Choosing the Right Sales Forecasting Service for Sustainable Revenue Growth

In the early 20th century, statisticians began treating economic activity as non-random, embedded with patterns that experts can predict.

Skipping forward to today, and those patterns apply to more economic activities — including sales. Forecasting solutions have become a critical component of revenue operations (RevOps).

Here’s a guide to what sales forecasting services offer, plus how 10 market-leading solutions compare to each other.

What Sales Forecasting Services Actually Do (and Why They Matter)

Sales forecasting is the process of predicting future sales or revenue over a defined period. Enterprises that manage complex sales pipeline activity might conduct forecasting monthly, weekly, or even daily, depending on how much they need to forecast and how. Organizations with more stable sales cycles typically conduct forecasts quarterly.

Sales forecasting services operationalize this function. They typically follow this four-step workflow:

1. Data collection

2. Data analysis

3. Model selection

4. Forecast generation

This workflow helps revenue operations professionals make data-driven, informed decisions, which materially impact revenue potential.

Top 10 Sales Forecasting Services to Consider

The solutions below are some of the best forecasting tools on the market. Here’s what they offer.

1. Rox

Rox is an AI-first revenue operating system. It lets sales teams automate core sales processes with ease, including offering advanced functionalities that support forecasting. As Snorkel’s Senior GTM Manager, Syed Jafri, put it: “If you are looking to consolidate tools and become an AI-first company, you need to implement Rox.”

Rox provides always-on AI agents that:

• Unify disparate revenue data into a centralized knowledge graph

• Identify market risk and opportunity signals

• Provide sales reps with next-best-action recommendations based on real-time intelligence

• Apply engagement-prediction modelling, including sequence optimization and response likelihood

Watch a demo of Rox today.

1. Clari

Clari is an end-to-end RevOps platform that deploys advanced AI models across sales processes, including sales forecasting — enabling teams to forecast with 95%-plus accuracy. In fact, Clari users report a 90% reduction in time spent on forecasting-related activities.

Clari offers custom quotes based on specific use cases. Its RevAI tier includes AI deal scoring, forecasting, and workflow enhancers. Users that opt for this tier gain:

• Unified, AI-driven forecasting across all revenue streams

• Automated roll-ups that aggregate forecast data across teams and time periods

• Continuous recalibration of forecasts using live sales pipeline and activity signals

1. Anaplan

Anaplan is a leading sales planning software. Its forecasting engine, Anaplan PlanIQ, uses enterprise-grade technology to simplify forecasting workflows. It lacks transparent pricing information, but verified users report tiers starting at $20,000 annually.

Anaplan PlanIQ lets sales teams:

• Select from leading ML algorithms (like DeepAR+ and Prophet)

• Access built-in explainability, allowing users to access — and place greater trust in — model outputs

• Seamlessly interoperate with core business systems, connecting forecasts to plans

1. HubSpot Sales Hub

HubSpot Sales Hub is a revenue operations platform within the broader HubSpot ecosystem (the Marketing Hub). Individual users can access HubSpot’s Marketing Hub for free, while SMB-based plans start at approximately $800 USD monthly.

With HubSpot Sales Hub, users can:

• Group deals by configurable forecast categories or deal stages

• Perform hierarchical forecast roll-ups.

• Produce AI projections (using “Breeze”) that estimate ranges from recent closed-won data

1. Outreach

Outreach’s forecasting capabilities are a core pillar of its broader sales engagement suite, which spans pipeline management, sales engagement, mutual action planning, and conversion intelligence. Forecasting is a natural extension of its end-to-end revenue execution framework.

Outreach structures its pricing around specific use cases. Buyers interested in forecasting can obtain tailored pricing aligned with this particular functionality, where they can:

• Automate forecast roll-ups, allowing teams to regain 44% of their preparation time.

• Access scenario-modelling for bull, base, and bear outcomes

• Match forecasting with customizable logic

1. Gong Forecast

Gong is a leading revenue operations system with five primary engines: Gong Engage, Agents, Revenue Graph, Collective, and Forecast. Gong offers custom pricing, which includes a platform fee, per-user license, and professional services costs.

Users gain access to Gong’s:

• AI Deal Predictor, which analyzes 300+ signals to forecast deal outcomes accurately

• Forecast Analytics, which provides real-time visibility across forecasting activities

• AI Revenue Predictor, which triangulates team-submitted forecasts against AI predictions

1. Pipedrive

Pipedrive is a customer relationship management (CRM) provider that offers sales forecasting as a built-in feature. Its CRM-based forecasting tools have an expansive user base, spanning startups to established enterprises across 179 countries. To access these tools, Pipedrive’s tiers start at $39 per month and increase to $79.

Here’s what these tiers include:

• AI-driven forecasting based on real-time deal progression

• Customizable forecast views across pipelines and timeframes

• Historical trend analysis to inform sales planning

1. Aviso AI

Aviso is an end-to-end revenue operations platform, offering advanced agentic AI capabilities primarily to GTM teams. Aviso doesn’t publicly disclose its pricing, although some users report accessing its platform for $50,000 annually.

While Aviso offers broad functionality, its core forecasting mechanisms include:

• Predicting end-of-quarter outcomes with a time-series AI engine

• Executing complex roll-up forecasts, independent of CRM hierarchy

• Supporting multi-model forecasting, including splits, overlays, pivots, and consumption-based models

1. Revenue Grid

Revenue Grid is a complete solution that helps organizations scale revenue operations. It seamlessly interoperates, requires minimal technical configuration, and offers advanced forecasting capabilities.

Revenue Grid’s forecasting functions are part of its “Revenue Grid Ultimate” tier, which users can access for $149 per user, per month. With this subscription, users can:

• Implement standardized forecasting cadences (weekly, monthly, or quarterly) across hierarchical roles, from executives to field sales reps

• Submit, review, and adjust individual and aggregated forecasts, within one easy-to-use platform

• Segment forecasts across revenue segments, like regional markets and product lines

1. Salesloft

Salesloft facilitates AI-driven sales execution across the revenue lifecycle. Its forecasting capabilities operate as a part of a broader revenue operations suite, supported by advanced agentic AI. Salesloft follows a custom pricing model, with forecasting offered as an optional add-on feature.

Salesloft lets you:

• Create customizable forecasts, filtered by segment, region, account size, and period

• Consolidate individual and team forecasts into visual roll-ups

• Apply AI modelling to continuously monitor forecast accuracy over time

How To Evaluate and Choose a Sales Forecasting Service

Before choosing one solution over another, consider these factors.

1. Systems Interoperability

A forecasting platform should integrate seamlessly with your existing systems for unified data flow across all relevant sources. This means your team works from a single source of truth, strengthening cross-team alignment and consistency in sales projections.

1. Predictive Model Fidelity

A model’s fidelity refers to its accuracy and reliability. Make sure the platform you choose produces accurate, autonomous sales predictions with reliable datasets. Vendors should disclose performance indicators — like mean absolute percentage error (MAPE) — to validate their model’s historical accuracy.

1. Model Interpretability

Users should understand why a forecasting model produces a particular outcome. This traceability fosters trust with stakeholders while facilitating the early identification of systemic faults within the model’s predictive framework.

1. Scalability

Forecasting platforms should scale in line with business growth. As operations expand, the platform should efficiently process higher data throughput, larger user bases, and increasingly sophisticated forecasting logic — without latency or computational degradation. This ensures long-term operational continuity.

Bridge Forecast Accuracy and Sales Productivity With Rox

Rox is for modern sales teams, offering best-in-class agentic AI. Rox’s swarms continuously gather competitor and market intelligence, proactively identify risks and opportunities, and present human sales reps with data-driven guidance.

These capabilities helped Tabs save seven hours per day on research. “I would think of [Rox] as your strategic commercial coworker,” Tabs’ Founder and CEO, Ali Hussain, wrote.

See for yourself. Watch a demo to learn more.

Frequently Asked Questions

What Is Forecasting?

In revenue operations, forecasting refers to the use of data — or, when data is limited, professional judgement — to predict future financial performance outcomes. To generate reliable forecasts at scale, teams use purpose-built SaaS sales forecasting platforms.

How Does AI in Sales Forecasting Improve Accuracy?

Compared to traditional forecasting processes, AI analyzes large-scale and complex datasets quickly and accurately. Most AI models employ closed-loop learning — a feedback mechanism that enables AI to “learn” by evaluating model outputs against realized results. It then uses those variances to iteratively refine predictions.

What Are the Main Types of Sales Forecasting Methods?

There are two primary types of forecasting methods: quantitative and qualitative. Quantitative forecasting methods are objective and data-based, using historical data to predict performance outcomes. Qualitative methods rely on subjective inputs, typically from domain experts. Teams apply qualitative methods when data is limited or lacks relevance.

How Often Should You Update Sales Forecasts?

Businesses often update sales forecasts on a monthly or quarterly cadence. But the best frequency depends on various factors, including market volatility, sales cycle complexity, and data availability. A business may adjust its forecasting cadence based on its environment.