Pros and Cons of Sales Forecasting

 In Sales

If you’re in a sales profession, you may make or receive sales forecasting reports as part of your job duties. Sales forecasting is a way of estimating future revenue by estimating the amount of sales a business may make in a particular timeframe. Understanding sales forecasting can help you make better business decisions around costs, risks or future plans, so it’s important to use a method that aligns with your business practices. In this article, we discuss different sales forecasting methods and explore their pros and cons.

Nine sales forecasting methods

Here are nine sales forecasting methods to consider:

1. Intuitive

The intuitive method relies on sales representatives to provide information to sales managers, business administrators and stakeholders on the probability of closing specific sales deals. In their predictions, sales representatives share how much they think the deal is worth and how long they approximate it takes to close the deal. This can be beneficial when there are new products that currently don’t have historical data to back their projections.

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2. Lead-driven

The lead-driven method combines other sales forecasting methods, including sales cycles, opportunities and historical sales for which the sales team supplies data and input. This method relies on assigning value to each lead based on the probability that the sale closes. This method may require data collection and teamwork between sales, marketing and finance teams so they can predict more accurate sales closing and profits.

3. Historical

The historical method relies on comparing the data from the same time period in years past to the current timeframe. This forecast relies on assumptions that your sales can be equal or greater to the data from the previous year. It can be better to use this data as a benchmark since you can’t predict the long-term interest of buyers or the consistency of demand.

4. Opportunity stage

The opportunity stage method relies on predicting the probability of closing sales during each stage of the sales process. With this method, sales teams decide on reporting frequency and make predictions on sales based on how close they are to close particular sales deals. This method may be less accurate than others because of its reliance on probability and predictions.

5. Length of sales cycle

The length of sales cycle method relies on the time to close each individual sales opportunity for its predictions. It requires accurate tracking of when a sales process begins with a client to when they close on the sale. It usually uses objective data to help with reliable forecasting.

6. Pipeline

The pipeline method relies on calculating the chances of closing every deal currently in the pipeline, or purchasing process. This method may have a long review process, depending on the size of the sales team and how many stages are in your business process. It’s considered a more accurate forecast because of its reliance on data. If this method seems like a viable option for your business, consider investing in automated software to streamline the review process so labor can focus elsewhere.

7. Qualitative demand

Qualitative demand forecasting relies on the thoughts and opinions of industry experts. Businesses may gather a group of experts in their industry to gather feedback on labor, budgeting and the growing trends of their industry. This can also include client or customer feedback through focus groups and surveys.

8. Quantitative demand

Quantitative demand forecasting relies on historical data of the business. It includes data related to inventory, sales, services and labor. Decisions with quantitative forecasting methods focus on numerical data only.

9. Multivariable sales analysis

The multivariable sales analysis forecasting method can provide accurate analyses because it combines multiple factors from other methods with predictive analytics. This may include the length of a sales cycle, the rate of success for various opportunities and the success rate of sales representatives in producing forecasts. Businesses with large budgets typically use this method because they can afford the necessary analytic software.

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Pros of sales forecasting

Here’s a list of nine pros of sales forecasting:

1. Provides insights into upcoming sales and revenue

Forecasting can provide insight into upcoming sales and revenue through reviewing past sales and labor costs and learning from experts and past customers. If your forecast combines customer feedback with expert projections and internal numbers, it may lead to a reliable forecast. Understanding where sales and revenue may come from can help businesses know where to best focus on spending and labor.

2. Aligns business strategies and results

Using a sales forecast to align business strategies and results can provide guidance on where your business can best allocate resources. For example, it can predict specific times when you may allocate resources to the distribution center or the materials departments. It may lead to increased production of products since development teams can plan accordingly for busier quarters.

3. Provides opportunities to make adjustments

Sales forecasts can provide businesses opportunities to make adjustments to their workflows based on projections. This is important for creating products and using raw materials. For example, forecasting can let decision-makers know when to produce more or less of a product so that they can use their money more efficiently.

4. Reveals patterns in data

Reviewing numerical data can help expose patterns and trends in data, such as when sales may be most lucrative and when sales may be lower. This can help with weekly and quarterly forecast predictions. It also can help plan focused marketing campaigns to increase sales in periods that historically are not as profitable.

5. Appeals to stakeholders

By using numerical data methods, you may attract investors or loan providers for your business. Investors typically trust numerical data because of the accuracy it provides in guiding projected sales. This type of data is something they might be interested in seeing before making investments.

6. Is easy to do

With the various methods available, it can be a fairly straightforward process to establish a sales forecast for a business. Businesses may try different methods while learning which method best aligns with their sales practices. Regardless of the selected method, it can help drive sales and clarify the best places for applying strategy and resources.

7. Helps integrate lead sources

Depending on the method your business uses, it’s easy to integrate new lead sources into upcoming sales forecasts. This can provide greater accuracy to forecasted projections. For example, if a sales team’s original forecast has lower numbers, but they have an increase in new leads, updating the forecast can help with having the right amount of resources available to meet the new projected numbers.

8. Begins gathering data on new products or services

Sales forecasts can help discover the value of newly developed products and services. For example, you can review past numerical data to see projections from current products and services and use the forecast to see the differences that new ones make. This can help businesses learn what new ideas are worth continuing and which ones to reevaluate.

9. Establishes a sales process

A sales forecast can help new sales teams develop a process and workflow for completing each stage of the sales process. For example, these processes can help predict the probability of closing sales, leading to more accurate future forecasts. Establishing guidelines can also create consistency in the process.

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Cons of sales forecasting

Here’s a list of seven cons of sales forecasting:

1. Is hard to predict

Sales forecasting can’t always account for unexpected occurrences that may happen. This can include natural disasters, shifts in the economy or scarcity of raw materials. While it may reveal some predictions through qualitative data, there are some things that are always unknown.

2. Creates biased opinions

If businesses rely solely on qualitative data for their sales forecasting, they may tailor projections based on biased thoughts and opinions. For example, current customer feedback data can help guide sales for existing customers but may lack insight on how to reach new customers. Consider reviewing numerical data for the trends and find ideas for a new customer base for targeted marketing efforts.

3. Requires clean data

It’s essential to have reliable and accurate forecasting, which means the data may require cleaning. Data cleaning can be a time-intensive process depending on the size of data and the amount of cleaning necessary. This process may not be necessary after completing a sales forecast process a few times. Plus, it may help your team find reliable methods for organizing data and keeping information consistent. For example, typos can easily skew the data.

Depending on the type of data, it may require multiple employees to gather, organize and analyze all of it to create a forecast. For example, with quantitative demand methods, there may be a lot of data for employees to review. If quantitative data provides an effective forecasting solution but is labor-intensive for your company, consider weighing the costs to profits or mixing qualitative and quantitative data.

5. Lacks essential details with numerical data

Numerical data may have accurate numbers from past sales, labor and inventory, but it may lack the details necessary to forecast the future popularity of particular products or services. For example, if your business sells food items become part of a health food trend, that may lead to a sudden unpredicted increase in sales. Consider combining customer or client feedback data from surveys or focus groups along with numeric data to help fill in details.

6. Affected by employee turnover

If your business unexpectedly experiences a high rate of employee turnover, it can affect the accuracy of your forecast. For example, if a mostly new sales team reviews past numerical data, they may not provide an accurate forecast because they’re not the ones who produced the numbers, and there may be new workflows in place. If you created forecasts with a more experienced sales team in place and have new replacements occur unexpectedly during the forecasted period, educate the new employees about the numbers as soon as possible.

7. Promotes false optimism

When creating forecasts, sales teams may plan their forecasts with optimistic projections rather than realistic projections. While their input is valuable, it’s important to incorporate other data sources for a more accurate projection. For example, compare the input from sales representatives to their historical data of sales the previous year and balance the difference in information in the forecast.


Source: Indeed

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