A Development Framework for Customer Experience Management Applications: Principles and Case Study
Bibliographic record
Abstract
Customer experience management (CEM) denotes a set of practices, processes, and tools that aim to personalize a customer's interactions with a company around the customer's needs and desires. This personalization depends on the purchase scenario at hand, and on how much a company knows about its customers. In turn, the purchase scenario depends, among other things, on the complexity of the product or service being offered (e.g., a carton of milk versus a house), and the complex set of motivations that can trigger a purchasing process. E-commerce software tool vendors need to provide the building blocks that enable retailers to configure and develop CEM functionalities that take into account these factors. In earlier work, we proposed such building blocks within the context of a CEM development framework that relies on a cognitive modeling of the purchasing process and identifies the touch points between seller and buyer and relevant influence factors. We envision a CEM scenario specification tool that enables business analysts to specify their purchase scenario, from which we generate data structures and algorithms to implement CEM functionalities by instantiating the framework. The framework is embodied in a set of ontologies and algorithm templates that can be instantiated with the specification parameters. In this paper, we present the principles behind our approach, and a prototype CEM scenario specification tool. We illustrate the tool with a moderately complex purchasing scenario, to validate the underlying theory, and to explore implementation strategies.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".