How Predictive Analytics Is Simplifying Sales Forecasting
Predictive analytics for sales forecasting is fast becoming a competitive necessity for sales organizations across industries. Today, more than half of companies say they’re using advanced and predictive analytics to drive strategy.
With markets that are increasingly uncertain, a business world that continues to move at a faster pace, and the sheer amount of data that companies must analyze today, it’s easy to see why predictive analytics are so important to sales organizations today.
And while a more sophisticated analytics approach may seem like a complicating factor, predictive analytics actually streamline and simplify the sales forecasting process in ways that drive smarter, more informed decisions.
Read on for more on exactly how predictive analytics can simplify and level-up sales forecasting capabilities for companies in every industry.
- Predictive analytics use artificial intelligence and mine both internal and external data sources.
- They take the guesswork out of sales forecasting — a key capability given that less than 25% of companies currently achieve high levels of forecast accuracy.
- Predictive analytics enables more comprehensive and automated data analysis to drive sales forecast accuracy.
- Lead scoring is a common use case for predictive sales analytics to forecast how profitable the current pipeline is likely to be at any time.
- Greater accuracy driven by predictive analytics for sales forecasting leads to numerous benefits such as shortened sales cycles and better lead management.
What are predictive analytics?
Predictive analytics is a sophisticated form of data analysis that utilizes artificial intelligence and machine learning to mine vast datasets and make accurate forecasts. Using algorithmic data modeling, predictive analytics allows users to look at insights created from thousands (or more) of data points from both internal and external sources.
Predictive analytics goes a step further than traditional analytics methods that tell the story of what and why something happened to also include what’s next.
It requires high levels of automation to execute, meaning predictive insights can be captured in real-time. This allows businesses to implement more agile business strategies, make better informed decisions, and maintain an overall better ability to navigate uncertainty — a priority for nearly every company after the challenges experienced over the past few years.
Predictive Analytics for Sales Forecasting: Why it’s a Game Changer
In short, predictive analytics takes the guesswork out of sales. Sales leaders and reps no longer need to operate based on intuition and can make decisions using highly objective and accurate insights.
The impact of this capability can not be overstated. Research shows that less than a quarter of companies have historically achieved sales forecasting accuracy greater than 75%. Many organizations still struggle with this challenge today. Without the right tools and technologies in place to leverage predictive analytics for sales forecasting, your forecasts will be less accurate and even negatively impact your revenue earning potential.
On the flip side, adding predictive analytics to your sales forecasting arsenal can have ripple effects when it comes to ROI. Your sales teams can act with more intention, operate using clear insights, convert leads at a higher rate, and execute at higher productivity levels long-term.
Altogether, these benefits equate to the ultimate win for your business — higher sales revenue.
How Predictive Analytics is Simplifying Sales Forecasting
You might be thinking that predictive analytics for sales forecasting sounds awesome — but also complicated. The good news is that most of the complexity exists behind the scenes. All you need to know is what you want to forecast, and your data analytics tool can execute the analysis for you.
Let’s look at some of the ways predictive analytics actually simplifies the sales forecasting process.
Comprehensive data analysis
Traditional sales forecasting required the gathering of data from multiple sources and significant time spent combining and standardizing said data. The process was long, manual, and tedious.
Predictive analytics tools source data from a wide variety of places, including internal databases as well as external sources that report on larger market events and trends. You can be sure, then, that your insights are complete and never missing an important data point that impacts its accuracy.
Lead scoring is a common way predictive analytics is used for better sales forecasting. Using a defined set of scoring criteria, predictive analytics tools can assess the likelihood of any single lead converting and, at scale, determine how much of your pipeline is likely to translate to real revenue (and when).
Adaptive forecasting is a type of predictive analytics that involves looking at many potential future scenarios using different combinations of data variables. It’s a highly automated, AI-driven process that allows sales decision makers to see all possible outcomes as well as the likelihood of each occurring so that they can prepare accordingly.
This level of predictive insight has become a near-necessity for sales organizations today amidst current market uncertainty. It helps to simplify the sales forecasting process by showing potential scenarios well ahead of time so companies can develop strategies proactively rather than scrambling later to respond to unexpected outcomes.
Ultimately, sales forecasting accuracy is what every organization aims for. And while predictive analytics are technically not new, the speed, scale, and accuracy with which they can be executed today have changed the ability for companies to make informed decisions and meet other important goals like shortened sales cycles, better lead management, and higher sales efficiency.
In short: modern predictive analytics equals greater accuracy, which equals more confidence in your sales strategy and higher ROI on your efforts.
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