The Self-Service Data Team is responsible for leveraging data to optimize for the self-service customer experience and drive nARR growth via sales efficiency. Data insights from this team feed: sales visibility, self-service fulfillment features, and growth/marketing experiments. The Self-Service Data Team also aims to create data tools to help with efficiency, prioritization, and decision making.
| Name | GitLab Handle | Title |
|---|---|---|
| Max Fleisher | @mfleisher | Mgr, Self-Service & Online Sales Data |
| Sara Gladchun | @sglad | Sr. Analyst, Self-Service & Online Sales Data |
Self Service Team - We partner with the overall Self Service team to provide data insight around the self service customers (currently AMER SMB FO and Pooled accounts) to drive nARR and growth through sales efficiency and strategy.
Central Data Team - We work with the central data team by staying involved in cross functional data initiatives, collaborating where possible, and providing feedback on data models and the data that live in Snowflake. We also work with the data science team by staying up to date on their projects and models and incorporate many of their predictive outputs into our analyses and triggers,
Product Analytics - We work with product analytics by staying up to date on what major projects they are working on and by leveraging many of their models in our own data work.
Fulfillment - We work with fulfillment to provide data around self service fulfillment features and feature requests.
Sales - We work with the Low Touch Sales team to provide data insights, data tools, and sales visibility to the AEs to increase efficiency and make the most up to date data available for quick response times and targeted outreach. We also provide forecasting models for the Low Touch sales teams (FO and Pooled teams).
Marketing Analytics - We partner with Marketing Analytics to provide data around FO Funnels as well as targeted digital outreach to the Pooled Account customers.
| Resource | About |
|---|---|
| Data Request Issue Template | Template that should be used for ad-hoc data questions and requests |
| Data Hub | All of our data assets and resources in one place |
| Quarter | Max | Sara |
|---|---|---|
| FY23-Q2 | Issue | Issue |
| FY23-Q3 | Issue | Issue |
| FY23-Q4 | Issue | Issue |
| FY24-Q1 | Issue | Issue |
| FY24-Q2 | Issue | Issue |
| FY24-Q3 | Issue | Issue |
Purpose: Outline how the broader Self-Service team can engage the Self-Service Data Squad (Max, Sara)
Goal: Minimize dependencies/blockers to insights while providing transparent engagement model
Disclaimer: Not all data questions will be able to be answered. Ultimately, taking time to answer ad-hoc questions means less time on projects (aka the zero-sum capacity problem). That is not to say that ad-hoc questions are not important; however, we do have “boulder” level projects in flight have been prioritized via the OKR process, which we also need to make progress on.
How to submit your ad-hoc data request or question:
Purpose: To ensure that we are all speaking the same data language, we have created clear metrics that align with our agreed business definitions.
General Definitions:
Retention, Renewal, and Churn Definitions:
SSOT data is necessary in order to have confidence in our metrics, have repeatable and replicable reporting, and for our data team to work more efficiently. We have created a GitLab repo to house our SSOT SQL queries for both our foundational base queries and for ad hoc analyses.
This allows us to keep a record of queries used for foundational projects like our dashboards and for one off analyses that may need to be repeated, tweaked, or modified in the future. Dashboard queries are also housed in Sisense as snippets in order for the data team to work more efficiently within the BI tool. We are currently updating these queries to work within Tableau as well.
The current workflow for creating or updating snippets and SSOT queries is the following:
Tableau Data Source Workflow (WIP - New Data Source):
Updating a published Tableau Data Source:
