Analytics & Reporting
Equals Review
Modern spreadsheet + SQL + BI hybrid — connect to your CRM and data warehouse, run SQL, build dashboards, all inside a familiar spreadsheet interface. The tool RevOps analysts actually enjoy using.
Equals at a glance
The RevenueHoop take on Equals
Equals is the modern spreadsheet built for RevOps analysts who want SQL power inside a familiar Google Sheets-style interface. Native connections to Snowflake, BigQuery, HubSpot, and Salesforce mean you write one SQL query, get live data in your spreadsheet, build a chart, share the dashboard — all without leaving the tool. For RevOps analytics work at Series A-C, Equals is often meaningfully better than the enterprise BI tools (Looker, Tableau) at a fraction of the cost.
Best for
- RevOps analysts building revenue dashboards and models
- Founders and finance teams doing SaaS financial modeling
- Sales ops teams building forecast and pipeline analytics
- Small-to-mid-market companies (Series A-C) without a data engineering team
- Any team where "SQL power + spreadsheet UX" is the sweet spot
When to use it
Use Equals when your analytics work is spreadsheet-native but needs to pull live data from databases and CRMs. The combination of familiar spreadsheet UX + native SQL + live database connections + BI-style dashboards makes Equals a genuine one-tool solution for most RevOps analytics work at Series A-C.
Equals also earns its place for finance teams doing SaaS financial modeling — cohort analysis, ARR waterfalls, unit economics — where the spreadsheet interface matches how finance folks think, but the live data connections eliminate manual refresh work.
When NOT to use it
Skip Equals if you have a dedicated data engineering team using dbt + Looker + a proper data warehouse — enterprise BI is meaningfully more powerful for enterprise use cases. Skip it if your analytics need is very complex data science; Hex or Mode fit that better.
For non-analytical financial modeling (budgeting, forecasting), Causal is more purpose-built.
Pros and cons
Pros
- Best-in-class modern spreadsheet UX
- Native SQL with live database connections
- BI-style visualizations inside a spreadsheet
- Native connections to CRMs (HubSpot, Salesforce) + data warehouses
- Fair pricing for mid-market analytics needs
- Fast time-to-first-dashboard (usually under an hour)
Cons
- Not designed for enterprise-scale BI needs
- Feature depth trails Looker/Tableau for complex data modeling
- Vendor stability less established than enterprise tools
- Advanced data science workflows better served by Hex/Mode
- Enterprise governance features maturing
Equals vs. the alternatives
Equals against the notebook-first BI tools and the enterprise BI alternatives.
Primary UX
- Equals
- Spreadsheet
- Mode
- Notebook
- Hex
- Notebook + spreadsheet
- Looker
- Semantic model + dashboards
Best for
- Equals
- RevOps analytics
- Mode
- Data teams
- Hex
- Data science teams
- Looker
- Enterprise BI
SQL support
- Equals
- Native ✅
- Mode
- Native ✅
- Hex
- Native ✅
- Looker
- LookML-based
Live database connections
- Equals
- ✅
- Mode
- ✅
- Hex
- ✅
- Looker
- ✅
Best stage
- Equals
- Series A-C
- Mode
- Series B-C
- Hex
- Series B-C
- Looker
- Series C+ / Enterprise
Learning curve
- Equals
- Low (spreadsheet users)
- Mode
- Moderate (SQL/notebook)
- Hex
- Moderate (data science)
- Looker
- Steep (LookML)
RH pick?
- Equals
- RevOps default ✅
- Mode
- Notebook-first analysts ✅
- Hex
- Data science teams ✅
- Looker
- Enterprise BI ✅
| Dimension | Equals | Mode | Hex | Looker |
|---|---|---|---|---|
| Primary UX | Spreadsheet | Notebook | Notebook + spreadsheet | Semantic model + dashboards |
| Best for | RevOps analytics | Data teams | Data science teams | Enterprise BI |
| SQL support | Native ✅ | Native ✅ | Native ✅ | LookML-based |
| Live database connections | ✅ | ✅ | ✅ | ✅ |
| Best stage | Series A-C | Series B-C | Series B-C | Series C+ / Enterprise |
| Learning curve | Low (spreadsheet users) | Moderate (SQL/notebook) | Moderate (data science) | Steep (LookML) |
| RH pick? | RevOps default ✅ | Notebook-first analysts ✅ | Data science teams ✅ | Enterprise BI ✅ |
The RevenueHoop opinion
Equals is the tool we recommend to RevOps analysts whose alternative is "duct-tape my Google Sheet with 15 Zapier automations pulling data every hour." The productivity difference is real. For Series A-C teams without a dedicated data engineering function, Equals often produces meaningfully better analytics work than teams get from more sophisticated tools they don't have the resources to operate well.
— Written by the RevenueHoop team. Last updated July 2026.
Equals FAQ
Modern spreadsheet + SQL + BI hybrid — connect to your CRM and data warehouse, run SQL, build dashboards, all inside a familiar spreadsheet interface. The tool RevOps analysts actually enjoy using. Equals is the modern spreadsheet built for RevOps analysts who want SQL power inside a familiar Google Sheets-style interface. Native connections to Snowflake, BigQuery, HubSpot, and Salesforce mean you write one SQL query, get live data in your spreadsheet, build a chart, share the dashboard — all without leaving the tool. For RevOps analytics work at Series A-C, Equals is often meaningfully better than the enterprise BI tools (Looker, Tableau) at a fraction of the cost.
Equals fits teams that need RevOps analysts building revenue dashboards and models; Founders and finance teams doing SaaS financial modeling; Sales ops teams building forecast and pipeline analytics; and Small-to-mid-market companies (Series A-C) without a data engineering team.
Skip Equals if you have a dedicated data engineering team using dbt + Looker + a proper data warehouse — enterprise BI is meaningfully more powerful for enterprise use cases. Skip it if your analytics need is very complex data science; Hex or Mode fit that better.
The alternatives we compare Equals against are Mode; Hex; Causal; and HubSpot. Equals against the notebook-first BI tools and the enterprise BI alternatives.
Equals is the tool we recommend to RevOps analysts whose alternative is "duct-tape my Google Sheet with 15 Zapier automations pulling data every hour." The productivity difference is real. For Series A-C teams without a dedicated data engineering function, Equals often produces meaningfully better analytics work than teams get from more sophisticated tools they don't have the resources to operate well.