Top 14 Predictive Analytics Tools for GTM Teams (2026)

The Forecast Accuracy Gap Is Costing Revenue Teams Millions

Only 7% of sales organizations achieve forecast accuracy of 90% or higher, according to recent Gartner research. Meanwhile, B2B teams using machine-learning-powered forecasting hit 88% accuracy compared to 64% with traditional spreadsheet methods. That 24-point gap translates directly into missed quotas, misallocated headcount, and board-level credibility problems.

The market has noticed. In 2025, Gartner published its first-ever Magic Quadrant for Revenue Action Orchestration, formally recognizing a category that barely existed three years ago. Gong, Clari, and Outreach were named Leaders. Forrester followed with its Wave for Revenue Orchestration Platforms in Q3 2024, validating the same cluster of vendors. The revenue intelligence market is projected to exceed $10 billion by 2034, growing at roughly 15% CAGR.

But "predictive analytics for GTM" now encompasses everything from conversation intelligence to third-party intent data to full revenue orchestration. Choosing the right tool means understanding what you actually need predicted and what data you have to feed the model.

This guide reviews 14 tools that GTM teams use for predictive analytics, organized by primary use case, with real pricing data and differentiation points so you can narrow the field before scheduling a single demo.

How We Evaluated These Tools

We assessed each AI-powered sales platform across five dimensions that matter most to GTM leaders:

• Prediction accuracy and methodology: What data does the model ingest? Conversation signals, CRM history, third-party intent, activity capture, or a blend?
• Integration depth: Does it plug into Salesforce, HubSpot, Outreach, and Salesloft natively, or does it require middleware and custom connectors?
• Time to value: Can a RevOps team get usable predictions within weeks, or does implementation take 6+ months?
• Pricing transparency: Is pricing published, or does every conversation start with "let me get you a custom quote"? We flagged hidden costs where we found them.
• Analyst recognition: Where applicable, we noted placements in the 2025 Gartner MQ for Revenue Action Orchestration and the Forrester Wave for Revenue Orchestration Platforms (Q3 2024).

Category 1: Revenue Forecasting and Pipeline Intelligence

These tools focus on predicting deal outcomes, identifying pipeline risk, and generating revenue forecasts. They form the core predictive layer for most GTM teams.

1. Clari

Clari is one of three Leaders in the 2025 Gartner Magic Quadrant for Revenue Action Orchestration, and also a Leader in the Forrester Wave (Q3 2024). Its RevAI engine analyzes CRM data, email activity, calendar events, and engagement patterns to predict which deals will close and when. The "Revenue Cadence" methodology adds structured forecast review processes for leadership.

Best for: Enterprise sales orgs (100+ reps) that need board-level forecast accuracy and structured pipeline review.

Pricing: Revenue Forecasting starts around $820/user/year for essentials, scaling to $2,100+/user/year for advanced features. Clari Copilot (conversation intelligence, formerly Wingman) adds $100/user/month. Implementation runs $15K-$75K. According to a 2026 analysis, total cost of ownership with required modules and professional services reaches $200-$310/user/month. Volume discounts begin meaningfully at 75+ users.

Key differentiator: Clari ingests activity data that never makes it into CRM fields. Given that up to 79% of deal-related data never gets logged by reps (per ESNA research), this passive data capture is where most of the predictive accuracy comes from. The downside: Clari's value scales with the volume of data flowing through your CRM and email, which means smaller teams see less model accuracy.

2. Gong

Gong placed highest on both axes in the 2025 Gartner Magic Quadrant for Revenue Action Orchestration, and ranked first across all four evaluated use cases in the accompanying Critical Capabilities report. Its predictive engine analyzes 300+ signals from customer conversations to forecast deal outcomes with 20% greater accuracy than CRM-based methods alone.

Best for: Teams that run a high volume of calls and demos, where conversation data is the richest buyer signal data source.

Pricing: In March 2025, Gong shifted from bundled to modular pricing. The Foundations plan starts at $1,600/user/year with a platform fee that scales from $5,000/year (10-20 users) to $50,000/year (enterprise). Add-ons include Gong Forecast ($700/user/year) and Gong Engage ($800/user/year). Onboarding runs around $7,500. For a 50-user team buying Foundations + Forecast, expect roughly $122K-$165K in year one.

Key differentiator: Depth of conversation analysis is unmatched. Competitor mentions, sentiment shifts, multi-threading patterns, and stakeholder engagement all feed the prediction model. Gong now serves 5,000+ companies and has positioned itself as a "Revenue AI Operating System" rather than just conversation intelligence. The risk is pricing complexity: the modular structure makes total cost hard to predict until you scope your exact needs.

3. Aviso

Aviso positions itself as an "End-to-End AI Revenue Platform" combining forecasting, conversational intelligence, deal guidance, and rep coaching in a single product. Its WinScore AI model blends CRM data, conversation signals, and engagement patterns to generate deal-outcome predictions and pipeline health scores.

Best for: Mid-market to enterprise teams looking for an all-in-one revenue operations platform that covers forecasting, coaching, and pipeline management without buying three separate tools.

Pricing: Approximately $165/user/month for full platform access, with implementation costs additional. Implementation typically requires 8-12 weeks including data integration and model training. More affordable than Gong or Clari for teams that want broad coverage without add-on complexity.

Key differentiator: Aviso was named a Strong Performer in the Forrester Wave for Revenue Operations and Intelligence. The platform claims 98% forecast accuracy on its marketing site, which is aggressive but reflects the breadth of signals (CRM, conversation, engagement, activity) feeding its models. The trade-off is lower brand recognition than Gong or Clari, which can matter when selling the purchase internally.

4. Terret (formerly BoostUp)

Terret (rebranded from BoostUp in 2025) focuses on RevOps teams with its AI Revenue Agent platform. It combines deal intelligence, forecasting, and revenue analytics to help RevOps leaders manage pipeline predictably. The platform emphasizes inspector-level deal analytics that surface risk signals before they become surprises in the forecast call.

Best for: RevOps-led organizations that want deep pipeline inspection beyond top-line forecasting numbers.

Pricing: Custom quote. Previously positioned as a more affordable alternative to Clari and Gong for mid-market teams. One source indicates starting from $79/month with a subscription model, though enterprise pricing scales significantly higher.

Key differentiator: Terret's strength is deal-level risk identification. Rather than just assigning a probability score, it flags specific reasons a deal might slip: lack of multi-threading, missing economic buyer, stalled next steps, or insufficient champion engagement. For RevOps teams tired of forecasting tools that just give a number without explaining why, this is compelling.

Category 2: Intent Data and Account Scoring

These platforms predict who is likely to buy by analyzing third-party intent signals, website behavior, and firmographic data. They sit earlier in the funnel than forecasting tools, helping marketing and SDR teams prioritize accounts.

5. 6sense

6sense is the most recognized name in predictive account identification. Its Revenue AI engine combines intent data from its publisher network, website de-anonymization, CRM data, and firmographic signals to predict which accounts are actively researching solutions. Accounts get assigned to buying stages (awareness through decision) and scored on predicted fit and timing.

Best for: ABM-focused marketing and sales teams that need to identify in-market accounts before they fill out a form.

Pricing: Custom quotes only, with credit-based access to features. According to Vendr benchmarking data, the median buyer pays $55K/year, with costs ranging from $35K to $130K+ depending on account volume and modules. Multi-year commitments unlock 15-37% discounts; end-of-quarter timing is a proven negotiation lever.

Key differentiator: 6sense's "dark funnel" insights reveal research activity that happens before prospects visit your website. G2 reviewers consistently cite this as the most valuable capability, though they also note the predictive model can be generous in scoring, meaning you still need human judgment to filter signal from noise.

6. Demandbase

Demandbase is 6sense's primary competitor in the ABM predictive analytics space. Its Demandbase One platform uses machine learning models to score accounts on company fit, intent actions, journey stage, and engagement across web, email, CRM, and marketing automation. The models are designed to work without manual configuration.

Best for: Enterprise ABM teams with ACV above $50K who target complex buying groups and want strong advertising capabilities alongside intent data.

Pricing: Custom enterprise pricing with a "platform fee + per user" model. A 2025 analysis estimates $18K-$32K/year for smaller teams (~200 employees), with a median annual cost around $65K. Enterprise deployments can reach $300K+ when you add advertising credits, AI orchestration, and onboarding services (which alone can run ~$29K).

Key differentiator: Demandbase's advertising capabilities are materially stronger than 6sense's. It can serve targeted ads to in-market accounts based on predictive scores, making it better suited for marketing-led ABM motions. The platform also predictively surfaces ICPs, flags deal-risk accounts, and identifies disjointed buying committees that could stall deals.

7. ZoomInfo

ZoomInfo is primarily a data platform, but its predictive capabilities have expanded substantially following the $575 million Chorus.ai acquisition. ZoomInfo now processes over 1.5 billion data points daily, including 58 million intent signals per week. The Guided Intent feature prioritizes trending topics proven to drive successful outcomes for your specific business.

Best for: Teams that need a combined data + intent + conversation intelligence stack without buying three separate tools.

Pricing: The Professional plan starts at $14,995/year (5,000 annual credits), Advanced at $24,995/year (10,000 credits), and Elite at $39,995+/year. Each additional user adds $1,500-$2,500 depending on tier. Intent signals (Streaming Intent) and data enrichment are add-ons that push total costs past $40K-$55K for most teams. Be aware: renewal prices often increase 10-20%.

Key differentiator: ZoomInfo's scale of data collection is unmatched. The integration of Chorus conversation intelligence with ZoomInfo's B2B contact database created 33% higher match rates on contact records, bridging the gap between "who to target" and "what to say." For teams already paying for ZoomInfo data, adding intent is a natural (if expensive) expansion rather than a separate vendor decision.

Category 3: CRM-Native Predictive Analytics

These tools add prediction capabilities directly within your existing CRM, avoiding the friction of adopting an entirely new platform.

8. Salesforce Einstein + Agentforce

Salesforce Einstein is the AI layer embedded across the Salesforce platform. Einstein Lead Scoring ranks prospects based on historical conversion patterns. Einstein Opportunity Insights predicts deal outcomes using won/lost patterns, engagement trends, and competitor mentions. And as of mid-2025, Salesforce has rolled these capabilities into its broader Agentforce platform, which includes autonomous AI agents for sales workflows.

Best for: Salesforce-native organizations that want predictive analytics without introducing another vendor, and those investing in the Agentforce ecosystem.

Pricing: The pricing tiers stack up quickly. Sales Cloud Einstein is $50/user/month; Revenue Intelligence (advanced pipeline analytics and deal-level predictions) is $220/user/month; Einstein Conversation Insights adds $50/user/month; and Agentforce starts at $125/user/month. A fully loaded enterprise deployment can reach $560/user/month before implementation costs ($50K-$500K+). Salesforce increased list prices by an average of 6% in August 2025.

Key differentiator: Zero integration overhead for existing Salesforce customers. Predictions live where reps already work, which dramatically improves adoption. Revenue Intelligence adds Tableau-powered dashboards and whitespace analysis for managers. The downside: Einstein's models are limited to data that actually exists in Salesforce, and reps log only about 21% of deal-related data. If your CRM hygiene is poor, Einstein's predictions will reflect that.

9. HubSpot Predictive Lead Scoring

HubSpot's predictive lead scoring uses AI to surface the most sales-ready contacts automatically, analyzing email engagement, website behavior, and demographic information. It combines manual and AI scoring in a single interface, available exclusively in the Enterprise tier.

Best for: SMB and mid-market teams already on HubSpot that want basic predictive lead scoring without adding a separate platform.

Pricing: Sales Hub Enterprise starts at $150/seat/month with a 10-seat minimum ($1,500/month base) plus a $3,500 onboarding fee. Predictive lead scoring is bundled into Enterprise and is not available at Professional or lower tiers.

Key differentiator: The lowest barrier to entry for CRM-native predictions. HubSpot's models are less sophisticated than standalone tools, but for teams under 50 reps who primarily need to prioritize inbound leads, the simplicity is a genuine advantage. You avoid the integration tax, the separate vendor contract, and the adoption challenge of switching between platforms.

Category 4: Revenue Action Orchestration

These platforms go beyond prediction to connect insights with execution, combining forecasting, engagement, and workflow orchestration. This is the category Gartner formalized with its 2025 Magic Quadrant.

10. Outreach

Outreach was named a Leader in the 2025 Gartner Magic Quadrant for Revenue Action Orchestration alongside Gong and Clari. Originally a sales engagement platform, Outreach has added predictive deal scoring, pipeline analytics, and AI-driven forecasting to its core sequencing and cadence capabilities. Its Smart Account Assist and Deal Health scoring use engagement signals, activity patterns, and conversation data to predict outcomes.

Best for: Teams that want to combine sales engagement (sequences, cadences, A/B testing) with predictive pipeline intelligence in a single platform.

Pricing: Custom quotes, but positioned competitively against Salesloft. Outreach does not publish pricing. Expect enterprise-tier investment for the full platform including forecasting and intelligence features.

Key differentiator: Outreach's advantage is the tight loop between prediction and action. When the platform identifies a deal at risk, it can trigger specific sequences, suggest next steps, or route the deal to a manager, all within the same interface the rep already uses for day-to-day outreach. The 2025 Gartner Leader placement validates this converged approach.

11. Salesloft

Salesloft earned the highest possible score in 14 criteria in the Forrester Wave for Revenue Orchestration Platforms (Q3 2024), including coaching, prospecting workflow, and third-party signal integration. Its Rhythm AI layer analyzes buyer signals and uses AI agents (including a Stalled Deals Agent and Analytics Interpreter Agent) to prioritize the highest-impact seller actions.

Best for: Teams already using Salesloft for sales engagement who want predictive deal intelligence embedded in their daily workflow.

Pricing: Two tiers: Advanced (sales engagement, deal tracking, AI insights) and Premier (adds revenue forecasting). Pricing is custom and not publicly listed. Rhythm is included in higher-tier plans, not available as a standalone product.

Key differentiator: Rhythm's predictions are action-oriented. Instead of just scoring deals, it tells reps specifically what to do next and why. The Stalled Deals Agent flags opportunities stuck in a stage too long, while the Analytics Interpreter surfaces trends that would otherwise require a RevOps analyst to find. The integration with Salesloft's cadence engine means predicted actions can be executed immediately.

12. People.ai

People.ai was recognized in the 2025 Gartner Magic Quadrant for Revenue Action Orchestration. It automatically captures sales activity from emails, calendars, dialers, and CRM systems, then uses AI-based matching algorithms to create accurate contact records and engagement histories. Its revenue intelligence layer identifies buying groups, tracks sentiment, and provides guided selling recommendations.

Best for: Enterprise teams focused on activity intelligence, particularly those struggling with poor CRM data hygiene who need to solve the data quality problem before layering on forecasting.

Pricing: Custom enterprise pricing via Individual and Enterprise plans. The company has raised $197M in funding and positions itself as an enterprise platform, so expect costs comparable to Gong or Clari for large deployments.

Key differentiator: People.ai solves the root cause of bad predictions: bad data. Rather than relying on reps to log activities, it passively records every email, meeting, and call, then matches that activity to the right accounts and opportunities using patented AI. For organizations where CRM data quality is the bottleneck, People.ai can make every other predictive tool more accurate.

Category 5: GTM Analytics and Attribution

These tools predict GTM performance more broadly, covering marketing attribution, pipeline contribution, and account engagement across channels.

13. HockeyStack

HockeyStack unifies marketing, sales, and product data to provide multi-touch attribution, account scoring, and pipeline forecasting. Its AI agents (Odin and Nova) analyze touchpoint data and surface next-best-actions for both marketing and sales teams. The platform works without cookies by default.

Best for: Marketing and RevOps teams that need to connect marketing spend to pipeline creation and predict which channels will drive revenue.

Pricing: HockeyStack has moved to a usage-based, custom-quoted model that scales with traffic volume and feature requirements. The core Platform plan starts around $2,200/month, with a median annual cost of $28K according to Vendr data. Startup pricing is available, and multi-year commitments unlock 20-50% discounts. This is a significant change from earlier published pricing tiers.

Key differentiator: HockeyStack bridges marketing and sales analytics in a single platform. Its lift modeling and multi-touch attribution show which campaigns and content drive pipeline, while account scoring predicts which accounts are most likely to convert. For GTM teams frustrated by the disconnect between marketing attribution and sales forecasting, HockeyStack eliminates the gap.

14. Mediafly Intelligence360 (formerly InsightSquared)

Mediafly Intelligence360, formerly InsightSquared (acquired by Mediafly in 2022), provides sales analytics, pipeline management, and forecasting intelligence as part of Mediafly's broader revenue enablement suite. It creates ML-driven pipeline forecasts and identifies patterns in historical data that correlate with won and lost deals.

Best for: RevOps teams that want deep pipeline analytics and historical pattern analysis combined with sales enablement (content management, value selling) in a single vendor.

Pricing: Custom quote through Mediafly. Generally positioned as a mid-market option, more accessible than Gong or Clari but with less standalone depth on the forecasting side.

Key differentiator: Intelligence360's pipeline analytics go deep on historical patterns, surfacing insights like "deals without activity for 30+ days in this segment are 80% less likely to close" and flagging phantom opportunities that inflate pipeline reports. The combination with Mediafly's enablement platform (content management, interactive presentations, value calculators) gives revenue teams both the intelligence and the tools to act on it.

A Decision Framework for Choosing the Right Tool

Fourteen tools is a lot to evaluate. Here is how to cut the list in half based on your team's situation.

Start with Your Primary Problem

• "Our forecasts are wrong." Start with Clari, Gong, or Aviso. Clari is strongest on pure forecasting methodology and process; Gong is strongest when conversation data is your richest signal; Aviso covers the most ground at a lower per-user price point.
• "We do not know which accounts to prioritize." 6sense or Demandbase for ABM-scale intent data. 6sense has broader intent coverage; Demandbase adds stronger advertising capabilities. For a more targeted ICP-scoring approach at lower cost, consider Keyplay ($12K/year for 50K tracked accounts) as a lighter-weight alternative.
• "Our CRM data is unreliable." People.ai for automatic activity capture first, then layer forecasting tools on top once the data foundation is solid. ZoomInfo if you also need contact enrichment + intent signals.
• "We want predictions inside our existing CRM." Salesforce Einstein or HubSpot Predictive Lead Scoring, depending on your platform. Fastest time to value, lowest vendor management overhead.
• "We need to connect engagement to prediction." Outreach or Salesloft, both of which now combine sales engagement with predictive intelligence in a single platform.
• "Marketing cannot prove pipeline contribution." HockeyStack for attribution + account scoring + forecasting in one platform.

Match to Team Size and Budget

• Under 20 reps, under $20K/year: HubSpot Predictive Lead Scoring (if on Enterprise), Salesforce Einstein Lead Scoring ($50/user/month), or Keyplay for ICP-based account scoring ($12K/year).
• 20-100 reps, $20K-$100K/year: Clari, Aviso, Terret, ZoomInfo with intent add-ons, or HockeyStack. This is the sweet spot where mid-market-friendly tools deliver real ROI without enterprise pricing.
• 100+ reps, $100K+/year: Gong, 6sense, Demandbase, Outreach, or Salesforce Revenue Intelligence. At this scale, even a 5% improvement in forecast accuracy can represent millions in revenue predictability. The 24-point accuracy gap between ML-powered and traditional forecasting justifies the investment.

Assess Your Data Readiness First

This is the step most teams skip, and it is the most important one. The accuracy of any predictive tool depends on the quality of data feeding it.

• Clean CRM with strong rep adoption (80%+ activity logging): You are ready for Clari, Gong, Aviso, or Outreach. Jump straight to the prediction layer.
• Inconsistent CRM with patchy logging: Start with People.ai or ZoomInfo to fix the data foundation. Simultaneously, consider tools like Autobound that surface signal data (job changes, funding events, hiring patterns, tech stack changes) to enrich thin CRM records without requiring reps to do manual research. Layer forecasting tools on top after 3-6 months of improved data quality.
• Minimal data history (new CRM, early-stage team): Start with HubSpot or Salesforce native tools, build 6-12 months of clean data, then evaluate standalone platforms. Investing in an enterprise forecasting tool before you have the data to train it is the most common (and most expensive) mistake we see.

Five Pitfalls That Sink Predictive Analytics Investments

Based on patterns across hundreds of GTM teams evaluating these tools:

1. Buying a forecasting tool when your CRM data is broken. If reps log less than 50% of activities, no prediction model can compensate. According to MarketsandMarkets research, organizations using AI in their sales processes achieved 3x higher quota attainment compared to traditional methods, but only when the underlying data was reliable. Fix data hygiene first, then invest in predictions.
2. Conflating intent data with purchase intent. A prospect researching "CRM comparison" could be writing a blog post, not shopping for a new CRM. Layer intent signals with firmographic fit, engagement data, and buying-stage context before routing to sales. This is where platforms like Autobound add value: they contextualize raw signals (job postings, funding rounds, tech stack changes) into personalized outreach that tests whether intent is genuine.
3. Ignoring time-to-value. A tool that takes 6 months to implement and 12 months to train its models is an 18-month bet. If you need predictions within 90 days, prioritize tools with faster onboarding: HockeyStack, HubSpot, or Salesloft Rhythm.
4. Overweighting algorithmic sophistication. The most accurate prediction model in the world is useless if reps do not trust it or cannot act on it. Gong Labs research found that AI is increasingly a trusted decision-maker in revenue teams, but adoption still depends on embedding predictions into the workflow, not presenting them in a separate dashboard.
5. Evaluating tools in isolation. Predictive analytics platforms do not generate pipeline by themselves. They tell you where to focus; you still need strong execution to convert that focus into meetings and revenue. The most effective GTM stacks pair predictive tools with signal-based outbound execution, so the insight becomes an action within minutes, not days.

Where This Market Is Heading

Two structural shifts are reshaping predictive analytics for GTM teams:

Convergence. The 2025 Gartner Magic Quadrant for Revenue Action Orchestration reflects what practitioners already feel: the boundaries between forecasting, engagement, conversation intelligence, and coaching are dissolving. Expect fewer standalone "predictive analytics" point solutions and more platforms that bundle prediction with execution. Gong, Clari, Outreach, and Salesloft are all racing toward the same converged vision.

Agentic AI. Salesforce's Agentforce, Gong's AI agents, and Salesloft's Rhythm agents all point toward a future where predictive models do not just surface recommendations but autonomously execute them. Gartner predicts that by 2028, 60% of B2B sales workflow will be partly or fully automated through AI, up from 5% in 2023. The teams that build clean data foundations and adopt predictive tools now will be best positioned to benefit as autonomous agents become the norm.

Start by defining your primary use case, assessing your data readiness, and matching to your budget. The best predictive analytics tool is the one your team actually uses, connected to data they actually generate, surfacing predictions they can act on in their existing workflow. Everything else is noise.