Sales Forecasting entails sales managers using historical data to map out future trends, potential mishaps, and the overall journey of the product/service. It creates a way to support decision analysis, estimate expenditure, and budget plans. However, many companies fail when it comes to sales forecasting with a percentage as high as eighty. The pain points come in the form of primary causes — poor sales execution, flawed forecast roll-up processes, and lack of accountability. The solution to these problems comes in the form of AI-based forecasting, which helps in mitigating such hurdles.
The Need for Sales Forecasting
Irrespective of the size of the company, whether it is a large organization or SMEs, every firm must determine future sales revenues, allocate workforce and monetary resources, and come up with strategies for future growth.
- Getting Capital — Sales forecasting is important for any business to start off. Businesses need capital, investments to set up offices, team, gather technology, and serve the industry. This business plan entails sales forecasting at its core prior to pitching to investors.
- Measuring Growth — With up and running, sales forecasting plays a bigger role after the establishment of the company. For instance, if the business is lacking in revenue in relevance to the predictions, then it is a sign of poor performance. Hence, sales forecasting also helps in maintaining the overall growth of the business.
- Mapping Supply based on Demand — Based on conclusive databases, sales forecasts help firms figure out interest in products or services and predict the same for future growth. Using this forecast, firms can also increase or reduce supply in order to meet demand and in the process, generate revenue without surplus.
- Creating Marketing Plans — By observing how well the forecast looks or how poor it looks, marketers can increase their campaigns, better target them, reposition or reassess the market. Projections also contribute to a quarterly or yearly plan of marketing budgets and serve as a benchmark for the campaigns to be carried out.
How AI-based Sales Helps Businesses
Salesforce reports that sales leaders expect their teams’ adoption of AI for sales to grow 155% by 2020. It also found that high-performing sales teams are 4.9 times more likely to use AI than others. Let’s see why these companies are eager to implement AI in their sales forecasting.
As mentioned earlier, sales forecasting is used to set budgets, fund marketing, set goals, determining expansion timelines, and much more. In the modern digital world, to succeed, marketers need to hit beyond the predicted numbers bringing precise accuracy to impact the hardest. With AI, ML, sales leaders will be able to go beyond the numbers and improve execution.
Geoff Birnes, SVP of Customer Engagement and co-founder of Atrium writes in his article that “Companies can analyze each opportunity individually and use logistic regression models to score a specific opportunity’s probability of success. By combining those predictions with the expected size of each deal, the system can project an expected Sales forecast across product groups, managers, regions, and the overall sales organization.”
AI-powered sales forecasting software gathers collected data and scouts through data signals such as emails, meetings, even phone calls, and analyzes how they relate to sales outcomes. These insights are implemented into the working pipeline and AI provides rating and visibility into the data, even advising sales representatives on what next steps to pursue. AI-powered sales forecasting also goes back and refreshes the ratings, thus learning from the past failure or success rate. It eliminates the need for forecasting using spreadsheets, emails, or other variables, where teams waste time and cannot learn what worked or failed previously.
Companies produce massive volumes of data every day. However, it is overwhelming for people to process this much information. There is a lot of data that they can’t go around on their own. As a consequence, marketers leave behind a lot of data without full control. This left data eventually lead to incorrect decisions. With the full, correct information at hand, AI will boost your revenue forecasts by supplying your employees with the right insights, comments, and recommendations.
Improved Lead Scoring
A 2016 study revealed that around 61% of companies listed misleading buying signals as one of the challenges in lead scoring. Also, while 68% of B2B sales teams have lead scoring strategies, only 40% say that it brings value. These misjudgments are based on gut feeling, incorrect, and wrong information.
AI can accurately predict the outcome of the sale pitch and support marketers to get a precise sales forecast. AI will screen and classify the most profitable sales channels using a large volume of real-time and historical data. It will use advanced analytics to fine-tune lead values by taking into consideration a number of various variables including socioeconomic, firmographic, and technographic, and then show the strongest leads. Analytics will also show the need of consumers for your product or service and may also create a timetable for a final conclusion.
Higher Retention Rates
Retaining customers plays a key role in driving the profitability of any business. It is found that existing customers are 50% more likely to try a business’ new product and 31% more when contrasted with new customers. As such, businesses can answers customers’ questions, engage, and resolve customer queries using AI-driven chatbots. They also help to monitor customer activity and solve their issues proactively. An Oracle research has shown that 8 out of 10 businesses have already implemented AI or are in the middle of implementing it in their customer service.
Improved Close Rates
With chunks of data up close, AI can study vast amounts of data and pick key purchase influencers not visible on the organizational charts of the client business. This information can create one-on-one conversations between the sales team and the person in question, leading to a shorter sales cycle. As per CSO Insights, only 50% of forecasted sales opportunities prove to be right. AI will bring insights about individuals in the client business who are more likely to complete the deal.
Organizations using predictive analytics have seen to reach 82% efficiency when it comes to sales forecasting. Predictive analytics scans the information, analyzes patterns, and make predictions. It incorporates AI, data mining, machine learning, statistics to create predictive models. These models capture relationships from different factors and assess risk with a particular set of specified conditions, and then assign a weight to the risk assessment.
Irrespective of the business model, sales forecasting is immensely important for businesses. It helps organizations to spot and mitigate issues in advance, make informed decisions, setting budgets, teams, and goals. With AI in the sales forecasting, business strengthens their hand in overseeing all these aspects with higher accuracy.