Sales forecasting software evaluates historical business data and produces a report of expected sales based on trends. Forecast reports show sales targets vs. achieved sales vs. potential sales.
Sales forecasting technology aims to answer these questions:
1. What amount of revenue can we expect? This can be organized by salesperson, territory or account.
2. How did actual sales compare to expected sales? If those two numbers don’t match, why not?
3. What method will produce the most accurate forecast?
Sales forecasting software includes templates. These templates run statistical analyses on data and produce sales forecast reports. Typically, users program the templates with “assumptions” to simulate sales behavior and market conditions.
Ultimately, forecasting is a process of self-assessment, market assessment, and self-reflection. Accurate forecasts require detailed finances and business records, as well as external market conditions.
You can create sales forecasts using spreadsheets or a general BI tool. However, the benefits of a dedicated sales forecasting tool are:
A UI for Sales (vs. IT or data scientists)
Forecast templates
Visuals for sales managers
Better contextual understanding of forecasts
Deep integration with other sales technology
Sophisticated CRM systems often include sales forecasting as a capability.
Functionality differs depending on the complexity of the product, and whether it’s a point solution or part of a Sales technology suite. Features may include:
Sales forecast benchmarking: Forecast sales and compare to company or industry benchmarks.
Quick forecasts / Dashboard: A quick overview of sales forecasts.
Sales forecast visuals: Clear and helpful visuals.
Sales forecast templates: Pre-built templates for forecast reports.
Sales forecasting factors: Multivariate forecasting algorithms that allow users to adjust the factors that influence projections.
Sales forecast review: Tracks forecast accuracy/model fit and confidence.
Sales forecasting management: Ability to manage models and assumptions based on forecast reviews.
Custom forecast models: Allows for granular control over the forecasting approach.
Record of previous sales forecasts: Record of previous forecasts/audit trail.
Sales forecast collaboration: Allows users to collaborate on sales forecasts. For example, different users may access, comment on and adjust forecasts.
Data import: Import historical data from CRM systems and other sales software.
Export to Excel
Sales forecasting serves two main use cases. First, projections can be used for demand planning. In this ERP-use case, forecasts inform product inventory and resource planning. The goal is to improve production scheduling, inventory management and workforce logistics.
Some sales forecasting tools offer static reports. More sophisticated tools offer dynamic forecasts that continually test model fit. In other words, they test how well certain assumptions predict sales accurately.
Either way, the software should document the models used and provide visibility into underlying assumptions. Sales forecasting templates help simplify the math and data analysis. However, they should not be a black box. The mechanisms should be available for review and adjustment.
Projected sales numbers are rarely 100% accurate. Therefore, it is important to understand trends in the inaccuracy of the sales forecasts themselves. This will help managers produce more realistic forecasts in the future. It will also provide insight into what type of operational inefficiencies might arise because of inaccurate forecasts. For example, under-estimation can lead to deliver limitations or customer service failures. Over-estimation can lead to wasted overhead due to over-investment in products and resources.
The most robust sales forecasting tools will perform meta-analysis on the type of errors made in forecasts, and improve model fit accordingly.
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