AI Marketplaces Leaderboard

Revenue Analytics

ChatGPT plugin

Non-trivial RevOps reports

View in ChatGPT directory
Featured
No

not on the featured shelf

Movement

From our daily snapshots over the last 30 days.

Rank by placement

#4378of 4,58170places down24h—7d
#4,201#4,250#4,299#4,348Sep 3Sep 9Sep 15Sep 21Sep 27Oct 2

Rank in Business & operations

#927of 97420places down24h—7d
#881#894#907#920Sep 3Sep 9Sep 15Sep 21Sep 27Oct 2

In its category

How this plugin compares with others in the same category. Positions are counted within the category, not across the whole directory.

Business & operations927th of 974 by placement
#PluginPosition7d
924Yournotify#4368363places down
925Butterfly#4371364places down
926Grid Control#4374—
927Revenue AnalyticsThis page#4378—
928TrueDialog#4383360places down
929Famewall#4387—
930Outfield#4388357places down

All 974 Business & operations plugins

Revenue Analytics by DataLabs.store connects your HubSpot data to ChatGPT. It continuously replicates your portal (custom objects, deals, contacts, companies, line items, engagements, email events) into a real relational Microsoft SQL Server database whose schema matches your portal, then lets ChatGPT query it in plain English and return answers backed by the exact SQL that produced them.

Why it exists HubSpot is built to run your CRM at scale, and its reporting tools are genuinely good at what they were designed for. The limit is structural: standard reports are built one object at a time, so questions that need several tables joined together before the math even starts do not fit. Per-deal margin (deals joined to their line items), multi-hop paths (deal to contact to company to source), correctly weighted stats (SUM(clicks)/SUM(delivered), not an average of averages), cohorts, and window functions all need a real database underneath. That is the job this fills.

What you can ask

  • "Rank owners by weighted pipeline (each deal's amount times its real stage odds) and their historical win rate."
  • "Unique click-through rate by acquisition source, weighted by delivered volume, for contacts who became customers."
  • "Which deals are aging past 90 days, and where does our ARR actually concentrate?"

Then keep drilling: "now split by pipeline... now only Q2... now show the trend."

What makes it different

  • Real joins across any objects, expressed as one query.
  • Every answer is auditable: the exact SQL ships with the number, so you can verify and reproduce it.
  • You bring the model: full frontier ChatGPT reasoning runs over your own data.
  • Complementary to HubSpot's own AI. Breeze works inside the CRM; this understands the data behind it.

A note on sensitive data This app's HubSpot integration requests only standard, non-sensitive OAuth read scopes for CRM analytics. It does not request HubSpot's sensitive scope grants (for example, crm.objects.contacts.sensitive.read). Because HubSpot enforces this at the API level, any property a customer has explicitly marked as a "sensitive data" property inside their own HubSpot account is withheld by HubSpot and never delivered to our sync process. It cannot enter the customer's synced database, and therefore cannot be returned by any of this MCP's tools, regardless of the query executed.

Example prompts

  • “Which emails actually turn prospects into deals - and how fast”
  • “For each pipeline stage average days a deal sits there, and how many activities we log while it's there”
  • “Which rep has the best pipeline once you account for who actually closes?”

Skills

  • revenue-analytics

    Use when the user has connected DataLabs' "Revenue Analytics" MCP connector (mcp.datalabs.store) - either as a custom connector exposing tools like execute_query/get_tables_list/get_full_database_schema/get_semantic_metadata/save_query/list_saved_queries, or as a Company Knowledge source exposing search/fetch - and asks analytical questions about their HubSpot data (deals, pipeline, contacts, companies, engagements, email campaigns, workflows). Also use for schema exploration, SQL generation/debugging against this connector, or reproducing known revenue-ops analyses (weighted pipeline, forecast calibration, stuck deals, engagement-vs-win-rate, revenue concentration, email attribution).

Categories
Made by
DataLabs.store · Website
Sign-in
On Install
Availability
Available
Version
1.0.1
Created
July 21, 2026
More info