Understanding Service Retention Within and Across Cohorts Using Limited Information  

Oleh:

Schweidel, David A. ; Fader, Peter S. ; Bradlow, Eric T.

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Journal of Marketing (EBSCO) vol. 72 no. 1 (Jan. 2008), page 82-94.
Topik: Service Retention; Proportional Hazards Model; Valuation

Ketersediaan

Perpustakaan Pusat (Semanggi) Nomor Panggil: JJ94.13 Non-tandon: 1 (dapat dipinjam: 0) Tandon: tidak ada

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Isi artikel

Service churn and retention rates remain central as constructs in marketing activities, such as valuation of service subscribers and resource allocation. Although extant approaches have been proposed to relate service churn to external factors, such as reported satisfaction, marketing-mix activities, and so on, managers often face situations in which the only information available is the duration for which subscribers have had service. In such cases, can they forecast service churn and understand the contributing factors, which may allow for subquent intervention? The authors propose a framework to examine factors that may underlie service retention in a contractual setting. Specifically, they use a model of retention that accounts for (1) duration dependence, (2) promotional effects, (3) subscriber heterogeneity, (4) cross-cohort effects, and (5) calendar-time effects (e.g., seasonality). Then, they apply the framework to subscription databases of seven services offered by a tellecomunications provider, mirroring the format commonly used to forecast future service churn (and to make managerial decisions). Across all seven services, the inclusion of promotional effects always improves the forecast accuracy of retention behavior, whereas including cross-cohort effects does not significantly improve it. In five of the services, customer heterogeneity, calendar-time effects, and duration dependence also contribute to improved forecasts. The authors use these results to understand how the expected value of a subscription differs across model specifications. They find considerable variation across model specifications, indicating that model misspecification can affect resource allocation decisions and other marketing efforts that are important to a firm.

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