Lean Data Case Study 5

1. PART 4
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2. As we know, Ziqitza wants
to determine if it is
reaching the poorest of the
poor. But assessing
something like “poverty”
can be challenging. Ziqitza
needs to identify a reliable
and valid set of questions,
metrics, or indicators to
quickly and accurately
judge whether it is
reaching its target
customers.
2
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3. 3
In Ziqitza’s case, the Progress
out of Poverty Index (PPI), a
tool managed by the Grameen
Foundation, is an ideal
instrument.
If you’re not familiar with the
PPI, you might want to check
out progressoutofpoverty.org
to download a copy of your
country’s survey and watch a
view overview describing the
instrument.
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4. 4
Ziqitza decided to have its
call center representatives
administer the PPI questions
as part of its routine
feedback calls. The PPI
questions were strategically
added to the client
satisfaction survey to ensure
that familiarity with Ziqitza
services leads to a congenial
situation in which to engage
in a longer survey.
Image Credit: Center for Health Market Innovations. All use, reproduction and distribution of this work is subject to a CC-BY-NC-ND license.

5. 5
After the first week, each call
averaged four to five minutes.
Normal business operations and
data collection could operate in
tandem.
Ziqitza successfully implemented
the PPI, but that is not to say
that everything went perfectly.
Indeed as far as Acumen or
Grameen Foundation know, this
was the first time the PPI had
ever been administered by
phone, and unsurprisingly we
learned some important lessons
about how to conduct the
interviews in the process.
. Image Credit: Center for Health Market Innovations. All use, reproduction and distribution of this work is subject to a CC-BY-NC-ND license.

6. In particular, at first we found that the first
three questions were particularly prone to
error and so adjusted the script the call-
center interviewers used. This speaks to the
importance, even within Lean Data
approaches, of testing the questions you
are planning to use as thoroughly as
possible before rolling out at scale, this will
help you spot any unexpected
inconsistencies or systematic errors in the
data you aim to collect.
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