Why Predictive Sales Analytics in B2B Sales?
B2B sales organisations have always been under pressure to hit sales targets, uncover new opportunities, and maximise productivity. But nowadays, in an increasingly competitive, ever-changing and globalised market, it is essential for sales leaders to make optimal use of limited sales resources.
Predictive Sales Analytics play a critical to improve sales productivity in B2B. It reduces the time sales managers and sales teams spent in unproductive non-customer-facing activities. Furthermore, they also provide a decisive competitive advantage in highly competitive industries.
Sales leaders should first define what their business goal is. Once the purpose of a predictive model is defined, sales managers should gather sales data. Sales leaders should evaluate the expected costs and benefits, to assess the ROI of the solution.
Finally, B2B organisations should instruct their sales forces to modify their behaviour based on the predictive analytics model. Companies also need to re-think the sales KPIs they are measuring. In other words, thanks to the implementation of predictive analytics, managers must define new sales KPI and sales controlling processes.
The method is, in spirit, the same. Sales reps ought to focus on a limited number of accounts with higher chances of closing deals, churning, buying from cross-selling or paying more.
Here some Predictive Analytics examples for B2B.
Thanks to predictive analytics, Big-Data is a big opportunity for B2B sales managers. However, this vast opportunity requires a thorough understanding of every sales situation, including the possible/available big data mining models.
Regardless of the size of Big Data, defining data mining and predictive analytics methods begins with understanding the type of information the sales team needs to be successful. CRM and ERP Data Mining for Predictive Analytics is a process for researching past sales data in search of patterns or relationships between variables.
Once B2B sales managers have discovered these relationships, they can use them as a model to make accurate forecasts, identify new sales opportunities and increase sales team efficiency.
In business-to-business sales situations, Big Data does not have to be so big: CRM and ERP Data Mining are sufficient as starting capital for predictive analytics. ERP and CRM sales data is one of the most valuable input a company can analyse. Therefore, B2B sales managers should analyse these sales data for useful insights.
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Predictive analytics is the technology that enables a look into the future. What data do you need? How do you get started with predictive analytics? What methods can you use?
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