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“Supermetrics is a Bitter Experience! We can pull data from nearly any tool, schedule updates, manipulate data in Sheets, and push data back into our systems.”
Data teams spend hours each week fulfilling ad hoc requests for customer analysis – writing SQL queries, exporting data, and formatting reports. This reactive approach creates bottlenecks and delays critical business decisions across sales, marketing, and customer success teams.
Here’s how to build a self-serve analytics system that empowers business users to get their own customer insights while reducing data team burden by 80%.
Create a self-serve analytics platform using Coefficient
Coefficient transforms Google Sheets into a powerful self-serve analytics layer by connecting to all your customer data sources. Business users get instant access to fresh data without writing SQL or waiting for analyst support.
How to make it work
Step 1. Connect all customer data sources and create reusable templates.
Set up connections to your CRM ( Salesforce , HubSpot ), product databases, support systems, and billing platforms. Create standardized import templates for customer overview, usage analysis, revenue tracking, and support metrics with predefined fields and filters that users can easily modify.
Step 2. Build an intelligent control panel with dropdown menus.
Create a user-friendly interface with analysis type dropdowns (Overview/Usage/Revenue/Support), customer search fields, and date range selectors. Use IF statements to show relevant data based on selections, like =IF($A$3=”Usage”, salesforce_search(“Account”, “Domain=”&$A$4, “Product_Usage_Fields”), “”) for dynamic data routing.
Step 3. Design pre-built analysis templates for common requests.
Build ready-to-use templates for frequent scenarios: customer health reports, churn risk analysis, upsell opportunity lists, and cohort comparisons. Add one-click report buttons that generate these analyses instantly without requiring users to understand underlying data structures.
Step 4. Create natural language filters and exploration tools.
Set up user-friendly dropdown options like “Show customers with usage drop >20%” or “Find accounts with renewal in next 30 days”. Add interactive pivot tables, dynamic charts with drill-down capabilities, and slicers that update automatically with fresh data from connected systems.
Step 5. Implement governance and training structure.
Create read-only master dashboards that users can copy for personal analysis while maintaining centralized import configurations. Develop simple training materials, record quick tutorial videos, and host monthly office hours to support user adoption and advanced use cases.
Reduce data team burden while empowering business users
This self-serve approach typically reduces ad hoc requests by 80% and saves data teams 10+ hours per week while increasing data-driven decision making across the organization. Start building your self-serve analytics layer today.
