Case study / Healthcare
A leading restaurant management software platform turns 320,000 scattered leads into one governed, AI-searchable view of its revenue engine
Marketo and Salesforce unified in Snowflake, enriched with 37 AI-ready features, searchable in Glean.
320,000
Scattered lead records brought into a governed revenue view
37
Firmographics, behavioral signals and AI-generated summaries added per lead
$9.55M
Representative 16,000-lead cohort pipeline value from roughly 382 opportunities
The Challenge
Data foundation, Snowflake-native pipeline, AI-ready enrichment, Glean indexing, revenue intelligence roadmap
A leading restaurant management software platform was sitting on its own revenue history and could not use it. Roughly 320,000 leads and millions of activity records were spread across Marketo and Salesforce, with no single trusted view of a prospect or how they moved through the funnel. The legacy state was fragmented and manual: raw Marketo activity, brittle exports and hand-tuned scoring rules, which meant pipeline was worked on instinct rather than evidence.
The stakes were the funnel itself. At current performance, a representative 16,000-lead cohort produces roughly 9,388 qualified leads and 382 opportunities, worth about $9.55M in pipeline. Every period that leads were mis-prioritized and under-penetrated segments went unseen, that revenue stayed on the table.
Marketo’s event data and field values had to be transformed into a curated, human-readable knowledge layer in Snowflake, carrying both raw and processed views plus a lead_summary construct that business users could actually understand. Stale records and incomplete fields had to be quantified and fixed first, since ICP, journey ops, retention and cross-sell modeling would only hold up on clean data.
The solution also had to stay Snowflake-native with no external orchestration, plugging directly into Glean, the company’s AI-search platform.
We used to joke that our best leads were buried somewhere in Marketo. Now they show up in a single view, enriched and scored, and my team can finally spend time selling instead of hunting for signal.
Revenue Operations leader
a leading restaurant management software platform
Why Gruve
Gruve connected the data foundation to the AI-search layer and the revenue roadmap
Gruve won the Marketo-to-Glean work on the strength of its data foundation capabilities: designing and building an automated, end-to-end pipeline that moved the company from a fragmented, manual legacy state to a governed, optimized, Snowflake-native platform feeding Glean.
That same capability is what set Gruve apart going forward. Rather than treating the pipeline as a one-off reporting project, Gruve connected Marketo, Snowflake and Glean into a single governed system, then layered domain-informed ICP discovery, blind-spot identification and lookalike modeling on top, grounded in restaurant-specific revenue signals rather than generic scoring formulas.
The result was a foundation built not just for Phase 1, but for a three-phase roadmap running from foundation to predictive intelligence to autonomous agents.
The Approach
Four steps turned scattered lead history into an AI-searchable revenue layer
Step 1
Automated data foundation
Built a fully automated, Snowflake-native data pipeline that ingests raw Marketo activity and lead data and materializes both raw and processed, human-readable lead views, including a lead_summary layer.
Step 2
AI-ready enrichment
Enriched each lead with roughly 37 AI-ready features, including firmographics, behavioral signals and AI-generated lead summaries, turning marketing exhaust into structured fuel for sales intelligence.
Step 3
AI-searchable in Glean
Indexed the curated Snowflake layer into Glean so any authorized user can ask natural-language questions about leads and journeys and get permission-aware, AI-generated answers grounded in this dataset.
Step 4
Roadmap to predictive and agentic
Designed Phase 2 and Phase 3: AI-driven ICP discovery, journey-ops and retention modeling, cross-sell and up-sell targeting, and autonomous routing and forecasting agents, with an ROI framework spanning ops savings, prep-time reduction, routing accuracy and revenue lift.
What a lead carries now
Roughly 37 AI-ready features per record, including firmographics, behavioral signals and an AI-generated lead summary, with both a raw view and a processed, human-readable view retained in Snowflake so analysts and business users work from the same records.
Where it runs
Snowflake-native end to end with no external orchestration dependencies, indexed into Glean as the AI-search layer, with permission-aware answers so access follows existing entitlements.
The outcomes
A governed foundation changed how the revenue team finds, trusts and acts on lead intelligence
Speed to production
Phase 1 delivered a fully automated, Snowflake-native pipeline and a curated knowledge layer, with roughly 37 AI-ready features per lead and AI-generated lead summaries surfaced in Glean. New questions about the funnel start from a search rather than a data pull.
Baseline: Raw Marketo activity reached the business through brittle manual exports, with no single trusted view of a prospect or their journey.
Cost per outcome
Estimated 40%+ operations savings on reporting and lead-ops tasks tied to the data foundation, plus roughly 25% prep-time reduction across the data foundation and AI intelligence levers. RevOps and marketing ops time shifts from CSV hygiene to designing plays and experiments.
Baseline: Manual reporting and list-building across Marketo, Salesforce and spreadsheets absorbed RevOps and marketing ops capacity every cycle.
Control and sovereignty
Governed, Snowflake-native platform with no external orchestration dependencies, raw and processed views both retained, and permission-aware AI answers in Glean so any authorized user can query enriched leads directly.
Baseline: Marketo behaved as a black box only specialists could query, with scoring logic hand-tuned outside any governed layer.
Risk reduced
Data quality quantified and remediated before any advanced AI modeling, covering stale records, incomplete fields and non-restaurant accounts, so ICP, journey-ops, retention and cross-sell models are built on trustworthy data.
Baseline: Hand-tuned scoring rules sellers had largely stopped believing, so pipeline was prioritized on instinct rather than evidence.
The path not taken
Without the foundation, every funnel question would still start with a manual data pull
Without Gruve, marketing and RevOps would have carried on with fragmented Marketo and Salesforce exports and manual scoring logic. Marketo would have stayed a specialist-only system, with no governed Snowflake foundation underneath it and no AI-searchable layer in Glean above it, so every new question about the funnel would keep starting with a data pull instead of an answer.
The old path also meant leaving roughly 320,000 leads and millions of activity records in systems only specialists could query, with prioritization running on scoring rules sellers had already stopped believing.
What would have happened
The cost of the old path showed up as pipeline worked on instinct instead of evidence
Staying on the old path meant leaving roughly 320,000 leads and millions of activity records in systems only specialists could query, with about $25K USD of revenue riding on each raw lead and prioritization running on scoring rules sellers had already stopped believing. The cost never showed up as a line item. It showed up as every quarter of pipeline worked on instinct instead of evidence, and every under-penetrated segment nobody could see.
What's next
The same foundation can support predictive intelligence and autonomous revenue agents
What happens next, as the design supports it: the company can empirically discover its true ICP, identify blind-spot segments with high conversion rates that Sales is currently under-penetrating, and run lookalike modeling to find more accounts like its best customers. The same foundation carries journey-ops and retention models with early churn signals, cross-sell and up-sell targeting, and autonomous agents that route, enrich and prioritize leads in real time, feeding a revenue flywheel that keeps re-training on what actually closed.
Anything not yet built is a target, not a result.
Who else this applies to: B2B revenue teams sitting on large Marketo and Salesforce histories they cannot activate, and any organization standing up Glean as the search layer over a governed Snowflake warehouse.
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