Data mining
Libraries have always had a large database of users, stocks and usage
statistics. The objective of collecting library statistics is ‘to
assess the quality and effectiveness of services [and resources]
provided by the library’ (Poll 2001). Whilst we examine these statistics
for such purposes, libraries seldom examine the profiles of their users
for marketing. Occasionally, they send out surveys to evaluate the data
in the context of their unique setting to enable sound decision-making
(Bland and Howard 2003). A continuing trend toward data-driven decision
making and accountability requires institutions to store more data in a
data warehouse. Data warehousing is simply storing information about
customers, clients or users that keeps the records as indexed files, in
an ordered usable management system which has been around for many
years. In the competitive business environment, this data is
increasingly being used in sophisticated ways through use of new
technologies in data mining. Data mining is the ability to extract and
interpret specific data held within the data warehouse.
Telecommunication companies, banks, insurance companies, utilities and
retailers compile data about customers by using this new technology. The
data gives insights into their customers’ future behaviour and product
needs. This newer technology is more sophisticated than previous data
collection and analysis software that gathered for analysis and
application simpler data such as customers’ ages, gender, income and
geographical location.
Data mining, customer analytics and predictive modelling software
often allow the user to select subgroups of customers, track cohort
groups, create queries, and build visual representations of data
patterns or trends to make changes in products and make long range plans
for new directions. Hence data-driven decisions can be made. This
actual concept is not so new (Chen, Han and Yu 1995). In the past, staff
of the local library could recommend and purchase new stock for
customers by developing a personal interest and knowledge of individuals
who used its services. Data mining attempts to do this on a larger and
more consistent scale and it seeks, through statistical analysis, to
reveal that often-elusive relationship between a business and its loyal
customers. Libraries have always kept statistics on usage. Customer
analytics are a way of making statistics more useful by applying
algorithms in a way that makes them easy to use. This yields valuable
information for target marketing such library patron profiles. In
theory, the more information you can gather about your customers, the
better you can anticipate and cater to their needs. By using advanced
profiling technology, libraries would be in the position of predicting
library users’ satisfaction rates rather than establishing current
satisfaction. This involves changing strategies to meet new realities in
user demands. It is a perfect vehicle for soliciting unbiased
information on current trends and anticipating future challenges. It
also facilitates clarification of current needs through the
administration of random surveys of pre-existent data that can be mined
without the necessity to impose on clients by ad hoc or sustained
interrogation.
The ability to develop effective marketing and promotion strategies
is another key benefit of utilising the customer profile. Target
marketing is available as a means of profiling the library user. For
example, even basic statistics can be used to ascertain the nature of
information retrieval habits of library users. Staff can identify usage
by such things as type of resource, time and how up-to-date the resource
is. A sophisticated yield of user profiling would also establish that
items were checked out by particular demographic groups, then by cross
referencing to a database of information about users, such things as age
groups and reading levels would be revealed for examination. As a
result we could query the user profile provided by a database and
disseminate information to staff of the library who would benefit from
the query results, such as appropriate times to run courses.
https://www.alia.org.au/about-alia/awards-and-grants/325/library-technician-research-award/previous-recipients-library-technician-research-award
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