Cited by 21Customer churn and segmentationCustomer Service Quality and LoyaltySpatial and Panel Data Analysis

The impact of geographical factors on churn prediction: An application to an insurance company in madrid’s urban area

Open access
de la Llave, Miguel Ángel, López, Fernando A., Angulo, Ana·2019·Scandinavian Actuarial Journal

Abstract

Geography has previously been noted as a decisive factor in business literature. This paper provides evidence of the significant role geography plays in customer lapse behaviour in an urban environment. This novel approach is based on the idea that the customers who cancel all policies and leave the company are not randomly distributed; rather, a mimetic performance of close individuals is noted. The physical proximity of the customer to the geographical focus (strategical centre, as insurance offices) and the interaction with nearby customer are spatial factors that increase (or decrease) the probability of churning. An empirical analysis using more than 7000 spatially georeferenced offline customers of a Spanish insurance company in the urban area of Madrid (Spain) demonstrated that the customer's proximity to offices of such insurance company under study decreases the probability of churning, whereas high lapse risk was detected in customers in the surroundings of the company's competitor branches. In addition, we identified spatial autocorrelation in churn probability, thus demonstrating that the probability of churn of a customer increases if nearby customers churn.

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Registry

Crossref · 10.1080/03461238.2018.1531781

Cites

46 of 46 references verified0 citation errors · 0 not checkable

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  • Competitive policies are not priced too expensively at other insurers, so it is relatively attractive to switch insurers.

    “Similarly, competitive policies are not priced too expensively at other insurers, so it is relatively attractive to switch insurers (De la Llave et al., 2019;Leiria et al., 2021;Spedicato et al., 2018).”

    From Customer price sensitivities in competitive insurance markets (2022) · cites this paper as De la Llave et al., 2019

    In the discussion around Figure 2 (page not supplied), the paper says the car-insurance market is highly competitive and suggests that younger customers may look online for cheaper alternatives. It also links higher premiums to greater likelihood of cancellation. It does not establish that other insurers’ policies are generally inexpensive or that switching insurers is therefore broadly attractive. (LLM, conf=0.83)

    Experimental — accuracy not yet measured
  • It becomes more worthwhile for customers to switch insurers at higher rate changes, even though this incentive decreases as the rate change increases in absolute sense.

    “First, it becomes more worthwhile for customers to switch insurers at higher rate changes, even though this incentive decreases as the rate change increases in absolute sense (De la Llave et al., 2019;Guelman et al., 2014;Leiria et al., 2021).”

    From Customer price sensitivities in competitive insurance markets (2022) · cites this paper as De la Llave et al., 2019

    The Results and discussion section reports that customers paying higher premiums are more likely to cancel, with a weaker increase in churn beyond €420. That supports the claimed nonlinear pattern, but the paper examines premium amounts, not changes in insurance rates, and does not establish that switching becomes more worthwhile to customers. (LLM, conf=0.86)

    Experimental — accuracy not yet measured
  • This amplifies the let sleeping dogs lie effect and makes it relatively attractive for very low and high risk customers to lapse their expensive policy and switch insurers, perhaps even more attractive when considering more insurers than the six largest competitors.

    “This amplifies the let sleeping dogs lie effect and it therefore tends to be relatively attractive for very low and high risk customers to lapse their (expensive) policy and to switch insurers, and perhaps even more attractive when considering more insurers than the six largest competitors in the market (De la Llave et al., 2019; Guillén et al., 2008Guillén et al., , 2012;;Jeong et al., 2018;Pared”

    From Customer price sensitivities in competitive insurance markets (2022) · cites this paper as De la Llave et al., 2019

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Cites

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1 of the 47 references couldn’t be matched to a known paper.

Research works printed but not linked: 4

  1. Collingham, Y.C., R.A. Wadsworth, B. Huntley, and P.E. Hulme, 2000, Predicting the spatial distribution of non-indigenous riparian weeds: issues of spatial scale and extent, Journal of Applied Ecology, 37: 13-27.
  2. Heagerty, P.J., and S.R. Lele, 1998, A composite likelihood approach to binary spatial data, Journal of the American Statistic Association, 93: 1099-1111.
  3. LeSage, J.P., R. K. Pace, N. Lam, R. Campanella, and X. Liu, 2011, New Orleans Business Recovery in the Aftermath of Hurrican Katrina, Journal of the Royal Statistical Society A, 174: 1007-1027.
  4. Soeini, R., and K. Rodpysh, 2012, Applying Data Mining to Insurance Customer Churn Management, International Proceedings of Computer Science, 30: 82-92.

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Authors
de la Llave, M. Á., López, F. A., & Angulo, A.
Affiliations
  • Grupo de investigación en Econometría Espacial, Universidad Nacional de Educación a Distancia, Madrid, Spain
  • Departamento de Métodos Cuantitativos e Informáticos, Universidad Politécnica de Cartagena, Cartagena, Spain
  • Universidad de Zaragoza (ES)
Venue
Scandinavian Actuarial Journal
Published
2018-11-15
Volume
2019
Issue
3
Pages
188-203
ISSN
  • 0346-1238 (ISSN-L)
  • 1651-2030
OpenAlex
W2901673744
Cited by
21
References
47
Citations by year
2026: 1 · 2025: 5 · 2024: 1 · 2023: 2 · 2022: 6 · 2021: 3 · 2020: 3
FWCI
2.68
Type
journal article
Language
en
Open access
Locations
  • Scandinavian Actuarial Journal (publishedVersion)
  • Zaguan (University of Zaragoza Repository) (submittedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
Funders
  • Ministerio de Economía y Competitividad
  • Gobierno de Aragón
  • European Regional Development Fund
Topics
  • Social Sciences › Business, Management and Accounting › Marketing › Customer churn and segmentation
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Customer Service Quality and Loyalty
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
Keywords
customer churn prediction, insurance customers, geographical factors, spatial proximity, spatial autocorrelation, insurance offices, urban environment, Madrid

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