Predictive Sales Analytics – How to use it in B2B Sales Controlling
Controlling sales in B2B is increasingly becoming a high-tech game. Since selling cycles in business-to-business are getting longer and sales is getting more expensive, controlling need to look further into the future.
Machine learning, a well-known example of weak artificial intelligence, represents a fantastic opportunity for improvement in B2B sales controlling and business intelligence. It enriches the world of sales analytics with a substantial competitive advantage.
Due to the increasing amount of sales data and a reduction of implementation costs, artificial intelligence has become an attractive technology for sales planning and controlling in B2B.
Predictive analytics plays here a central role. A Predictive Analytics Software has the power to predict which customer is going to churn, to take a new offer or to accept a higher price.
Let’s discuss how to implement Predictive Analytics using AI within your sales controlling.
Sales controlling is dead, long live sales controlling!
Do not let fancy new buzzwords disregard your current sales controlling. Artificial intelligence is not going to replace your sales controller tomorrow. Use what you have and build on your strengths.
First, use the sales data that you already have. Be it an ERP or a CRM, consider the value you can extract from it. If there is a Business Intelligence or a sales reporting system in place, evaluate the option of extending it using external intellect. If this way is not viable, then it might be worth considering a substituting.
Do not change your sales analytics and controlling processes, before learning what precisely predictive analytics can do for your company. This last point includes the sales KPI you are using.
Most companies and B2B sales teams measure already a mixture of output, input and some advanced KPI. Focus on those that better help your company water current challenges. Reduce them to a bare minimum.
Predictive analytics for sales controlling? You don’t have to reinvent the wheel.
Define new KPI using predictive analytics methods. Examples of performance indicators using predictive analytics include sales potential, churn risk, pricing analytics and sales forecasting.
Use tested methods for predictive analytics.
You do not need to reinvent the wheel. Although machine learning is pushing the performance of predictive analytics to new highs, there are well-known methods you can start testing using your sales data.
A product recommendation predicts the items that a customer would most likely purchase. Not-so-advanced statistics power some of the most effective product recommendation engines. For example, an a priori algorithm can we used to find products for cross-selling.
Machine learning is making inroads in the reduction of customer attrition. By using our customer churn prediction software based in a random forest, companies can detect 70 to 80 % of customers at risk of churning. By implementing the appropriate retention strategies, they can reduce attrition to its half.
A five per cent improvement in pricing can double profits in an average B2B company. This potential is the reason why sales managers should not underestimate predictive analytics methods for pricing analytics. A pricing analytics software can deliver three digits ROI in a short time.
These last three examples of predictive analytics methods are well proven. B2B companies can, using the right tools and experienced partners, adjust their sales controlling process to implement them.
Get started today with predictive analytics and keep a long-term perspective.
Predictive analytics and artificial intelligence (AI) analyse and summarise KPIs for sales controlling and, if required, take over part of the slow side of sales management. The added value, the time saving, is obvious.
A recent study of German companies working in the field of Predictive Analytics listed the sales department as the second field where most of the investments in Predictive Analytics will flow.
There are, however, two main vital factors to consider before implementing Predictive Analytics in sales controlling.
First, the predictive analytics software should be fast to implement and should be easy to integrate into existing sales controlling processes.
Second, insights gained from Predictive Analytics should follow adequate sales actions. For example, if a company is implementing a customer churn prediction software in sales controlling, it should also define the necessary retention strategies and specific activities.
Predictive analytics? The effort is worth.
In summary, companies looking for competitive advantages thanks to Predictive Analytics should get started with ready to deploy solutions, using the sales data they already have. They should also keep an open eye to possible adjustments in existing sales controlling. The effort is worth.
Predictive Sales Analytics – How to use it in B2B Sales controlling – Summary.
Predictive Analytics is a cause and effect of the digital change in B2B sales. Regardless of its (well-deserved) fame, B2B executives should understand predictive analytics as an addition to their existing sales analytics and controlling capabilities.
Artificial intelligence and predictive analytics will not replace the wisdom of an experience sales controller in the foreseeable future. Nevertheless, both technologies can reduce costs and save valuable time. Companies should consider applying them to existing processes and using the current ERP and CRM data that they have.
Furthermore, B2B sales managers have today several proven predictive analytics methods and intelligent sales reporting software solutions ready to implement. They will still need adjustments in sales controlling. It is worth the investment. Companies should not blindly trust predictive analytics models that they do not understand.
Our recommendation is to get started using the current data and sales analytics capabilities while keeping a long-term vision regarding expanding the application of predictive analytics in B2B sales.