2 December 2016
Proprietary and Confidential
Measuring Progress:
Pursuing Transparency for Scale
2 December 2016
Initial observations from mapping analytics for FI
Page 2
Improving transparency, especially at national level, w/ more extensive data for a few select markets
• Ability to compare high-level progress across markets to assess impact of overall initiative portfolios
• Some opportunity to overlay national socio-economic data to consider structural drivers of FI
• Localized data available for some “headline” markets in FI, but are somewhat outliers given adoption
Granularity is resource intensive, but needed at the operational level, requires a new approach
• Localized pain points and segmentation difficult to assess, makes pre-implementation much more risky
• Lacking these data, it can be difficult to contextualize pilots when then looking to scale more broadly
• Digitization offers efficiencies for data collection; complexity calls for collaboration and standardization
Allows stakeholders to focus on bottom-up innovation, shift resources to targeted implementations
• Funders facilitate data aggregation / synthesis w/ more robust impact assessments to justify initiatives
• Stakeholders align market definitions, segmentation, or opportunity assessments to their propositions
• Panelists are illustrative of opportunities for a more data-driven approach to planning / operationalization
2 December 2016
High-level metrics useful in profiling financial inclusion gaps
Page 3
Sessions over the past couple of days have highlighted the need for and benefits from
financial inclusion, and we now take a look at the potential role of measurement for
identifying key gaps to address, developing new initiatives, and optimizing overall impact
7%
22%
0%
25%
50%
75%
100%
Adults Did NotReceiveWages
As Cash WithdrewAll
KeptBalance
Savings at FI
Low & Middle Income Markets
40%47%
0%
25%
50%
75%
100%
Adults Did NotReceiveWages
As Cash WithdrewAll
KeptBalance
Savings at FI
High Income Markets
Irregular incomes and cash payments tilt table
against FI, so propositions will need to both push and pull funds into formal channels / accounts
Global metrics facilitate greater benchmarking, and
can provide for interesting comparative profiles, and potentially as upper layers for dashboards
Source: Mondato analysis, World Bank Global Findex
2 December 2016
Findex regional / nat’l data provides more nuanced views
Page 4
East Asia & Pacific
Developing Europe & Central Asia Latin America
& Caribbean
Middle East
South AsiaSub-Saharan
Africa
0%
25%
50%
75%
100%
Below AverageAbove Average
Accounts at a Financial
Institution by Segments…
…and Penetration by Use Case
East Asia & Pacific Developing
Europe & Central AsiaLatin America
& Caribbean
Middle East
South Asia
Sub-Saharan Africa
0%
25%
50%
75%
100%
National level segmentation allows stakeholders to track objectives, but not targeted local initiatives
Increasing use cases adds additional data cuts, yet not granular enough for tracking intra-market experiments
Source: Mondato analysis, World Bank Global Findex
Acco
un
t a
t a
FI
% U
sin
g S
erv
ice
in
Pa
st Y
ea
r
2 December 2016
GSMA reveals MM traction, but use case adoption is “narrow”
Page 5
Airtime
P2P
Bill Payment
Bulk Disbursements
Merchant Payments
Int'l Remittamces
0%
25%
50%
75%
100%
2011 2015
%TxnVolume
P2P
Bill Pymnt
Bulk Dis.Airtime
Merchant PymntInt'l Rem.
$0
$3,000
$6,000
$9,000
2012 2015
Txn Value $US Mil
Source: Mondato analysis, GSMA State of the Industry Report – Mobile Money
Limited Change Txn
Composition / Diversity
Growth Historically Driven by
P2P, just one element of FI
• GSMA has taken a leading role in financial inclusion measurement w/ mobile money focus
• Significant growth in electronic transactions over past few years, but somewhat one dimensional
• Broader set of metrics beyond MNOs required for broader context, national benchmarking
2 December 2016
Empowering the wider ecosystem to accelerate innovation
Page 6
Situational
Context
Scaled Data
Aggregation
Targeted
Facilitation
Use Case Ecosystem
Scala
bili
tyF
und
ing
Current Situation: Increasing number of data points from multiple sources, and start of time series for tracking
Opportunity: Leverage digitization in building out
wider set of data points through collaboration
To-be Desired State: Shift resources to operational
assistance / facilitate wider ecosystem innovation
Limited private sector business case
Trusted third party to align interests
Alternative KPIs unlock synergies / ROI
Development Focus
Co
ntr
ibu
tio
n P
ha
se
Consider opportunities to bring a unified data
library to the wider
ecosystem
*Top-down initiatives are considered to generally be once-off interventions reliant on continued stakeholder funding, whereas a bottom-up approach is organic and continuous, such as collaborations to aggregate device-level data
2 December 2016
Moving towards more actionable insights on a localized basis
Page 7
Increased sample sizes and making these representative at the sub-national level would
be costly but increase ability to localize use cases (e.g., at provincial level as above)
Coast Province
Mombasa County
Less defined by geographic sampling, and more about aggregation of real-time device data to
explore supply-demand dynamics / impact
As-is: Moving Beyond Nat’l Level*
*In reality, localized data are available for a few select markets that have been focus areas for mobile money (e.g., India, Kenya, Tanzania), but these tend to be one-off exceptions, and other countries
tending to be based on a sample size of approximately 2,000 respondents per market
To-be: Organic Database Aggregation
2 December 2016
Panelists provide insight into measurement and impact
Page 8
• Initiatives include small-plot agriculture insurance, education savings, and financial diaries
• Illustrative of challenges in measuring impact, and how data collection is resource intensive
• While always the case to a certain extent, consider where efficiencies are possible:
• Initial hypothesis formulation, research methodology, and validation of key learnings
• Innovative data collection channels / collaborations undertaken for the studies
• Potential areas where collaboration might be both most effective and manageable
• Possible challenges to be considered (e.g., data ownership / privacy, unstructured)
• Further impact assessments being considered if data were available