Create and deploy IT solutions for business

Machine learning in the service management industry | Microsoft Dynamics CRM 2016

, / 4494 0
Machine learning in the service management industry | Microsoft Dynamics CRM 2016

In a previous blog we briefly discussed how a service management organisation can implement machine learning capabilities to increase customer satisfaction, through automating operations to reduce response times. With the acquisition of FieldOne in 2015, Microsoft Dynamics CRM 2016 is set to add a new level of functionality to the machine learning capabilities for field operatives. In this blog, we will look to highlight why more companies are becoming service oriented and how Dynamics CRM machine learning capabilities can improve efficiencies within the service management industry.

How machine learning is responding to changes in the service management industry

Speaking at Convergence 2016, Carr Phillips highlights the continuous merging of the product and service world aka “Everything-as-a-Service”. “Business models are expanding with field services companies focusing on subscription services and how they can expand into value-added project services, with a need to integrate across business processes and pull everything together.”

Machine learning and analytics are already beginning to restructure certain areas of the service spectrum, particularly as some enterprises look to evolve from the traditional break-fix to a more customer-centric and cost-efficient predictive service model.

Machine learning and Microsoft Dynamics CRM 2016

The Microsoft Dynamics CRM 2016 release has a greater focus on providing customers with market leading capabilities for field service with the introduction of project service, and a continued investment in enhancing intelligence capabilities. According to an Information Week article, the CTO of FieldOne described how Microsoft Cortana Analytics can bring together service order history, with data transmitted from customers’ equipment, to distinguish fault patterns, forecast when a maintenance issue is likely to occur, and detect a solution. Using machine learning in this manner will surely become a key aspect of field service strategy.

By taking advantage of the powerful capabilities of machine learning and predictive analytics, service organisations can gain a competitive advantage with increased connectivity, by streaming data directly from the products / equipment your customers have on site. Predictive technology will enable service organisations to add value to their customers with zero-downtime service levels, increasing customer satisfaction.

How machine learning can benefit service organisations – embrace predictive service management

There are other benefits to accessing predictive learning and analytics. Aside from moving to a predictive service-based model, management can make decisions based on more information than they’ve ever had access to before. Machine learning can be used to optimise workflows, add value to processes for technician scheduling and routing, streamline inventory throughput, make recommendations to the customer service teams about other services customers may be interested in and even make strategic decisions based on detailed, actionable predictions.

Carr also highlights some key benefits from Dynamics CRM machine learning with optimised travel times allowing companies to benefit from a typical 20-22% total saving in fuel and vehicle maintenance (wear and tear), and more accurate predictions of arrival time resulting in an increase in customer satisfaction and allowing companies to action more calls.

Driving business growth

Suppliers and providers are continually needing to integrate diverse product and service operations to deliver customer-specific outcomes in a reliable manner and at an acceptable margin. With so much data flowing into your organisation and the volume only likely to grow in the near future, failing to understand and act on it would represent a huge waste of resource and opportunity. By leveraging systems of intelligence and machine learning to spot otherwise invisible patterns and trends, you have the chance to transform not only the service you provide, but also the level of effectiveness with which it’s managed resulting in:

  • Increased customer satisfaction
  • Reduced downtime
  • Better analytics on failures and symptoms
  • Reduced service costs
    • Reduced manpower
    • Better accuracy of diagnosis
    • Exceed Service Level Agreements (SLA’s)
    • Prevent small problems from becoming big ones

More efficient field resources enhances total value and create new revenue-generating opportunities while building loyalty and trust. Learn how Microsoft Dynamics CRM 2016 will empower your business to execute a service management strategy that drives business growth as well as enhance operational efficiency and effectiveness. Contact Prodware today for more information.

Leave a reply

Your email address will not be published.