Assessing the Impact of Chokepoints in a Customer Onboarding Process

A. Rodriguez () and John Rosen

MPRA Paper from University Library of Munich, Germany

Abstract: Customer onboarding processes have become dysfunctional, especially with regards to the increasing number, complexity, and often, competing demands, of regulatory and law enforcement bodies with oversight over a firm’s practices. Prospective customers are screened across any number of considerations ranging from conventional ones such as financial considerations (i. e., “Does this customer have an acceptable balance sheet?”) to the more recent socio-cultural ones (i. e. “does this customer have an effective diversity program?” “Has this customer expressed a commitment to environmentally sustainable business practices?”). An impaired sales pipeline resulting from an impaired customer vetting process may lead to lowered economic returns, reduced profitability, erosion of market share. A firm intent on repairing their customer intake processes could examine whether rescinding or reducing extant customer acceptance thresholds will enhance their performance. However, many firms are beset by a peculiar outcome that complicates auditing the onboarding process. Customer portfolios are routinely culled of non-performing customers or costly-to-serve customers, leaving a selection of seemingly successful customers – a data artifact known as a one-class problem. In this paper we simulate the onboarding process to isolate the effect of changes in established acceptance thresholds on customer’s likelihood of success. However, to do so we first address the One-Class problem. When only One Class (“Successful” or “Performing”) customers are available, allows for the deployment of two well-known One-Class algorithms: Support Vector Machines and Isolated Random Forests. This study shows their use in reconstructing a representative sample of the customer pool. Aside from showing how to treat the One Class artifact, our objective is to establish a platform for discussion. For plausible initial conditions this study highlights a tradeoff between reductions in customer thresholds and the firm’s commitment to ensuring customer success.

Keywords: Customer onboarding; Model-shift; Data-shift; One�Class; Isolation Forests; Support Vector Machines (search for similar items in EconPapers)
JEL-codes: L22 L23 M14 (search for similar items in EconPapers)
Date: 2023-06-21
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:117997

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