In a broader sense, a sales forecast is the process of evaluating key metrics to predict future sales. Key metrics such as sales quota, lead closed, pipeline coverage, and historical conversion can be used to accurately forecast sales.
A sales forecast takes into account all of the sales processes that are within a company and analyze them. One common way companies determine projected sales is by using historical data to make informed decisions about future sales.
When companies don’t have historical sales data because they are less than a year old or they are small businesses with not enough customer data, they have to rely on other financial planning methods to predict sales.
Typically, a company relies on sales projections from reps based on the rep’s current sales. This method of determining sales forecasts is often referred to as intuitive forecasting.
What is the Intuitive Forecasting Method?
Intuitive forecasting is strictly based on the reps’ perception of the number of deals they believe they can close for the forecasting period. It is a tricky forecasting method because it is strictly based on intuition.
Traditional forecasting methods such as historical sales forecasting rely on past sales trends, average deal size, and deal amount to predict future sales. Methods that rely on quantifiable data have a more rigorous forecasting process, unlike intuitive forecasting. This forecasting approach is typically used for the current reporting period where each rep makes sales predictions as to the likelihood they are to meet/go above their sales goal.
There isn’t a quantifiable metric that can be taken into account when using this forecasting tool which is why companies often deem it as being unreliable. The way the rep presents this forecasting model can vary, but the basic underlying component is the same.
Example of Intuitive Sales Forecast?
The basic component of intuitive forecasting is simple. Usually, a rep will predict the dollar amount he or she is going to bring in in a given period of time. Reps will often use language similar to /
During this quarter, I plan on bringing in X number of dollars within X number of days.
With intuitive forecasting, each sales rep examines all of the sales and the potential value of each of the sales in the pipeline in addition to all of the prospective opportunities that they have planned in the foreseeable future. This determines the deal size for the quarter for the rep.
The rep takes everything into account and based on their analysis of all of the opportunities and deals presented to them, the reps forecast that they will bring in, for example, $1,000,000 in sales for this sales quarter.
Who is Intuitive Forecasting For?
Due to the vague nature of intuitive forecasting, many companies don’t use this forecasting method. This method, however, is a good for companies that have been around for less than a year because they have no previous sales forecasting data to reference. New companies don’t have any previous revenue that they can use as a baseline which is why intuitive forecasting is the best method for them.
Traditionally, companies use past forecasting data or historical forecasting, to set benchmarks for current and future sales. Historical data typically dates back anywhere from one to three years. Without this data, companies don’t have a set metric to use as a baseline to predict current and future sales.
As companies mature and create more clean data, they can use other forecasting techniques like historical sales forecasting, pipeline sales forecasting, length of sales cycle forecasting, and opportunity stage forecasting to verify intuitive data.
If companies want to have intuitive sales forecasting as part of their revenue projections, and they are not financially restrained, then they can also use AI technology, regression analysis and multivariable assessments to further determine the accuracy of the intuitive data.
This is why companies that are fairly new use intuitive forecasting to predict the number of sales they will be able to close for a given period.
Psychology Behind the Forecast Method
Companies prefer not to use intuitive forecasting especially when they have data from the past that they can use to predict sales. The reason for this is that intuitive forecasting stems from an instinctive feeling rather than conscious reasoning.
Based off of this feeling and the economic phenomenon of loss aversion, reps’ forecasting data may not be accurate. In order to understand the theory behind loss aversion, check out this short YouTube video.
In a nutshell, loss aversion is the ability to make decisions based on the mathematical equation of risk in relation to what’s at stake. The psychological component of risk aversion is that the negative impact that we have when we feel loss is twice as big as the impact of what we have gained.
From a sales rep perspective, there are a lot of pressures that are in place for a sales rep to successfully close a deal. Job security and peer competitiveness are some of the factors that can negatively impact a rep’s forecasting ability. In addition, an optimistic interaction with a lead can also negatively impact a rep’s forecasting ability because he or she might over-inflate his or her ability to close a deal.
There is no accurate way of knowing if the forecast that the rep is giving is an accurate depiction of his or her pipeline which is why companies turn to AI-based software to confirm a rep’s intuitive forecast.
Leads Closed
As mentioned in our previous article, The A-Z Guide of Roll-Up Forecasting, not all sales opportunities that enter the sales pipeline convert to leads. The number of leads a rep closes can be measured against the overall quota.
The amount of leads a rep closes gives insight to the rep’s performance as well. If a rep is in need of coaching or support, the best thing to do is to track a rep’s daily performance. This way, help, and coaching can be offered early enough to get the reps back on track.
One of the issues that can come up is that of the lead source. Typically, a company’s BDR team compiles a list of leads and presents them to a sales account sales manager who gives the leads to the sales reps. In addition, the marketing team of a company also compiles a list of leads based on KPIs that they measure from the website or other digital platforms in addition to the market research that they do. Based on the digital data, marketing teams are also able to compile a list of leads to give to the account sales managers.
Sales Quota
A sales quota is the entire sales goal for a company and each of its reps within a specified period of time. Sales teams of a company can have an overall quota for the quarter or year and each sales rep would be responsible for a certain portion of the quota.
A company needs to have a quota to be able to set goals. Without these goals in place, it becomes very difficult for a sales team to predict sales forecasts for the quarter or year. Companies can also use past quotas for particular times of the year to predict current quotas.
For example, if you have a company that sells gift wrapping paper, the fourth quarter of the year will have the highest sales because most people purchase wrapping paper around Christmas.
You might also be able to spot anomalies in quarters, such as selling more wrapping during May for Mother’s Day, that can help you predict the current quota for the period.
Sales Pipeline Coverage
Sales pipeline coverage gives a rep an idea of how much cushion they have to hit their target quota. The larger the pipeline, the larger the buffer a rep will have in case a deal falls through. Pipeline coverage is your total amount of sales opportunities in relation to your sales quota.
With accurate sales forecasts, companies are able to make informed business decisions that aid in predicting short and long-term financial performance.
Companies that have been around for at least a year can use past sales data from reps and the key metrics mentioned above to predict future sales. This method of forecasting can be accurate because companies are using trends in past performance to predict future sales.
The Need for Artificial Intelligence
Artificial Intelligence is used in customer relationship management to help absolve forecasting errors and lost sales in addition to, increasing resource optimization. Artificial intelligence software analyzes a rep’s communication with the lead and comes up with a forecast of its own. The theory behind AI forecasting is that it reduces any biases the rep might have when predicting sales.
The software uses text and voice analysis to get an idea of how a rep is performing with a current lead. For example, a rep says that he is 80% sure that he is going to close a deal. However, based off of the rep’s email exchanges and underlying tone conveyed in email analyzed by software, the AI forecast will predict that the rep, in reality, only has a 60% of closing a deal.
While this method seems like a reliable way to check a rep’s intuitive forecast, there is an ethical issue involved. With AI technology tapping into personal emails, phone calls and other forms of communication, some argue that this is an invasion of privacy.
By using AI technology, a company is taking away empowerment from its employees and essentially sending a message that the company doesn’t trust its own sales team. Keep in mind, not every company uses this method because they deem it unnecessary and expensive. There are many ways a company can analyze sales forecasts. It really depends on each individual company as to what method they would like to use.
Benefits of Intuitive Forecasting
While intuitive forecasting might not be for everyone, there are some benefits to this forecasting method.
For one, your reps work closely with the prospects and so they know what their needs are. If used correctly and if the sales reps are able to accurately capture their prospects’ intentions, then this method can be somewhat accurate.
Intuitive sales forecasting can also have companies plan possible scenarios that might impact revenue projections. Since both these forecasting methods don’t rely heavily on hard data, they should be verified with more data intensive sales forecasting techniques.
In addition, this method doesn’t require any historical data. As mentioned above, newly formed companies do not have historical data they can use as benchmarks. Companies might use competitor forecasts to calculate their own forecast but this is extremely unreliable and inaccurate because each company is unique and has a different sales structure.
If a company is less than a year old, intuitive forecasting is the best method to predict sales for a given time period.
Potential Issues with Intuitive Sales Forecasting
The issue with this method is that intuitive forecasting relies on a rep’s forecasting ability. Individual rep performance is measured differently amongst the sales team. For example, an account manager might view each rep’s performance based on quantifiable data while the reps might rely on intuition in addition to their respective historical data to determine their performance.
It is purely subjective and because each rep is inherently different, this method doesn’t translate from one rep to another. This method essentially is based on an educated guess.
Normally, people have a hard time setting realistic goals because they are biased. In the sales world, more often than not, reps inflate their conversion rate of leads so that quarterly forecasts are closer to, if not greater than the sales goal.
In addition, due to its subjective nature, intuitive forecasting cannot be scaled or replicated so each time this forecasting method is used, it can produce different results.
Internal Factors for Intuitive Sales Forecasting
There are multiple factors that that need to be taken into account when using an intuitive sales forecasting method. One internal factor that can either hinder or be beneficial for intuitive forecasting is setting sales goals.
When the sales reps are given realistic targets that they are able to hit, reps are less likely to inflate forecasting numbers and give more accurate revenue projections. In addition, companies should always be planning for their sales budget.
Sales budget accounts for the hiring of additional employees. When companies don’t have a large enough sales budget to hire additional reps due to poor resource management or extreme external factors, the company puts more pressure on the individual sales reps to outperform previous years’ sales goals.
Another internal factor that can impact intuitive sale forecasting is a product change. Companies might launch a new version of a product that might not be selling as well as it used to. Reps that have high sales goals may now be able to meet their sales goal for the new products which can cause them to have a biased intuitive sales forecast.
External factors are factors that the rep has no control over because they are factors outside the company that can impact a rep’s sales goal.
Market changes resulting in a decrease in buyer demand can impact a reps intuitive forecast numbers because their total sales might be less than anticipated for the quarter or year. In addition, as technology advances, there might be industry changes resulting in the change of industry trends for a particular product.
When this happens, demand either increases or decreases for certain products. Companies need to always be innovating or else their product will fall behind. If company products can’t keep up with market demand, then reps will have a hard time selling the product which can cause them to inflate their forecast numbers.
How to Resolve the Bias Mindset with Intuitive Sales Forecasting
There is no way to completely resolve a biased mindset a rep may have when using this forecasting method. However, companies can use other forecasting methods in conjunction with intuitive sales forecasting to make it more reliable.
Some forecasting methods that work well with intuitive sales forecasting are; opportunity stage forecasting, pipeline forecasting, and length of sales cycle forecasting. Companies can offer training to their sales reps to help them reduce some of the bias involved. However, as previously mentioned, this method is quite subjective.
Conclusion:
Having accurate predictions with this sales forecasting method can be a challenge because of the biased nature of the forecasting method. When this forecasting method is used with AI technology or other forecasting methods, intuitive sales forecasting can have somewhat accurate forecasts.