For Effective Staffing
Employing predictive technologies can help carriers deal with
fluctuations in staffing needs. By Krishna Mehta
Predictive analytics enables users to extract in- formation from historical data and use it to predict future outcomes. One of
the most effective ways the insurance
industry is leveraging predictive analytics is to gain valuable insight to make
effective employee staffing decisions.
Predictive analytics provides insurance executives with critical insights—
not normally available to them via traditional methodologies—that can help
improve the efficiency of operational
issues, such as employee staffing.
Staffing decisions in the insurance domain tend to be demand-driven. For example, in a customer service support operation, an organization needs to make
decisions around how many employees
are required for different shifts. Factors
such as volume of calls coming in and the
types of calls being placed are key data
points that help facilitate staffing decisions. This requirement extends to other
areas dependent on volume, such as underwriting or claims administration.
Effective staffing decisions can be
made if quality estimates regarding demand fluctuation are available. In the
customer service call center context,
this might involve predicting how call
volume varies over the course of a day,
day of the week, time of month or by
month. Since the skill sets needed vary
by call type—marketing versus claims,
for example—this analysis would need
to be performed separately for individual call categories.
A common staffing challenge organizations face is in predicting the numbers required. An insurance provider
might require a quarterly projection of
demand to make hiring decisions. However, weekly projections might be necessary to make decisions related to staff
work schedules, and half-hourly level
projections might be needed by shift
managers to make the right decisions
on a day-to-day basis.
The need for these numbers at different levels of aggregation poses some
challenges to an organization. One
could roll the data up to the highest
level (quarterly, for example) of aggregation and perform analysis at that
level. Lower-level numbers (weekly
and half-hourly) would be obtained
by using some splitting rules. This is
commonly referred to as the top-down
Alternatively, the forecast could be
generated at the lowest level (
half-an-hour slots, for our example) and high-er-level numbers could be obtained by
rolling up these numbers. This is called
the bottom-up approach.
A top-down or bottom-up approach
will always be less accurate at one of the
levels. One could use independent forecasts for the different levels, but this will
lead to internal inconsistencies in numbers. A hybrid approach can help resolve
this problem. The hybrid approach will:
• Generate lower-level forecasts using granular data
• Generate top-level forecasts with
• Use the lower-level forecasts to
generate proportions, which are then
used to split the top-level number.
This approach is useful because it
takes advantage of the higher accuracy
of the top-level numbers, and then fac-
tors in some of the lower-level informa-
tion by calculating proportions from
lower-level forecasts. The use of predic-
tive analytics to develop a hybrid fore-
casting approach will allow insurers to
better manage staff schedules to deal
with fluctuations in call volumes, and
serve their customers better and in a
more cost effective manner.
ElEmEnts of a
There are key factors to consider when
designing a framework to include predictive analytics in the staffing process. A successful strategy involves a number of
components, including having the right
type of data available for analysis, access to
the right tools and techniques, as well as a
solid understanding of implementation
capabilities and limitations.
Before any analysis can be performed,
the data asset needs to be created. While
the data elements are being put in place,
one needs to start thinking about tools
and techniques needed to perform the
analysis. Organizations take different
routes to accomplish this step. One approach is to build the analytical capability in-house by hiring trained modelers
and acquiring the tools needed to perform the analysis. Another approach is to
outsource this process to external vendors. Both methods have their merits.
Finally, a successful paradigm should
account for all the constraints and needs
of the implementation environment.
One needs to be cognizant of these limitations upfront, and then design the
analysis plan accordingly. INN
Krishna Mehta is VP, Transformation at EXL
Service, New York.
The use of predictive analytics
For more about analytics, search “Business Analytics
to develop a hybrid forecasting
approach will allow insurers to
better manage staff schedules to deal
with fluctuations in call volumes.
SaaS to Grow” at www.insurancenetworking.com.