Sales forecasting software platforms tend to focus on different areas and have different strengths. Some platforms predict pipeline from CRM signal, others build forecasts from sales conversation data, and still others generate forecasts directly from the opportunity data already in the CRM. A separate category takes the finished forecast and translates it into quota, capacity, and comp decisions.

None of these tools solves the same problem, and shortlisting them against each other without knowing which problem each one solves usually leads to the wrong purchase. This guide breaks the market into seven distinct categories and names the platform that wins each one.

Key Takeaways

• Sales forecasting software splits into seven categories: pipeline forecasting, conversation-based forecasting, AI-driven predictive forecasting, forecast operationalization into quota and comp, CRM-native forecasting, connected enterprise planning, and SMB-to-mid-market CRM forecasting.
• The right tool depends on the forecasting problem the team is solving, not on team size or category prestige.
• Real-time platforms suit teams that re-forecast weekly. Scenario-modeling platforms suit teams that re-forecast on a planned cadence.
• A forecast that doesn't translate into quota, capacity, and compensation decisions tends to live in a silo. Confirm that the forecast updates those systems automatically, not via manual exports.
• HubSpot Sales Hub and Salesforce Sales Cloud publish per-seat rates; Clari, Gong, Aviso, Anaplan, and CaptivateIQ Planning use custom enterprise pricing.

Sales Forecasting Software Comparison

Here is how the seven platforms compare at a glance based on category fit, key capabilities, pricing model, and current G2 rating. 

The 7 Best Sales Forecasting Software Platforms

Each of the seven platforms below solves a different forecasting problem, which is why they're listed by category rather than ranked against each other. A tool built to predict pipeline from call data doesn't compete with a tool built to translate the forecast into comp impact. 

Before choosing your platform, define what problem you’re trying to solve. 

Clari

Best for: Revenue intelligence and pipeline forecasting

Clari is built for revenue teams whose pipeline is too big and too multi-threaded to forecast from the CRM alone. The forecast needs to hold up not just internally but also in board meetings where revenue leadership has to defend the number against scrutiny. The platform ingests data from CRM, ERP, customer success tools, and rep email activity, then converts that combined signal into commit, most-likely, and best-case projections that refresh as the pipeline moves. Clari's own benchmark is a 98% forecast accuracy claim by week two of the quarter. 

Clari rolled out a Model Context Protocol (MCP) Server in April 2026, which lets external AI agents query Clari's revenue data directly. That move tells you category leaders think pipeline forecasting is headed toward becoming the source of truth that other AI tools read from, rather than another closed dashboard. 

Worth noting: Clari is enterprise-only, with no published pricing. The product also assumes the surrounding data stack is mature enough to feed it clean inputs. Teams without that foundation tend to buy more Clari than they can actually operationalize.

Gong

Best for: Forecasting from sales conversation data

Gong made its name in revenue intelligence by recording and analyzing sales calls. Gong Forecast applies that same conversation data to forecasting. A deal where the buyer has stopped engaging on calls gets downgraded automatically, even when the rep's commit is still confident. Conversely, deals showing strong buying signals in conversation can override a hedged forecast call. This gives sales leaders a forecast that's anchored to what's really happening between reps and buyers, rather than what reps are reporting upward.

The best fit here is sales orgs where the call is the center of gravity for the deal, and where rep-reported forecasts have a history of diverging from how quarters actually close.

Worth noting: Gong's forecast is only as strong as its call coverage. Teams with patchy recording adoption or low call volume get a thinner data layer feeding the forecast, which means the model has less to work with than the marketing suggests.

Aviso

Best for: AI-driven predictive forecasting

Aviso runs machine learning (ML) models against CRM data, deal characteristics, rep behavior, and external signals to produce deal-level probability scores that combine into a forecast. The user-facing layer is WinScore, which shows whether a deal is likely to close and the reason why. This way, reps and managers can see why a deal is flagged at risk and override the model's call when they have context it doesn't. 

Like Gong, Aviso includes a conversation intelligence layer, too. The difference is where the weight sits. Gong's forecast leans on call data first; Aviso's leans on the predictive ML layer first, with conversation data as a supporting signal. The best fit for the tool are enterprises that want the AI-driven forecasting layer to do most of the deal-level lift, and reps to defend exceptions, rather than the other way around.

Worth noting: No published pricing, and Aviso's forecast accuracy is only as good as the data hygiene in the systems feeding it. CRM fields that reps update inconsistently, or engagement data that's patchy, both degrade the model's output regardless of how sophisticated the ML layer is.

CaptivateIQ Planning

Best for: Connecting the forecast to quotas, capacity, and comp plans

This is the category most listicles in this space miss. A forecast (whether it comes from Clari, Gong, the CRM, or finance models) has to translate into quota assignments, capacity decisions, and compensation plan adjustments. Otherwise, the operating plan stays stuck on whatever it was set to at the start of the quarter. Reps work against quotas the forecast no longer supports, finance budgets against payout figures that no longer match attainment projections, and the team ends up missing a forecast it was technically equipped to hit.

CaptivateIQ Planning translates forecasts into the operating plan that runs against them. Territories, quotas, capacity models, and compensation plans share the same data layer, which means a forecast change reaches each one without exporting, reconciling, or rebuilding anything by hand. A mid-quarter forecast shift becomes a quota and comp update on the same day, not a multi-week rework across Sales, RevOps, and Finance.

CaptivateIQ Catalyst extends Planning with machine learning for two especially useful jobs: attainment forecasting (predicting whether reps will hit existing quota at current performance) and commission expense forecasting (projected payout cost across roles and plans). Both forecast what happens to comp and capacity given the sales forecast, not the sales forecast itself.

The translation work depends on a mature compensation and quota engine on the receiving end, which is where CaptivateIQ has a deep track record. CaptivateIQ was named a Leader in The Forrester Wave™: SPM Solutions for Incentive Compensation, and holds a 4.7/5 rating on G2 from over 3,000 reviews.

Worth noting: If the primary need is predicting which deals will close, CaptivateIQ Planning isn't the platform for that job. Pair it with a pipeline forecasting tool like Clari, Gong, or Aviso to generate the forecast, then let Planning translate it into quota and comp impact.

Salesforce Sales Cloud Forecasting

Best for: CRM-native forecasting for Salesforce-heavy organizations

Salesforce Sales Cloud (now known as Agentforce Sales)includes a forecasting layer that runs directly against the opportunity data already sitting in the CRM. Reps and managers get pipeline, commit, and best-case views without exporting anything. Sales Cloud's Einstein add-on includes Einstein Forecasting, a machine learning layer that analyzes historical close patterns to predict revenue and flag close-date risk on open opportunities. It’s a good fit for teams already standardized on Salesforce who want their forecast in the same environment as their opportunities, dashboards, and pipeline reports. 

Worth noting: Einstein Forecasting needs at least 12 months of opportunity history and a standard fiscal year to produce useful predictions. Teams new to Salesforce, or teams running custom fiscal calendars, will get less out of the AI layer until enough data accrues to train it.

Anaplan

Best for: Connected enterprise planning across sales, finance, and workforce

Anaplan is a connected planning platform, which means sales forecasting runs inside a much larger modeling environment that also covers FP&A, supply chain, and workforce planning. Sales teams use it to model territory coverage, quota changes, and revenue outcomes against base, upside, and downside scenarios. It’s a strong fit for enterprises where the CRO and CFO need to plan from one shared model, rather than reconciling separate sales, finance, and workforce forecasts after the fact.

Worth noting: Anaplan is a significant investment in licensing, implementation, and ongoing model management. Teams that want forecasting alone tend to find it heavier than they need. Its value compounds when used across more than one function, which means evaluating Anaplan as a forecasting tool in isolation usually understates the case for buying it.

HubSpot Sales Hub

Best for: Growing teams replacing spreadsheet forecasts

HubSpot Sales Hub (Professional and Enterprise tiers) includes a forecast tool that pulls deal data directly from the HubSpot CRM and rolls it into the standard four-bucket view: Pipeline, Best Case, Commit, and Closed. Forecasts refresh automatically when a rep updates the CRM, which replaces the gut-feel commit conversation with reproducible math. The Weighted Pipeline metric goes a step further by applying close-probability weighting to each deal so the forecast reflects the math of the pipeline. Sales Hub Enterprise also includes Breeze AI for predictive deal-risk scoring.

It’s a good fit for SMB and growing mid-market teams who are leaving spreadsheet forecasts behind and want a forecasting layer that lives where the deal data already does. The lower lift to set up is part of the appeal. Forecasting works on day one, with deals reps are already updating.

Worth noting: HubSpot's forecasting depth scales with the tier and with how disciplined the team is about keeping deals current. Teams that need multi-segment, multi-region, or consumption-based forecasting usually outgrow Sales Hub before they're ready for a full enterprise platform.

How to Choose the Right Sales Forecasting Software

To choose the right sales forecasting software, ask the following four questions:

1. Which forecasting problem is the team trying to solve? 
2. Which data sources does the team trust enough to forecast from?
3. How should the forecast flow into the rest of the revenue stack? 
4. How often is the forecast expected to change? 

Below, we’ll work through each one with practical advice for evaluating the software.

Define the Forecasting Problem Before the Shortlist

The biggest mistake you can make is shortlisting by brand recognition before defining the problem. Start by writing down the forecasting question the team is trying to answer. Some useful questions include:

• "Which deals will close this quarter?" Points to pipeline forecasting platforms.
• "What does payout cost look like at projected attainment?" Points to forecast operationalization.
• "How do we re-cut quotas when the forecast moves?" Points to connected planning or operationalization tools.
• "Can we get a forecast inside the CRM without buying another tool?" points to CRM-native forecasting.

A team that needs deal-level pipeline prediction (Clari, Gong, Aviso) ends up with a different shortlist than a team that needs the forecast to flow into quota and comp execution (CaptivateIQ Planning), or a team that wants forecasting inside the CRM they already own (Salesforce Sales Cloud Forecasting, HubSpot Sales Hub). 

Trust the Data Source That Drives the Forecast

Every forecasting platform draws from a primary data source, and that source determines whether the forecast lands or gets ignored. Pick the platform that forecasts from the data the team already trusts most. A forecast built on signals the team doubts gets second-guessed every time it surfaces, no matter how accurate the underlying model claims to be. Call sentiment is the wrong primary signal for a team with patchy call coverage. CRM stage progression is the wrong primary signal for a team whose reps update deals inconsistently.

Clari forecasts from CRM and pipeline activity data. Gong forecasts from call and engagement data. Aviso runs ML models across both. Salesforce Sales Cloud Forecasting and HubSpot Sales Hub work from opportunity history alone. CaptivateIQ Planning operationalizes against compensation and quota data once a forecast exists.

Map Where the Forecast Has to Flow Next

When going through a vendor demo, ask the team to walk through what happens specifically when the forecast changes mid-quarter. How do quota assignments update? How does the comp model reflect new attainment projections? What does finance see in their planning tool? If the answer involves a manual export and a spreadsheet, the forecast is going to live in a silo regardless of how good the forecasting layer itself looks in the demo.

The answers to these questions are important because a sales forecast isn't the final product. Quota planners use it to reset targets, finance uses it to update FP&A models, and comp teams use it to project payout costs. A poorly integrated forecasting tool won't reveal that problem at purchase. It reveals it the first time the forecast changes, when quotas, comp plans, and finance models all have to be updated by hand.

Account for How Often the Forecast Will Change

Forecasts that change weekly need different infrastructure than forecasts that change quarterly. Teams running consumption-based revenue, multi-region pipelines, or fast-shifting GTM strategies need both real-time forecasting and scenario modeling. Teams running a stable annual plan with quarterly true-ups can get by with less. Getting the match wrong will prove costly. A real-time platform for a quarterly cadence is overkill. A scenario-modeling tool for a weekly cadence leaves the team a step behind every Monday.

Clari, Gong, and Aviso are built around continuous re-forecasting, refreshing as new pipeline, conversation, or behavioral signal lands. Anaplan and CaptivateIQ Planning are built around scenario modeling and what-if analysis, where the team works through base, upside, and downside cases on a planned cadence. Salesforce Sales Cloud Forecasting and HubSpot Sales Hub sit in the middle. They refresh automatically as deal data updates, but without the depth of either real-time intelligence or full-scenario modeling.

FAQs

What is sales forecasting software?

Sales forecasting software produces revenue projections by analyzing historical sales data, current pipeline, and behavioral or conversation signals. The strongest platforms refresh continuously as deal data updates, and most rely on one of a handful of well-established revenue forecasting models (top-down, bottom-up, opportunity stage, time-series, AI-driven).

What is the difference between sales forecasting and revenue intelligence?

Sales forecasting is the specific job of projecting future revenue. Revenue intelligence is the broader category that includes forecasting plus pipeline analysis, deal health scoring, and rep performance tracking. Clari and Gong are revenue intelligence platforms whose forecasting capabilities sit inside a larger product.

How accurate is AI-powered sales forecasting?

Vendor claims range from 90 to 98% accuracy, usually measured by week two of the quarter. These are vendor benchmarks, not independent ones, and they assume strong data hygiene in the systems feeding the predictive sales forecasting model.

How much does sales forecasting software cost?

Pricing splits cleanly into two camps. HubSpot Sales Hub and Salesforce Sales Cloud publish per-seat rates available on their websites. Clari, Gong, Aviso, Anaplan, and CaptivateIQ Planning all use custom enterprise pricing, which is standard for the category but harder to evaluate without a vendor conversation.

How does sales forecasting connect to compensation?

The sales forecast sets the expected attainment, which directly drives commission expense projections. A 110% attainment forecast means higher expected payouts; an 85% forecast means lower. Platforms like CaptivateIQ Planning operationalize this connection so finance can model expected compensation costs against any forecasted scenario in real time.

Can sales forecasting software integrate with my CRM?

Yes, with some variation. CRM-native tools like Salesforce Sales Cloud Forecasting and HubSpot Sales Hub work directly on the CRM data layer. Standalone platforms (Clari, Gong, Aviso) all integrate with Salesforce, HubSpot, Microsoft Dynamics, and other major CRMs as a baseline. Custom CRMs require checking specific vendor support.

Connect Your Sales Forecast to the Rest of the Revenue Stack

CaptivateIQ Planning is built to translate the sales forecast into quota changes, capacity decisions, and compensation plan impact. Quotas update in the same workspace as the comp model, so finance sees the commission expense impact of a forecast shift before it lands in payroll, and sales, RevOps, and finance stay aligned on the same numbers as the forecast moves. When the forecast moves, the operating plan moves with it, which is the difference between hitting the plan and explaining why it slipped.

See how CaptivateIQ Planning connects the forecast to the rest of the revenue stack.