Sales Forecasting Software: The 11 Best Tools Compared (2026)

87% of enterprises missed revenue targets in 2025 (Clari Labs, 2026). The sales forecasting software you choose is part of the problem or part of the solution. But picking the right one requires understanding what each tool actually does versus what it claims to do.

ORM has spent more than 12 years building forecast models for B2B SaaS companies, and I will tell you this: most sales forecasting software is really pipeline visibility software that generates a number as a byproduct. Real forecasting requires custom models calibrated to your specific revenue engine. That distinction matters when you are choosing where to invest.

This is an honest assessment of the 11 best sales forecasting tools and software platforms on the market. ORM is on the list because we belong here. But I will tell you when a competitor is the better fit for your situation.

What Is the Difference Between Forecasting Software and a Managed Service?

Before you compare features, understand the two ways to buy forecasting. The choice shapes everything else.

Sales forecasting software is something you operate. You buy licenses, connect your CRM, configure the model, and your team runs it day to day. The platform produces a forecast based on patterns it has learned across many customers. The strength is control and visibility. The limit is that the model is generic until your team makes it specific, and most teams never get the time to. Managed forecasting is something you engage. A team builds a model on your data, runs it, and delivers the forecast plus the actions to hit target. You own the outcome without operating the software. The strength is accuracy and the analytical depth most companies cannot staff internally. The limit is that it is not a dashboard your reps open every morning.

The honest cost comparison is software plus the internal time, expertise, maintenance and analysis it takes to make a model useful, set against a managed service that includes that work. ORM talks with every customer at least every two weeks, and fits the model to the customer's data rather than asking the customer to reshape their data for the model.

Neither is better in the abstract. A frontline manager who needs to inspect 40 deals before a Monday pipeline review needs software. A CFO who needs a number the board can trust needs a model built for the way the company actually sells. Plenty of companies run both: a platform for daily visibility, a forecast model for board-level accuracy. The list below covers both categories so you can decide which problem you are solving.

How Do the Top Sales Forecasting Tools Compare?

1. ORM (Best for Forecast Accuracy and Prescriptive Action)

ORM is not a tool. It is a managed forecasting service. You do not buy licenses and operate a platform. You engage a dedicated team that builds custom mathematical models on your CRM data and delivers prescriptive analytics with specific actions to close the gap between forecast and target.

What sets it apart: Every ORM client gets a different model because every client has a different revenue engine. A $200M ARR enterprise-focused company needs fundamentally different conversion rate assumptions, pipeline coverage models, and segment analysis than a $75M ARR company running product-led growth. ORM builds that specificity. Forecast accuracy: we target 95%. We score it each quarter against what actually closed. Prescriptive output: ORM does not only produce a number. The model tells you which deals to accelerate, where to add pipeline coverage, how to reallocate rep capacity, and exactly what needs to change to hit target. The difference between a forecast and a plan is the difference between knowing and doing. Best fit: B2B SaaS companies where forecast accuracy has board-level consequences. Companies that want the outcome (accurate forecast plus action plan) without building an internal analytics team. Limitation: ORM is not a daily-use dashboard. If your primary need is real-time pipeline visibility for frontline managers, pair ORM with a platform like Clari or your CRM.

2. Clari (Best Revenue Intelligence Platform)

Clari built the revenue intelligence category. The platform connects to your CRM, email, calendar, and conversation data to provide pipeline visibility, AI-generated forecasts, and deal health signals.

Strengths: Pipeline visibility is exceptional. The platform gives CROs a real-time view of the pipeline without chasing reps for updates. Activity capture reduces CRM hygiene issues. The Wingman acquisition added conversation intelligence. For companies that need a daily operating platform for the revenue team, Clari is the market leader. Forecast capability: Clari's AI generates forecasts based on patterns across its customer base. The accuracy depends heavily on implementation quality and CRM data completeness. Companies with clean data and strong RevOps teams see meaningful accuracy improvements. Companies with messy data see the same problems reflected back. Best fit: Companies that need daily pipeline visibility and revenue intelligence tooling. Revenue teams that want a platform their managers and reps operate every day. Limitation: The forecast is generated by algorithms trained on aggregate patterns, not models built on your specific sales motion. For board-level precision, this approach has a ceiling. Pricing: Not published. Vendr reports a median negotiated contract of $76,000 a year across 291 purchases, with a range from $19,065 to $415,001.

3. Gong (Best for Conversation Intelligence + Forecasting)

Gong started as a conversation intelligence platform and expanded into forecasting. The platform records and analyzes sales calls, surfaces deal risks based on conversation patterns, and generates pipeline forecasts.

Strengths: Call analysis is best-in-class. Gong identifies which deals mention competitors, which champions are disengaging, which conversations lack next steps, and which reps are following the methodology. For sales teams that want coaching insights layered with forecasting, Gong provides unique visibility. Forecast capability: Gong's forecasting uses conversation data alongside pipeline data. This is a genuine differentiator because it adds behavioral signals that CRM data alone misses. If a champion has not been on a call in 30 days, Gong catches it. Best fit: Sales teams where call coaching and conversation analytics are a priority alongside forecasting. Companies where most selling happens on recorded calls. Limitation: Gong's forecast accuracy depends on conversation data availability. Deals where interactions happen offline, via email, or through partners are less visible. The platform is also expensive for organizations that only need the forecasting component. Pricing: Not published. Vendr reports a median negotiated contract of $55,040 a year across 1,132 purchases.

4. Salesforce Einstein (Best Native CRM Forecasting)

Salesforce Einstein is the AI layer built into the Salesforce platform. It provides opportunity scoring, forecast predictions, and pipeline analytics without requiring a separate tool.

Strengths: Zero integration overhead. If your team lives in Salesforce, Einstein is already there. Opportunity scores update automatically. The forecast leverages your CRM data natively. For companies that want forecasting without adding another tool to the stack, Einstein is the path of least resistance. Forecast capability: Einstein analyzes historical opportunity data to predict close probabilities and generate forecast numbers. Accuracy depends entirely on the quality and completeness of your Salesforce data. Companies with disciplined CRM hygiene see reasonable results. Companies with inconsistent data entry see noise. Best fit: Salesforce-native companies under $50M ARR that want incremental forecasting improvement without a separate vendor. Limitation: Einstein is a feature, not a product. It lacks the depth of custom modeling, the prescriptive recommendations, and the dedicated analytical support that purpose-built forecasting solutions provide. Pricing: Included in higher Sales Cloud editions or sold as an add-on. Salesforce publishes current list prices.

5. HubSpot Forecasting (Best for HubSpot-Native SMB/Mid-Market)

HubSpot includes forecasting in its Sales Hub Enterprise tier. The tool provides deal-based forecasting, pipeline reporting, and goal tracking within the HubSpot ecosystem.

Strengths: Simple to configure, easy for reps to use, and natively integrated with HubSpot CRM. For HubSpot-native companies, it eliminates integration complexity. The deal pipeline view and forecast rollups give managers basic visibility. Forecast capability: HubSpot's forecasting is primarily weighted pipeline with some AI enhancements. It aggregates deal amounts by stage and applies probability weights. This is functional but basic compared to purpose-built forecasting tools. Best fit: Companies under $30M ARR running HubSpot that need basic forecasting without a separate tool. Limitation: The methodology is simple. No custom models, no prescriptive recommendations, no segment-level decomposition. Companies outgrow HubSpot forecasting as pipeline complexity increases. Pricing: Included in Sales Hub Enterprise. HubSpot publishes current list prices.

6. Aviso (Best AI-Native Forecasting Platform)

Aviso positions itself as an AI-first revenue intelligence platform with a strong emphasis on forecast accuracy.

Strengths: Aviso's AI models analyze CRM data, email, calendar, and conversation signals to generate forecasts. The platform provides scenario modeling, deal guidance, and revenue insights. The AI layer is more sophisticated than most competitors. Best fit: Mid-market to enterprise companies that want a platform-based approach with strong AI capabilities. Limitation: Ask for the methodology behind any accuracy figure a vendor quotes, and run your own history through the model before you sign. That applies to every tool on this list, ORM included. Pricing: Not published.

7. BoostUp (Best for Deal Inspection)

BoostUp combines deal inspection, pipeline management, and forecasting in a single platform. The focus on deal health and risk identification is strong.

Strengths: The deal inspection layer surfaces risks based on engagement signals, helping managers prioritize pipeline reviews. The forecasting component layers on top of deal-level insights. Best fit: Revenue teams that want detailed deal-level visibility and risk flagging alongside their forecast. Companies where pipeline review discipline needs improvement. Limitation: Forecasting is secondary to deal inspection in BoostUp's architecture. For companies where forecast accuracy is the primary requirement, purpose-built solutions go deeper. Pricing: Not published.

8. People.ai (Best for Activity Data Capture)

People.ai focuses on capturing and analyzing sales activity data: emails, meetings, calls, and engagement signals. The platform maps activities to accounts and opportunities to provide activity-based pipeline intelligence.

Strengths: Activity capture is thorough. People.ai provides visibility into rep activity patterns, account engagement levels, and buying committee mapping. For organizations where CRM activity data is incomplete, People.ai fills the gaps. Best fit: Enterprise sales organizations where understanding rep activity and account engagement at scale is critical. Limitation: Forecasting is not People.ai's primary use case. The platform provides pipeline intelligence and activity data that can improve forecast inputs, but it does not generate the forecast models that dedicated solutions offer. Pricing: Not published.

9. RevCast (Best for Revenue Planning and Scenarios)

RevCast focuses on revenue planning and scenario modeling. The platform helps revenue leaders model headcount plans, territory assignments, quota distribution, and pipeline scenarios.

Strengths: The planning layer is strong. RevCast answers questions like "what happens if we hire 5 AEs in Q2?" or "how should we reallocate territories after losing 3 reps?" The scenario modeling capability is useful for annual and quarterly planning. Best fit: Revenue leaders focused on capacity planning and territory design alongside forecasting. Limitation: RevCast is more of a planning tool than a forecasting tool. The forecasting component supports the planning use case but does not offer the depth of custom models or prescriptive analytics. Pricing: Not published.

10. InsightSquared (Best for Mid-Market Revenue Analytics)

InsightSquared provides revenue analytics, reporting, and forecasting for mid-market B2B companies. The platform offers pre-built reports, activity capture, and pipeline analytics.

Strengths: Comprehensive pre-built reporting covers the metrics most revenue leaders need without extensive configuration. The platform is more accessible than enterprise tools. Best fit: Mid-market companies ($10M-$75M ARR) that need revenue analytics and reporting alongside basic forecasting. Limitation: Forecasting capabilities are lighter than dedicated platforms. The tool is stronger as a reporting and analytics solution than as a primary forecasting engine. Pricing: Not published.

11. Anaplan (Best for Enterprise Financial Planning)

Anaplan is an enterprise planning platform that extends well beyond sales forecasting into financial planning, supply chain, and workforce planning. Some enterprise companies use Anaplan for revenue forecasting as part of their broader planning infrastructure.

Strengths: Powerful modeling capabilities. Anaplan can build complex, multi-variable forecast models that account for financial, operational, and sales inputs. For companies that want forecasting integrated with broader financial planning, Anaplan provides a unified platform. Best fit: Large enterprise companies ($500M+ revenue) that need revenue forecasting embedded in their enterprise planning ecosystem. Limitation: Anaplan is complex to implement and expensive to operate. It requires dedicated administrators and modelers. Overkill for companies that only need sales forecasting. Pricing: Not published.

What to Look for in Sales Forecasting Software

Most buyers evaluate forecasting software on the demo. The demo always looks good. Here is what actually predicts whether the tool will hold up after the contract is signed.

Methodology, not only the number. Ask how the forecast is produced. If the answer is "AI" with no detail, push harder. A tool that weights your pipeline by stage probability is doing arithmetic, not forecasting. You want to know what inputs the model uses, how it handles your sales motion, and what it does when the data is incomplete. Accuracy you can verify. Vendors quote accuracy figures that are rarely independent. Ask for the methodology behind any number, and ask to run your own historical data through the model before you commit. A forecast that was right last quarter on someone else's pipeline tells you nothing about yours. It survives messy CRM data. Every forecasting tool performs well on clean data. The ones worth paying for degrade gracefully when reps skip fields, deals sit in the wrong stage, and close dates slip. If the platform needs perfect pipeline hygiene to work, you are buying a second job, not a forecast. Prescriptive output, not only a dashboard. A number tells you where you will land. It does not tell you what to do about it. The tools that earn their cost decompose the gap between forecast and target and point to the specific deals, segments, and capacity changes that move it. The difference between a forecast and a plan is the difference between knowing and doing. Fit to your stage and complexity. A $30M ARR company running one motion does not need an enterprise planning platform, and a $400M company with three segments and two sales motions will outgrow a native CRM feature fast. Match the depth of the model to the complexity of the revenue engine. Paying for capability you cannot use is as costly as buying a tool you outgrow in a year.

How to Choose

If forecast accuracy is your top priority: ORM works to a 95% accuracy target through custom models and prescriptive analytics. Aviso is the strongest platform-based alternative. If daily pipeline visibility matters most: Clari is the market leader. Gong adds conversation intelligence. BoostUp adds deal inspection depth. If you want to avoid adding another tool: Salesforce Einstein or HubSpot forecasting eliminates integration overhead, with obvious accuracy trade-offs. If you need planning and scenario modeling: RevCast or Anaplan (for enterprise) provide planning capabilities alongside forecasting. If activity data is your biggest gap: People.ai captures and structures the activity data that makes every other forecasting tool more accurate.

How Should You Measure a Forecasting Tool's Accuracy?

Measure it on new and expansion revenue, separately from renewals, and check that it holds for the whole quarter. Across B2B SaaS, accuracy on new and expansion revenue usually reaches about 90%, but it takes a lot of manual effort and goes stale as conditions change. The useful test is whether a tool holds its number from day one to day 90 without someone adjusting it by hand.

Renewals flatter any accuracy figure they are blended into, because the amount is usually known in advance. It is the same as last year, or it changes with a price increase or a change in seats.

How Do You Know It Is Time to Replace Your Forecasting Software?

Usually one of two things happens. The company misses a quarter, or Sales Ops runs out of capacity to keep doing the work by hand.

A missed quarter is rarely the whole problem. The real damage is that nobody saw it coming. By the time leadership sees the forecast is off, it is too late to act. That is what breaks trust. When Sales commits to a number and misses it a few times, leadership starts looking for a more independent view.

The second trigger is operational. Sales Ops can end up maintaining 37 spreadsheets with 12 tabs in each. Then the team gets asked to run the forecast call as well. At that point the team spends more time assembling the forecast than analyzing the business. They usually need help the most and are the least likely to say so.

The CRO is focused on selling, so it is often the CEO or CFO who makes the call. They want to know whether the company will hit the number, and if it will not, they want to know early enough to do something about it.

What Is the One Question to Ask Every Forecasting Vendor?

Ask: "How will this work for my business?"

If a vendor cannot explain how the tool adapts to the specifics of your sales process, be careful. Some buyers accept whatever process the vendor imposes, on the theory that it must be best practice. That turns the implementation into a change management project, and those are much more likely to fail.

A good answer covers five things:

- how you define your sales stages - what counts as qualified pipeline and what does not - how expansion deals differ from renewals - how renewals differ from new-logo deals - who is actually responsible for the forecast

The last one matters because the sales leader, Sales Ops, the regional VP and Finance each read the forecast through a different lens.

The bad answer is easy to recognize. If a vendor says the forecast can be set up in 15 minutes, walk away. A forecast you can trust takes an understanding of your process, a validated data set and trained models that reconcile with how the company actually operates.

Two more questions are worth asking before you sign. Is my data secure, and are you using it to train your models? Do the results come from our own systems, with a trace back to the raw data?

How Long Does It Take to Implement Sales Forecasting Software?

At ORM, four to six weeks to a fully trained model built on your own historical sales performance. Most of that time goes into process rather than plumbing.

The biggest source of delay is understanding what the data means, more than connecting to it. One ORM customer could not say which field held the closed-won booking amount. Getting it right took five to seven iterations and some custom logic.

Is Your CRM Data Ready for Forecasting Software?

Almost certainly, even if it does not feel that way. The objection ORM hears most is "I don't think our data is ready." Some version of "garbage in, garbage out" usually follows.

Machine learning models look for predictive signal. If your organization records data in a way that is consistently imperfect, those patterns can still predict what happens next. Plenty of CRMs have half-kept fields and loose stage definitions. The behavior in that data still tells you a lot.

The answer is to find the signals that are reliable, model them and improve the data over time. Once something is measured, the data gets better. Perfect data never arrives. Predictive data is what you need.

Can You Run Two Forecasting Tools at the Same Time?

Yes, and some companies choose to. Teams that rely on a roll-up tool alone often tell ORM the same thing: "We had to turn off the forecast in the last four weeks because it was way off." A clean interface for rolling up numbers is a different thing from an accurate forecast.

In some companies ORM and Clari run side by side. ORM carries the statistical forecast, and Clari manages the rep and manager roll-up as an independent view. Leadership then sees two perspectives: what the field believes will happen, and what the data says is likely to happen. When they disagree, that gap is worth a conversation.

The Bottom Line

The sales forecasting tool market is crowded with platforms that do adjacent things to forecasting. Pipeline visibility, conversation intelligence, activity capture, and revenue planning are all valuable. But they are not forecasting.

Real forecasting is building mathematical models calibrated to your specific win rates, conversion rates, sales cycle length, and pipeline dynamics. It is decomposing the gap between forecast and target by segment and recommending specific actions. That requires custom work, not a platform configuration.

Choose the tool that matches your primary need. Then evaluate whether the forecasting component is a feature or a foundation. The difference shows up in the accuracy, and the accuracy shows up in the board room.

Related reading: - Best RevOps Tools - ORM vs Clari - ORM vs Gong - ORM vs 6sense - Sales Forecasting: Complete Guide - Forecast Accuracy - Win Rate

What the Benchmark Data Says About Choosing

Feature comparisons converge. Two published findings do more to narrow a shortlist than any matrix.

Cadence beats capability. Companies tracking pipeline velocity weekly reach 87 percent forecast accuracy against 52 percent for irregular tracking, with revenue growth of 34 percent against 11 percent. A 35-point accuracy gap from operating rhythm is larger than the gap between any two products on this list. Ask how long a weekly review takes with the tool in front of you, not what it can theoretically produce. Your assumptions have moved. The average B2B sales cycle runs 84 days and has lengthened 22 percent since 2022 across a study of 939 companies, while new-logo win rates across 655,000 opportunities and 48 billion dollars of pipeline sit near 19 percent.

Any tool carrying hardcoded stage weights, coverage rules or close-date thresholds is calibrated for a market that no longer exists. The question worth asking every vendor is what re-fits when those numbers move, and what stays where someone last set it.

For what the license price leaves out, see the real cost comparison.