research-article

Predictors without Borders: Behavioral Modeling of Product Adoption in Three Developing Countries

Authors:

Muhammad R. Khan,

Joshua E. BlumenstockAuthors Info & Claims

KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 145 - 154

https://doi.org/10.1145/2939672.2939710

Published: 13 August 2016 Publication History

Abstract

Billions of people around the world live without access to banks or other formal financial institutions. In the past several years, many mobile operators have launched "Mobile Money" platforms that deliver basic financial services over the mobile phone network. While many believe that these services can improve the lives of the poor, in many countries adoption of Mobile Money still remains anemic. In this paper, we develop a predictive model of Mobile Money adoption that uses billions of mobile phone communications records to understand the behavioral determinants of adoption. We describe a novel approach to feature engineering that uses a Deterministic Finite Automaton to construct thousands of behavioral metrics of phone use from a concise set of recursive rules. These features provide the foundation for a predictive model that is tested on mobile phone operators logs from Ghana, Pakistan, and Zambia, three very different developing-country contexts. The results highlight the key correlates of Mobile Money use in each country, as well as the potential for such methods to predict and drive adoption. More generally, our analysis provides insight into the extent to which homogenized supervised learning methods can generalize across geographic contexts. We find that without careful tuning, a model that performs very well in one country frequently does not generalize to another.

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Cited By

Marco RPerez-Saiz H(2025)Formal financial inclusion and digital mobile money adoption in sub-Saharan Africa: do bank concentration and competition matter?Applied Economics10.1080/00036846.2025.2453756(1-21)Online publication date: 5-Feb-2025
https://doi.org/10.1080/00036846.2025.2453756
Reppas DMuschert G(2020)Mobile Money Systems as Avant-Garde in the Digital Transition of Financial RelationsThe Palgrave Handbook of Corporate Sustainability in the Digital Era10.1007/978-3-030-42412-1_8(157-168)Online publication date: 7-Oct-2020
https://doi.org/10.1007/978-3-030-42412-1_8
Gazzaz SRao P(2019)Extracting Meaningful Insights on City and Zone Levels Utilizing US Open Government DataCompanion Proceedings of The 2019 World Wide Web Conference10.1145/3308560.3316470(1279-1285)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308560.3316470
Show More Cited By

Index Terms

Predictors without Borders: Behavioral Modeling of Product Adoption in Three Developing Countries

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cover image ACM Conferences

KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2016

2176 pages

ISBN:9781450342322

DOI:10.1145/2939672

General Chairs:
Balaji Krishnapuram
IBM
,
Mohak Shah
Bosch
,
Program Chairs:
Alex Smola
Amazon
,
Charu Aggarwal
IBM
,
Dou Shen
Baidu
,
Rajeev Rastogi
Amazon

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 13 August 2016

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KDD '16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 13 - 17, 2016

California, San Francisco, USA

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KDD '16 Paper Acceptance Rate 66 of 1,115 submissions, 6%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Marco RPerez-Saiz H(2025)Formal financial inclusion and digital mobile money adoption in sub-Saharan Africa: do bank concentration and competition matter?Applied Economics10.1080/00036846.2025.2453756(1-21)Online publication date: 5-Feb-2025
https://doi.org/10.1080/00036846.2025.2453756
Reppas DMuschert G(2020)Mobile Money Systems as Avant-Garde in the Digital Transition of Financial RelationsThe Palgrave Handbook of Corporate Sustainability in the Digital Era10.1007/978-3-030-42412-1_8(157-168)Online publication date: 7-Oct-2020
https://doi.org/10.1007/978-3-030-42412-1_8
Gazzaz SRao P(2019)Extracting Meaningful Insights on City and Zone Levels Utilizing US Open Government DataCompanion Proceedings of The 2019 World Wide Web Conference10.1145/3308560.3316470(1279-1285)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308560.3316470
Berger PKompan M(2019)User Modeling for Churn Prediction in E-CommerceIEEE Intelligent Systems10.1109/MIS.2019.289578834:2(44-52)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1109/MIS.2019.2895788
Centellegher SMiritello GVillatoro DParameshwar DLepri BOliver N(2018)Mobile MoneyProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/32870352:4(1-18)Online publication date: 27-Dec-2018
https://dl.acm.org/doi/10.1145/3287035
Suri T(2017)Mobile MoneyAnnual Review of Economics10.1146/annurev-economics-063016-1036389:1(497-520)Online publication date: 2-Aug-2017
https://doi.org/10.1146/annurev-economics-063016-103638

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