Use Prescriptive Opportunity Scoring to Win More Deals This Quarter

Prescriptive Opportunity Scoring Chart with XANTEven with all its metrics and indicators, sales is very subjective. Despite the advances in online procurement, there are still many businesses that rely on human-to-human contact to make sales. And humans are prone to making judgments based on emotions.

We’re all familiar with conversations we’ve had with managers or reps who rely too heavily on gut instincts and intuition when determining which deals will close and which will fall through.

The end result is far from perfect.

Many organizations achieve less than 60 percent accuracy in their sales forecasting because of these best-guess techniques. Worse, the further away from the end of the quarter you are, the less accurate this method of forecasting becomes.

Everything comes down to sales

Most students would be upset if they scored 60 percent on an exam. Yet, for whatever reason, that number is acceptable when it comes to sales forecasting.

60 percent isn’t much better than a coin toss, and you certainly wouldn’t plan important business decisions on heads or tails.

Without reliable figures, organizations are destined to struggle.

That’s because companies, whether product-based or service-oriented, are all driven by sales.

Your sales and revenue determine how many employees you need and where people should be assigned. Sales forecasting influences multiple functions across the business, from supply chain and marketing all the way to finance.

If you’re horribly inaccurate in your sales forecasting, then you’re asking the rest of the organization to be horribly inaccurate in its planning.

Using machine learning to increase accuracy


In order to increase forecast accuracy, organizations must adopt technology that removes personal biases from the process and keeps personal judgments in check.

The best way to do this is to leverage data-driven, machine-learning technologies.

Machine learning isn’t emotionally invested in any of the deals it analyzes.

Instead of relying on instincts, data-driven solutions analyze hundreds of attributes and tens of thousands of historical instances.

Based on what has happened in similar past deals, the machine can score the opportunities in your pipeline and determine their likelihood to close. And even more importantly, will they close this quarter?

By identifying correlations and patterns in the data, you can reduce inaccuracies in your reporting. You don’t want to completely ignore sales judgment, so you should look for a system that blends sales judgment with historical data and predictive analysis.

Using data to determine overall pipeline health

Alongside scoring specific opportunities, data-driven solutions can also evaluate the overall quality of your pipeline.

Organizations often mistake a full pipe as indicative of a healthy one, which can be a misleading and costly assumption.

Many organizations attempt to reach quota by having what they call coverage. If they have a specific sales target, they need X times that number in their pipeline to be on track.

But even if a company has the “right” coverage, they may consistently miss their number.


Because salespeople might stuff any opportunity, no matter how poor, in their queues in order to get sales ops and managers off their backs.

Using machine learning, sales organizations get a deeper look at their pipeline and can see which opportunities are quality, and which have no hope of ever moving forward.

That way, you can work toward creating the coverage you really need for sales success.

Machine learning helps managers gain a better understanding of legitimate opportunities and pipeline health.

Moving from predictive to prescriptive

While it’s great having technology that can score your opportunities and tell you if your pipeline is healthy, that is not the whole story.

Unless you make the appropriate adjustments, that information isn’t likely to change anything.

Enter prescriptive opportunity scoring.

Prescriptive technology evaluates an opportunity and searches for the attributes that historically have had a positive or negative impact on winning or losing a deal.

A prescriptive solution can help identify if your reps are speaking to the wrong people at the wrong level or waiting too long to contact their prospects.

Prescriptive opportunity scoring uses data from historical cases to provide actionable advice at the point in the sales process when it will have the most impact.

For example, let’s say a deal has been stuck in a stage longer than average. The machine can determine which actions in the past helped move an opportunity from dormant to active — and prescribe those same actions in this case.

Sales teams can also use opportunity scores to decide where to assign the right people and devote the most time.

Instead of pooling all your best resources on opportunities that are very likely to close, a manager may assign strong performers to work on deals that are somewhat likely, to see if they can get them across the line.

Predict the future while there’s still time to change it

Near the very end of the quarter, everyone can predict where they stand and which deals will or won’t go through.

That doesn’t do any good.

You need technology that predicts where you stand while it’s still early enough to make changes, and provides your salespeople with the confidence to commit to deals early in the cycle.

By combining the power of prediction with prescriptive opportunity scoring, sales teams and organizations can know the strength of the pipeline, likelihood of hitting targets, where to apply resources to maximum effect, and which actions they should take to win more deals.

To learn how you can become a predictive sales organization, download the free ebook below.

Becoming a Predictive Sales Organization

Free eBook:Becoming a Predictive Sales Organization

Discover how data science can help you improve your sales forecasts and increase revenue.

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