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Analytics and Performance Measurement Frameworks for Social Customer Relationship Management

2014· book-chapter· en· W4255984321 on OpenAlex
Anteneh Ayanso, D.J.G. Visser

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvances in social networking and online communities book series · 2014
Typebook-chapter
Languageen
FieldComputer Science
TopicSentiment Analysis and Opinion Mining
Canadian institutionsBrock University
Fundersnot available
KeywordsAnalyticsBig dataComputer scienceData scienceKnowledge managementCustomer relationship managementProcess managementBusiness analyticsBusinessBusiness modelBusiness analysisData miningMarketingDatabase

Abstract

fetched live from OpenAlex

This chapter provides an overview of the analytics and performance measurement frameworks for social customer relationship management (SCRM). Based on a review of academic research and industry practices, the chapter discusses the limitations of traditional CRM, and the technology and analytical capabilities that support SCRM. The chapter also provides a review of existing measurement frameworks for SCRM strategies and outlines the various metrics that have been proposed and/or are currently in use as part of SCRM systems. Furthermore, in view of the opportunities and challenges of big data and the social media environment, the chapter highlights current business practices as well technology and analytics trends that facilitate the implementation and maintenance of SCRM systems.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.955
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.065
GPT teacher head0.296
Teacher spread0.231 · 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