Thinking about the different ways that libraries could be making use of user-activity data and it seems to me that there are two distinct categories:
- Internal or indirect – i.e. using the data to improve library services (e.g. looking at loans data to show value and use of stock – Evidence-Based Stock Management), or to assess Value for Money (e.g. cost of resources being used)
- Direct – i.e. using the data to help users make more informed choices when using library services. (e.g. users on your course borrowed these items)
Internal or indirect use of user-activity data
Apart from loans data libraries might also have access to data from OPAC and other search systems, Ezproxy-type systems, or systems such as SFX. In many cases there are institutional VLEs that also track user-activity. For academic libraries the key to making use of much of this data is being able to identify the activity with a particular course.
Ideas around using this type of data include being able to assess the Value for Money of e-resources by breaking down the use by course. Such an approach might also lead to some interesting ideas around cost and charging models. For a library showing the print and online resources that are ‘consumed’ by an individual course could go some way to helping libraries prove their value.
With e-resources libraries are tied in to licence deals that don’t really take account of actual levels of use of those resources other than at a crude ‘size of institution’ level. Accumulating data of actual use of those resources by different courses showing peaks/troughs and the maximum number of concurrent users could help the library sector in negotiating better licence terms.
User-activity data can also be used to identify weaknesses and deficiencies in services, such as investigating search terms that are used on OPACs and websites as sometimes they can indicate areas that need addressing with more relevant content or metadata.
Direct use of user-activity data
There are several areas where user-activity data can be used to provide direct services to users. Search results could be analysed by course and fed back to people on that course ‘People on your course are searching for this, using these databases, borrowing these books’.
A key point is to feed that data back into wherever your users are accessing the library service, whether that is the OPAC, VLE or elsewhere through widgets or gadgets. Getting the data fed back in real-time is the ultimate challenge as too many systems still rely on batch processing of data.
Loans data from other HEIs is also valuable particularly if it can be mapped to your own courses so your students can see what books students on similar courses elsewhere are borrowing.
One area where user activity data might help is in providing support to people using e-resources. Users often seem to struggle understanding what databases to use. Taking database search terms and the results and then building some form of success or ranking system might enable us to build a more intelligent system that could guide users to appropriate resources by indicating what people searching ‘Web of Science’ for example, were searching for; or by taking their search query and suggesting suitable databases.
Resource or Customer?
One final thing strikes me. Libraries have long made use of statistical data as a decision-making tool but in the main have tended to use data at a category level (e.g. books on a particular subject, or at a particular class number range). Data also tends to be analysed on a location and time basis, e.g. books on these subjects are popular in this library or less popular at that library. I think you could perhaps categorise this as a ‘resource-centric’ view of usage data. What happens with the resource is what libraries are concerned with.
In contrast the retail sector seems to take a different approach that you could perhaps categorise as a ‘customer-centric’ view of their user-activity data. What is the customer buying?, what will encourage the customer to buy x? There is still a time and location element, with different patterns in different stores.
For libraries, this would need us to start to look at the pattern of our customers use and use the data to predict what they might be interested in, from their previous loan or search patterns. If they are following a course, be able to push relevant resources to them at the appropriate time in much more of a real-time fashion.