3 Ways SalesTech with Artificial Intelligence Improves Forecast Accuracy

In the last 35 years, CRM functionality has provided a more sophisticated method of forecasting with features promoting consistency across teams.

Even with improved systems and increased investments in SalesTech, data is often exported into spreadsheets for Sales Ops or Sales Leaders to perform additional analysis—applying their own judgment to probabilities and timing. The reality is that no one knows which opportunities will close or when—that’s why it’s called a forecast. With inaccurate predictions, forecast accuracy is frequently cited as a top concern for Sales Leaders, CEOs, and the Board.

Sales reps are also spending 2.5 hours every week updating their forecasts with less than 75% accuracy. Forecasting Intelligence solutions that work inside of your CRM can help. These solutions ensure that data is collected and logged into CRM, leading to more accurate, data-driven forecasting.

Here are three ways SalesTech with artificial intelligence improves forecast accuracy:

1. More Complete Data

  • Old Way: Manual. Sellers must remember to log their meetings, meeting notes, and emails into CRM. Many wait until Friday to enter activities for the entire week. Activities, details, context, and next steps often are forgotten. Much of the data needed to assess a forecast is missing.
  • New Way: Automated data capture and history. Manual entry is a thing of the past. Email, calendar, conversation recordings, and phone calls are logged into CRM automatically. Rich information like topics, action items, next steps, current and new stakeholder identification are captured and assessed against the forecast.

2. Insightful Analysis

  • Old Way: Limited. Forecast changes can only be analyzed manually and only if each forecast was saved in Excel. No automated analysis of which opportunities have pushed or pulled, how often or why, or how deal size is trending. Judgments on buyer sentiments and other accuracy indicators are based on seller perception.
  • New Way: AI-powered analysis. All engagement and activity data are analyzed by AI. This includes the ratio of emails initiated to emails received, emotion and tone of communication, buyer sentiments, competitor mentions and other indicators that are typically associated with the art of selling. Snapshotted and versioned forecasts allow you to analyze every change from forecast categories and stages, to deal, managers and sellers. Smart scoring allows you to identify where risks are highest. Using spreadsheets limits insights.

3. Best in Class Execution

  • Old Way: Manual. Stage advancement based on static lists of qualifying tasks can lead to inaccuracy because they don’t take into account differences in buying dynamics. Without machine-learning and artificial intelligence, the many moving parts of a deal are left to be deciphered, actioned, and dispositioned manually.
  • New Way: Consistent execution. Identification of winning plays and recommended actions eliminate time spent on guesswork and task scheduling. Automation of repetitive rep tasks and alerts that guide actions and deal prioritization increase rep productivity and forecast predictability. Static lists and manual entry only leave accuracy at the mercy of human error.

Relying on manual entry and human predictions alone will place organizations behind the competition. With consistent and disciplined forecasting and pipeline management processes, SalesTech is a powerful tool to improve forecast accuracy, help A-player talent increase selling time, reduce friction in the employee experience, and arm the CEO, ELT, and Board with accurate data to drive decisions aligned with the revenue growth strategy. 

To better understand which SalesTech works best for your organization, connect with SBI Commercial Tech Practice Lead, Nancy Nardin, to navigate the 1200+ tools in the market.