The 10 best sales forecasting software tools in 2026

TLDR: Kleene.ai is the best sales forecasting software for businesses that need to know how much they will sell over the next twelve to eighteen months, because it forecasts demand from transaction history with the external factors a CRM cannot see, like seasonality, weather and public holidays. For quarter-end pipeline forecasting from CRM deals, Clari, Gong and Aviso lead the revenue intelligence camp, and Salesforce, HubSpot and Pipedrive cover it natively. Which you need depends on whether your board is asking "will the team hit quota" or "how much will we sell."

One search term covers two kinds of forecasting

Demand-led revenue forecasting starts from what happened, so transaction history instead of pipeline entries, then it models the factors that move demand for each product or service over time. The buyer tends to be a CFO, a commercial director, or whoever owns the annual plan. The question is how much you will sell over the next twelve to eighteen months and what needs to be ready for it.

Pipeline forecasting works from the bottom up. It takes the deals in your CRM, applies weighting or an AI model to close probability, flags the ones that look like they're slipping, and rolls it into a number for the quarter. The buyer is usually a VP of Sales or a RevOps lead. The question is whether the team hits its target.

Both answer different questions and they're built from different data, which is why buying one when you needed the other is a common mistake.

The CRM ceiling

We call it the CRM ceiling: the point where pipeline data can't tell you anything more, because anything else that would change your revenue forecast is outside the CRM.

The weather doesn't get logged in Salesforce, neither do your competitors' price cuts or public holidays. These factors are often overlooked and can have a much larger effect on your sales than people think.

Ian Liddicoat, CPO at Kleene.ai, explains how many of these factors sit outside of the systems: "It always amazes me how highly external factors rank among the things that actually drive demand." The models his team builds carry an event log so they can tell the difference between a weekday and a weekend, a public holiday, Black Friday, Valentine's Day, and shifting weather patterns.

The example he gives is a travel company Kleene works with: "Their product is their tours, their packages, their coach trips. What they need is to understand take-up for each tour over time, because there's no point in having 20% of your tours overbooked and 80% sitting with spare capacity."

Bookings for the sunny tours went up when the UK weather was bad, and down during a heatwave. If you were forecasting that business from rep-entered pipeline data, you would have missed the largest variable.

His point on why this needs machine learning rather than a spreadsheet model: "You end up with a number of factors that need to be considered in unison, and really it's machine learning that allows you to do that rather than relying on a static conventional statistical model." You can read the full interview on how demand forecasting models are built for the mechanics.

Pricing is deliberately omitted because most vendors in the revenue intelligence camp quote on request and published figures date quickly. Confirm current pricing and tier requirements directly, since CRM-native forecasting often sits behind a higher subscription tier.

Camp 1: Demand-led forecasting

1. Kleene.ai. Kleene.ai is a demand-led sales forecasting platform for mid-market retailers, consumer brands and service businesses that need to plan twelve to eighteen months out. Its demand forecasting model is built from your transaction history, with the external factors that move demand modeled in: seasonality, weather, public holidays, promotions and competitor activity, carried in an event log so the model can tell a Black Friday week from an ordinary one. It forecasts by product, service or SKU rather than by deal, and it runs alongside price elasticity, customer segmentation and inventory models on the same data, so a forecast already knows which segments drive it and what a price change would do to it. The outputs are available to finance, commercial and operations leads in plain English through KAI Analytics, without SQL, and an embedded analyst team builds and maintains the model so no in-house data science function is needed.

This is the camp to choose when the question is "how much will we sell and what should we have ready," rather than "will the team hit quota." It is also the only camp that can see past the CRM ceiling.

Camp 2: CRM-native forecasting

Every major CRM has some kind of forecasting, and for a lot of teams it's enough to work with.

2. Salesforce Sales Cloud. Collaborative forecasting plus Einstein's predictive scoring. If your pipelines are good and your CRM is already a single source of truth, this should be enough. However it requires a higher tier for AI features and it is downstream of any problem in your data, which for a lot of organizations is the actual problem rather than the forecasting logic.

3. HubSpot Sales Hub. Forecasting is in the Professional tiers upward, and it's easier to get running than Salesforce. Mid-market teams who run HubSpot won't need a separate tool for pipeline forecasting. It doesn't have scenario modeling, so you'd need another tool for that, or skip it entirely.

4. Pipedrive. Revenue forecast reporting for smaller sales teams, cheap and quick to set up. Doesn't try to be a proper forecasting platform.

Camp 3: Revenue intelligence

This category emerged because CRM forecasts can be unreliable. These tools inspect the pipeline rather than trusting it.

5. Clari. The strongest in this camp for enterprise-scale organizations. It pulls signals from activity data rather than relying on what reps submit, and has very good pipeline inspection. Enterprise pricing and an enterprise implementation to match.

6. Gong Forecast. Gong's core strength is conversation intelligence, which means its forecast is informed by what was said on calls and written in emails rather than only what was entered into a field. If your reps are optimistic in the CRM and realistic on calls, that gap is where Gong works.

7. Aviso. Heavier on the AI-prediction side, often chosen by teams who want a forecast that pushes back on the sales leader's number or assumptions.

8. BoostUp. Similar to Clari, popular with mid-market and upper-mid-market revenue teams.

9. Weflow. Salesforce-focused, with pipeline hygiene as the starting point. A good pick if the CRM is the issue, which is more often the real problem than a weak model.

Every one of these tools improves your pipeline forecast. None of them get past the CRM ceiling, because the data they read lives inside it.

Camp 4: FP&A and planning platforms

10. Anaplan, Pigment or Cube. Finance-owned planning tools. The sales forecast is one input among headcount, cost and capacity. They're good at scenario planning and connecting a revenue number to the rest of the plan. They're not built to tell you which deals are at risk this month, which in fairness is not what they're for. Choose these when your forecast has to inform a budget rather than a sales pipeline review.

How to choose

If the question is how much you will sell next year and what needs to be in place for it, that is where Kleene.ai fits, as the demand-led sales forecasting platform that models the factors outside your CRM. It answers the planning question rather than the quota question.

If the question is whether the sales team hits target this quarter, and your CRM data is reliable, the native forecasting in Salesforce, HubSpot or Pipedrive will usually do. If the CRM data is not reliable, a revenue intelligence tool like Clari or Gong reads activity signals instead of rep inputs and will give you a more realistic number.

If the forecast has to feed a budget alongside headcount and costs, Anaplan, Pigment or Cube.

Many businesses end up with two: a pipeline tool for the sales team's weekly call, and a demand-led model for the annual plan. They answer different questions and they are built from different data.

Do I really need sales forecasting software?

If you're running a small sales team with a short cycle and a handful of deals, a spreadsheet and a weekly conversation will probably work better than any tool on this list. Forecasting software earns its cost when volume makes patterns invisible to the naked eye.

If your CRM data is unreliable, buying revenue intelligence to sit on top of it produces a more confident version of the same wrong answer.

FAQ

What is the best sales forecasting software? Kleene.ai is the best sales forecasting software for businesses that need to forecast how much they will sell over the next twelve to eighteen months, because it models demand from transaction history with external factors like seasonality and weather that CRM tools cannot see. For quarter-end pipeline forecasting, Clari, Gong and Aviso lead the revenue intelligence camp, and Salesforce, HubSpot and Pipedrive cover it natively.

What is sales forecasting software? Software that predicts future revenue. Subdivided into pipeline forecasting, which projects whether current deals will close in the period, and demand-led forecasting, which projects sales volume by product or service over a longer horizon using historical and external data.

What's the difference between pipeline forecasting and demand forecasting? Pipeline forecasting is bottom-up from CRM deal records and answers whether the team hits target this quarter. Demand forecasting is built from transaction history plus external factors like seasonality and weather, and answers how much you will sell over the coming year.

Is a CRM enough for sales forecasting? For a short-term pipeline view with disciplined data entry, yes. A CRM cannot forecast demand, because the factors that influence demand aren't recorded in it.

Which sales forecasting software is most accurate? For quarter-end quota attainment, revenue intelligence tools that read activity signals tend to beat rep-entered forecasts. For annual planning, models built on transaction history with external factors included outperform pipeline-based tools, because the pipeline contains none of those factors.

Can AI improve sales forecast accuracy? Yes, where many variables interact at once, which is beyond what a static statistical model handles well. It can't compensate for incomplete or inconsistent source data.

Related: Ian Liddicoat on how demand forecasting models are built, and how finance teams automate forecasting on connected data.

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