Choosing sales forecasting software comes down to five decisions: whether your CRM’s built-in forecasting is already enough, which forecast method matches how your team calls the number, how clean your pipeline data is, how much you trust AI predictions, and what a specialist tool will cost next to the time managers spend in spreadsheets today. Most teams should run at least one full quarter on native CRM forecasting before buying anything else.
The good specialist tools do not invent a better number from nothing. They make it faster to collect rep and manager calls, show what changed since last week, and flag the deals most likely to slip.
Start with what your CRM already does
Salesforce and HubSpot both include forecasting on their mid-tier plans, and for many teams that is the right place to start.
- Salesforce Sales Cloud lists sales quoting and forecasting on Pro Suite at $100 per user per month on annual billing, with advanced pipeline management and forecasting on the $195 tier. Predictive forecasting is part of its AI features.
- HubSpot Sales Hub includes default and custom forecasting on Professional at $90 per seat per month on annual billing, plus a one-time $1,500 onboarding fee. Enterprise, at $150 per seat, adds forecasting across team hierarchies.
Native forecasting works well when you have one or two layers of managers, a single sales motion and reps who keep the CRM current. A specialist tool starts to pay off when managers spend hours each week in spreadsheets reconciling the number, when you have several layers of roll-ups, or when leadership keeps asking what changed and nobody can answer quickly.
Match the tool to your forecast process
Forecasting tools are built around different ways of calling the number. Pick one that fits the process you run, not the one in the vendor’s slides.
Roll-up forecasting
Reps submit commit, best case and pipeline numbers, managers adjust them, and the call rolls up through each level to the CRO. This is the most common process in B2B sales. Clari Forecast is built for it, with roll-ups across every management level and a view of what moved week over week.
Deal-based and signal-based forecasting
Here the forecast leans on evidence from each deal: who has engaged, what was said on calls and whether next steps are booked. Gong Forecast takes this approach, scoring deals with call data so managers can check what buyers actually said before they commit a deal.
AI and statistical projections
Models predict the outcome from historical win rates, deal age, stage movement and activity. Most tools show this projection next to the human call. Aviso puts AI predictions at the center and also offers consumption forecasting for usage-based businesses, which matters if your revenue depends on customer usage rather than signed bookings alone.
Data quality and CRM fit
Every forecasting tool is only as good as the pipeline underneath it. Before a demo, check these basics:
- Close dates and amounts are updated at least weekly, not left at the default.
- Stages have written exit criteria so a deal in Stage 4 means the same thing for every rep.
- Forecast categories (commit, best case, pipeline, omitted) are set on every open deal.
- Closed-lost reasons are captured so the model can learn from losses as well as wins.
- Products, renewals and expansion deals are separated if you forecast them differently.
Also confirm CRM support early. Some tools are built for one system. Forecastio, for example, works only with HubSpot, which makes it a strong fit for HubSpot teams and a non-starter for everyone else. Ask how often the tool syncs, whether it writes forecast categories back to the CRM, and how it handles custom objects and multiple currencies.
Judge AI claims carefully
Nearly every vendor now advertises AI forecasting. The useful questions are practical:
- How many quarters of history does the model need before its projection is reliable?
- Can you see why the model scored a deal the way it did, or is it a black box?
- How did the projection compare with actual results for customers similar to you? Ask for this in writing rather than accepting a headline accuracy figure.
- Can managers override the model, and does the tool track whether human calls or the model were closer at quarter end?
AI helps most when it flags the deals that need attention, such as stalled deals, single-threaded deals or deals with no recent buyer activity. It helps least when it replaces judgment with a single number nobody can explain to the board.
Pricing models and total cost
Pricing in this category falls into three groups:
- Built into the CRM. Salesforce and HubSpot include forecasting in mid-tier plans, so the cost is the CRM upgrade, not a separate tool.
- Published specialist pricing. Forecastio’s Sales Forecasting plan costs $249 per month on annual billing with two seats, extra seats are $49 per month each, and the Forecasting and Revenue Intelligence plan costs $369 per month with two seats.
- Quote-only enterprise platforms. Clari Forecast, Gong Forecast and Aviso do not publish prices. Gong charges per-user licenses plus a platform fee, so buying it only for forecasting is rarely the cheapest route.
Count who needs a seat. Reps who only update categories in the CRM may not need a license, while every manager who adjusts the call usually does. Also ask about migration help. Aviso, for instance, offers free migration and contract buyouts for teams switching from another vendor. For ratings and prices side by side, see our ranked list of the best sales forecasting software.
Implementation and adoption
A forecasting tool succeeds when it replaces the spreadsheet in the weekly forecast call, not when it sits beside it. Plan the rollout around that meeting.
- Map your forecast hierarchy, from rep to front-line manager to director to CRO, and agree on who submits a call at each level.
- Define forecast categories and the rules for each one, such as what a deal needs before a rep can mark it commit.
- Connect the CRM and clean up the fields the tool depends on before managers log in.
- Run the weekly forecast call from the tool from the first week, and retire the old spreadsheet within a month.
- Review accuracy at quarter end and adjust stage definitions, categories or model settings based on what slipped.
Expect specialist tools to take a few weeks to configure for a mid-size team and longer for a global organization with several sales motions. Native CRM forecasting is faster to switch on, but it still needs an admin to set up forecast types and hierarchies properly.
Vendor changes to watch
Consolidation has reached forecasting. Clari Forecast is now the forecasting product from Salesloft, which merged with Clari and kept the Clari name on this tool. If you are evaluating it, ask how packaging and pricing will change inside the Salesloft platform and whether you can buy forecasting on its own. For Gong Forecast, the question is the reverse: how much of Gong’s wider platform you need to license to get the forecasting you want.
How to test forecast accuracy before you buy
The best trial is a back-test plus a live quarter. Ask the vendor to load your last four to six quarters of CRM history and show what the tool would have predicted at weeks 4, 8 and 12 of each quarter compared with what actually closed. Then run the tool alongside your current process for one live quarter.
During that quarter, track three things: how much time managers save preparing for the weekly forecast call, how early the tool flagged deals that later slipped, and whether the final call landed closer to actuals than your old method. Forecastio offers a one-month pilot and Salesforce offers a 30-day trial, while enterprise vendors usually run a structured proof of concept on request.
Questions to ask in every demo
- Which CRMs do you support, and how often do you sync?
- Can you back-test against our last four quarters of pipeline data?
- How does the forecast roll up through our management layers?
- Can we see why the AI scored a specific deal the way it did?
- Who needs a paid seat: reps, managers or both?
- What migration help and implementation support are included?
- How will recent mergers or packaging changes affect our pricing at renewal?
See the full ranking
Best Sales Forecasting Software in 2026
We compare six sales forecasting tools on features, CRM fit, and price, and name the right pick for enterprise teams, Salesforce shops, and HubSpot users.
Frequently asked questions
When should you move off spreadsheet forecasting?
Move once managers spend more than a couple of hours a week rebuilding the number, or once you have more than one layer of managers rolling up calls. Spreadsheets also struggle to show what changed since last week. If leadership keeps asking which deals moved and nobody can answer quickly, a dedicated tool or native CRM forecasting will pay off.
How much pipeline history do you need for AI forecasting?
You need enough closed deals for patterns to show, which usually means several quarters of clean history. Ask each vendor for its minimum and test it with a back-test on your own data. If your stages, products or pricing changed recently, older history may mislead the model, so tell the vendor before it trains.
Should reps or managers own the forecast in the tool?
Both, with clear roles. Reps should own deal-level data: amounts, close dates, next steps and forecast categories. Managers should own the call they submit upward and record why they adjusted a rep’s number. Good tools keep both views side by side so leaders can see where judgment and data disagree.
Can you forecast renewals and new business in the same tool?
Usually yes, but set them up as separate forecasts. Renewals behave differently from new deals, with different risk signals and timing. Ask whether the tool supports multiple forecast types, and for usage-based products, whether it handles consumption forecasting. Aviso, for example, lists consumption forecasting as a core feature.