Knocked out by too much data? Sales forecasting AI + CRM pump up wins
Connecting all of your sales data with AI paints you an accurate picture of your relationships and pipeline, while boosting intelligent sales forecasting.
Sales forecasting is one of the most important things a company does. It fuels sales planning and is used throughout an enterprise for staffing and budgeting. Despite its importance, many organizations use outmoded practices that produce bad forecasts.
A comparison could be drawn with times past, when farmers depended on signals like cats washing behind their ears or the ache in an old-timer’s knee to forecast the weather. With the advent of supercomputers, weather prediction has vastly improved. But in large enterprises, the tools used to foresee sales remain only somewhat more reliable than an arthritic knee.
Just how dubious are sales forecasts? A full 55% of sales leaders, and 57% of quota-carrying sellers lack confidence in forecast accuracy, according to Gartner.
While you might think this state of affairs will improve over time, Gartner estimates that even by 2025, “90% of B2B enterprise sales organizations will continue to rely on intuition instead of advanced data analytics or their B2B CRM, resulting in inaccurate forecasts, sales pipelines and quota attainment.”
Sales forecasting is the process of estimating a company’s sales revenue for a specific time period – commonly a month, quarter, or year. A sales forecast is prediction of how much a company will sell in the future.
Producing an accurate sales forecast is vital to business success. Hiring, payroll, compensation, inventory management, and marketing all depend on it. Public companies can quickly lose credibility if they miss a forecast.
Forecasting goes hand-in-hand with sales pipeline management. Getting an accurate picture of qualification, engagement, and velocity for each deal helps sales reps and managers provide data for a reliable sales forecast.
A forecast is different than sales targets, which are the sales an enterprise hopes to achieve. A sales forecast uses a variety of data points to provide an accurate prediction of future sales performance.
Although different organizations can have vastly different sales structures and processes, the majority tend to use one or a combination of the following primary approaches to sales forecasting:
Connecting all of your sales data with AI paints you an accurate picture of your relationships and pipeline, while boosting intelligent sales forecasting.
The pressure is on for sales teams to deliver, putting the spotlight on forecasting. Facing stiff competition and an uncertain market, expectations for salespeople keep rising – and forecasts are the means by which sales activity, and by extension the health of the business, is most readily monitored.
Unfortunately, enterprises continue to make the same mistakes in their forecasting processes. Here are some of the common pitfalls: Learn the best practices for the sales audit process, including what it is, who to include, what questions to ask, and how to complete the audit.
Fortunately, there are ways sales organizations can build a forecast process that helps achieve greater accuracy – and, ultimately, better sales results.
At the most fundamental level, improving sales forecasting means using data to more accurately predict performance and manage planning to ensure sales success. This includes steps like:
One commonality across these points is that they illustrate the need for cultural change in the sales organization. In other words, you can only drive accuracy in forecasting if salespeople don’t feel pressure to inflate the forecast.
And, by extension, they need to feel comfortable sharing information about deals even when it is not favorable.
Given all the benefits of accurate sales forecasting, what’s keeping companies from pursuing more modern approaches?
For one thing, regardless of approach, the quality of forecasts is inextricably linked to the quality of the data on which they are based. And it’s not enough to merely have all the data available; it needs to be integrated in such a way that it can be readily analyzed in real time.
Unfortunately, this type of data integration is anything but common. According to APQC’s Planning and Management Accounting Benchmark, only 14% of organizations currently house operational and finance data in a single integrated system. This means that for most companies, forecasting requires the gathering of data across organizational silos and disparate systems, which becomes time consuming and costly.
The good news, however, is that data integration enables organizations to take better advantage of technologies such as AI and machine learning that are ideally suited for spotting the types of trends that data can reveal.
By incorporating state-of-the-art tools into an integrated approach for data analysis, organizations can transform sales forecasting into a strategic advantage.