Greater Predictions with Sales Forecasting Software.

Sales forecasting software aggregates data from the company's CRM system, as well as from other structured and unstructured customer data sources, including emails and call records, to provide an overall view of identified sale opportunities in the pipeline. Using predictive techniques, sales forecasting software allocates risks and estimates deal closure rates from historical records to create a realistic sales projection that takes into account the current sales pipeline and future sales opportunities.

There are many factors to consider. The most important factor is selecting software that matches your organization's sales forecasting strategy and that has a proven track record of accurate forecasting capabilities. Data-driven forecasting software uses granular information, and it should incorporate true predictive analytics capabilities. Other factors to consider include data visualization capabilities, sales modeling, scalability and the ability to automatically integrate data from corporate systems.

The purpose of Pipeline analytics software is to measure, monitor and predict sales. Pipeline analytics give visibility into the sales process and help the sales manager track and manage the sales process. The software tracks each potential sale as it progresses from initial interest and prospecting through to lead qualification and conversion. Analytics measure this progress, providing visibility, insights and metrics into the selling process, helping sales managers guide and coach their teams.

A sales forecast determines the predicted sales for future sales cycles. This crucial information drives purchasing and manufacturing decisions and allows finance to predict corporate revenue and profit margins for the next forecast period. It should take into account factors such as sales cycles, market trends, competitive pressures and potential headwinds. Adaptive and accurate sales forecasts are essential components of corporate planning and key factors in determining product pricing strategies.

Because revenue flow and sales don’t necessarily happen at the same time, most companies need to forecast sales and revenue separately. The sales forecast determines when the company expects to conclude a sale. It considers factors such as the sales pipeline, lead prospecting, lead nurturing and lead conversion rates up to and including the sale. Revenue forecasting is concerned with how sales are recognized as income (i.e., when the company gets paid), which may not occur at the same time as the sale. Revenue forecasting measures and predicts cash flow, whereas sales forecasting measures the number of sales

Achieve accurate forecasts with Xactly sales forecasting software. Using data-driven pipeline analytics, Xactly Forecasting empowers revenue leaders to inspect and manage their pipeline more effectively and sellers to close deals more efficiently.

A simple sales forecast can be achieved through historical analysis or weighted pipeline modeling. Using historical data, you simply apply your previous year’s growth rate to current month-over-month performance. In a weighted pipeline model, you assign a probability percentage to each stage of your CRM (e.g., 25% for Discovery, 75% for Contract) and multiply that by the deal value. While these manual methods provide a baseline, they often fail to account for "human bias," where sales reps may be overly optimistic about a deal’s closing date.

AI-powered models improve predictability by shifting from "gut feel" to unbiased data signals. Unlike traditional methods, AI analyzes thousands of data points, including email sentiment, meeting frequency, and historical rep performance, to assign a "Health Score" to every deal. By identifying patterns that lead to wins or losses, AI can flag "at-risk" revenue weeks before a human manager might notice. This allows leadership to move from reactive troubleshooting to proactive strategy, consistently landing within 5% of their quarterly targets.

Both Xactly and Clari are industry leaders in revenue intelligence, but they approach accuracy through different lenses. Clari excels in "Revenue Cadence," focusing heavily on conversation intelligence and real-time activity signals to predict deal outcomes. Xactly Forecasting provides a unique advantage by integrating incentive compensation data into the forecast. By understanding how a rep’s "path to quota" and potential commission impact their behavior, Xactly provides a more holistic view of the "human element" behind the numbers, often resulting in higher accuracy for organizations with complex, multi-tiered sales teams.

Leaders can benchmark accuracy by tracking their Mean Absolute Percentage Error (MAPE) and comparing it to industry standards. For B2B SaaS organizations, "World-Class" performance is typically defined as maintaining 90%+ accuracy within the first 30 days of a quarter. Leaders also use cohort analysis to benchmark internal teams against each other, identifying "sandbaggers" (those who consistently beat low forecasts) versus "optimists" (those who consistently miss high forecasts), ensuring the entire organization moves toward a standardized "accuracy culture."

Improving accuracy across global regions requires standardizing the "Definition of Done." Accuracy often fluctuates because a "Qualified Lead" in North America might mean something different than in EMEA. To fix this, organizations should: Implement Global Stage Gates: Ensure every region follows the same objective criteria for moving deals through the CRM. Centralize Data Intelligence: Use a single platform to eliminate "regional data silos" where local teams might be using different spreadsheets. Adjust for Regional Seasonality: Use AI to automatically adjust forecast models for regional differences, such as varying fiscal year starts or local holiday cycles, ensuring that global projections remain balanced and realistic.