These tools provide robust capabilities to use different methods to do the forecast and flexibility to adapt the tool to your business.
Sales Forecasting Methods
Sales Rep Intuition
This is the most basic method used to forecast sales by seeking rep opinion on closing every deal in the pipeline. Usually, the sales reps tend to overestimate forecasts and hence it may not be very reliable. For example: if a rep has a very strong relationship with a customer, and he is trying to cross-sell a product he will tend to overestimate the winning probability. Also, there can be a lot of subjectivity involved and if multiple people are working on a deal, no two people may agree on a closing rate. Rep feedback can be included as one of the factors to the forecast but relying only on this can be detrimental.
This method involves looking at only historical data to make the forecast. For example, if the sales in December were x, January sales will be December sales +- growth rate over January sales. This works fine for extremely stable businesses with commodity-like products. Although it has certain shortcomings like it doesn't incorporate seasonality, customers need changes in time period etc. Historical data is important but exclusively relying on it can be harmful.
This method is based on estimating the probability of conversion based on the stage of the pipeline and the sales activity with customers. For example, deals that are sales qualified leads SQLs with demo done have a higher probability than deals that are MQL without a demo done. Combining the activity and opportunity stage, a probability is assigned to each deal and the forecast is done. The drawback of this method is that it doesn't account for the time of a deal at any stage in the pipeline. For example, a deal that comes in freshly as SQL is given the same weight as the deal that has been there for 2 quarters.
Pipeline forecasting (MultiVariate with lead scoring)
This is a very robust way of forecasting and many software today are built for this. This software creates a forecast based on the current pipeline and an estimated future pipeline by combining past sales patterns. This is a very detailed forecast method which uses predictive forecasting which includes multiple factors such as
- probability of closing (deal health) based on rep history/opportunity stage,
- average sales cycle length,
- individual rep performance,
Forecasting software allows for modeling internal and external factors and checking their impact on the forecast. Sales managers can adjust the forecast and do a detailed what-if analysis by changing these variables to understand which metric impacts the revenue the most. You can configure the forecast using any method or combination of them. This software usually provides deep filters to dissect the forecast by the Sales rep (or anyone in sales hierarchy), Sales team/company, Opportunity stage, Opportunity value and Other custom filters you create, etc
Factors that affect the Sales Forecast
Even though there are so many variables and prerequisites to get the forecast accurate, software enables us to account for all these variables and improve forecast accuracy.
Most of this software eases up the bottom-up forecasting by allowing you to incorporate the smallest unit that contributes to the forecast and add it up to get the actual sales projection.
Make Sales Forecasts with flexibility
- Ability to change sales variables (like pricing, discounts, sales reps, etc) to make forecast simulations
- Ability to forecast across geographies, accounts, products, enterprise
- Ability to have real-time forecasts and parallel forecasts
- Ability to incorporate trends, promotions, competition, seasonality, etc in forecasting
- Use deep filters to analyze be SKU, city, accounts, opportunity stage, etc
What-if Scenario Analysis
- Create what-if modeling scenarios to check the impact on revenue
- Allows the team to prepare for some new trends and market realities
- Create tactics accordingly
Use a variety of forecasting models
- Predictive analytics model
Analyze projections and performance
- Dashboards, reports, and analytics
- Data visualization for important KPI and metrics
- Sales performance analytics
AI in Sales Forecasting Software
Software differentiate themselves mainly on technology (how well they use AI).
AI can be used to leverage the deep integrations with your existing systems (email, SMS, calls etc) to capture data sales activity accurately. Some software even integrates with your engagement, enabling solutions to capture sales activity data to improve the accuracy of the forecast.
Another way sales forecasting software differentiate themselves is in the flexibility they provide in terms of customizations you can do to fit your sales process. This includes how many internal, external variables u can include, the number of forecasting methods u can apply, do simulations across business scenarios, etc.