How to prevent your customers from failing
Bibliographic record
Abstract
Customers are often involved in the design or delivery of services, and in this respect, they function as coproducers of the service. What happens when customers fail to perform their roles effectively? Customer failure is not uncommon; the authors cite research indicating that customers cause about one-third of all service problems. To study the issue of customer failure and its prevention, the authors conducted interviews with managers and customers in a variety of industries about experiences of customer failure, developed case studies related to the topic and conducted secondary research to identify examples of best practices in customer-failure prevention. From their research, the authors conclude that recovering from instances of customer failure is difficult, in part because the customer and the company may have different views of the causes of the problem. As a result, companies should focus on preventing customer failures. An effective three-step approach is first to collect diagnostic data about where customer failures occur and then to analyze the root causes of cases of customer failure ? whether the root causes are issues of technology, people, processes or the design of the physical environment that customers encounter. The third step is to establish preventive solutions, such as process redesign. The authors cite a number of examples of companies that try to prevent customer failures. For instance, customers of Weight Watchers International Inc. may offer each other encouragement at Weight Watchers?meetings and thus help prevent one another from failing in their weight-loss plans.
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.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.013 |
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".