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Enregistrement W154586159

CRM R.I.P.? Not Exactly: Today's CRM Is Business Process Focused around Smaller, Provable Initiatives in Sales and Marketing

2004· article· en· W154586159 sur OpenAlex

Pourquoi ce travail est dans la base

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aboutLe titre ou le résumé porte un signal canadien du lexique géographique.
no affAucune affiliation canadienne : ce travail est invisible pour une base fondée sur la seule affiliation.
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Notice bibliographique

RevueABA banking journal · 2004
Typearticle
Langueen
DomaineBusiness, Management and Accounting
ThématiqueSecurities Regulation and Market Practices
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésBusinessCustomer relationship managementMarketingService (business)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Around the time the recession put a chokehold on capital spending, relationship management projects became another casualty of the ailing economy. popular refrain at the time that as an infrastructure or application set, CRM had died, failed, or left many companies high and dry after a huge spend, proving unable to provoke useful insight on customers or significantly boost cross selling. When Steven Fehlings, professional services director, financial services with Pivotal Corp., a CRM software and services supplier based in Vancouver, Can., heard such commentary, or hears it today, he thinks of some clients who were, in fact, frustrated with earlier CRM deployments. They were disenchanted with projects that didn't add significant value after years of effort, he notes. Problems resulting from not improving or automating business process stalled these CRM efforts, as did issues with the user interface. (Basically, they weren't intuitive enough.) Or, integration problems impaired project effectiveness. In recent Pivotal has developed a finely segmented approach to avoid too broad, off-target deployments. We have segmented our offerings around lines of business such as wealth management, Fehlings explains. The product for each division each gets different treatment while meeting certain common management objectives that automate sales and service and make processes more efficient. The idea, says Fehlings, was to eliminate the need for custom coding by making the application relevant 'out of the box'. Darlene Mann, CEO of Siperian, Inc., San Mateo, Calif., thinks that customer facing and solutions didn't die as much as shrink to a more nimble, manageable size. In her view, CRM solidified--if not around a line of business, as Pivotal has done--then around some discrete aspect of sales, service, or marketing process. Whatever the application, however, the data collection issues remain a challenge. Her clients are making progress in building better data models and in their data warehousing efforts generally--though she knows that other institutions struggle with it. That all the information an institution has on a is housed in the CRM database has always been a myth, Mann relates. Most of the good information resides in transactional systems. From that misunderstanding came a lot misguided expectations about what focusing initiatives and systems could achieve. In large institutions, there could be literally thousands of transactional systems, Mann adds. Donald Layden, president with NuEdge Systems, Brookfield, Wisc., has worked in with banking issues for running the trust organization under M&I Data Services (now Metavante), at Fiserv, and at other organizations before joining NuEdge. In his view, bankers have long understood the value of clean, accurate information, but have perhaps been frustrated by the limitations of older generation marketing information file (MCIF) systems--which required them to adhere to a stricter product focus and had other problems associated with them. Layden thinks that today's systems are more flexible, making it simpler to blend data from third-party sources into enterprise systems. When his company begins working with a client, overlaying their technology and analytics, a bank goes from having 100 data sets on a given to ten times that amount. We've been helping our customers use data driven strategies for improving retention and boosting cross-sell ratios for 15 years, he says. CRM were dead, I doubt we'd have a business. Always a complicated proposition Sanju Bansal also bristles when he hears word of CRM's supposed demise. If anything, he believes such claims are as exaggerated as the original capabilities attributed to the applications. …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Communication savante
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,306
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,000
Communication savante0,0020,006
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,024
Tête enseignante GPT0,256
Écart entre enseignants0,232 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle