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[Proposed Experiment]: Solar Malawi: (FINANCE) Locating Existing Customers

Any consequences of non-payment are not enforceable without a way of knowing exactly where to find existing customers. Having details of where they live will increase pressure on them to repay their loans, and make consequences more enforceable. Even though the feasibility of us following up loans is not necessarily cost-effective, it may also give more pressure to customers if they know we know where they live.  Lean Phase:  Revenue/channels Assumption: People are able to give...

Kurt Michl
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Kurt Michl

[Proposed Experiment]: Malawi SoCon: Methods for tracking loan customers

Lean Phase:  Channel/Revenue Assumption:  Lending institutions (banks, MFI, Village banks) have ways of knowing exactly how to find their customers/members. Such methods may include geo-pinning, sketch maps and a dependence on village chiefs.    Time Period:  <1 week (July 2019) Criteria:  existence of a method for tracking location.  Success Metric: Success Point:  all three types of lending institutions have methods of locating customers.  Green Light:  use these...

Kurt Michl
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Kurt Michl

[Proposed Experiment]: Malawi - Texting Communication Experiment - February, 2019

Lean Phase: Channel Assumption: Sending texts manually to all existing customers on the payment plan will improve relationships with customers and generate a more efficient communication system. The content of the texts will initially be payment reminders and then, when we have the capacity, we will also include balance updates and confirmation texts. These texts will help customers to make their repayments in a more timely manner and will provide more confidence regarding this payment...

Kyana Chan
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Kyana Chan
Adopted Experiment

[Proposed Experiment]: Malawi - Revised Risk Profiling Testing for Existing Defaulting Customers and Non-Defaulting/Proxy Customers - February, 2019

Lean Phase: Customer Segment Assumption: The previous risk profiling tool was inaccurate (the weightings aren’t accurate or the metrics may not be explanatory), with less than 50% of the defaulting and non-defaulting customers being identified correctly ( see data here ). This may be due to the scores/weightings not being based on any data, as well as potential irrelevancies in the metrics. Via surveys of the remaining customers, we will be able regress the data to fit a model on which...

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Kyana Chan

Sydney, Australia

Joined this community on Nov 15, 2018

University or Company
USYD

Degree or Work area
Business

Country you Trekked/are Trekking in
Malawi

LinkedIn Profile
www.linkedin.com/in/kyana-chan-7bb166159

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