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Record W2045124712 · doi:10.5539/ass.v6n10p178

The Application of Customer Relationship Management in Investment Banks

2010· article· en· W2045124712 on OpenAlex
Fan Wang, Fang Hu, Li Yu

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAsian Social Science · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer churn and segmentation
Canadian institutionsnot available
Fundersnot available
KeywordsCustomer relationship managementBusinessInvestment (military)Value (mathematics)ChinaCustomer baseForeign direct investmentMarketingIndustrial organizationFinanceEconomicsComputer science

Abstract

fetched live from OpenAlex

Customer Relationship Management was put forward in the 90s by American companies. CRM has been widely applied by the investment banks abroad. The investment banks in China (securities) are in the early stage of CRM. The institutions which carried out CRM successfully and profitably were few .Based on the importance of CRM to the investment banks management, this essay compares the application of CRM between domestic investment banks and the foreign ones, and points out that in applying CRM, domestic investment banks must make clear their cognitional misunderstanding, base themselves on the customers center and customers satisfaction, and their own status and characteristics, take efficient measures to perfect CRM, then realize the maximizing value.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.930
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.016
GPT teacher head0.271
Teacher spread0.256 · 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