What is sales forecasting?
Sales forecasting helps companies estimate future revenue by predicting demand over a specific period. It involves historical data analyses, tracking market trends, and other relevant factors to project sales performance. In this comprehensive guide, you’ll learn how to improve and optimize the accuracy of your SaaS sales forecast with top strategies and tips.
For SaaS companies, it often involves projecting metrics like monthly recurring revenue (MRR) (and resultant annual recurring revenue (ARR), customer acquisition rates, and churn.
Examples:
- A SaaS startup might forecast user growth to smartly plan for, say, server capacity and customer support needs.
- An established SaaS firm could project renewal rates and upsells for the next quarter/year.
Accurate forecasts are the bedrock of a SaaS company’s success by enabling better:
- Revenue projection and financial planning
- Demand anticipation and feature prioritization
- Strategic growth planning and market expansion
- Resource allocation across departments
- Risk management
- Investor relations and funding strategies
- Pricing optimization
What is the difference between sales and revenue forecasting? Improve SaaS forecast accuracy
In ‘one-time’ traditional sales models, sales and revenue forecasting go hand in hand. A product sale translates directly into recognized revenue. Let’s say a bike sold is revenue earned.
However, in SaaS, sales forecasting is distinct from revenue forecasting as it focuses on new business and expansions. Revenue forecasting focuses on recognized recurring revenue.
Let’s dig a bit deeper with the table below: Keep reading to learn how to improve and optimize the accuracy of your SaaS sales forecast with top strategies and tips.
| Difference | Sales forecasting | Revenue forecasting |
| Focus | Predicts new customer acquisitions (new logos) and expansions/upsells. | Projects actual recurring revenue to be recognized. |
| Key elements | Focuses on price and timing of new sales, renewals, and upsells. | Considers sales forecasts, churn rates, contract terms, and billing cycles. |
| Outcome | A sale in SaaS typically results in recurring revenue. Used for pipeline management and sales strategy. | Tied to financial planning and budgeting. |
What are the most common methodologies for sales forecasting? Improve SaaS forecast accuracy
The common sales forecasting methods include: Continue exploring to discover how to improve and optimize the accuracy of your SaaS sales forecast process in this guide.
- Bottom-up forecasting: Each sales rep analyzes his/her pipeline, estimating closure likelihood and potential revenue for each opportunity. Individual forecasts are then consolidated by the sales leader for an organizational projection.
- Stage-weighted forecasting: In this method, probability weights are assigned to each sales pipeline stage (lead, qualification, proposal, negotiation). As one can imagine, later stages are given higher probabilities.
- Pipeline-based forecasting: One uses historical data to calculate conversion rates between pipeline stages, projecting future closures in this method. This could help account for seasonality and also incorporate qualitative inputs.
- Funnel forecasting: Finally, this method works backwards from sales targets to determine the required lead generation.
Most companies start with bottom-up forecasting and incorporate other methods as they mature and gather more historical data. So, the best method can be determined by factoring in the company’s growth stage, industry, market dynamics, and the nature of the sales cycle.
What are the best practices for effective sales forecasting?
- Regularly review and adjust forecasts: Regular forecast updates are crucial. Ideally, update your main forecasting method (e.g., bottom-up) weekly. However, triangulation with other methods can be done quarterly.
- Accountability through forecast accuracy: Implement and track a forecast accuracy metric. This will hold team members accountable for their projections and discourage overly optimistic and overly conservative estimates.
- Develop forecasting skills: Create clear guidelines on forecasting processes and probability assignments. It is prudent to conduct training sessions led by experienced sales leaders covering methodologies and best practices. Companies can also implement a mentorship program by pairing new reps with seasoned team members for hands-on learning.
- Stay market-savvy: Proactive sales and RevOps leaders consistently monitor industry trends, market conditions, and external factors. Incorporate these insights into forecasts to anticipate potential shifts and disruptions. Finally, keeping predictions aligned with market realities.
How can technology help with quicker and accurate sales forecasting?
The right technology can significantly enhance sales forecasting accuracy and speed. CRM systems and financial planning and analysis software integrate sales and financial data, while AI and machine learning improve prediction accuracy.
Cloud-based tools can help with real-time updates, while automation can reduce errors. Intelligent FP&A software allows for quick scenario modeling, and interactive dashboards make complex data more actionable, leading to more informed decision-making.
The post How to Improve the Accuracy of Your SaaS Sales Forecast? appeared first on Visualmodo.
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