LAC Session Type
Poster
Name
From Data to Action: Custom Dashboard Solutions for Virtual Chat Reference Excellence
Description

Purpose & Goals

The primary aim of this presentation is to showcase our efforts in enhancing our virtual reference service through improved metrics and insights. Our goals include: Identifying unanswered chats to improve response times and service quality. Monitoring operator staffing to optimize resource allocation. Implementing keyword-based search functionality to facilitate data retrieval and analysis, allowing us to extract valuable insights from chat transcripts. Streamlining access to chat data without reliance on the service provider's dashboard. By achieving these goals, we aim to demonstrate the effectiveness of our approach in improving the efficiency and effectiveness of our virtual reference service.

Design & Methodology

Scholars Portal uses LibraryH3lp as a chat service provider. They created a Python package for their client to use to retrieve or update data from their web server. Each client (consortia/school/organization) can only access its own data. Since 2019, we have written various programming scripts using the LibraryH3lp Python package. In 2020 we created an Excel Based dashboard and In 2022, we created a Web Based dashboard to get more interactivity. We use a Django web framework application so that the dashboard can be accessed via the web. Our System team allocated a subdomain and setup the server so that the data can only be accessible internally as chat transcripts may contain confidential information.

Conclusions

In the age of ubiquitous systems, where users and services can access information via APIs, our findings demonstrate the value of creating a dashboard customized to our specific requirements. Our dashboard not only fulfills our needs but also extends the functionality beyond what is offered by the default dashboard provided by the chat service software.

Implications & Value

Readers may benefit from having a foundational understanding of a programming language such as Python, as well as knowledge of querying an API. Additionally, it is important for readers to assess whether their chat service software supports the necessary API query capabilities. Understanding these aspects can significantly enhance the efficiency and effectiveness of virtual reference systems, enabling users to quickly retrieve valuable insights from their chat service software. By leveraging this knowledge, organizations can optimize their virtual reference services and improve their capacity to provide timely assistance to front-end users, their operators, and their managers.

Keywords
API, Metrics, Dashboard, Virtual chat reference