Purpose:
The Open University is the UK’s largest academic institution dedicated to distance learning, with over 173,000 students. Library Services within the University provide students and staff with access to predominantly electronic information resources; digital and information literacy skills training and a 24/7 virtual enquiry service.
In 2004 the University introduced an institutional wide customer relationship management (CRM) system to record all student enquiries which was subsequently introduced to manage Library enquiries in 2008. This wealth of knowledge on our customers has provided the Library with an opportunity to exploit management information to improve our service. In 2016 a change of management saw a change in strategic direction for the enquiry service, with a shift to provide proactive support based on customer insight.
In order to achieve this objective the data capture and reporting processes for the CRM required an overhaul. All enquiries are categorised by a fixed taxonomy of topic areas. Established initially in 2008 with a few incremental improvements, the taxonomy was overdue a review. The list was long, ambiguous and in some cases repetitious. Initial research with the enquiries team back in 2015 found inconsistent practices and understanding on what each topic area meant.
Data usage focussed primarily on meeting the service level agreement of enquiries answered within two working days of receipt. Wider opportunities to use the customer insight for service improvements had not been established.
Design, methodology or approach:
The initial focus for the CRM analytics was to identify the service resource needs. As this could be achieved with the existing data without any system adjustments the team set about analysing the data to predict the staffing levels needed. The team focused on the daily volume of enquiries received via email, webchat, telephone and in person to identify appropriate staffing levels. Each communication channel has a different average enquiry handling time. Daily resource requirements were estimated for a whole year to identify the staff resource required to operate the service on any given day. This was completed by August 2016, the results of which have led to improved resource alignment and service level agreements being consistently maintained.
Following this work the team turned their attentions in September 2016 to improving the data capture and reporting processes. The team has three overall key aims:
- Identify common enquiries throughout the annual student journey and develop timely proactive push-communications and training.
- Use enquiry analytics to pinpoint spikes in queries from specific courses; using that insight to work with faculty to improve the learning experience.
- Exploit the customer data to enhance the overall student experience by using the insight with teams across the Library Service to improve our service delivery.
The team initially conducted a data needs analysis with key stakeholders. This included enquiry librarians responsible for developing push communications; academic liaison librarians responsible for managing the faculty relationships and service owners across the library. Guidance from the University team with overall responsibility for the CRM was sought to identify if and how CRM system could be used to extract the data needed by the stakeholders.
The project team have also worked with the wider enquiries team to improve the taxonomy of topics used to categorise enquiries. Following and initial review by the team of the enquiry data they worked with metadata specialists to develop a taxonomy of subject areas. As these are separated into area and sub area the project team also conducted a card sorting exercise with the whole enquiries team to instil a common understanding.
Working with the systems team, data from the CRM has been included in the library data visualisation tool Kibana to enable patterns in enquiries to be identified quickly and easily. Trends in enquiries by topic area can be visualised over a time series. Enquiries received by specific course cohorts can be graphed out over time to identify spikes in activity. The number and nature of enquiries can be reported to service owners on a regular basis.
Findings:
Work is ongoing to take the data analysis into proactive support and is scheduled to be completed before the conference. This includes the exploitation of the data analysis to develop proactive support which can be pushed out to students via our social media and training materials to increase our one-to-many support. The data will also be used to support faculty liaison conversations to instil the need to improve learning materials through embedded library tuition or improvements to existing research activities. Monthly reporting to service owners is being established to drive service improvements.
Conclusions:
Customer relationship management systems in Libraries can be powerful tools to aid enquiry handling, but the real benefits come from exploiting this data in multifaceted ways to improve the student experience. This paper will discuss how the CRM data at the Open University Library has been exploited to the benefit of students.
