In 2018, we have firmly established our position - IT service management (ITSM) and IT services are still operating, despite ongoing talk about how long they will last during the digital revolution. Indeed, the demand for technical support services is growing - in the Technical Support Report and Salary Report (Help Desk Institute) for 2017 indicates that 55% of technical support services have noted an increase in the volume of requests over the past year.
On the other hand, many companies saw a decrease in support calls last year (15%) compared to 2016 (10%). A key factor contributing to the reduction in the number of applications was self-support. However, HDI also reports that the cost of an application rose to $25 last year, up from $18 in 2016. This is not what most IT departments aim for. Fortunately, automation powered by analytics and machine learning can improve help desk processes and productivity by reducing errors and improving quality and speed. Sometimes it goes beyond human capabilities, and machine learning and analytics are a key foundation for an intelligent, accommodating and responsive IT help desk.
This article takes a closer look at how machine learning can solve many help desk and ITSM issues related to ticket volume and cost, and how to create a faster, more automated help desk that employees in the enterprise will enjoy using.
Effective ITSM through machine learning and analytics
My favorite definition of machine learning comes from the company :
“Machine learning teaches computers to do what comes naturally to humans and animals—to learn from experience. Machine learning algorithms use computational methods to learn information directly from data without relying on a predefined equation as a model. Algorithms adaptively improve their own performance as the number of samples available for study increases.”
The following capabilities are available for some ITSM tools based on machine learning and big data analytics:
- Bot support. Virtual agents and chatbots can automatically suggest news, articles, services, and support offers from data directories and public requests. This 24/7 support in the form of end-user training programs offered helps resolve issues much faster. The key benefits of the bot are an improved user interface and fewer incoming calls.
- Smart news and notifications. These tools allow you to proactively notify users of potential issues. In addition, IT can recommend workarounds with personalized notifications that provide end users with up-to-date and useful information about issues they may be experiencing, as well as tips on how to avoid them. Informed users will appreciate active IT support and incoming calls will decrease.
- Smart search. When end users search for information or services, a context-sensitive knowledge management system can provide recommendations, articles, and links. End users usually skip some results in favor of others. These clicks and views are weighted as content is re-indexed over time, so search capabilities are dynamically tuned. Since end users provide feedback in the form of like/dislike voting, this also affects the rating of content that they and other users can find. In terms of benefits, end users can find answers quickly and feel more confident, and help desk agents are able to process more tickets and reach more service level agreements (SLAs).
- Analysis of popular topics. Here, analytical capabilities reveal patterns for structured and unstructured data sources. Information about popular topics is graphically displayed in the form of a heatmap, where the size of the segments corresponds to the frequency of certain topics or groups of keywords that are in demand by users. Recurring incidents will be detected instantly, grouped and resolved together. Popular Topic Insights also detects incident clusters with a common root cause and greatly reduces the time it takes to identify and resolve the underlying issue. The technology can also automatically create knowledge base articles based on similar interactions or similar issues. Finding trends in any data increases the activity of the IT department, prevents incidents from recurring, and therefore increases end-user satisfaction while reducing IT costs.
- Smart applications. End users expect that submitting a ticket is no more difficult than writing a tweet, namely a short message in natural language describing a problem or request, which can be sent via email. Or even just attach a photo of the problem and send it from your mobile device. Registering a smart ticket speeds up the case creation process by automatically filling in all fields based on what the end user wrote or a scan of an image processed using optical character recognition (OCR) software. Using a set of observational data, the technology automatically categorizes and directs tickets to the appropriate help desk agents. Agents can forward tickets to different support groups and can overwrite auto-filled fields if the machine learning model was not optimal for the case. The system learns from new patterns, which allows it to better cope with emerging problems in the future. All this means that end users can easily and quickly open tickets, resulting in increased satisfaction when using work tools. This capability also reduces manual work and errors and helps reduce resolution time and costs.
- smart email. This tool is similar to smart requests. The end user can send an email to the support team and describe the problem in natural language. The helpdesk tool generates a ticket based on the contents of the email and also automatically replies to the end user with links to suggested solutions. End users are satisfied as it is easy and convenient to open tickets and requests, and IT agents have less manual work.
- Smart change management. Machine learning also supports modern analytics and change management. Given the frequent number of changes that businesses require today, intelligent systems can provide change agents or change managers with suggestions to optimize the environment and increase change success rates in the future. Agents can describe the necessary changes in natural language, and analytics capabilities will check the content for affected configuration items. All changes are scheduled, and automatic indicators tell the change manager if there are any problems with the change, such as risk, scheduling in an unscheduled window, or "not approved" status. The key benefit of smart change management is faster time to value with fewer configurations, fewer tweaks, and ultimately lower cash costs.
Ultimately, machine learning and analytics transform ITSM systems with intelligent assumptions and recommendations about ticket issues and the change process that help agents and IT support teams describe, diagnose, predict, and prescribe what has happened, what is happening, and what will happen. End users get proactive, personalized and dynamic insights and fast decisions. At the same time, much is done automatically; without human intervention. And as technology learns over time, processes only get better. It is important to note that all the smart features described in this article are available today.
Source: habr.com