Close Deals

Manage Open Opportunities

Every open opportunity has the right data, the right contacts, and the right next step — always.

Of open opportunities in the average CRM have at least one critical data quality flag — placeholder close date, blank economic buyer, or unverified primary contact.

THE brıef

Open opportunities are the most valuable records in the CRM — and often the least accurate ones. Close dates are placeholder dates that haven't been updated in 6 weeks. The economic buyer field is blank or wrong. Deal stage reflects where the rep put it last month, not where the deal actually is. Key contacts have changed companies. The agent continuously audits every open opportunity, flags data quality problems, verifies contacts, and surfaces actionable next steps — so the pipeline reflects reality instead of wishful thinking.

Continuously audits open opportunities for data completeness and accuracy

The agent runs a continuous audit of every open opportunity against a set of required data completeness standards: close date (is it a real date or a default placeholder?), deal stage (has it been updated in the last 14 days?), primary contact (is the contact still at the company?), economic buyer (is this field populated with a verified contact?), next step (is there a logged next activity scheduled?), and deal value (is it populated and realistic?). Each missing or suspect field is flagged on the opportunity record — not in a separate report that requires manual reconciliation.

• Pipeline audit: 84 open opportunities.
• Audit flags: 23 with placeholder close dates (Dec 31 or last day of quarter), 18 with no economic buyer field, 14 with contact not verified in 90 days, 9 with no logged next activity.
• Total flagged: 41 (49%).

Verifies that key contacts are still at the company

One of the most common reasons deals stall is that the champion or primary contact left the company — and no one on the rep's team noticed for weeks. The agent monitors all key contacts on open opportunities for job change signals: LinkedIn updates, enrichment data changes, and email bounce patterns. When a contact's status changes — or when their LinkedIn shows a new employer — the opportunity is flagged immediately with the departed contact highlighted and a task to identify and verify a replacement. This turns what's typically a surprise discovery into a managed transition.

• Opportunity flag: Ridge Systems — Jordan M. (champion, VP Sales) updated LinkedIn to show new employer 4 days ago.
• Opportunity affected: Stage 3, $180K. Recommended: identify new champion from buying committee map. Manager notified.

Surfaces the right next step for every stalled opportunity

A stalled opportunity isn't just a data quality problem — it's a rep execution problem. The agent identifies opportunities where the next step is missing, overdue, or unlikely to progress the deal (a generic 'follow up' task with no specific action) and generates a contextually appropriate next step based on the deal's current stage, the last activity, and the risk signals present. 'Follow up' becomes 'Send the security questionnaire to Marcus T. (IT Director) — it's been 21 days since it was requested.' Specific, actionable, grounded in deal context.

• Prism Analytics — next step surfaced: 'Security questionnaire sent 21 days ago, no response. Next: call Marcus T. directly (verified phone available). Reference the procurement close date to create urgency.'
• Prior next step: 'Follow up on questionnaire.'

Reports pipeline data quality trends and ownership patterns

CRM data quality isn't a one-time fix — it's an ongoing discipline. The agent reports on data quality trends across the pipeline: which field types are most commonly flagged, which reps have the highest and lowest data quality scores, and whether data quality is improving or declining week over week. Managers use this report not to penalize reps but to identify where coaching is needed and where the CRM fields themselves might need restructuring. A field that's almost always blank is either poorly positioned in the layout or not actually useful.

• Data quality report: week 12.
• Overall score: 74% complete.
• Improving: close date accuracy (+8% vs. week 8).
• Declining: economic buyer field (-5% vs. week 8).
• Low performers: 3 reps below 60% data quality score.
• Recommended focus: economic buyer capture in Stage 2.

Today vs. with

Manage Open Opportunities

Today

• Pipeline review calls spend the first 20 minutes on data quality — updating close dates, confirming stage, and chasing reps for missing fields.
• A champion leaves the company, the deal goes dark, and the rep finds out 3 weeks later when emails start bouncing.
• Next steps are generic — 'follow up' tasks with no specific action, no contact named, and no urgency tied to the deal timeline.

With ABM Strategist

• Data quality flags are surfaced to reps as CRM tasks — pipeline review focuses on deal strategy, not data hygiene.
• Contact departures are detected within days from LinkedIn and enrichment signals — replacement contacts are recommended before the deal goes cold.
• Next steps are contextual and specific — tied to the deal stage, the last activity, and the risk signals present in the opportunity.

Works with

• Forecast Pipeline
  Opportunity data quality directly impacts forecast accuracy — clean pipeline data produces reliable forecasts.
• Track Champions
  Champion departures from active deals are a primary use case for real-time contact monitoring.
• Manage Deal Risk
  Data gaps in opportunities are classified as risk signals — missing economic buyer is a deal risk indicator.

Three layers, one platform by Lantern

Every agent runs on three layers: a unified data model, 150+ enrichment providers, and an open-source engine where every decision is auditable.

Data Waterfall

150+ enrichment providers. Sequential routing optimized per segment. The best answer wins. No vendor lock-in.

Agent Engine

Open-source execution engine. Workflows defined in code. Human-in-the-loop checkpoints. Full audit trail on every action.

Revenue Ontology

Every data source normalized into one model. Entity resolution across systems. Relationships stored, not inferred. Schema that evolves with your business.

FAQ

• Which CRM platforms does the agent monitor?
• Can we configure which fields are required vs. optional for the data quality audit?
• What happens to opportunities that have been open for more than 12 months — are they treated differently?
• Can managers review their team's data quality scores without the reps seeing manager-only metrics?

A clean pipeline is the foundation of accurate forecasting and effective deal management. This agent maintains it continuously.