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What is ‘Predictive Sales Analytics’?

Vendavo< Vendavo October 25, 2021

Seeing the future may not be possible, but predictive sales analytics can provide a solution that comes in a close second. 

As the name suggests, predictive sales analytics involves using algorithms, patterns in historical data, and machine learning to anticipate future outcomes as they relate to sales. Heavily reliant on data sets, its power ultimately derives from the accuracy, depth, and breadth of the data it is fed – and this is why it is becoming a key player in the increasingly data-led field of B2B sales.

Predictive Sales Analytics: A Game-Changer For B2B?

We’ll preface this section by saying that predictive analytics isn’t exactly a new concept. The application of it on a wider scale and with a high degree of sophistication, however, is. As data access has grown and more powerful software and hardware has become available, the possibilities of predictive analytics have opened up.

Predictive sales analytics has the potential to drive widespread positive changes within the B2B space, helping companies find customers, score leads, and pinpoint sales opportunities. Outside of these areas, predictive analytics can also help to boost revenue in fast-changing markets. Using data, AI, and machine learning it’s possible to spot opportunities, ascertain how likely they are to be successfully won, and then understand which strategic pricing decisions will help to improve sales performance now and in the future.

The uptake of AI and predictive analytics has, historically, been slow-going. This is starting to change as the increasingly complicated nature of the B2B landscape becomes harder to navigate without powerful data insight. As such, even the so-called ‘old school’ firms are opening their eyes to the benefits. 

How Can Predictive Sales Analytics Actually Be Used?

In terms of maximizing efficiency, predictive sales analytics are becoming increasingly valuable for eliminating guesswork and reducing the amount of manual data handling that occurs during pricing and sales processes – a slow and error-prone way of doing things that can’t keep pace with the increasing rapidity of B2B sales, especially in e-commerce. 

The knock-on effect of this includes better company-wide accuracy and reliability.  Here are some of the other practical applications of B2B predictive analytics: 

Forecasting

Forecasting remains an integral part of a lot of organizations’ respective sales processes. For many, there are few things more important than sales forecasting for financial and inventory planning. Inaccurate predictions therefore have the potential to cause disruptions further down the line. 

Success in this regard is built on having the right products in the right quantities in inventory and selling them at the right price. Looking closer at the on-the-ground decisions that impact profitability, applying predictive features can help to ensure optimizations are based on more than just a hunch. By forecasting demand across different conditions and geographic locations, it’s possible to approach every sale with a tailored and agile strategy. 

To really maximize productivity here, teams should be careful not to overlook the fundamentals. Unless you have access to a crystal ball, forecasting is always (in some way) informed by historical data. In other words, the quality and reliability of a forecast is tied to the quality of the available data. This is why data collection is paramount.

For larger-scale enterprises, maintaining a handle on data collection can present more of a challenge – particularly if it is stored across a variety of resources. Investing in the right technology and driving its company-wide adoption will make it a lot easier to capture useful data.

Improve Pricing Decisions

Uncertainty surrounding a pricing strategy causes a lack of confidence within sales teams. Within companies yet to embrace the power of intelligent pricing software, guesswork is more often than not planting these seeds of doubt. 

Predictive analytics can reduce this uncertainty by helping teams gain valuable perspectives on customers’ buying intent and willingness to pay. With historical transactional data, it’s possible to anticipate and set optimized price points that will simultaneously maximize margins while minimizing the risk of lost deals. 

This has wider implications too. To win at CPQ selling, businesses need to be quicker, more agile, and more intelligent. It wouldn’t be a big stretch to assert that predictive sales analytics is shaping the future for this approach to sales, helping to deliver smarter pricing guidance across the entire sales cycle, resulting in much faster and more confident decision-making at every level. 

Refine Discounts

Naturally, offering deals will entice new customers. But knowing how much to do this by is a different story. ​​Predictive analytics in sales can be used to identify the optimal discount level to pique the interest of customers. An analysis of previously run deals, promotions, and price strategies will help with developing a discount initiative that prospects will be receptive to, while still contributing to margin growth.

That last part should definitely not be overlooked. In an ultra-competitive environment, the natural inclination is to look towards discounts to win over customers, but beware of the double-edged sword. After all, over-discounting is a sure-fire way to lose revenue. That’s why it’s so important to integrate these insights within a wider price optimization strategy and develop a mutually beneficial approach. 

The Future of Predictive Analytics in Sales

Predictive sales analytics has longer-term implications for customer lifetime value, customer retention, and margin optimization. Looking ahead, it’s clear that this technology will continue to reshape the sales landscape for years to come. 

One thing is already being demonstrated?  When a business understands the value of predictive analytics technology and can drive its company-wide adoption, this typically leads to greater success. However, the critical question remains: Is the enterprise willing to fully embrace the technology on offer – or risk being left behind?


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