Sales Forecasting Software 2024: Ultimate Guide

Sales forecasting is the process of predicting sales revenue monthly, quarterly and annually. These forecasts help sales managers to alter sales strategy and tactics to hit revenue targets.

Suppose, your forecast predicts your sales team will hit quota and there are opportunities to grow further. This would mean hiring additional resources to keep up with the demand. Another way round, if the forecasts predict your team will miss quota, you can change your strategies to focus on certain opportunities and maybe let go of a few salespeople. Thus they are also used for other business decisions like budgeting, hiring, resource management, etc.

Sales forecasts are sensitive to competition, market, government regulations, company policy, seasonality, employee count, etc. Sales forecasts are directional and usually try to be as accurate as possible. Even with a small range of error, they can be immensely useful in motivating the team to push harder to hit quota.

Sales forecasting software uses sales activity data and historical data to predict sales revenues for a given time period.

They also have strong data visualization (for KPIs and metrics) and reporting capabilities (e.g. win-rate reports, cycle time reports, activity by rep reports).

Who uses Sales Forecasting Software?

Sales Operations / Managers

Marketing

The marketing team can use the deal risk profiling in the software to adjust their ABM strategy and also understand their contribution to sales revenue.

Finance

The biggest benefit of the sales forecasting software is it gives the finance and accounting teams the information they need on revenue metrics which helps them improve their resource planning.

Improve Motivation and Performance of Teams

The primary question the forecast answers is if the company will meet sales quota or not. If yes, managers can motivate the team to beat the forecast especially the teams that are driving growth. If not, it motivates the underperforming teams to push harder to reach the numbers.

Improve Sales Planning

Sales forecasting gives sales managers a peek into the future expected revenue. Depending on the forecast, the sales managers can adjust their strategy to hit the numbers. If the forecast exceeds the quota, then it helps the sales ops team plan better for additional sales reps needs, allocate more marketing budget, and new technology needed to support the growing organization.

On the contrary, the forecast looks weak, corrective action needs to be taken to adjust to new realities which could include downsizing the team, reducing marketing budget etc.

Thus having visibility into the expected revenue at quarter/year-end helps improve the planning and be better ready for the market realities.

Sales planning improves around

• Hiring decisions

• Budgeting/cash flow decisions

• Marketing and growth decisions

• Revenues expectation setting

• Post-sales support decisions

Improve Sales Forecasting Process

A good sales forecast should be collaborative across teams and business (sales, marketing, finance, supply chain), data-driven and real-time. Organizations usually struggle with different teams using different methodology / tools to do the forecasting. Standardizing and bringing consistency in the forecasting process across the organization is the biggest benefit the sales leadership sees in forecasting tools.

Improve Sales Activity Management

Sales activity management includes keeping track of what your sales reps are doing for every deal in the pipeline (and sometimes to get new deals in the pipeline). These activities serve as good signals to determine the risk associated with each deal which is one of the most important factors that goes into the sales forecasting model. This software keeps track of all the activities done by the sales rep across channels and gives managers the visibility into rep activity.

Improve Company-wide Accountability

Forecasts help in identifying weak contributions to revenue. This can be further drilled down from enterprise-level to geographical level to account level to sales rep level. You can also slice it if contributions from certain products are lower. Hence forecasts can help identify areas to double down on at most granular levels to meet targets.

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.

Historical forecasting

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.

Opportunity forecasting

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,

• opportunity value,

• historical data,

• marketing funnel etc.

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

Internal Factors

• Prices

• Promotions

• Product mix

• Channels mix

• Customer mix

• Hiring / Firing

• Territory

• Sales Compensations

External Factors

• Market trends

• Competition

• Policy changes

• Industry changes

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

• Qualitative model

• Time series model

• Regression model

• 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.

Prerequisites to buying Sales Forecasting Software

Consistent Sales Process

Having a defined sales process which outlines the steps needed to close a sale if a must. If the reps are following different processes, then it becomes extremely difficult to assign closing probabilities and forecast suffers.

Defined Sales Goals

The forecast has to be benchmarked against the target so that you can leverage your sales team accordingly. These goals have to be defined at the rep level, team level, department level, business unit level and ultimately at the org level.

Standard Sales pipeline

Known Average Sales Cycle

Having a good understanding of time taken to generate a lead, prospect, pitch, close, onboard, renewal rates etc helps improve the forecast accuracy.

Known Average Deal Size

Knowing your deal size for the product you are trying to forecast is key to getting forecast right.

High CRM Adoption

Size of the Sales Team

Horizontal vs Point Solutions

Integration

The most important factor that decides how good your forecast is the quality of your data. So all the current systems capturing data must be integrated with forecasting tools. It may also be possible that you have to start capturing new data points and create a robust infrastructure so that all the data goes to the tool in the format that the tools need. Hence be ready with an enterprise wide commitment before you buy a sales forecasting tool.

Challenges in Adopting Sales Forecasting Software

Data Consistency

Subjectivity

Forecasts are ultimately a function of the assumptions that go into the model which are subjective decisions made by the forecaster about how to use data. Certain subjectivity is always inherent in the sales forecast if it takes in sales feedback to decide the closing probability for a deal.