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Leveraging social media contributions for dynamic skill-based routing

IP.com Disclosure Number: IPCOM000235940D
Publication Date: 2014-Mar-31
Document File: 3 page(s) / 46K

Publishing Venue

The IP.com Prior Art Database

Abstract

Disclosed are a method and system to leverage social media content to build relevant and dynamic skill repositories associated with customer support agents, better process incoming issues, and then match the issues to the agent best equipped to find the solution.

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Leveraging social media contributions for dynamic skill-based routing

Skill-based routing is an approach used in call centers and support organizations to assign incoming issues/trouble tickets to agents with skills in a particular area, among other uses. This approach has been in place for over a decade, although the focus has been on improving algorithms for assignment and staffing needed. No attention has been given to automating or improving the identification of agent skills; current approaches depend on a manual selection of skills from a pre-defined list. This makes skills tracking and routing more rigid and less able to adapt to changing skills or side interests and is unable to take advantage of abilities, interests, and skills of employees based on overall activity vs. a rigid skill table. It is also less flexible in matching issues to agents based on a more flexible analysis of both queries and abilities, or based on potential mutual interest between the caller and agent.

Current systems perform skill-based routing through basic matching, given a list of skills related to products, problems, etc., matching reported issues to items in the taxonomy, and then identifying an agent with the matching skill identified. This is a simplistic system with limited flexibility and little ability to take in broader and tacit knowledge or adapt based on agent experiences, past history, or affinity. This matching is also unable to accommodate unexpected terms or expertise outside of the fixed taxonomy, even when those are crucial to the particular issue.

The novel method provides dynamic identification of primary and secondary skills and knowledge based on a range of activities in social media. Social media content, potentially including both internal and external social networks, is used to identify interests and skills. This information can be combined with other content, most significantly including previous assigned and resolved issues, in addition to other collaborative or content sources such as email or created content. This overall set of data is used to create a dynamic profile of topics, skills, and expertise.

When new issues are raised, the relevant key topics/areas of focus can either be manually entered (e.g., through the caller/submitter selection or other method) or determined through a straightforward topic extraction. This information is then used to identify agents with a greater affinity, enabling better assignment. Previous issues resolved are given increased weighting, or in the case of new products, topic affinity in social networks or other content may be given higher weighting.

Additional analytics can further tune results. For example, the system can identify not only affinity with a topic but also for time-to-resolution (or fewer escalations), social network closeness to subject experts, lack of escalation for prior issues on a topic, having provided answers vs. just questions in associated social media

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