Artificial intelligence could be the key to helping businesses improve their forecasting.
A recent study found that 81% of the more than 2,000 leaders in private companies in the U.S.A. and U.K. said they had missed their revenue targets over the past two years. AI could improve forecasting accuracy by taking advantage of these missed opportunities.
Revenue intelligence firm GongOn Tuesday, released an in-depth report that highlights the challenges faced by companies when developing sales forecasts and revenue estimates.
The report states that the rapidly changing environment is prompting teams reevaluate their forecasting methods. The outdated and inaccurate technology was a major factor in the incorrect diagnosis of the bottom line.
Only 35% of respondents said that they were investigating or had invested in more advanced tech systems which leverage AI for more accurate forecasting. Companies that failed to meet their sales forecasts missed at least one quarter from the first quarter of 2020 until the third quarter 2023.
Fixing the accuracy of revenue forecasts
Gong released enterprise-grade improvements to Gong Forecast last year to help address forecasting issues.
The Gong revenue intelligence platform captures more than 3 billion customer interactions, which are analyzed by the upgraded system. The AI-powered system for forecasting helps teams to make more accurate revenue forecasts.
Amit Benjamindov, co-founder and CEO of Gong, said: “Predictions of the business’s trajectory and impact are crucial to the role of a revenue manager.” “But, not all predictions are equal.”
Bendov stated that accurate forecasts can positively impact a business when a company has gathered a baseline understanding of risks and opportunities. Gong Forecast fills in those gaps, so that companies can run their businesses with greater accuracy.
AI Forecasting with Tailored Insights and Customer Analysis
Over the years, the process of quarterly reporting and forecasting for business leaders has remained largely the same. Decision-makers — CEOs, CFOs, CROs, and VPs of Sales — are tasked with reporting a prediction on revenue to their stakeholders and the market within 5% of the actual outcome.
In recent years however, sales teams have struggled to cope with the organizational changes and unpredictable behavior of buyers. It is becoming increasingly difficult for revenue leaders, to resolve these two factors, to equip their teams with the insight they need to make accurate forecasts, manage their pipeline effectively, and meet their targets.
According to Gartner67% agree that creating accurate forecasts of sales is harder than three years back. 78 % of RevOps & sales leaders claim they don’t have enough data to make accurate forecasts.
Gong Forecast sets a high bar for providing highly accurate, and reliable forecasts. Gong’s AI strategy relies on the insights of customers to deliver more accurate results. These elements include a thorough analysis of customer interaction and the key personas in a transaction.
AI-Powered Input Can Make a Difference
Gong’s AI platform has been upgraded since its inception in 2015. Bendov explains that the platform’s upgrade includes better data analytics to better understand context, tone, intent and outcome.
Gong, a CRM Buyer spokesperson, said that as part of this work he had created the largest dataset in the industry of customer interactions.
Gong uses over 40 AI models that are trained using sales-specific data. These models can identify events like customer objections, risky deals, and opportunities. These models generate accurate and relevant recommendations for the next steps.
The hybrid approach combines general-purpose models, which the company augments with their own sales expertise, and its self-built data as well as models trained on customers’ interactions.
Bendov added that “Gong’s proprietary models provide a level accuracy two times higher than general-purpose, off-the shelf models.”
AI-based Forecasting: How Does It Work?
Gong aggregates precise predictions of deal outcomes based on 300 purchasing signals from the entire pipeline. This allows for high accuracy in forecasting revenue outcomes. For traditional forecasting, CRM data is manually entered to calculate a number.
Bendov said that another key difference was the platform’s capability to use positive and negative signals as a way to generate insights, which can help teams identify early issues to maximize revenue.
Gong, for example, automatically tracks conversations around pricing, legal reviews, and whether or not competitors are mentioned at the right stage of a transaction. These elements are used to score deals and create an overall forecast.
According to Forrester ResearchSales professionals spend 77% of their working time on non-selling activities like updating CRM systems and performing other administrative tasks. The lack of time leaves little room for sales or developing relationships. This also creates the possibility of human error and bias when manually entering CRM data.
AI Tools Elevate Sales Efficiency
AI gives sales professionals new ways to collect critical data and close more deals. Bendov explained that AI tools could directly impact the sales performance of a company in four important ways:
- Capture, analyze and report on communication between sales reps and their customers or prospects.
- Nuanced concepts in conversations can influence the closing of a deal, for example pricing discussions and future strategic initiatives.
- Share next steps to closing a deal.
- Automate these steps by composing highly personalized emails using the captured information.
Gong’s approach to business can have a significant impact. The report says that Gong’s approach can have a big impact on business.
In the U.S. 42% of respondents reported that they were forced to freeze hiring due to missed forecasts. Nearly 40% of respondents also said they were forced to stop planned bonuses and pay increases.
Only 19% of respondents said that they hadn’t missed a forecast in the past seven quarters. However, 28% said they were forced to let go people.
AI Forecasting Drives Corporate Strategy Changes
Separate studies by CensusWide This month, in the U.S.A. and U.K., it was found that using AI-powered models to forecast delivered results 20% more accurate than projections made solely using CRM. 18% of respondents also said they spent too much time on forecasting.
The apparent need for new tech is evident as businesses have access to more accurate and advanced forecasting technologies thanks to huge advances in AI. Research shows that businesses are changing their methods based on the forecasting problem.
In the U.S., 34% respondents said they have made or will make changes to their company’s forecasting process. 35% of U.S. respondents have said that their companies are investing in more advanced technology or systems to improve forecasting accuracy.
According to Gong’s research, revenue projections are on the rise. 68% of respondents said they were increasing their revenue projections. In the U.S. the majority of respondents expect increased revenue this year. 16% have lower projections and 16% keep their projections the same.
