Dynamics 365 Customer Service Insights Released

Dynamics 365 Customer Service Insights

Microsoft Dynamics 365 Customer Service Insights has now entered general availability. This new app uses Artificial Intelligence to help customer service managers improve client satisfaction and increase productivity by gaining greater visibility into performance metrics and emerging trends.

Crucially, this uses natural language understanding to group cases by topics without the need for users to manually tag cases. 

As a result, new insights are instantly available so growing issues can be understood at an early stage before these reach critical levels.

Licensing Customer Service Insights

Best of all, Customer Service Insights is included with the Dynamics 365 Customer Engagement Plan or the Customer Service Enterprise app.

By default, this includes capacity for up to 100k cases per tenant / month. If required, additional capacity can be purchased in increments of 500k cases per tenant / month. 

Customer Service Insights can be purchased as an add-on to Dynamics 365 for Customer Service Professional licences while Dynamics 365 on-premise customers are able to purchase this as a standalone application.

How Customer Service Insights Helps Service Managers

  • Pre-built dashboards provide visibility into KPI metrics for your organisation
  • Understanding what service topics are frequently showing up in cases helps focus on highest impact areas.
  • Pinpoint emerging topics to address issues before these become critical service problems 

Incoming Cases Dashboard

Incoming Case Dashboard for Customer Service Insights

This view includes visuals of your system's recent support cases with AI driven tables to help identify persistent issues, and emerging topics.

Consistent with all AI for Customer Service Insights dashboards, this can be filtered by time period (currently last 60 days, yesterday, last week or last 1 month). Data can cover all cases, or these can be filtered by product, channel and team.

'Current Popular Topics' is one example of natural language understanding in action. That's because these topics aren't entered by a human but are the result of the AI looking at cases to highlight items about the same topic.

This display focuses on the topics which have the highest case volume:

Popular Topics identified in Customer Service Insights

In the above example, we've focused on one specific topic in the list which references a frequent customer suggestion in an e-commerce scenario.

This hasn't resulted in the highest case volume but whereas other items on the list have seen a volume reduction during the selected time period, this topic has seen a 14% rise and amounts to almost 10% of all items during this time. Definitely an issue to make sure our web developers are working to fix!

Drilling down into an individual topic will also update supporting charts that depict case priority, channel and the time of day logged.

The Emerging Topics table, shown below, provides greater insights specifically around the trends in volume so that increases in topic frequency can be identified at an earlier stage and action taken before problems become critical.

Emerging Topics Identified in Customer Service Insights

In this example, a highlighted topic reveals an upturn in problems reported by customers during order processing.  

Approaching 50 cases are automatically grouped under this topic which represents an increase of more than 12% during this time period. Worryingly, the resolution rate is only 61% which suggests potential orders are being lost. 

Case Resolution Time Dashboard

Here service managers can see which topic clusters have the biggest negative impact in terms of time consumed.

This display reveals a recurring issue with a coupon code which is resulting in both high volume and a long resolution time. Overall, the AI app has ranked this the most time impactful issue on the overall case resolution time.

Monitoring Case Resolution in Customer Service Insights

Clicking an individual list topic will filter supporting charts enabling support managers to see at a glance which agents have been frequently handling these issues, and instantly see a day by day analysis of volumes and average resolution times. 

A deeper drill down is also available that provides further underlying detail to answer questions including:

  • How many cases are covered by this topic?
  • What total have been resolved?
  • Which agents have the most unresolved cases on this topic?
  • Who has the longest resolution time on this topic?
Customer Service Insights Dashboard including customer satisfaction metrics

This will also include a Sankey chart depicting how cases on this topic arose which flows across an analysis of escalation and resolution breakdown.

KPI Summary Dashboard

The Key Performance Indicator summary dashboard provides an overview of your organisation's customer service experience with AI technology highlighting key impacts and performance trends.

KPI Summary in Customer Service Insights

This includes:

  • KPI summary charts - across total cases, resolutions, SLA compliance, escalations, average resolution and average resolution time
  • Total case breakdown between new and backlog during the specified period
  • Case tracking chart - identify spikes and troughs in new cases showing the trend in new 
  • Unresolved cases by age chart - number of cases for the time period by how many days ago these were created

Customer Satisfaction Dashboard

An overview of customer satisfaction (CSAT), including using AI technology to promote topics that are having the greatest impact on CSAT scores. The Customer satisfaction dashboard includes a variety of charts with graphical views of your system's customer satisfaction data.

Customer Satisfaction Dashboard dashboard and metrics

AI Driven Topic Clustering

Customer Service Insights automatic parses case titles into the system and determines which cases share similar topics. 

Artificial intelligence and natural language processing models provide topic clustering out of the box. For example, our model can understand that different words can have the same semantic meaning without any user intervention.

In addition, the app also provides controls to remove common tags and prefixes from case titles, to ensure the most meaningful part of the case is used for the clustering.

Users can rename topics, combine or split these to achieve desired granularity, provide feedback on cases within a topic, and automatically identify emerging support topics before they reach an overwhelming volume.

Training Customer Service Insights AI engine with product information


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