SERVICE DESK EFFECTIVENESS SELF SERVICE REIGNS SUPREME
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1 SERVICE DESK EFFECTIVENESS SELF SERVICE REIGNS SUPREME 1
2 MAXIMIZING THE VALUE OF SELF SERVICE Situation: Flat IT budgets are putting pressure on Support teams to do more with less staff while still delivering higher Customer Satisfaction and more value. A Service Desk typically handles about fifty tickets per day per thousand End Users with a Staff of about twenty analysts per thousand End Users, and Bring Your Own Device policies and increasing IT complexity are straining the ability of the Service Desk to keep up. But all is not lost. A best in class Self Service implementation can deflect as much as 50% of the tickets. Service Desk teams attempting to drive Self Service adoption and effectiveness need reports that highlight what the top requests are and where there are gaps or failures the content library. The basic reporting in an ITSM tool cannot typically provide the necessary insights to attain that level of deflection. Without these insights it s impossible to know which content to develop to offload work from the Service Desk. Solution: Driving user adoption of Self Service requires a deep understanding of which content is relevant and effective in addressing user problems. Modern analytic applications such as Numerify360 can provide the insights that traditional reporting cannot. The freedom to drill down on complex data sets, filter on multiple attributes, across multiple tables provides answers to the important questions that can make or break Self Service. Stunning visuals make reports appealing to IT and business users alike. Enable a Shift-Left strategy Faster resolution Increased Customer Satisfaction Lower Cost of Service 2
3 The IT Help Desk creates 50% of the perception of IT Source: Gartner Getting the most from Self Service Defining the goals for implementing Self Service and communicating them is important, because there may be conflicting objectives. The reason most often cited is to allow end users to solve their own issues without waiting for IT. But in the very next breathe, customers also cite lower costs as a goal which may not align with increasing customer satisfaction. Building a business case may require you to understand your current costs. It s helpful to establish for each transaction the following: the current roles involved in ticket handling, the tickets likely to be addressed through self service - for example ticket status, password reset, How-to questions, etc. - and tools used. Don t forget to include staffing costs - such as estimating how much time is spent weekly to coordinate each transaction in the current state. For comparison, HDI* does provide estimates of industry averages. The cost of the average ticket resolved at Level 1 by a Service Desk analyst to be about $20. And they estimate the cost for Self Service to be in the range of $1-5. Resolution at Level 2 costs about $60, and a desk side visit or Level 3 costs in excess of $85. The second step is to analyze what proportion of tickets are likely to move to Self Service. Deciding whether you are going to measure the ROI on an ongoing basis will define the metrics required, which may include: Proportion of tickets resolved using Self-Service Customer satisfaction of Self-Service interactions vs Assisted Service Once you calculate the potential ROI check to see whether the value matches your expectations. Remember the potential impact on morale for IT staff who may be fearful of losing their job through this automation. If it s going to be a hard sell, it may make sense to consider the timing of the project or roll out with a limited number of transactions to test the estimates and get more feedback. The real value comes from the ability to free up staff from conducting mundane work to work on the harder incidents that impair business productivity and additional training that increases IT productivity. As the workload in the Service Desk changes, watch out for unexpected changes in existing metrics. The complexity of incidents being worked is likely to increase which may increase the Mean Time To Resolve at Level 1, and First Call Resolution rate may decrease as Users resolve simpler issues themselves. * Source: HDI Metric of the month: Percent resolved level 1 capable 3
4 BEST PRACTICE APPROACH SELF SERVICE Most Requested Items Notice outlier on Add User Password Reset Add User Account Document Creation Repair Printer Provision Laptop I.D. Card SAP Outage 1 Content is King The first step is to find the killer Content, Capabilities and Catalog requests. At some organizations this may be the corporate Address book, at another it may be password reset. Analysis of the top categories of information being sought will guide you to create content that users need. Key analysis: Successful and Failed Searches Insight into the top and bottom searches provides the guidance needed to identify top Service issues and requests. It s highly recommended to select a target customer group. A distributed sales force will have different needs and interests from the marketing team located at headquarters. Selecting an initial target audience allows a content team to meet the target users and define home run starting content. Establish a content team that will drive the content strategy and track progress. The team should create persona for the target user group(s) which can be used to create a focus on customer needs especially as they evolve over time. Look for gaps in the Knowledge or Keywords that users are searching for in Self Service. 4
5 2 If Content is King, then Adoption is heir to the throne The next step is to define goals for usage and adoption. Making sure that the content or requests that you created are being used is a critical test of the content and tools, for end users and the Service Desk analysts themselves. Ensure your target groups successfully adopt Self Service, and analyze Searches to close any information gaps with internal or external content. Key analysis: Incidents by Channel Number of Incident requests by channel vs. total Incident requests (%). This metric tracks the adoption of the various channels. Drilling down by group highlights which segment uses which channel. Key analysis: Self Service Success Rate Number of Incident requests resolved with knowledge content vs Incidents resolved at that level. This metric provides a view into the effectiveness of knowledge content at that level. Incident breakdown by channel Incident breakdown by self service success rate phone % self service % walk-in 76 0 SEP Self Service Success OCT NOV DEC Escalated to Service Desk 5
6 3 Track effectiveness for the keys to the Kingdom Finally, to ensure continued adoption it s essential to keep the content from going stale. Track which content or tools drop off in usage or supporting successful resolutions. Content should be curated continuously and tagged for ongoing maintenance or removed. Users are fickle. Don t forget to analyze content searches to identify common misspellings or acronyms. Key analysis: Top-n content usage Rank ordered list of content items ranked by usage (Knowledge articles). This metric tracks Knowledge Articles attached to an incident record. Track how the list changes over reporting periods to identify trends and patterns. For example, a pattern for major application releases: short term - authentication, navigation; longer term - reporting etc. Key analysis: Bottom-n content effectiveness Rank ordered list of content items ranked by effectiveness (Knowledge articles). This metric tracks which Knowledge Articles were attached to an Incident in which the Knowledge Article was marked as effective in supporting resolution. Articles that are becoming less effective should quickly be updated to address deficiencies. What was my top content last month? Tip: Filter Self Service versus Analyst resolution to learn which Articles are most effective at different stages and drill deeper to understand why. 6
ELIMINATE RECURRING INCIDENTS
SERVICE DESK EFFECTIVENESS ELIMINATE RECURRING INCIDENTS 1 ELIMINATE RECURRING INCIDENTS Situation: A Service Desk typically handles about fifty tickets per day per thousand end users with a staff of about
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