Best Sales Forecasting Tools for Growth Teams

Best Sales Forecasting Tools for Growth Teams

Updated June 23, 20262,985 words5 tools compared

Accurate sales forecasting separates thriving growth teams from those constantly caught off-guard by revenue shortfalls. When you're scaling from seed to Series B, the difference between predicting 80% pipeline accuracy versus 50% can determine whether you hit your targets or disappoint investors.

The challenge is that most forecasting tools are built for enterprise sales organizations with hundreds of reps and complex deal structures. Growth teams need something different: tools that work with your actual sales process, integrate with your existing stack, and don't require a dedicated revenue operations hire to implement.

This guide reviews nine leading sales forecasting platforms designed for growth teams, comparing their strengths, pricing, and use cases. Whether you're managing deals in spreadsheets today or looking to upgrade from basic CRM forecasting, you'll find actionable insights to make an informed decision.

Quick Comparison

ProductBest ForStarting PriceRatingKey Feature
ClariEnterprise forecastingContact sales4.6/5Revenue Orchestration Platform with AI-driven deal insights
GongRevenue intelligence at scaleContact sales4.7/5Multimodal AI agents processing conversation data
ChorusSales team enablementContact sales4.5/5Conversation intelligence with deal tracking
AvisoMid-market forecastingContact sales4.6/5Prescriptive forecasting with confidence scoring
People.aiActivity-based forecastingContact sales4.5/5Automatic deal scoring from buyer engagement patterns
WeflowSales team productivityCustom pricing4.3/5Workflow automation and pipeline visibility
ScratchpadSales rep efficiencyCustom pricing4.4/5CRM data capture without manual entry
BoostUpTeam collaborationCustom pricing4.2/5Real-time sales activity synchronization
DoolyPipeline accelerationCustom pricing4.3/5Deal tracking and rep activity dashboard

Scroll horizontally to see all columns

Detailed Reviews

In-depth analysis of each platform to help you make the right choice.

#1

Clari

Top Pick

Best For: Enterprise sales organizations and scale-ups with $10M+ ARR managing complex, long-sales-cycle deals

Clari positions itself as the Revenue Orchestration Platform for enterprises that need enterprise-grade forecasting. Built for complex, multi-stakeholder deal environments, Clari combines AI-driven pipeline analysis with revenue context to surface hidden deal risks. It's designed to scale with organizations managing hundreds of millions in ARR and thousands of active opportunities. The platform goes beyond simple forecasting to provide prescriptive guidance on deal progression and risk mitigation.

Pricing: Contact sales for custom pricing. Enterprise-tier only, typically $50K-$150K+ annually depending on organization size and deal complexity

Key Features

  • AI-powered deal risk assessment and opportunity scoring
  • Revenue context combining CRM data, activity signals, and conversation intelligence
  • Real-time pipeline visibility with automatic forecast updates
  • Prescriptive guidance suggesting deal actions to improve close probability
  • Custom revenue models reflecting your specific sales process

Pros

  • +Highest accuracy forecasting available through multimodal data analysis combining deal data, rep activity, and conversation intelligence
  • +Powerful executive dashboard that answers critical questions about pipeline health without manual reporting
  • +Integrates deeply with major CRMs (Salesforce, HubSpot) and supports native workflows rather than adding complexity
  • +Prescriptive AI that suggests specific actions to improve deal progression, not just warning flags

Cons

  • -Enterprise-only pricing makes it inaccessible for Series A and early Series B teams with limited budgets
  • -Requires significant implementation time and RevOps resources to configure custom revenue models and ensure data quality
  • -Learning curve is steep for sales teams accustomed to simpler tools; adoption can take 2-3 months
  • -May be overkill for teams with under 20 sales reps or less than $5M ARR

Verdict

Clari is the clear choice if you're an enterprise organization or late-stage scale-up with the budget and complexity to justify it. The AI-driven insights and forecasting accuracy are genuinely differentiated, and the platform pays for itself through better deal management and reduced forecast error. However, growth teams without significant RevOps support should consider mid-market alternatives first.

#2

Gong

Best For: Sales organizations generating significant call and email data that want conversation-based forecasting and buyer insights

Gong Revenue AI OS has emerged as a dominant player in revenue intelligence by processing conversation data from sales calls, emails, and meetings to derive actionable insights. The platform's strength lies in automatic deal scoring based on actual buyer behavior rather than rep subjective assessments. For growth teams, Gong offers a more accessible entry point than Clari while still providing enterprise-grade forecasting through conversation analysis. The multimodal approach means you get forecasting accuracy improvements simply by connecting your communication tools.

Pricing: Contact sales for custom pricing. Typical enterprise contracts start at $40K annually; some customers report $100K+ annually at scale

Key Features

  • AI-powered conversation intelligence analyzing calls, emails, and meetings automatically
  • Deal scoring based on buyer behavior signals rather than rep opinion
  • Sales coaching and training recommendations derived from successful call patterns
  • Forecast accuracy improvements through conversation trend analysis
  • API access for custom integrations and data extraction

Pros

  • +Automatic conversation processing removes manual data entry burden on sales reps, improving adoption
  • +Deal scoring based on buyer engagement patterns is more objective and predictive than rep sentiment
  • +Conversation intelligence creates secondary value through rep coaching and training insights beyond forecasting
  • +Works with your existing CRM and communication stack without replacing them
  • +Increasing adoption among Series B and C companies, creating strong community and best practices

Cons

  • -Requires robust conversation data (calls recorded, emails synced) to work effectively; limited value if your team rarely records calls
  • -Conversation processing involves sensitivity to customer data privacy; some organizations face data governance concerns
  • -Implementation requires careful integration with call recording systems and CRM; can take 6-8 weeks
  • -Price point approaches Clari for larger teams, limiting appeal for early-stage growth companies

Verdict

Gong is ideal for sales organizations with mature communication practices (regular call recording, email-based workflows) that want to unlock insights from existing data. The conversation intelligence creates value across sales coaching, training, and forecasting simultaneously. Growth teams should evaluate if the implementation effort and cost justify the forecasting improvement over simpler alternatives.

#3

Aviso

Best For: Mid-market and enterprise sales organizations focused on forecast accuracy and wanting prescriptive deal guidance

Aviso focuses specifically on predictive forecasting accuracy through prescriptive guidance and confidence scoring. Unlike platforms emphasizing conversation intelligence or activity tracking, Aviso concentrates on the forecast problem itself: predicting which deals will close and when. The platform's prescriptive engine analyzes your historical close patterns and current pipeline to identify deals at risk and suggest corrective actions. For growth teams that prioritize forecast accuracy above all else, Aviso provides targeted functionality without extra features you may not need.

Pricing: Contact sales; typically $30K-$80K annually depending on organization size, starting around the mid-market segment

Key Features

  • Predictive forecasting engine using historical close rate analysis
  • Opportunity confidence scoring indicating close probability by deal stage
  • Prescriptive recommendations highlighting deals needing attention and suggested next steps
  • Territory and rep performance analytics with trend analysis
  • Executive dashboard with forecast accuracy tracking and variance analysis

Pros

  • +Laser-focused on the forecasting problem; no unnecessary features diluting core functionality
  • +Prescriptive guidance is specific and actionable: 'Move this call to next week' or 'Contact champion at this account'
  • +Confidence scoring provides clarity on forecast reliability, not just point estimates
  • +Faster implementation than enterprise platforms; many teams go live in 4-6 weeks
  • +Mid-market pricing is more approachable than Clari and Gong for Series B teams

Cons

  • -Doesn't include conversation intelligence or activity tracking; requires clean CRM data to function well
  • -Prescriptive recommendations quality depends heavily on accurate opportunity data and sales process definition
  • -Less focus on sales team coaching and development compared to conversation intelligence platforms
  • -Smaller ecosystem and community compared to larger competitors; fewer integrations available

Verdict

Choose Aviso if forecast accuracy is your primary pain point and you're willing to maintain clean CRM data in exchange for focused functionality. The prescriptive guidance and confidence scoring directly address the forecasting problem without requiring you to manage conversation intelligence infrastructure. Particularly strong for Series B teams scaling without major RevOps resources.

#4

People.ai

Best For: Sales organizations with strong email and meeting-based workflows wanting activity-driven deal scoring

People.ai takes an activity-based approach to forecasting by automatically tracking buyer engagement patterns and scoring deals based on actual interaction momentum. Rather than relying on rep assessments or conversations, the platform monitors email opens, meeting attendance, document views, and other engagement signals to infer deal health. For growth teams hesitant about the privacy implications of conversation recording or lacking call data, People.ai provides an alternative forecasting signal based on observable buyer behavior.

Pricing: Contact sales for custom pricing; similar range to mid-market competitors, typically $25K-$70K annually

Key Features

  • Automatic activity tracking across email, meetings, and communication tools
  • AI-driven deal scoring based on buyer engagement velocity and patterns
  • Stakeholder mapping identifying key decision-makers and their engagement level
  • Pipeline trending showing deal momentum changes week-to-week
  • Automatic CRM data enrichment reducing manual rep entry

Pros

  • +Activity tracking is automatic and requires no rep behavior change; adoption is high since data flows without user action
  • +Deal scoring based on engagement momentum is more predictive than rep sentiment for early-stage opportunities
  • +Privacy-friendly compared to conversation intelligence; monitors engagement patterns without recording content
  • +Automatic data enrichment saves reps time and improves data quality without friction
  • +Works well for long-cycle, committee-based deals where stakeholder engagement is the key predictor

Cons

  • -Dependent on email and meeting engagement; limited effectiveness if buyers engage via phone or non-tracked channels
  • -Engagement signals can be noisy (auto-replies, mass emails) requiring filtering and tuning
  • -Doesn't provide coaching insights or conversation intelligence for sales enablement
  • -May require integration with multiple tools (email, calendar, CRM) to capture full activity picture

Verdict

People.ai works well for B2B sales teams using email and meetings as primary communication channels and wanting activity-based forecasting without conversation recording complexity. The engagement momentum scoring is surprisingly predictive for mid-stage pipeline deals. Best suited for Series A and B companies in the $2M-$10M ARR range.

#5

Dooly

Best For: Series A and early Series B companies where reps are reluctant to log CRM data and pipeline visibility is inconsistent

Dooly focuses on sales rep productivity and deal acceleration by reducing friction in daily work. Rather than being a dedicated forecasting platform, Dooly serves as a rep-facing dashboard that surfaces CRM data, automates activity logging, and accelerates deal progression through workflow shortcuts. For growth teams where sales reps spend more time in spreadsheets than the CRM, Dooly improves forecasting indirectly by ensuring deal data stays current and accurate. The platform emphasizes ease of use for reps, addressing adoption challenges that plague many enterprise tools.

Pricing: Custom pricing; typically $10K-$40K annually depending on team size, starting at the lower end for smaller teams

Key Features

  • Rep-friendly dashboard consolidating deal data, tasks, and next steps in one view
  • Automated activity logging from email and calendar reducing manual CRM entries
  • One-click deal progression shortcuts speeding up pipeline management
  • Manager coaching dashboard highlighting reps needing support
  • Mobile app allowing reps to update deals from anywhere

Pros

  • +Dramatically improves CRM data quality by removing friction from rep data entry
  • +Lower price point makes it accessible for seed and Series A companies
  • +High adoption among reps because it simplifies their work rather than adding bureaucracy
  • +Proven to accelerate deal cycles by 15-30% through workflow optimization
  • +Quick implementation; most teams see value within 2-3 weeks

Cons

  • -Provides pipeline visibility and acceleration rather than advanced forecasting; no AI-driven deal scoring
  • -Limited integrations compared to larger platforms; fewer data sources available
  • -Doesn't solve for conversation intelligence or activity-based insights
  • -Forecasting accuracy depends entirely on rep data entry quality; garbage in, garbage out

Verdict

Dooly is the smart choice if your primary challenge is getting accurate pipeline data into the CRM, not sophisticated forecasting algorithms. By improving CRM hygiene and rep adoption, Dooly indirectly improves forecasting accuracy while reducing rep frustration. Ideal for early-stage growth teams where simple, fast implementation matters more than advanced AI features.

Frequently Asked Questions about best sales forecasting tools for growth teams

Sales forecasting tools predict which deals will close and when using historical patterns, current pipeline composition, and buyer signals. Pipeline management tools track deals and ensure accurate CRM data. In practice, the best tools do both: they maintain clean pipeline data while applying forecasting models on top. For growth teams, this distinction matters because your primary need is likely pipeline visibility (getting deals into the CRM correctly) before advanced forecasting. Tools like Dooly excel at pipeline management and accuracy, while Clari and Gong add sophisticated forecasting on top of that foundation. Start with pipeline management if you're still struggling with data entry; upgrade to forecasting-focused tools once your pipeline data is consistently accurate.

Organizations implementing dedicated forecasting tools typically see 15-25 percentage point improvements in forecast accuracy, moving from 60-70% accuracy to 80-90% accuracy. This translates directly to better cash flow planning, more reliable investor communication, and reduced month-end surprises. However, the improvement depends heavily on data quality and sales process maturity. If your reps aren't consistently updating deal stages or accurately assessing close probability, even the best AI won't help. Before investing in expensive forecasting platforms, audit your current forecasting process: what's your actual accuracy today? How much of the error is due to poor data versus process issues? Many early-stage companies can improve forecasting accuracy 10-15 percentage points simply by enforcing consistent deal stage definitions and weekly manager forecast calibration sessions, costing nothing.

For Series A and early Series B companies, your CRM's forecasting is likely sufficient to start. Salesforce and HubSpot forecasting provide basic accuracy improvements through weighted pipeline analysis and historical close rate tracking. The main limitation is they rely entirely on rep forecast accuracy and stage progression, without external signals. Use your CRM's forecasting for 6-12 months while you mature your sales process and data practices. Then evaluate dedicated tools when you reach $2M+ ARR and have consistent month-over-month processes. That said, if your team is already struggling with forecast accuracy despite clean CRM data, moving to a dedicated platform faster may be worth the investment. Services like RevAlign.io can help you evaluate whether a platform upgrade or process improvement should come first.

For seed and early Series A companies, prioritize tools focused on pipeline management and rep adoption over AI forecasting sophistication. Dooly is arguably the best choice because it improves CRM data quality through automation and rep-friendly workflows without requiring significant RevOps infrastructure. Weflow and Scratchpad are also strong alternatives if you need to improve basic sales process consistency. Most AI-driven forecasting tools (Clari, Gong, Aviso) assume you already have solid pipeline data and mature sales processes; they add 10-15% forecasting improvement on top of a good foundation. For early-stage companies, that 10% improvement is less valuable than the 30-40% improvement you'll get from simply getting all deals properly logged and regularly updated. Start simple, evolve to complexity as you scale.

Conversation intelligence adds 5-10 percentage points of forecasting accuracy above standard pipeline-based forecasting, according to independent studies. Gong and Chorus users report that deal scoring based on call sentiment and buyer engagement questions improves early-stage pipeline prediction. However, this requires your team to actually record calls consistently and have mature sales processes where call quality correlates with close probability. For early-stage companies where you're still figuring out your ideal customer profile and sales messaging, conversation intelligence adds complexity without proportional benefit. Focus on conversation intelligence after you've established baseline forecasting accuracy through clean pipeline management. The exception: if your sales process is already call-heavy and you're recording calls for coaching anyway, Gong's conversation processing adds value with minimal implementation overhead.

Conclusion

Selecting the right sales forecasting tool depends on your current stage, budget, and data maturity. Early-stage growth teams (seed to Series A with under $1M ARR) should prioritize pipeline management and rep adoption through tools like Dooly, Weflow, or Scratchpad. These platforms improve forecasting indirectly by ensuring accurate, consistently-updated pipeline data in your CRM. The investment focuses on removing friction from your sales process rather than implementing complex AI.

Series B companies with $2M-$10M ARR and mature sales processes can justify investment in mid-market forecasting platforms like Aviso or People.ai. These provide meaningful forecasting accuracy improvements (15-20 percentage points) through predictive modeling and engagement-based deal scoring without the enterprise implementation burden of Clari or Gong.

Late-stage companies and enterprises managing $10M+ ARR with complex, long-cycle deals should evaluate Clari and Gong. The investment in these platforms becomes justified when you have the RevOps infrastructure to maintain them, the deal complexity to benefit from their features, and the budget to absorb $50K-$150K+ annual costs.

Regardless of which platform you choose, success requires three fundamentals: consistent sales process definition (stages and criteria), accurate rep pipeline updates, and regular forecast calibration reviews with managers. No platform can overcome poor data quality. Before implementing any new tool, audit whether your primary forecasting problem is process-related (undefined stages, inconsistent updating) or data-related (bad data despite good process). That diagnosis will guide you toward the right solution and prevent expensive tool purchases that don't address your actual constraints.

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