Sales Forecasting is highly crucial, regardless of the business you’re running. It assists organizations in safeguarding themselves by detecting and resolving conflicts in advance. Sales forecasting aids businesses in planning by estimating potential market growth and, as a result, assisting them in shaping their business model.
This crucial sales forecasting process also helps companies make coherent budgeting, recruiting, and goal-setting decisions. However critical as this is, most companies are inaccurate in this regard. This situation is where artificial intelligence (AI) enters the sales forecasting game.
Anthony Iannarino – “The only way to improve the sales forecast is to review each opportunity.”
Some recent statistics on the sales forecasting scenario:
1. The majority of companies are unable to make anything near to reliable revenue predictions. Businesses believe that their efforts are unsuccessful in 69% of cases.
2. Companies who make reliable predictions are 10% more likely to increase sales year over year and more likely to be at the top of their field.
3. The reality is that only about 28% of revenue predictions are very close to being correct.
Now that we know, most sales forecasting predictions aren’t of much help to businesses. Let us look at the causes of this effect and answer the question, “Why are Sales Forecast Inaccurate?”
1. Scarcity of accurate data:
Your forecasts will be far from reliable if you don’t feed your databases with accurate and high-quality data. According to Experian’s research, 94% of companies have data errors on their side.
Human error is the most common source of data inaccuracy. Automation will save the employees the time and effort of entering data, as well as reduce inaccuracies.
2. The Competitor Effect:
As businesses adjust their strategies to changing environmental conditions, competitive pressure affects many business decisions, including accounting and financial decisions. Any significant changes made by your competitors can affect your revenues. If they lower their prices, you will have to lower yours or risk losing sales.Marco Maria Mattei, Professor of Accounting at the University of Bologna, published an article discovering how competitive pressure on the commodity affects the accuracy of analysts in sales forecasting.
3. The Sales Team Effect:
There two cases through which the sales team affects the sales forecasting.
Case 1: Often, it sounds like the sales representatives are inhaling optimism rather than oxygen. That isn’t always a negative thing; being upbeat aids their performance. The only problem is that their revenue estimates are often inaccurate.
Case 2: Revenues will drop if salespeople leave the company. If they leave suddenly, the revenue projections will be off once again.
4. Negligence in Accountability for Sales Forecast:
Today, only a few businesses have a framework to ensure that revenue forecast accuracy is measured and analyzed. Sales forecasters are not held personally responsible for the predictions they make. The risk is that if they have no desire to change how they predict revenue, they will never figure out how to make it as reliable and practical as possible.
Inaccurate sale forecasting takes a toll on critical business processes.
How Does Inaccurate Sales Forecasting Affect Businesses?
Inventory takes the brunt of lousy forecasting more than any other aspect of the company. Inaccurate sales forecasting or failing to foresee spikes or troughs in consumer demand can result in inventory shortages or excesses, all of which can be costly.
A lack of goods erodes consumer interest, lowers earnings, and gives rivals a golden opportunity to fill the void in the market. An oversupply raises manufacturing costs and creates a disparity between production costs and sales receipts. In any case, inventory issues caused by inadequate forecasts can significantly impact a company’s cash flow and profit margins.
Kristen Zhivago – “The key to forecasting accurately is to know how and where these other influences are occurring.”
The Solution; How AI helps in Sales Forecasting?
1. Predicting Through Analytics:
Predictive analytics examines the data, looks for trends, and makes predictions based on those patterns. Machine learning is used in predictive analytics platforms and software to accomplish this. Machine learning is an AI technique for detecting patterns in large datasets.
In the business and marketing spheres, predictive analytics has a broad variety of uses. It can be used for everything from estimating customer turnover to forecasting equipment maintenance to detecting potential fraud. These forecasts have the potential to save a company money or increase its value.
2. Processing of Data:
Regularly, businesses produce enormous quantities of data. However, humans have a difficult time processing this information. There is just too much information for humans to process on their own. As a consequence, we neglect a large portion of our results.
Since there is so much data to handle, valuable information is discarded. This uncertainty means we’re making decisions based on incomplete information. As a result, there are inaccuracies. AI will help you improve your sales forecasting efforts by analyzing data and presenting insights, observations, and advice to your team, all while enhancing the overall sales process.
A chatbot design mimics rather than replaces interactive communication. It’s the start of a dialogue. Chatbots, when created correctly, represent humans and help to make more meaningful human connections.
Chatbots that are deployed internally will assist the employees in making better forecasts. These bots could save your employees time and effort by allowing them to gather data and information from your system quickly. Instead of wasting their time scouring your plans, they may send a message, and the bot will retrieve the information for them.
4. Optimizing Lead Scoring:
Too many times, salespeople fall into the risky pit of scoring leads based on deceptive purchasing signals. The predictive lead scoring is mostly based on the gut instincts of salespeople and their unreliable knowledge.
AI gives companies better lead scoring capabilities, allowing them to predict the fate of sales opportunities more effectively and, as a result, deliver more reliable sales forecasts. AI will sift through massive amounts of real-time and historical data to find the most profitable sales leads.
To sum it up
Artificial intelligence has evolved a long way from its origins as a science fiction plot device. AI is being used by businesses worldwide to optimize processes, inform decisions, and improve the customer experience. Organizations in the sales industry are no exception.
AI empowers sales teams to accelerate their understanding of customers, accurately assess their actual value, and increase forecasting accuracy. It’s no coincidence that AI is the fastest-growing technology for sales teams, with adoption projected to rise at an unprecedented rate over the next three years.
We hope this piece of information gives you enough value for your upcoming sales endeavors and brings you some clearance over sales forecasting. We would also appreciate it if you could share your valuable feedback with us.
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