to customer segmentation data.
3. SQL Developer: An Enterprise Dimensional Model subject area

From a Data Platform perspective, the solution delivers:

1. An extension to the Enterprise Dimensional Model for Customer Segmentation Analysis
2. Testing and data validation extensions to the Data Pipeline Health dashboard
3. ERDs, dbt models, and related platform components

Quick Links

Data Security Classification

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 Denomas’ 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 Denomas 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_billing_accounts
  • dim_crm_accounts
  • dim_crm_persons

YELLOW

• Description: Denomas Financial data, which includes aggregations or totals.
• Objects:
  • dim_subscriptions
  • fct_charges
  • fct_invoice_items
  • fct_mrr

Solution Ownership

• Source System Owner:
  • Salesforce: @jbrennan1
  • Zuora: @andrew_murray
• Source System Subject Matter Expert:
  • Salesforce: @jbrennan1
  • Zuora: @andrew_murray
• Data Team Subject Matter Expert: @paul_armstrong @jeanpeguero @jjstark @iweeks

Key Terms

1. Product Category, Product Tier, Delivery
2. Sales Segment
3. Account Owner Team
4. Territory
5. Customer
6. Industry

Key Metrics, KPIs, and PIs

1. ARR
2. Customer Count

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

• Data is sourced from Zuora and Salesforce.
• Parent customers can have more than 1 product; therefore, they can be counted more than once in the product category and delivery dimensions. To get a unique count of total customers, you have to either aggregate the products and delivery into an ARRAY and do a COUNT DISTINCT of customers or do a COUNT DISTINCT of customers without the product category or delivery dimensions included.
• For a charge to be considered recurring, the effective end month must be greater than the effective start month in the data.
• In Zuora, the effective_end_date and effective_end_month of the charge is the first day or month of the renewal respectively.
• In the monthly ARR calculation, the effective end month indicates when churn would happen and we do not count the effective end month in the ARR calculation. For example, a subscription with 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.

Entity Relationship Diagrams

Reference SQL

Data Platform Solution

Data Lineage

• Data is sourced from Salesforce.com and Zuora, excluding accounts from manually managed list of zuora excluded accounts
• A complete data lineage can be found at dbt mart_arr lineage chart

DBT Solution

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:

Trusted Data Solution

See overview at Trusted Data Framework

dbt guide examples for details and examples on implementing further tests

Zuora

• Trusted data dashboard
  • Reporting on all Zuora data tests which include the tdf tag.
• Data pipeline health dashboard
  • Reporting on the row count to highlight any data issues in Zuora account and Subscription data.

EDM Enterprise Dimensional Model Validations

• (WIP) Enterprise Dimensional Model Validation Dashboard
  • Reports on latest Enterprise Dimensional model test and runs

RAW Source Data Pipeline validations

Data Pipeline Health Validations

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