Sales forecasting is the most important figure for any organization. It forms the basis for all investment and spending decisions that will drive growth. Therefore! the sales forecasting process is a key activity that cannot be ignored. It is essential for companies to create predictions based on demand! performance! and trends to stay ahead.
Furthermore! the rapid pace of the technology industry! which often involves uncertainty! makes planning ahead even more important.
However! in today’s world! sales forecasting is a challenge for many companies! as they fail to produce accurate forecasts: less than 20% of sales organizations have phone number list a forecast accuracy of 75% or higher! according to the Miller Heiman Group . The primary reason is the lack of a well-constructed forecasting strategy across the entire organization.
Fortunately! this doesn’t have to be the case! as there are certain things a company can do to stay ahead of the curve and make inaccurate sales forecast results a thing of the past.
With this in mind! in the following article! we’ll take a closer look at the root of the problem and suggest how to implement proper sales forecasting practices.
4 barriers to effective sales forecasting
Sales forecasting practices are incredibly useful for businesses in more ways than one. After all! they allow companies to be better prepared for future events. At the same time! sales forecasting ensures that a company can make well-informed decisions using reliable data! rather than relying germanic portions resulting in dishes on information that may be inaccurate. But to achieve the best results! it’s important to adopt the right strategy to avoid the common mistakes that many businesses around the world continually make.
01. Incomplete! incorrect or unavailable data
One of the most common issues affecting forecast accuracy is data quality. Most organizations make decisions based on incomplete! incorrect! or unavailable data. One of the most common mistakes is not including data as a key driver of business strategy. This impacts the type of forecast data that should be collected in business processes. If this hasn’t been taken into account! you’re already working with a significant gap.
Other common issues affecting data quality could include! for example! a sales representative not having sufficient information about established deals and/or (which is most often) not entering the information into the CRM.
The latter is especially a behavioral problem that usually review business occurs for two main reasons.
- A stigma has been created around data entry into CRM as “administration.” Organizations must move away from this stigma and incorporate data collection and capture as part of sales reps’ roles.
- Salespeople are overconfident! conservative! or try to fudge their sales forecasts.
02. Subjective versus quantitative methods
Due to uneven data and! often in the case of large companies! disjointed (legacy) technologies that show different numbers for the same question! companies are unable to use quantitative techniques for sales forecasting and instead rely on judgment or “finger-in-the-air” decisions.
One of the most notable mistakes is using subjective sales forecasting methods! as they are often considered to provide inaccurate results (at least when used alone). By leveraging quantitative sales forecasting methods! companies can get a more accurate picture of what the future holds.
Fortunately! in recent years! many companies have realized that subjective sales forecasting can be improved by combining it with quantitative! data-driven methods! which are reputed to be far superior at providing predictions for the future.
03. Lack of forecasting policy and processes throughout the organization
Although sales forecasting is a key business activity that organizations regularly perform! no organization-wide processes and policies have been established. Most organizations tend to use a highly independent approach to sales forecasting. Each functional department prepares a sales forecast geared toward its specific needs. The problem with this approach is the lack of functional integration. There is little to no communication! and there is no coordination or collaboration in the process.
Because of this! different departments and regions tend to use different forecasting processes and methods without first aligning them with the needs of the entire company. Organizations with multiple product lines sold through different routes to market channels! in particular! need to consider appropriate methods and a process to ensure upward visibility.