The field mapping session is where most marketing intelligence implementations quietly fail. Your RevOps team spends two weeks mapping fields, and then the vendor confirms which fields their model actually supports. Intent scores in a custom object: unsupported. Territory assignments across three lookup fields: unsupported. Internal account tier flags your sales team built over four years: unsupported. The demand engine view that emerges reflects the tool’s schema, not your GTM motion.
Marketing Intelligence is built to read your CRM schema rather than map it to a fixed model. Tags are how that works in practice.
Key takeaways: Filters and Tags in Marketing Intelligence
- Filters read your CRM schema, not the other way around. Standard filters in Lative’s Marketing Intelligence pull from mapped fields like opportunity owner and industry; Tags extend that to almost any CRM custom field your RevOps team has built.
- Tags turn intent and ownership signals into filter toggles. Third-party intent data, territory flags, and account-tier categories living in custom Salesforce fields become filterable inside the demand engine view, no custom report build required.
- Schema mapping no longer blocks implementation. A marketing intelligence CRM rollout used to slow down when the tool could not accommodate the field structure the company already had; Tags bridges that gap directly.
- Account scoring and lead scoring stay connected to the source of truth. Because Tags surface the same custom fields powering your scoring models, segment-level reporting and attribution dashboards line up with the way reps already prioritize accounts.
- Marketing-sourced pipeline reporting becomes segment-specific. Filter Revenue Insights by any combination of standard and custom fields, then trace pipeline contribution back to the segments your GTM model actually plans against.
How Tags work
Standard filters in Lative’s Marketing Intelligence pull from defined field mappings: opportunity owner maps to the CRM’s opportunity owner field, industry maps to the firmographic industry field, and so on. Tags work differently. A Tag can be mapped to almost any custom field in your CRM, simultaneously, across multiple fields if needed.
An April 2025 Gong Labs analysis of 1.8 million deals found that multi-threading (actively engaging more than one stakeholder per account) boosts win rates by an average of 130% in deals over $50K. Tracking which contacts within each target account are showing intent, through signals that often live in custom CRM fields, is the operational mechanism behind that win rate advantage. Tags make those signals available as first-class filters inside Lative’s demand engine view.
Common Tag configurations marketing and RevOps teams apply:
- Third-party intent data: map intent platform signals from custom Salesforce fields directly into demand engine filters, so you can see which accounts with active pipeline intent are ahead of or behind their coverage targets
- Territory and segment flags: filter Revenue Insights by territory assignment, region, or custom segment categories your team tracks outside of standard Salesforce fields
- Relationship coverage: surface accounts where sales has flagged personal connections or relationship history, and compare conversion rates against accounts without that coverage
- Product line interest: filter demand analysis by product line or solution category when your CRM tracks multi-product accounts with custom fields
A practical example: your CRM tracks third-party intent data in a custom field, which accounts are showing high web activity on your category keywords this week.
Surfacing Intent Signals as Filter Toggles
Tags lets you map that field as a filter in Lative’s demand engine view. You can now filter your Revenue Supply Chain and Revenue Insights by the accounts displaying the most intent activity, alongside your standard firmographic and stage filters. That is a filter toggle, not a custom report build.
Another example: your team tracks a custom CRM field for whether a sales rep has a personal connection at a given account. Tags can map that field too, allowing you to filter demand engine analysis by accounts with relationship coverage alongside accounts with high intent. Both data points living in your CRM, both surfaced in the same view.
Why this matters for implementation
Spencer Stuart identified technology integration as the most common barrier marketing teams face when deploying AI-driven marketing analytics: the CRM schema problem is almost always at the center of it.
A highly customized CRM, one built over years to reflect a specific go-to-market motion with specific data capture requirements, does not need to be restructured for Lative to work.
The Tags layer bridges the gap between Lative’s standardized RevOps data model and whatever custom schema the CRM contains. Marketing Intelligence implementations slow down or fail when the tool cannot accommodate the CRM structure the company actually has. Tags address that problem directly.
The Spreadsheet Workaround Tags Replaces
Without this layer, teams work around the gap by exporting data to spreadsheets, requesting one-off reports from RevOps, or building custom BI queries for each analysis. Each workaround takes hours and produces a static result that is outdated the next time the underlying data changes.
For the executive-level view that Tags-filtered data feeds into, see Marketing Highlights. For the campaign-level attribution view that benefits from custom CRM field filtering, see Campaign Cards.
When Seismic‘s marketing team needed to incorporate their intent data platform signals into demand engine analysis, Tags mapped the custom Salesforce fields carrying that intent data directly into Lative’s filtering layer. What had previously required a custom report build for every analysis became a standard filter toggle their RevOps team could apply in any view.
Custom segments, account scoring, and attribution dashboards on one schema
The deeper value of schema-flexible filtering shows up when custom segments, account scoring, and attribution dashboards all read from the same CRM custom fields. Most marketing intelligence platforms force teams to maintain two parallel data models: the CRM schema the GTM team plans against, and a vendor-defined model the reporting tool understands. Every change to one has to be reconciled with the other, and the segment-level reporting that leadership asks for becomes a quarterly project rather than a daily view.
With Tags, the schema mapping is the segmentation logic. The same custom field that powers your account scoring model (for example, a third-party intent score, a strategic-account flag, or a product-line interest field) becomes a filter inside Lative. Lead scoring stays consistent across systems because the scoring inputs your RevOps team curates in the CRM are the inputs Marketing Intelligence reads from. Attribution dashboards inherit the same logic, so marketing-sourced pipeline can be sliced by territory, segment, intent tier, or any combination your GTM motion already tracks.
The practical effect: when leadership asks how a specific segment is converting, the answer comes from one view that matches the CRM schema the team plans against, rather than a reconciled extract from three different tools.
How Seismic Mapped Intent Data Into Demand Engine Analysis
Once your CRM schema is mapped, Lative carries it through to sales capacity planning, so the same data drives both marketing intelligence and capacity.
If your demand engine view is missing data that lives in custom CRM fields, Tags was built to close that gap. See how Lative’s Marketing Intelligence adapts to your CRM schema.
FAQ: schema-flexibility and custom segments in a marketing intelligence CRM workflow
Can Marketing Intelligence filter on CRM custom fields without remapping the schema?
Yes. Standard filters in Lative’s Marketing Intelligence read from defined field mappings, and Tags extend that to almost any custom Salesforce field, simultaneously, across multiple fields if needed. There is no requirement to restructure the CRM schema to accommodate the tool. A custom intent field, a territory flag, or a relationship-coverage marker can be mapped as a filter in the demand engine view and used alongside firmographic and stage filters.
How do Tags support custom segment-level account scoring and lead scoring?
Because Tags read directly from the CRM custom fields powering your account scoring and lead scoring models, the segments visible in Lative match the segments your scoring logic already uses. Marketing and RevOps can filter Revenue Insights by the same intent tiers, strategic-account flags, or product-line categories that feed the scoring model, so segment-level reporting and prioritization stay aligned with how reps work the pipeline.
What does this mean for marketing-sourced pipeline and attribution dashboards?
Marketing-sourced pipeline can be filtered and reported on by any combination of standard and custom CRM fields surfaced through Tags. Attribution dashboards inherit the same schema mapping, so the pipeline numbers leadership reviews tie back to the exact segments, territories, and intent tiers the GTM team plans against. The reconciliation work that usually sits between marketing reports and the CRM source of truth gets compressed into a single view.
Lative Team — Lative is the AI-native GTM platform that connects marketing intelligence to sales capacity planning on one shared data foundation.
