Previously, I suggested that now is a good time to assess how to
improve your manual review operations. Today we
explore what metrics we should look into.
As a productivity measure, many organizations look at the
orders reviewed per hour (or per shift). Typical reviewer productivity varies
by size of the online business. For
large organizations reviewing 20 orders per hour per reviewer is common (see the
CyberSource
11th Annual Fraud Report for reviewer productivity for smaller
online businesses). One additional measure
that has proven to be useful is the average time in queue, which looks at the
difference between the time an order was decisioned and the time it passed from
the automated screen into the review queue. If you’re able to determine the
time that each of these transaction status changes occurred, you’ll be able to
directly relate your reviewers’ productivity to customer experience commitments
and objectives.
The other piece is to measure the effectiveness of your
review tools. It depends on the number of tools you have in use, and the
complexity of your review processes. If you haven’t already, you should
establish a mechanism for tracking the use of your review tools, preferably
with relationship to the transaction and its outcome. If you’re monitoring at
the transaction level, remember that you should evaluate the effect of a
particular tool, such as an external address validation service, in light of
both the accepted good transactions as well as the fraud chargebacks. You
should also identify whether reviews that were completed without the use of
services that cannot be automated (for example, bank verifications, or customer
contact) might be automatically screened and dispositioned using your existing
automated fraud screening tools.
In the next post, I’ll discuss looking at feedback on the
quality of reviews that were performed.
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