Your best marketing data is stranded in Snowflake. Marketo can't touch it.
Review counts, category traffic, engagement signals. All of it lives in Snowflake, behind a CSV upload and a 72-hour lag before Marketo can use it. Conversion reads Snowflake natively, triggers on G2's custom Salesforce objects in real time, and puts AI agents in charge of the MOps work your team no longer wants to do by hand.
72 hr → live
Custom-object sync lag gone
No CSV
Snowflake data activated natively, no exports
< 4 weeks
Parallel migration alongside Marketo
MAP + CDP
One platform, no separate data layer
Marketo was not built for G2's object architecture
G2's Salesforce instance is built around product profiles, 1:many profile join bridge objects, standalone category objects, and lifecycle bridges, all fed by a sub-hourly ETL from G2 admin. Marketo can read some of these at a surface level but cannot trigger on them, cannot use their fields in emails, and falls behind by 25 to 72 hours when objects update. Conversion treats every relationship as a first-class primitive.
Salesforce standard
Contact
Lead / Contact
Account
~98% from G2.com
Custom objects (Marketo cannot trigger on these)
Product Profile
Custom Object
Profile Join
Bridge: Contact ↔ Profile (1:many)
Category Object
Custom Object (standalone)
Lifecycle Bridge
Lead ↔ Contact
Your Snowflake data should arrive in the email, not in a CSV two days later
The motivating use case: "you have 8 of 10 reviews needed to qualify for reports, here is how to close the gap." Today that message requires a Snowflake export, a CSV upload to Marketo temp fields, and a Velocity script workaround, and it arrives stale. Conversion reads the number directly and computes it in the message at send time.
- 1Snowflake query runs
Review counts, category traffic, and engagement signals computed in the warehouse.
- 2Exported to CSV
Data manually exported, formatted, and mapped to Marketo temp fields.
- 3Uploaded to Marketo
Bulk import to temp fields. Custom-object sync shows up to 72-hour lag in some cases.
- 4Email finally sends
"You need 10 reviews, you're at 8" arrives a day or more after the data was true.
Every Velocity script workaround adds another manual step and another opportunity for data drift.
- 1Snowflake connected natively
Conversion reads review counts, category traffic, and engagement signals directly from Snowflake via a service account you control.
- 2Fields available everywhere
Every Snowflake field is queryable as a segment filter, personalization token, or scoring input, no export required.
- 3Email sends with live math
"You have 8 of 10 reviews" computed in the message at send time. No Velocity scripts. No lag.
The CSV workaround, the Velocity scripting, and the 72-hour lag are gone. Your data is live the moment it changes in Snowflake.
Salesforce, Snowflake, and product data in one unified record
Conversion pulls every source into a single contact record, enriched in real time. Salesforce standard and custom objects, Snowflake review and engagement data, and AI-enriched signals all land in one place so you can segment, score, and personalize on all of it without writing SQL or running exports.
Salesforce
CRM
Snowflake
Warehouse
Product
Events
AI Agents
Enrichment
Sarah Chen
VP of Marketing · Acme Corp
Lead Score
0
Agents that build campaigns, score leads, and run the MOps queue
Marketo needs a human to orchestrate everything. Conversion is agent-native from the ground up, so the AI is a blank canvas, not an add-on layer. Agents can build campaigns from a brief, run recurring ABM programs, and create their own dynamic nurture tracks based on how contacts actually engage.
Campaigns from a brief
Describe the campaign goal in plain language. An agent builds the audience, emails, scoring adjustments, and flow in under a minute, with a draft artifact your team reviews before anything goes live.
Self-tuning lead scoring on Snowflake data
Scoring trained on G2's closed-won and closed-lost history, with review counts, category traffic, and engagement signals as first-class inputs. The model retrains itself instead of waiting on manual recalibration, and you edit it in natural language.
Recurring ABM agents
Set an agent running against your target account list and let it monitor signals, update scores, trigger sequences, and surface next-best actions, without a weekly MOps queue.
Dynamic nurture tracks
Agents that build their own paths based on how a contact engages, not just pick among pre-built tracks. Configurable autonomy lets you run some flows fully supervised and others with lighter review.
Built-in controls
Human-in-the-loop by default
Every output reviewable before it goes live
Full audit trail
RBAC to object and field
No training on your data
Enterprise-grade security
Conversion vs Marketo
Marketo has been reliable for B2B table stakes. On the capabilities that matter for a PLG pivot, warehouse activation, custom-object triggers, and AI agents, here is the honest read.
| Capability | Conversion | Marketo |
|---|---|---|
| Data and warehouse | ||
| Direct Snowflake read in-platform | CSV upload only | |
| Custom objects as first-class trigger sources | Read-only, no triggers | |
| Real-time sync (no 25-72 hour lag) | ||
| In-message math on product and review data | Velocity scripting | |
| MAP and CDP in one platform | CDP sold separately | |
| AI and agents | ||
| Agents that build full campaigns end to end | ||
| Self-tuning lead scoring on your data | Static, manual models | |
| Dynamic nurture tracks built by agents | ||
| MCP available today | ||
| Configurable agent autonomy guardrails | ||
| Foundations | ||
| Drag-and-drop email and forms parity | ||
| Salesforce campaign sync and attribution | ||
| LinkedIn, Google, and Meta ad orchestration | Limited | |
| Closed-lost recovery workflows | ||
| Partnership | ||
| Forward-deployed engineer in Slack | Standard support | |
| Parallel migration on your timeline | ||
| Pricing model | Marketable contacts | Contacts + modules |
Enterprise B2B teams with complex data models have already made this switch
Fintech and enterprise SaaS companies with Salesforce custom objects, strict security requirements, and large warehouse footprints are migrating to Conversion and reclaiming the hours Marketo's workarounds consumed.
Plaid consolidated its GTM stack onto Conversion, unifying Salesforce and warehouse data under strict security controls.
Veriforce migrated off Salesforce Marketing Cloud and Pardot onto Conversion with a forward-deployed team running the migration in parallel.
Frequently asked questions
Conversion treats joins and custom objects as first-class relationships. Product profiles, profile-join bridge objects, category objects, and lifecycle bridges are all modeled natively. Triggers fire in real time off any object, and custom-object fields are available in emails and scoring without a sync-lag window. This is the gap Marketo cannot close, because it was not built with custom objects as a core primitive.
Yes. Conversion connects to Snowflake through a service account you own, with table-level scoping you set. Review counts, category traffic, and any other field in your warehouse are queryable in the platform and available as segment filters, personalization tokens, and scoring inputs, without any export or import step.
The flagship use case for G2 is sending a message that says something like: you have 8 of 10 reviews needed to qualify for reports, here is how to close the gap. That number is computed at send time from Snowflake, not from a CSV snapshot from two days ago. No Velocity scripting required.
Every deployment runs in parallel with Marketo until you are ready to cut over. Our forward-deployed team replicates your live programs, dedup rules, lifecycle objects, and Salesforce sync in Conversion, and migrates in phases with clear milestones. The full migration target is under four weeks, compared to the three-month HubSpot-to-Marketo migration G2 ran previously.
Pricing is based on marketable contacts, not total records. For a database of 400,000 to 500,000 marketable contacts out of roughly one million total, the number is predictable in advance and AI usage is unlimited within the plan.
Every agent action produces a draft artifact first. Nothing reaches a contact until a human reviews and approves it, so you get the efficiency of automation without giving up editorial control. Autonomy is configurable per agent and per workflow, from fully supervised to lighter review, depending on the risk level. RBAC scopes access to the object and field level.
Let's build the G2 use case together
Run a working session scoped to G2's Snowflake data, custom-object architecture, and PLG goals. Our team handles the heavy lifting and runs the migration alongside Marketo.