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Record 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 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
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

VenueABA banking journal · 2004
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicSecurities Regulation and Market Practices
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessCustomer relationship managementMarketingService (business)
DOInot available

Abstract

fetched live from 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. …

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.306
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.006
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 head0.024
GPT teacher head0.256
Teacher spread0.232 · 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