Other benefits of sales forecasting include:

  • Avoiding cash flow problems

  • Budgeting more efficiently

  • Planning for more accurate staffing

  • Aligning sales quotes with revenue expectations

  • Estimating future revenue more accurately

  • Focusing on high-revenue opportunities and eliminating sales failures

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Some basic sales metrics to measure include:

  • Number of opportunities generated. The number of leads that have expressed interest in a product or service.

  • Conversion rate. The percentage of leads that have become customers.

  • Average deal size. The average amount of money customers spend.

  • Number of deals closed. The total number of purchases.

  • Average sales cycle. The average amount of time it takes for a company to complete a full sales cycle.

  • Win/loss ratio. The amount of sales that have been won versus those that have been lost.

  • Customer retention rate. The percentage of customers that continue doing business with a company.

  • Customer lifetime value. The total value a customer brings to a business over the entire course of their relationship.

  • Sales productivity per sales representative. A measure of the effectiveness of each sales representative, usually measured by the number of sales closed divided by the number of working hours.
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Here are the most essential types of data to gather and analyze:

  • Sales data. Sales data is the most critical data to collect when forecasting. It can include sales performance information, inventory data, purchasing history, and any of the other pre-established sales benchmarks.

  • Market and industry data. Market data includes any recent and relevant analysis about the current economic and industry climate. This tells businesses what industry factors to take into consideration when making sales forecasts.

  • Customer data. Customer data includes any relevant information about a business’s customers as it relates to sales. This could include purchasing history, preferences, patterns, and demographics. Customer data helps businesses better target their customer base to improve the sales cycle.

  • Internal data. Internet data provides information about internal corporate changes that may affect sales. For example, turn over rate, new company policies, and new sales team hires are all internal data.
  • Historical forecasting. Historical forecasting is when sales professionals look at past data to predict future trends. This methodology operates under the assumption that history repeats itself. For example, a business could look at past Black Friday sales and use that information to predict future Black Friday sales. While historical forecasting is a common methodology, it’s important to remember it doesn’t take drastic external or internal changes into consideration.

  • Pipeline forecasting. Pipeline forecasting is a methodology where sales professionals analyze the current sales pipeline and compare it to other sales data. This approach evaluates every potential opportunity a sales team has to close deals and sets goals based on that data. There are several tools that help sales forecasters dive deep into a current sales pipeline and make predictions.

  • Opportunity stage forecasting. Opportunity stage forecasting dives slightly deeper into the sales pipeline. It focuses on different potential sales in different stages of the deal. Sales forecasters use this data and compare it with metrics like the average length of a sales cycle to predict future closes. 

  • Intuitive sales forecasting. Intuitive sales forecasting is a method where sales forecasters interview sales teams and gather qualitative data and stories to understand the sales process better. While capturing first-hand information into the sales process is highly useful, it’s important to use it along with data-driven forecasting methods.

  • Length of sales cycle forecasting. Length of sales cycle forecasting focuses on how long it takes sales representatives to close a deal. Typically, sales forecasters will look at historical data and compare it with the length of the sales cycle. Then, they will use that information to make forecasts about future sales. 

  • Multi-variable sales forecasting. Multivariable sales forecasting is the most detailed and comprehensive forecasting methodology. It reviews comprehensive data and uses predictive analytics to make sales forecasts.

Here are some best practices for excellent sales forecasting:

  • Integrating findings into other business processes.

  • Involving other departments in the planning process.

  • Communicating findings with all relevant stakeholders.

  • Collaborating with sales and marketing teams to capture qualitative data.

  • Using technology to capture and analyze data and automate processes.

  • Revisiting sales forecasts regularly and anytime there’s a market change.

  • Considering all historical, economic, and market trends in forecasts.

Here are some of the most common challenges to consider when creating sales forecasts:

  • Lacking historical data or sufficient data.

  • Facing unpredictable market and economic conditions.

  • Adapting to rapid changes in consumer behavior and preferences.

  • Improving visibility into the future sales pipeline.

  • Anticipating the impact of promotions and other events accurately.

  • Planning for market disruptions.

  • Encouraging collaboration and communication among departments.

  • Overcoming resistance to change and adoption of new forecasting methods.

  • Encountering inaccuracies in data collection and analysis.

  • Addressing inconsistent sales performance across different regions or product lines.