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Record W2080524058 · doi:10.1108/10878570010379392

Measuring customer capital

2000· article· en· W2080524058 on OpenAlexaff
Jan Duffy

Bibliographic record

VenueStrategy and Leadership · 2000
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer churn and segmentation
Canadian institutionsCentre for Disability Prevention and Rehabilitation
Fundersnot available
KeywordsBusinessCustomer retentionCustomer advocacyCustomer to customerCustomer intelligenceCustomer equityIndustrial organizationVoice of the customerMarketingCompetitive advantageProcess managementService quality

Abstract

fetched live from OpenAlex

Abstract Measuring customer capital – customers' contribution to current and future revenues – is fundamental to assessing how successful an organization is in turning customer relationships into sustainable competitive advantage. The new business environment requires that companies shift from being product‐centric to being customer‐centric. This shift affects every business process. This article describes the factors that must be considered in assessing customer capital and outlines the steps for introducing such an initiative into the company.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.152
GPT teacher head0.239
Teacher spread0.086 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations77
Published2000
Admission routes1
Has abstractyes

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