There
has been recent progress in the analysis of call-center data. Call-by-call
data
from a small number
of sites have
been
obtained and analyzed, and these
limited
results
have proven
to
be fascinating. In some cases, such
as the characterization of the arrival
process and of the
delay of arriving
calls to the system, conventional assumptions and models of system performance
have been upheld. In others, such as the characterization of the service-time distribution and of customer patience, the data have revealed fundamental, new views of the nature of the service process.
Of course,
these limited studies
are only the beginning,
and the effort
to collect and analyze call-center
data can and should
be expanded in every dimension
in Kenya and Africa at large.
Perhaps the most pressing practical need is for improvements in
the forecasting of arrival rates. For
highly utilized call centers,
more
accurate, distributional forecasts
are
essential. While
there exists some research that develops methods
for estimating and predicting arrival rates, I strongly believe there is surely room for additional improvement to be made both here at home and the entire
continent. However, further
development of models for estimation and prediction will depend, in part, on
access to richer data sets.
Some of us believe that much of the randomness of Poisson arrival rates may be explained by covariates that are not captured in
currently available data.
Procedures for predicting waiting-times are also worth pursuing.
Field-based
studies that characterize the performance of different statistics and methods
would also be of value.
More broadly,
there is need for the development of a wider range of descriptive
models. While a characterization of arrival rates, abandonment from
queue, and service times are
essential for the management
of
call centers, they
constitute only a part of the complete
picture of what goes
on. For example, there exist (self ) service times
and abandonment (commonly
called “opt-out”) behavior that arise from customer use of IVRs.
Neither
of these
phenomena is likely to be the
same
as its CSR analogue. Similarly, sojourn
times
and
abandonment from web-based
services
have not been
examined
in multi-media centers.
Parallel, descriptive studies are also needed to validate or refute the robustness
of
initial findings. For example,
lognormal service times have been reported in
two call centers,
both
of which are part of retail financial
services companies. Perhaps the
service-time distributions of catalogue retailers or help-desk operations have different characteristics.
Similarly, one would like to test some finding that the waiting-time messages customers
hear while tele-queueing promote, rather than discourage,
abandonment.
It would also be interesting to put work on abandonment (Palm, Roberts,
Kort, Mandelbaum with Sakov and Zeltyn)
in perspective. These
studies provide empirical and exploratory models for (im)patience on
the phone in Sweden in the 40’s, France
in the late 70’s, the U.S. in the early 80’s, Israel in the late 90’s
and now Africa(Kenya in particular under
this research) in the early millennium. A systematic comparison of patience across countries,
for current phone
services, should be a worthy, interesting undertaking.
There is the opportunity to
further develop and extend the scope of explanatory models.
Indeed, given
the
high
levels of system utilization in
the QED Quality and Efficiency Driven (operational) regime, a small percentage error in
the forecast of the offered load can lead to significant, unanticipated changes in system performance. In particular, the state of the art
in
forecasting call volumes is still rudimentary. Similarly, the fact that service times are lognormally distributed enables the use of standard parametric techniques
to understand the effect of covariates on the (normally
distributed) natural log of service times.
In well-run
QED call centers, only a small fraction of the
customers
abandon (around 1-3%), hence about
97% of the
(millions
of ) observations
are
censored. Based on such figures,
one can hardly expect any reasonable estimate of the whole patience distribution, non-parametrically at least. Fortunately, however, theoretical analysis suggests
that
only the behavior of impatience near the origin is of relevance, and this is observable
and
analyzable.
Indeed,
call-center data are
challenging the state-of-the-art
of statistics, and
new statistical
techniques seem to be
needed
to
support their analysis. Two
examples
are
the
accurate non- parametric estimation of
hazard rates, with corresponding
confidence intervals, and
the survival
analysis of tens
of thousands, or even millions, of observations, possibly correlated and highly
censored.
Last
but certainly
not
least, a broader goal should
be, in fact, the analysis of integrated operational, marketing, human resources, and psychological
data. That is, the analysis of these integrated
data is essential if one is to understand and
quantify the role of operational service quality as a driver
for
business success.
A prerequisite for understanding the financial effects of operational
decisions is the ability to analyze an integrated
data set that includes operational (ACD) automatic call distributor and marketing / business (customer information systems) data. With this information, one can attempt to tease
out the longer-term, financial effects of operational
policies.
My experience
has
been that both
types of data are very
difficult to access,
however. One reason for this
is
technical. Only recently have the manufacturers of telephone equipment given customers something of an “off the shelf ” ability to capture, store,
and
retrieve detailed, call-by- call data. Similarly, the integration of these operational data with the
business
data
captured in
customer information systems is only now becoming widely
available. Another
reason stems from confidentiality concerns; most of our Kenyan companies
are rightly wary of releasing customer information. Once managers recognize the great untapped value of these data,
i
believe they will employ mechanisms for
preserving confidentiality in order to reap the benefit.
Ultimately, i envision a data-repository
that is continuously fed by many call centers of varying
types. The collected data would be continuously and automatically analyzed, from both operations
and marketing perspectives.
Then the data
would be both archived and fed back to the originating
call centers, who would use it (through visualization tools) to support ongoing operations, as well
as tactical and strategic goals.
Little imagination is required for appreciating the
value of such a data-base. As a start, its
developer could become a benchmark that sets industry standards, as far as customer-service
quality and call-center efficiency are concerned.
As already mentioned, such a data-base would enable
the identification of
success-drivers of call-center business transaction.
Researched
& Compiled: Samwel Kariuki
Date: 12th
Feb 2017
![IMG_5605[1]](https://sknjeri.files.wordpress.com/2016/08/img_56051.jpg?w=680)
