Use Case: Sales Controlling Optimization

Increase sales force productivity by detecting process strengths and weaknesses with intelligent automation

The problem for sales teams

Sales control systems cannot be perfect for every sales team so there’s always room for optimization. One of our international banking clients was spending 50% of their sales resources manually reviewing the only the top tier customers from many thousands for follow-on sales opportunities or risk of churn. There are two problems here, 1) the sales team should be spending more time selling rather than manually searching through data, and 2) more than 90% of the customers were essentially being ignored.

Our solution

Inspirient’s automated analytics platform combines general anomaly detection and the Fraunhofer IAIS to identify and explain strengths and weaknesses in the sales process. Once the key drivers for process inefficiency have been identified and prioritized, Inspirient’s automated trend detection and forecasting module was used to surface clients needing further action from their whole database.

Why Inspirient?

Increase operational efficiency and reduce the risk of missed sales opportunities with automated analytics. Inspirient’s solution supports your current sales control system, so can be implemented with minimal disruption to the current process.

Typical Efficiency Gain

Efficiency gain is modeled via the FTE resources currently performing the process that is to be automated.


0
FTE days gained / year
€0
Savings / year

Would you like to achieve superior efficiency through automation?

In cooperation with   Logo of Fraunhofer IAIS

The Inspirient Automated Analytics Engine automates the entire data analytics process end-to-end: From the assignment of input data, pattern and outlier detection, automated visualization of patterns, weak points and opportunities to automatic generation of textual explanations and recognition of the underlying relationships and rules. Most other analytics solutions rarely include these textual explanations and observations regarding the underlying data relations, which are both critical to provide a deeper level of analysis and more actionable conclusions.

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