Gong AI Platform Delivers Improved Accuracy to Revenue Forecasting

Artificial intelligence is poised to become a key player in helping businesses fix their broken forecasting efforts.

According to current research, 81% of more than 2,000 business leaders at privately held companies in the U.S. and U.K. admitted they missed revenue targets in the last two years. Such missed opportunities open the door for AI to improve business forecasting accuracy.

Revenue intelligence firm Gong, on Tuesday, released a report that spotlights companies’ challenges when developing their sales forecasts and revenue projections.

The report notes that rapidly changing conditions are prompting teams to reevaluate how they handle forecasting. A significant factor in the misdiagnosed bottom line was outdated and inaccurate technology.

Despite one-third of the respondents attributing responsibility to antiquated business systems, only 35% of those surveyed said they are investigating or have invested in new, more advanced tech systems that leverage AI to forecast more accurately. Notably, companies that missed their sales forecast involved at least one quarter between the first quarter of 2021 and the third quarter of 2023.

Addressing Revenue Forecast Accuracy

Late last year, Gong announced enterprise-grade enhancements to Gong Forecast, its AI-powered forecasting and pipeline management solution to address forecasting shortfalls.

The upgraded system focuses on more than 300 buying signals across more than three billion customer interactions captured on the Gong revenue intelligence platform. The AI-powered forecasting system helps teams drive more accurate forecasts and deliver predictable revenue.

“Predictions about the trajectory of the business and its impact are essential to a revenue leader’s role,” said Amit Bendov, CEO and co-founder of Gong. “But not all predictions are created equal.”

It’s critical to receive a core baseline understanding of the true risks and opportunities within the pipeline to make accurate predictions that positively impact the business, Bendov offered. Gong Forecast addresses those understanding gaps so companies can rely on a complete picture of what is happening within their pipeline to run their business with greater accuracy.

AI Forecasting With Tailored Insights, Customer Analysis

The quarterly forecasting and reporting process for business leaders has mostly stayed the same over the decades. 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.

However, revenue teams have grappled with organizational changes and unpredictable buyer behavior in recent years. Resolving those two factors is increasingly challenging for revenue leaders to empower teams with the insights needed to call accurate forecasts, effectively manage their pipeline, and hit their numbers.

According to Gartner, 67% of sales operations leaders agree that creating accurate sales forecasts is harder today than three years ago. At the same time, 78% of RevOps and sales leaders say they lack the correct data to forecast accurately.

Gong Forecast aims to set a new bar for delivering highly accurate, reliable forecasts in turbulent times. Gong’s approach to AI relies on insights specific to customers to provide more accurate prediction results. These elements include a keen analysis of customer interactions and key personas involved in a deal.

AI-Powered Input Makes a Difference

The platform’s upgrade tweaks the AI development Gong has used since it started in 2015. New to the process is better data analysis to understand context, intent, tone, and outcome deeply, Bendov explained.

“As part of that work, Gong has generated the largest dataset of customer interactions in the industry,” he told CRM Buyer.

Gong employs over 40 proprietary AI models, each trained on sales-specific data and capable of identifying events such as customer objections, deal risks, and opportunities. These models, in turn, generate relevant and accurate recommendations for next steps.

The hybrid approach uses both general-purpose models that it augments with its own sales expertise and the company’s self-built data and models trained on customer interactions.

“Gong’s proprietary models deliver a level of accuracy that’s two times better than off-the-shelf, general-purpose models,” added Bendov.

How AI-Based Forecasting Works

Gong aggregates precise deal predictions based on 300 buying signals from across the entire pipeline to forecast revenue outcomes with high degrees of accuracy. Traditional forecasting solutions rely on CRM data entered manually to predict a number.

Another key difference is the platform’s ability to leverage positive and negative signals to drive insights that can help teams identify and address issues early to maximize revenue opportunities, according to Bendov.

For example, Gong automatically tracks discussions around pricing, legal review, and whether competitors are mentioned in the appropriate stage of a deal. It uses those elements in scoring deals and creating an overall forecast.

According to Forrester Research, sales professionals devote 77% of their time to non-selling tasks like notetaking, updating CRM systems, and other administrative activities. That time glut leaves little time for selling or relationship development. It also introduces opportunities for human error and bias associated with manual CRM data input.

AI Tools Elevate Sales Efficiency

AI offers new ways for sales professionals to capture critical data effortlessly and close more deals. Bendov offered that AI tools can directly impact a company’s sales prowess in four essential ways:

• Capture and analyze communication between a sales rep and customers or prospects at scale
• Identify nuanced concepts within conversations that could influence the deal-closing, such as pricing discussions and upcoming strategic initiatives
• Share recommended next steps on how to proceed best to close a deal
• Automate many of these steps, such as drafting highly personalized emails based on the information captured

Gong’s approach can have a big impact on business. According to the report, it can prevent a company from resorting to freezes on hiring, raises, and layoffs.

In the U.S., 42% of respondents said they had to freeze hiring because of missed forecasts, and nearly 40% said they had to pause planned pay increases and bonuses.

Only 19% said they had not missed a forecast over the last seven quarters, but 28% of those surveyed said they had to let people go.

AI Forecasting Drives Corporate Strategy Changes

A separate study by CensusWide this month in the U.S. and U.K. found that forecasting using AI-powered predictive models delivered 20% more accurate results than projections made using CRM alone. Almost as many, 18%, also believe they spend too much time forecasting.

An apparent appetite for updated tech arrives as huge advancements in generative AI have made new, more accurate forecasting technology widely available to businesses. Based on the forecasting dilemma, research shows that companies are beginning to change their methods.

When asked how their companies were handling their forecasting process this year, 34% of respondents in the U.S. said that they are or plan to make changes. Thirty-five percent said they are investigating or have invested in new, more advanced technology and systems to forecast more accurately.

Revenue projections are looking up, according to Gong, with 68% saying they are increasing revenue projections. In the U.S., the majority of respondents are projecting increased revenues for this year, 16% see decreasing revenue projections, and 16% are keeping projections steady.