Sales Forecast

Build a Revenue Forecast

Help the user build and validate a revenue forecast — from category modeling through pipeline coverage analysis, deal-level inspection, and gap planning.

Step 1 — Gather context

If references/learnings.md exists, read it first for accumulated knowledge.

Ask the user:

1. Scope:

• A) Individual rep forecast
• B) Team / pod forecast
• C) Regional forecast
• D) Company-wide forecast

2. Time period:

• Current quarter
• Next quarter
• Half / full year
• Custom period

3. What numbers do you have? (provide what you know)

• Quota / target
• Closed-won so far this period
• Commit (deals you're confident will close)
• Best case (deals that could close with things going right)
• Total open pipeline
• Average sales cycle length
• Historical win rate

4. What's your primary concern?

• A) We're behind quota and need a gap plan
• B) I need to validate my commit number
• C) I need to present a forecast to leadership
• D) Pipeline coverage feels thin
• E) Too many deals are slipping from commit to best case
• F) Other — describe it

If the user's request already provides most of this context, skip directly to the relevant step. Lead with your best-effort answer using reasonable assumptions (stated explicitly), then ask only the most critical 1-2 clarifying questions at the end — don't gate your response behind gathering complete context.

Step 2 — Forecast model

Build a forecast model table:

These are typical probability ranges — adjust based on the team's historical conversion data if available. Teams with strong qualification tend toward the higher end; teams early in building pipeline discipline should use the lower end.

Forecast summary

Forecast scenarios

• Worst case: Closed Won + (Commit × 80%) — assumes some commit deals slip
• Most likely: Closed Won + (Commit × 90%) + (Best Case × 40%)
• Best case: Closed Won + (Commit × 95%) + (Best Case × 60%) + (Pipeline × 15%)

Present as a range: "Based on current pipeline, expect to land between $X (worst) and $Y (best), most likely around $Z."

Step 3 — Pipeline coverage analysis

Coverage analysis rules

• Coverage < 2x: Critical — not enough pipeline to hit quota even optimistically. Immediate pipeline generation needed.
• Coverage 2-3x: Thin — likely to miss unless win rates are above average. Focus on deal acceleration and pipeline gen.
• Coverage 3-4x: Healthy for new business — focus on execution and deal quality.
• Coverage 4x+: Strong coverage — focus on deal progression and closing, not more pipeline.
• Commit > remaining gap: You have enough in commit to cover the gap. Focus shifts to deal execution and preventing slippage.
• Deals closing after period end: Flag any "commit" deals with close dates after the period ends — these aren't real commit.

Step 4 — Deal-level inspection

For each deal in Commit and Best Case, create an inspection table:

Risk flags to check

• Close date pushed more than once
• No activity in 14+ days
• Single-threaded (one contact)
• No compelling event
• In stage longer than 2x average
• Economic buyer not engaged
• Competitor mentioned but not addressed
• Budget not confirmed

Recommendations per deal

For each deal, recommend one of:

• Keep in Commit: Strong deal, high confidence, clear path to close
• Move to Best Case: Has potential but too many open risks for commit
• Move to Pipeline: Significant unknowns — not ready for commit or best case
• Pull in: Deal could close faster than planned — what would accelerate it?
• Push to next period: Won't close this period — move out and plan accordingly
• Qualify out: Deal isn't real — remove from pipeline

Step 5 — Gap plan

If there's a gap between the forecast and quota, build a plan to close it:

For each lever, answer:

1. How much can it realistically contribute? (be conservative — gap plans are usually optimistic) 2. What specific actions will you take this week? (not "do more outbound" — specific companies, people, messages) 3. Who owns it? (rep, manager, SE, executive) 4. When will you know if it's working? (set a checkpoint date)

Gap plan math

• Gap: Quota − (Closed Won + Weighted Commit + Weighted Best Case)
• Gap plan target: Gap × 1.5 (plan for more than you need, since not all levers will work)
• Minimum viable gap close: At least 50% of gap plan should come from Pull-in and Accelerate (fastest to materialize)

For platform-specific forecasting guidance (Momentum, Sybill, Oliv, Outreach, Weflow, People.ai, Veloxy, Scratchpad, Dialpad), see references/platforms.md.

Before recommending a specific platform skill

This skill covers a strategy domain across many platforms. Before pointing the user to any specific platform skill (any /sales-{platform} listed in ## Related skills, e.g., /sales-mailshake, /sales-klaviyo, /sales-apollo), read that platform skill's actual SKILL.md first. The 1-line description in ## Related skills is enough to identify a candidate — it's not enough to commit to it or to write a prompt that invokes it well.

How to read it:

• If ~/.claude/skills/{skill-name}/SKILL.md exists locally, Read it.
• For sales-* skills, WebFetch directly from this repo: https://raw.githubusercontent.com/sales-skills/sales/main/skills/{skill-name}/SKILL.md — e.g., for sales-mailshake: https://raw.githubusercontent.com/sales-skills/sales/main/skills/sales-mailshake/SKILL.md.
• For non-sales-* skills (third-party), look up {org}/{repo} in ~/.claude/skills/sales-do/references/skill-sources.md if installed and fetch the same skills/{skill-name}/SKILL.md path under that repo.

After reading, ground your recommendation in something concrete from the SKILL.md (its scope, a sub-flow, its argument-hint shape, or a "Do NOT use for..." negative trigger). Align any generated invocation with the platform skill's argument-hint. If the platform skill turns out not to fit the user's situation, swap to another or handle the question here directly rather than recommending a poor fit.

Related skills

• /sales-gong — Gong platform help (Gong Forecast module — widely considered weak, ~40% of customers stack Clari)
• /sales-salesloft — Salesloft Forecast module for submission workflows and AI-assisted predictions
• /sales-deal-inspect — Deep-dive on individual deals in your forecast
• /sales-cadence — Build outbound cadences for gap-plan pipeline generation
• /sales-clari-copilot — Clari Copilot platform help — conversation intelligence tightly integrated with Clari's forecasting engine, deal scoring from call signals
• /sales-revenue-io — Revenue.io platform help — Revenue Intelligence dashboards and deal scoring for Salesforce-native teams
• /sales-momentum — Momentum platform help — AI revenue orchestration with automated CRM updates, MEDDIC Autopilot, AI coaching, churn signals, executive briefs (acquired by Salesforce Feb 2026)
• /sales-modjo — Modjo platform help — EU-native deal intelligence, Ask Modjo AI pipeline queries, CRM auto-fill for forecast accuracy, GDPR-compliant
• /sales-veloxy — Veloxy platform help — Salesforce-native field sales with predictive intelligence and automatic activity logging for better forecast data
• /sales-do — Not sure which skill to use? The router matches any sales objective to the right skill. Install: npx skills add sales-skills/sales --skill sales-do

Gotchas

• Don't use pipeline total without weighting by stage. Claude will sometimes say "you have $2M in pipeline against a $1M quota, so you're covered." Raw pipeline total is meaningless — only weighted pipeline matters. Always apply stage-based win probabilities.
• Don't assume historical win rates apply to the current quarter. Win rates shift based on deal mix, market conditions, new competitors, and team changes. If the user provides historical rates, use them as a starting point but flag that current-quarter dynamics may differ.
• Don't forget to account for slipped deals from last quarter. Deals that pushed from last quarter inflate current-quarter pipeline but often have lower close probability (they already missed one deadline). Flag these and weight them more conservatively.
• Don't ignore seasonality. Q4 and fiscal year-end typically see higher close rates due to budget pressure. Q1 often sees longer cycles as budgets reset. Ask about the company's fiscal year when it matters.
• Don't present a single forecast number without a range. Always give worst/most likely/best case scenarios. A single number creates false precision and sets the user up for a bad forecast call.

• Self-improving: If you discover something not covered here, append it to references/learnings.md with today's date.

Examples

Example 1: Quarterly forecast build

User says: "Build my team's Q2 forecast. Quota is $2M, we've closed $800k, commit is $600k across 4 deals, best case is $400k, total pipeline is $1.8M, 6 weeks left." Skill does: 1. Builds a forecast model with weighted values across all categories 2. Calculates coverage ratio (1.5x — flags as thin) 3. Presents worst/most likely/best case scenarios 4. Creates a gap plan with specific levers to close the gap Result: Complete forecast ready for the leadership call, with a gap plan if needed

Example 2: Commit validation

User says: "Validate my $500k commit — Deal A ($200k, negotiation, strong champion), Deal B ($150k, proposal, no EB meeting), Deal C ($150k, demo stage, verbal interest only)." Skill does: 1. Inspects each deal against risk criteria 2. Recommends keeping Deal A in commit, moving Deal B to best case, moving Deal C to pipeline 3. Adjusts commit to $200-350k with reasoning Result: Defensible commit number the rep can present to their manager

Troubleshooting

Don't have all the numbers

Solution: Start with what you know. The skill can build a useful forecast from just quota + closed-won + pipeline total. It will flag what's missing and estimate where possible. Even a rough forecast with assumptions stated is better than no forecast.

Forecast keeps missing — always too optimistic

Solution: Apply stricter win probability weights. Most teams over-weight commit (use 85% not 95%) and best case (use 40% not 60%). Check for "commit creep" — deals that sit in commit for multiple forecast periods without closing. The deal-level inspection step catches these patterns.

Gap plan feels unrealistic

Solution: Apply the 50% rule — at least half of gap plan value should come from pull-in and accelerate levers (fastest to materialize). New outbound takes 4-8 weeks to generate pipeline, so it won't help this quarter. Be conservative on each lever and plan for 1.5x the gap.

Build a Revenue Forecast Learnings

Accumulated tips, gotchas, and corrections discovered during use. Claude reads this at the start of each invocation and appends new learnings as they're discovered. Once significant learnings have accumulated, use /sales-request-skill to share them back to the community. Shared and declined entries are marked so they won't be re-prompted.

<!-- Add entries below in format: YYYY-MM-DD: Learning description -->

Platform-Specific Forecasting Guide

Forecasting capabilities and integration details for each platform that feeds into or supports revenue forecasting. Use this reference when the user's forecasting question involves a specific tool.

Table of contents

• Sybill
• Modjo
• Momentum
• Oliv
• Weflow
• Scratchpad
• People.ai (Backstory)
• Dialpad
• Veloxy
• MaxIQ (ForecastIQ + EchoIQ)

In Sybill

• Deal Workspace (Business+, $90/user/mo): Pipeline views with deal health signals, risk indicators, and activity timeline. Shows which deals are progressing, which are stalled, and which have risk signals from recent calls.
• Ask Sybill for pipeline queries: Natural language queries about pipeline health — "Which deals mentioned budget concerns this quarter?" or "Which Stage 3 deals haven't had a call in 2 weeks?" Cross-references calls, emails, CRM, and Slack data (Business+).
• CRM Autofill for forecast accuracy (Business+): Auto-populates CRM fields from calls using MEDDPICC/BANT/SPICED frameworks. Forecast accuracy improves because CRM data reflects what was actually discussed, not stale rep estimates.
• Compound Intelligence: Sybill's context graph connects conversations, deals, and outcomes over time. The more calls it processes, the better it identifies patterns that predict deal outcomes — useful for forecast risk signals.
• Limitations: No dedicated forecasting module like Clari or Gong Forecast. Sybill improves forecast inputs (cleaner CRM data, deal health signals) rather than generating forecast numbers. API/MCP (Enterprise only) required for exporting deal intelligence to external forecasting tools. No weighted pipeline automation — forecast logic must live in CRM or external BI.
• Best for: Teams wanting to improve forecast accuracy by having better CRM data quality and deal visibility. Sybill makes the data feeding your forecast more reliable — it's a forecast-enabler, not a standalone forecasting tool. Pair with CRM native forecasting or Clari for the full stack.

In Modjo

• Modjo Deals: Pipeline visibility with deal health signals derived from conversation data. Shows which deals are progressing, stalled, or at risk based on actual call content rather than rep self-reports. Surfaces churn risk, expansion opportunities, and competitor mentions across all conversations.
• AI Insights for forecast accuracy: Aggregated business signals across all calls — budget concerns, timeline shifts, competitor mentions. Managers can ask Modjo AI "Which deals mentioned budget concerns this quarter?" to identify forecast risks.
• CRM auto-fill for forecast data quality: Auto-populates CRM fields from conversations (claims 90% of fields). Forecast accuracy improves because CRM data reflects what was actually discussed, not stale rep estimates. Supports Salesforce, HubSpot, Pipedrive, Zoho, Sellsy, Dynamics.
• Ask Modjo AI for pipeline queries: Natural language queries — "Which Stage 3 deals haven't had a call in 2 weeks?" or "Show me deals where the champion changed." Cross-references calls, CRM data, and deal information.
• Limitations: No dedicated forecasting module like Clari or Gong Forecast. Modjo improves forecast inputs (cleaner CRM data, deal health signals from conversations) rather than generating forecast roll-ups, weighted pipeline, or AI-predicted numbers. No standalone forecasting purchase. For dedicated forecasting, pair with CRM native forecasting or Clari.
• Best for: Teams wanting to improve forecast accuracy through better CRM data quality and deal visibility. Modjo makes the data feeding your forecast more reliable — it's a forecast-enabler, not a standalone forecasting tool. Strongest for EU teams needing GDPR-compliant deal intelligence. Pair with Salesforce/HubSpot native forecasting or Clari for the full stack.

In Momentum

• AI CRO Agent (Transformation tier, $99/user/mo): Provides executive-level insights on pipeline health, account trends, and team performance. Auto-generates Executive Briefs summarizing forecast-relevant data across all calls and deals.
• MEDDIC Autopilot: Automatically extracts qualification data from calls and writes it to Salesforce. This feeds forecast accuracy — reps no longer estimate MEDDIC scores from memory; the AI captures what was actually said on calls.
• AI Signals: Detects deal risk signals from conversations — budget objections, timeline shifts, champion changes, competitor mentions. These signals inform which deals should be downgraded in the forecast.
• Churn risk signals: Customer Retention Agent detects churn indicators from CS calls. Feeds renewal forecasting accuracy — flagging at-risk renewals before they appear in pipeline metrics.
• Deep Research: Cross-deal AI analysis. Ask questions across all calls (e.g., "Which deals mentioned budget cuts this quarter?") and get AI-synthesized answers. Usage-based credits.
• IQ Reports: Aggregated intelligence dashboards showing pipeline trends, signal patterns, and team activity across calls.
• Salesforce integration: Real-time bi-directional sync means forecast data in Salesforce reflects actual call content, not stale rep-entered data. Auto-populated fields improve weighted pipeline accuracy.
• Limitations: Salesforce-only (no HubSpot or Pipedrive forecast integration). AI CRO Agent and Executive Briefs require Transformation tier ($99/user/mo). Deep Research is usage-based credits on top of subscription. Forecasting features depend on call data volume — small teams with few calls get less signal.
• Best for: Teams already using Momentum for CRM automation who want forecast accuracy improved by AI-extracted deal data. The forecasting value comes from cleaner CRM data (MEDDIC auto-fill, risk signals) rather than a separate forecasting module — Momentum makes the data feeding your forecast more reliable.

In Oliv

• Forecaster Agent ($199/manager/mo): Generates weekly presentation-ready pipeline reports with AI commentary. Shows target vs actual, gap to goal, and deals likely to slip. Begins delivering reports immediately upon deployment — no 8-12 week implementation cycle.
• Deal Insights module ($29/user/mo): AI Deal Health Score for every opportunity. AI Deal Lifecycle Tracking shows where each deal sits and how it's progressing. Roll-up and aggregated forecasting across the pipeline. Win-loss analysis identifies patterns in closed deals.
• Deal Driver Agent: Identifies at-risk deals with specific remediation steps. Flags stalled opportunities and suggests actions — useful for forecast hygiene (downgrade deals that show risk signals before they slip).
• CRM Manager Agent (~$19/user/mo): Auto-populates CRM fields from conversation data. Improves forecast accuracy by ensuring CRM data reflects what was actually discussed on calls rather than stale rep estimates.
• Cross-deal analysis: Analyst Agent performs pattern analysis across all deals — useful for identifying systemic forecast risks (e.g., "3 deals in Stage 4 all mentioned budget cuts this quarter").
• Two-way CRM integration: Real-time bidirectional sync with Salesforce, HubSpot, Zoho, Freshworks, Copper, Close, Pipedrive, Dynamics 365. Forecast data in CRM reflects actual conversation content.
• Limitations: No public API — cannot export forecast data programmatically or build custom forecast dashboards. Forecaster Agent is $199/manager/mo on top of per-rep costs. Very early-stage platform (0 G2 reviews) — limited independent validation of forecast accuracy claims. No historical forecasting benchmarks published.
• Best for: Teams replacing expensive Gong + Clari stacks who want forecasting bundled with conversation intelligence at a fraction of the cost. The Forecaster Agent's instant deployment (vs 8-12 week Clari implementation) is useful for teams that need forecast visibility quickly. Multi-CRM support (8 CRMs) is an advantage over Momentum (Salesforce only).

In Outreach

• AI Forecasting (Amplify Pro tier, ~$160/user/mo): Pipeline coverage analysis, gap-to-goal visibility, and AI-driven revenue predictions. Flexible rollup categories (commit, best case, pipeline). Manager override and submission workflows.
• Deal velocity tracking: Time-in-stage metrics, engagement trend analysis, and risk signal detection feed forecast accuracy. Stalled deals get flagged before they slip.
• Pipeline views: Customizable pipeline filters and rollups by team, stage, segment, or custom fields. Real-time aggregation across the org.
• Kaia integration: Conversation intelligence data (sentiment, topics discussed, next steps captured) enriches deal health scores, which feed forecast confidence.
• CRM sync: Bi-directional Salesforce sync means forecast data reflects actual deal state. Outreach deal data flows to Salesforce forecast views.
• Reduces forecast prep time: Vendor claims ~44% reduction in forecast prep time via automated pipeline rollups and AI-generated summaries.
• Limitations: Forecasting only available on Amplify Pro (~$160/user/mo) — not Core or Plus tiers. Annual contracts required. Outreach forecasting is newer than Clari/Gong Forecast — less battle-tested at enterprise scale. No standalone forecasting purchase — requires full Outreach platform.
• Best for: Teams already on Outreach for sequences who want forecasting integrated with engagement and conversation data in one platform. Avoids the need to stack a separate forecasting tool (Clari, Gong Forecast) alongside Outreach. If you need best-in-class standalone forecasting, Clari remains the category leader.

In Weflow

• Deal Intelligence & Forecasting ($39/user/mo): 50+ AI-generated deal health signals, pipeline management views, automated forecast roll-ups, and AI prediction. Combines bottom-up, weighted, and AI forecasting methods. Real-time bi-directional Salesforce sync means forecast data lives in Salesforce — no separate system of record.
• Activity Capture feeds forecast accuracy ($19/user/mo): Server-side auto-sync of emails, meetings, and contacts into native Salesforce objects. Eliminates the "garbage in, garbage out" cycle — forecasts improve because CRM data reflects actual activities, not stale rep estimates.
• Conversation Intelligence feeds deal signals ($39/user/mo): AI extracts MEDDIC/BANT fields from calls and auto-fills Salesforce opportunity fields. Deal health signals come from actual call content, not rep self-reports.
• Ask Weflow AI for pipeline queries: Natural language queries — "Which Stage 3 deals haven't had a call in 2 weeks?" or "Which deals mentioned budget concerns this quarter?" Cross-references calls, activities, and CRM data.
• Full bundle economics ($79/user/mo): Activity capture + CI + forecasting. The forecasting module's accuracy depends on the data quality from the other two modules — buying all three together is the intended deployment.
• Limitations: Salesforce-only (no HubSpot or Dynamics forecasting). No public API — cannot export forecast data to external BI tools programmatically (data lives in Salesforce, queryable via SOQL). Not suited for complex enterprise forecast hierarchies at 1,000+ rep scale. Newer platform — less battle-tested at enterprise scale than Clari.
• Best for: Mid-market Salesforce teams (10-100 reps) where forecast inaccuracy traces back to stale CRM data. Weflow's approach is "fix the data first, then forecast" — the forecasting module is most powerful when paired with activity capture and CI. If you already have clean CRM data and need standalone forecasting, Clari or Gong Forecast may be better fits.

In Scratchpad

• No dedicated forecasting module — Scratchpad does NOT generate forecast roll-ups, weighted pipeline, or AI-predicted numbers. It is a Salesforce CRM overlay that improves the data feeding your forecast, not a forecasting tool itself.
• AI Field Updates feed forecast accuracy ($19/user/mo Solo): Automatically extracts methodology fields (MEDDPICC, BANT, SPICED) from calls and populates Salesforce opportunity fields. Forecast accuracy improves because CRM data reflects actual call content rather than stale rep estimates.
• Hygiene Monitor for forecast data quality (Solo+): Tracks missing fields, stale deals, and overdue tasks across the pipeline. Surfaces opportunities with incomplete data that would make forecasts unreliable.
• Sales Sheets for pipeline visibility: Modern spreadsheet and Kanban views of pipeline data. Faster than native Salesforce for reviewing pipeline during forecast calls — but views are Salesforce data, not a separate forecast system.
• Deal & Account Agent: AI assistant that can answer pipeline health questions by cross-referencing calls, emails, and CRM data. Useful for pre-forecast-call prep.
• Limitations: No forecast roll-ups, no pipeline analytics, no deal health scoring, no AI forecast predictions, no forecast submission workflow. All forecasting logic must live in Salesforce native forecasting or a dedicated tool (Clari, Gong Forecast, Weflow). Scratchpad makes the data cleaner — it doesn't generate the forecast.
• Best for: Teams whose forecast inaccuracy traces back to stale CRM data and reps not updating opportunity fields. Scratchpad fixes the data input problem. Pair with Salesforce native forecasting or Clari for the actual forecast. Also useful for teams already using Gong who want better daily pipeline views (Scratchpad Team integrates with Gong).

In People.ai (Backstory)

• Activity-backed pipeline analytics: People.ai captures every email, call, and meeting automatically and ties activity data to CRM records. Pipeline views show real engagement levels — not rep self-reports — giving managers a more accurate picture of deal momentum and pipeline health.
• Deal Intelligence for forecast accuracy: Risk flagging identifies at-risk deals based on declining engagement, single-threading, or missing stakeholders. Engagement scoring quantifies deal health based on actual activity patterns. These signals inform which deals should stay in commit vs. move to best case.
• Revenue forecasting: Predictions backed by actual activity data rather than CRM-entered estimates. Win/loss pattern analysis identifies which rep behaviors and engagement patterns correlate with wins, improving forecast model calibration over time.
• Historical baseline: Analyzes 2 years of prior activity on day one — immediate pipeline visibility and baseline for engagement benchmarks without waiting months to accumulate data.
• MCP Integration (Feb 2026): Query revenue data through Claude, ChatGPT, or Copilot. Example: "Which commit deals have had declining engagement this month?" or "Which accounts are single-threaded in Stage 4?" Enterprise tier required.
• Multi-CRM support: Salesforce, Microsoft Dynamics, Oracle — simultaneous multi-CRM support for global teams with different CRM instances per region.
• Limitations: People.ai does NOT record or transcribe calls — it captures activity metadata only. No dedicated forecast submission workflow (like Clari). No standalone weighted pipeline module — forecast roll-ups must live in CRM or a tool like Clari. Enterprise-only pricing, no free or self-serve tier. Activity data processing can take 24-48 hours for call data — not real-time.
• Best for: Enterprise teams whose forecast inaccuracy traces back to reps not logging activities in CRM. People.ai fixes the data layer — once CRM data reflects actual engagement, forecasts built on that data become dramatically more reliable. Common stack: People.ai (data layer) + Clari (forecast governance) + Gong (conversation insights). If you need standalone forecasting with submission workflows, Clari is the category leader.

In Dialpad

• Dialpad Analytics: Call volume, duration, disposition, and agent performance dashboards. Provides the data layer for understanding call activity trends — how many calls per rep, average handle time, call outcomes.
• Stats API for custom reporting: Async report generation (POST to create, GET to retrieve CSV). Two export types: Stats (aggregated data like call counts by status) and Records (per-call detail). Enables custom forecasting dashboards by feeding call activity data into BI tools.
• AI Scorecard: Automated call scoring can surface quality trends that correlate with pipeline outcomes. Not a forecasting tool itself, but provides signal for forecast accuracy.
• Limitations: Dialpad does NOT have a dedicated forecasting module. No pipeline analytics, deal health scoring, AI forecast predictions, or CRM-native forecast roll-ups. Analytics are limited to call activity metrics (volume, duration, outcomes), not revenue metrics (pipeline, stage progression, win rates). For revenue forecasting, use Clari, Gong Forecast, Weflow, or Salesforce native forecasting. Dialpad's analytics complement these tools by providing the call activity data layer.
• Best for: Teams using Dialpad as their phone system who want to feed call activity data into a separate forecasting tool. Not a standalone forecasting solution.

In Veloxy

• Predictive Sales Intelligence: AI analyzes and prioritizes leads and contacts based on buyer intent signals. Surfaces real-time actionable signals across desktop and mobile — helps reps focus on highest-probability prospects rather than working through lists sequentially.
• Pipeline management: Dashboard views show pipeline health, deal progression, and activity metrics. Managers can track team performance and identify gaps.
• Activity-backed data: Veloxy automatically logs emails, calls, and activities to Salesforce, reducing the gap between actual rep activity and CRM data. More accurate CRM data improves forecast inputs.
• Geolocation for pipeline visibility: Thematic mapping visualizes opportunities by deal size, stage, or custom criteria on a map — useful for territory-based pipeline reviews and identifying geographic concentration risk.
• Limitations: Veloxy does NOT have a dedicated forecasting module. No weighted pipeline, AI-predicted revenue, forecast submission workflows, or rollup views. No conversation intelligence to analyze deal sentiment from calls. Predictive intelligence prioritizes leads for reps — it doesn't predict revenue outcomes for managers. For dedicated forecasting, pair with Salesforce native forecasting or Clari.
• Best for: Field sales teams whose forecast inaccuracy traces back to missing CRM data because reps don't log activities. Veloxy fixes the data input problem through automatic activity capture and email sync. Once CRM data is cleaner, forecasts built on that data become more reliable. Not a standalone forecasting tool — it's a forecast-enabler through better data hygiene.

In MaxIQ (ForecastIQ + EchoIQ)

• ForecastIQ module: Signal-based forecasting — predicts revenue from conversation signals and deal data rather than stage-based CRM assumptions. AI-driven predictions adjust in real-time as new calls happen and deal signals change.
• AI Forecaster agent: Adjusts win likelihood in real-time based on call signals. Every conversation updates deal momentum, forecast accuracy, expansion potential, and renewal health automatically.
• AI Revenue Planner agent: Aggregates deal signals across the pipeline into defendable forecasts. Surfaces which deals are driving the commit number and which are at risk of slipping.
• AI Watchdog agent: Monitors pipeline for stalls, slippage, and single-threading risks — flags deals that should be downgraded in the forecast before they miss.
• InspectIQ pipeline visibility: Real-time pipeline tracking with deal scoring and gap/risk identification. Change detection alerts when deals move or stall.
• CRM sync for forecast accuracy: Bi-directional Salesforce sync means deal data in CRM reflects actual conversation content, not stale rep estimates. Auto-populates MEDDIC fields and next steps from calls.
• Limitations: New platform (34 G2 reviews) — limited independent validation of forecast accuracy. No public API to export forecast data. Some features in active development. HubSpot forecast integration feature parity with Salesforce not confirmed. No standalone forecast submission workflow like Clari — forecast governance may need to live in CRM.
• Best for: Teams that want conversation intelligence and forecasting in one platform instead of stacking Gong + Clari. Usage-based pricing is attractive for organizations where not all reps are forecast-relevant. Startup Program (free 1 year) allows low-risk evaluation. Strongest when Salesforce is the CRM. Pair with CRM native forecasting for formal submit/override workflows.

In Rafiki

• Deal Intelligence dashboard: Revenue Agent surfaces at-risk deals and tracks buying signals from conversation data. Pipeline reports show which deals are advancing and which are stalling based on conversation patterns, not just CRM stage updates.
• Gen AI Reports: One-click revenue insights from complex sales data. Ask anything about deals, pipeline, or team performance using natural language. Surfaces trends that would take hours of manual analysis.
• Conversation-backed pipeline visibility: Deal health scoring based on actual call content — competitor mentions, objection frequency, stakeholder engagement — rather than rep-entered CRM data. More accurate signal for forecast accuracy.
• CRM auto-sync of deal signals: Methodology scores (MEDDIC/BANT/SPIN/GAP) auto-captured from calls and synced to CRM opportunity fields (Premium). Managers can validate forecast categories against actual conversation evidence.
• Limitations: Rafiki does NOT have a dedicated forecast submission workflow (like Clari). No weighted pipeline module — forecast roll-ups must live in CRM or a dedicated tool. Deal intelligence is conversational (based on what was discussed) not activity-based (email/meeting frequency). Gen AI Reports use add-on credits on Premium. No API access below Enterprise — can't pipe deal intelligence to BI tools. For forecast governance, use Clari or Salesforce native forecasting. For activity-backed pipeline data, use People.ai.
• Best for: SMB/mid-market teams ($19-49/user/mo) whose forecast inaccuracy traces back to reps not accurately reflecting deal reality in CRM. Rafiki's conversation-backed deal signals give managers a second source of truth beyond rep self-reports. Common stack: Rafiki (conversation signals) + CRM native forecasting (roll-ups). Not a standalone forecasting tool — it's a forecast accuracy improver through conversation intelligence.

In Swan

• Penguini agent (pipeline health): Monitors for stale deals, flags missing next steps, identifies at-risk opportunities, and suggests recovery actions. AI-driven analysis of deal health based on CRM data and engagement patterns.
• Autonomous alerts: Swan notifies via Slack when deals stall, stages regress, or close dates slip. No manual pipeline review needed — the agent monitors continuously.
• CRM-connected: Works with HubSpot (primary) and Salesforce to read deal data and update records.
• Limitations: Swan does NOT have dedicated forecasting workflows (commit numbers, forecast submissions, weighted pipeline). It's a pipeline health monitor, not a forecast governance tool. For formal forecasting, pair with Clari or CRM native forecasting.
• Best for: Small teams wanting autonomous pipeline monitoring and deal alerts without a dedicated RevOps analyst. Not a standalone forecasting tool — it's a pipeline health companion.
• Platform skill: /sales-swan