A Case Study on Key Success Factors in Delivering e-CRM Solutions
Bibliographic record
Abstract
Over the past 10 years, a number of studies have pointed out that many e-CRM projects fail to deliver the expected benefits. In the business-to-business e-CRM market, many customers have faced issues with technology implementation, management of organizational change, and/or e-CRM effectiveness. However, none of these studies mentioned PeopleSoft. In fact, PeopleSoft’s e-CRM has been a best-of-breed solution. The purpose of this case study is to explain the determinants of the success of PeopleSoft’s e-CRM. This study was carried out in PeopleSoft’s Canadian subsidiary in 2004 (before the acquisition by Oracle). The findings reveal the superiority of PeopleSoft on the financial, marketing, and technological dimensions. In fact, the sustainable competitive advantage of PeopleSoft’s e-CRM lies in what is called a value-based business model. This unique business model is based on a 100% Internet architecture, a pricing model customized according to the value delivered to the customer (not the number of users), and the sharing of e-CRM risk with customers. This case study describes a striking reality: PeopleSoft’s CRM vision is the key success factor. Other eCRM vendors, including Siebel, lack a vision for selling their e-CRM technology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".