Title: Leveraging Performance Improvement Platforms and Data Analytics for better customer service
Author Name: Matthew Blake McLain
1. Introduction
Within the tech space, information is in constant flux. User experience, feature sets, troubleshooting tips, and standard operating procedures change weekly, if not monthly. Within this space, technical support agents often have disparity in expected knowledge and questions asked of them by users. To solve this, many technical organizations provide support agents (as well as general customer-facing teams) with performance management platforms (Emerson, L. C., & Berge, Z. L.,2018). These platforms allow customer-facing agents to search for, and retrieve live information in the middle of their workflows. In addition the storage and retrieval of information, performance management platforms also allow customer-facing agents to empower and easily provide information to customers with quick links, images, and help center articles.
In addition to the aforementioned benefits, digital performance management platforms can also passively track and obtain information on their use and efficacy. Specific performance management platforms, such as Guru, provide analysts and instructors with learning analytics (Ruiz-Calleja, A., Prieto, L. P., Ley, T., 2017) information such as the usage of their tools and search terms. Guru also tracks the accuracy of its information, and allows stakeholders to keep information updated and accurate to ensure only accurate information is being shared.
Both of these aspects together offer instructional data to both customer facing agents, and the enablement teams that empower them to do their best work. This allows large complex organizations to kill “two birds with one stone.” saving valuable resources and minimizing the labor and human capital costs many of these programs would otherwise cost.
2. Overview of the Case
This case focuses on an organization within the tech space with roughly 100 customer-facing agents. A rapidly growing company, this team often has trouble accessing accurate information, leading to low ticket volume, low issue resolution percentages, higher instances of churn, and lower-than-average customer satisfaction scores.
In addition, new hires to this team often feel un-empowered and unable to onboard and learn about the platform they support, scale, and sell. All of the above leads to increased costs and the desire for organizational stakeholders to hire a knowledge management team. This team is tasked with the responsibility of increasing ticket volume, ticket resolution percentages, and customer satisfaction scores, as well as decreasing churn and employee turnover.
3. Solutions Implemented
The knowledge management team implemented a software known as Guru, a knowledge/performance management platform. This team is responsible for maintaining and updating the Guru library – a database of useful information that customer-facing agents can use mid-workflow.
For example, within the support service platform known as Zendesk, support agents are able to either open the extension via the ‘Guru’ icon at the bottom of their text entry, or open the Google Chrome browser extension by pressing cmd+G on their keyboards. This allows users to search for cards – rather, live pages the user can reference mid-workflow that contain information. In addition, agents can also use Guru within company communication platform such as Slack, to democratize data sharing and further empower agents to find and share the correct answers.
Once this extension is opened, the user will see a panel appear on the right of Zendesk. Then, the user can search for the relevant search term they are wanting to learn more about (mid workflow). That said, in most cases, this platform will also learn what cards to suggest based on the text included within the ticket itself. For example, in the video below where the title is “Help with calendar connection,” Guru knows to suggest cards to the agent associated with that topic.
In addition to reviewing the information within the cards, agents can also copy, share, and upvote cards from the taskbar on the left (as shown in the video above).
To ensure the integrity of the cards, stakeholders can comment on cards to un-verify and note cards as ‘out of date’ to mark for review by knowledge management.
Knowledge management will review this information and ensure a majority (above 80%) of cards are up-to date by consulting experts and compiling information to share with users of the Guru platform.
In addition to being an extremely useful tool for performance improvement, Guru can also be leveraged for learning analytics. For example, in the screenshot below you can see total adoption of the Guru platform over time – in addition to the ‘trust’ score (percentage of verified cards) currently in use.
Guru can also show most searched for terms for content both created and not created. This allows the knowledge management team to identify gaps of information for both the customer facing agent as well as the database.
Guru can also reveal the most viewed cards – as well as the count of views to help the knowledge management team dig deeper into knowledge gaps as well as opportunities for coaching for the entire team.
Lastly (and most importantly), Guru can allow stakeholders to view the usage per user. This allows the knowledge management team to view total usage and compare with customer satisfaction scores, issue resolution, churn rate, on a per agent basis – comparing that with their usage of Guru itself to measure a possible positive correlation.
4. Outcomes
Adoption of the Guru platform typically stays at near 100% levels. In addition, trust scores for the Guru platform have stayed at or above 92% since it was implemented. Agents that utilize Guru perform 40% better on customer satisfaction scores, 80% better on issue resolution scores, and take 25% more tickets weekly on average compared to agents that do not use Guru at all.
In addition, the top 3 agents who use Guru in a given quarter are twice as likely to receive promotions into senior positions (or other departments). Most importantly, Guru has allowed customer-facing teams to scale at breakneck pace with little friction and less resources expended on human capital to facilitate coaching and enablement sessions overall. Curiously, since Guru’s implementation, we have also seen a slight but growing increase in customer retention for the overall company platform.
5. Implications
This case study emphasizes the importance of performance management platforms within the tech space, as well as stakeholders ability to measure the efficacy of these platforms. For a potentially extreme return on investment, tech spaces are able to empower customer-facing teams to do their best work, increasing their performance and likelihood for career growth, and the companies own performance and ability to retain customers.
References
Emerson, L. C., & Berge, Z. L. (2018). Microlearning: Knowledge management applications and competency-based training in the workplace. Knowledge Management & E-Learning, 2018 Volume 10 Issue 2, 125–132.https://files.eric.ed.gov/fulltext/ED354883.pdf
Gottfredson, C. (2013). From scattered information to transformational performance support: Where are you? Retrieved from http://www.learningsolutionsmag. com/articles/1156/from-scattered-information-to- transformational-performance-support-where-are-you
Gottfredson, C., & Mosher, B. (2010). Innovative perfor- mance support: Strategies and practices for learning in the workflow. New York: McGraw-Hill.
Reiser, R. A., & Dempsey, J. V. (2018). Trends and issues in instructional design and technology. New York, NY: Pearson Education.
Rossett, A., & Schafer, L. (2007). Job aids and performance support in the workplace: Moving from knowledge in the classroom to knowledge everywhere. New York: Pfeiffer/ Wiley. Associated web site and tool: http://www.colletandschafer.com/perfsupp/
Ruiz-Calleja, A., Prieto, L. P., Ley, T. (2017) Learning Analytics for Professional and Workplace Learning: A Literature Review. Conference: European Conference on Technology Enhanced Learning DOI:10.1007/978-3-319-66610-5_13