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The landscape of IT Service Management (ITSM) is fraught with challenges that seem perennial. Among these, the issue of reopened tickets looms large, consistently draining resources and pulling down both service quality and customer satisfaction. Multiple tickets opened by users for the same issue, whether to expedite a resolution or because the initial solution was unsatisfactory, are not just operational inconveniences. They signify a more considerable systemic problem, one that traditional analytics tools have been woefully inadequate at capturing.
The %Reopened KPI, which measures the percentage of incidents reopened after being marked as resolved, has been the go-to metric for many organizations. However, this KPI falls short in capturing the entire scope of the problem. It does not account for cases where a dissatisfied user opens a new ticket for the same issue instead of reopening the existing one or when a ticket is closed and a new one is created because the initial ticket cannot be reopened. As a result, organizations, unaware of the true scale of the problem, often underestimate its impact, leading to misdirected efforts and resources.
Enter Swish AI’s % Repeated Incidents KPI, a groundbreaking innovation that revolutionizes how ITSM organizations measure and address the issue of reopened tickets. This comprehensive and precise KPI provides a more accurate reflection of the problem’s true scale, enabling organizations to make informed decisions and take targeted actions to prevent repeated incidents.
A ‘repeated incident’ is defined as an incident opened by the same caller, belonging to the same Swish cluster, and with a creation date difference of less than ‘X’ days, where ‘X’ is a user-defined parameter, typically in the range of 3-5 days. To qualify as a repeated incident, the incident must:
By considering these criteria, the % Repeated Incidents KPI provides a comprehensive and accurate measure of the true scale of the problem.
The innovation of the % Repeated Incidents KPI lies in Swish AI’s advanced capabilities in Natural Language Processing (NLP) and process mining. Swish AI’s platform analyzes incidents, identifies patterns indicating repeated incidents, and clusters similar incidents together. This deep contextual understanding of each ticket enables the accurate identification and measurement of repeated incidents.
NLP is employed to understand the context of each ticket, identifying key terms and concepts indicating the incident’s nature. Process mining is utilized to analyze the incident flow, identifying patterns that signal repeated incidents. By combining these two potent technologies, Swish AI can accurately identify and cluster repeated incidents, providing a clear and comprehensive view of the problem.
The Ripple Effect of the % Repeated Incidents KPI
The % Repeated Incidents KPI is more than just a metric; it’s a catalyst for transformation. By providing a clear and accurate view of reopened tickets’ true scale, ITSM organizations can:
The advent of the % Repeated Incidents KPI represents a seismic shift in the ITSM world. Organizations no longer need to rely on incomplete and inaccurate metrics to gauge performance and customer satisfaction. With the % Repeated Incidents KPI, ITSM leaders can make informed decisions, prioritize efforts, and implement targeted improvements that will meaningfully impact their organization’s performance.
A Fortune 500 company, a global behemoth in the food and beverage industry, offers a compelling case study of the % Repeated Incidents KPI’s impact. This company, like many others, relied on traditional analytics tools and believed their rate of reopened tickets was around 2%. However, after implementing Swish AI’s platform and utilizing the % Repeated Incidents KPI, they discovered that their actual rate of reopened tickets was, in fact, a staggering 14%.
This insight was a game-changer for the company. It became evident that their reliance on traditional analytics tools was not only providing an incomplete picture of the problem but also leading them to underestimate the impact of repeated incidents on their operations, service quality, and customer satisfaction. Armed with this newfound insight, the company could make targeted improvements to their incident management process, leading to a significant reduction in repeated incidents.
The % Repeated Incidents KPI by Swish AI heralds a new era in ITSM. This revolutionary metric lifts the veil on the true nature of reopened tickets, empowering ITSM organizations to optimize operations, enhance service quality, and elevate customer satisfaction to new heights. The future of ITSM is here, and it’s time to embrace the change.
Reach out to our expert team today and chart your journey to the future of ITSM with the guidance of an AI Copilot.
