We will take a look at the 13 individual types of analysis that can be performed and their brief descriptions. We already reviewed the data fields needed for these analysis types.

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Stage one. Time Series Analytics

-Determine if there are any time-series patterns to the issue. Time of day, day of week, week of month,month of year.

-Use Statistical rules to determine if patterns/trends/behavioural changes and signals exist.

Stage two. Client Analytics

- Who is calling, where are they calling from, when are they calling, how many times have they called. What type of issues have they called about. How were those issues resolved. Were those issues reopened. Were those issues escalated or reassigned multiple times.

Stage three. Client Experience Analytics

- Are the Client issues being resolved on first call. Are the Clients escalating. How is the Client Satisfaction. How are the response and resolve times. How is the Client backlog. Are Clients having to call about the same issues over and over.

Stage Four. Categorical Analytics

 - What types of issues are client facing, what are the affected Categories, Subcategories, CIs.

Stage Five. Comparative Analytics

- Compare current week to previous week across all dimensions and see what is changing, and why its changing.Compare any two periods to understand changes. 

Stage Six. Technician and Towers Analytics

- Who is resolving these issues. How is their effectiveness. How is their productivity and utilization. Are there any skills, capabilities, and capacity issues. Is the entire team equally contributing to the work. 

Stage Seven. Dispatch Analytics

- How many tickets are being dispatched from SD to L1.5, DSS, and L2+ teams? What types of tickets are being dispatched? Who is dispatching the tickets? 

Stage eight. Backlog Analytics

 - Understand ticket backlogs and its impact on quality of service and client dissatisfaction.

Stage nine. Automation of Analysis

 - Leverage automation to conduct preliminary analysis so time can be better spent on drill-downs.

Stage ten. Statistical Analysis

-Conduct an analysis using Control Chart Rules to harvest insights.

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Stage eleven. Sentiment Analytics

- Leverage sentiment analytics to harvest insights from CSAT, Escalation data.

Stage twelve. Semantic Text Analytics

- Use S.T.A to extract insights from unstructured data.

Stage thirteen. Predictive Analytics

- Use predictive analytics to determine what can happen if things change or don’t change.

In Order to conduct proper ITSM/Ticket Analytics, it is essential that key data fields are provided. The quality of the data will not be an issue as insights can also be derived from poor quality.