Dynamics 365 CRM opportunity scoring based on Machine Learning

作者 WaveAccess

WaveAccess invites customers to create a CRM opportunity scoring system for better business results

WaveAccess invites customers to create a Dynamics 365 CRM Opportunity Scoring system to accent opportunities with higher deal probability and reveal the gaps and areas of close attention for lower deal probability opportunities.

Business value of CRM opportunity scoring:
  • – Rating the opportunities by chance of conversion
  • – Relevant customer service due improved analytical insight
  • – Salespersons efficiency rating by various metrics.

How it works

Machine learning is a technique of data science that helps computers learn from existing data in order to forecast future behaviors, outcomes, and trends. This process involves collecting data from one or multiple sources, and feeding the data into the Machine Learning models. These models then use the data to predict future outcomes. Essentially, Machine Learning uses past data to predict future data.

Connecting Dynamics 365 CRM data to Azure Machine Learning allows predicting Leads and Opportunities probability and calculate a Opportunity scoring.

Based on the previous sales patterns (sales reps' history and customer history), Azure Machine Learning can model and predict which opportunities should be taken by which sales representatives and also reveals the gaps such as non-filled fields, non-defined influencers: technical buyers, competitors etc. The opportunity with poor gap filling will have a lower score and will need more attention of sales representatives.

It ultimately increases the likelihood of a sale.

This solution is highly customizable. WaveAccess can help you configure Lead and Opportunity scoring in your Dynamics 365 instance to match your specific needs.

The other options of using Azure Machine Learning to enhance your Dynamics 365 CRM efficiency are:
  • Machine Learning based Lead Scoring system for fast and proper leads processing
  • Product Recommendations
  • Customer Requests processing and their distribution based on sales people success score
  • Topic mining based on key words extracting from speech