The Quest for Customer Intelligence to Support Marketing Decisions: A Knowledge-Based Framework
Bibliographic record
Abstract
The quest for customer intelligence to create value in marketing has highlighted the significance of the research focus of this paper. Customer intelligence, which is defined as understandings or insights resulting from the application of analytic techniques, plays a significant role in the survival and prosperity of enterprises in the knowledge-based economy. In this light, the paper has developed a framework of customer intelligence to support marketing decisions through the lens of knowledge-based theory. The proposed framework aims at supporting enterprises to identify the right customer data for the right customer intelligence corresponding with the right marketing decisions. In this light, four types of customer intelligence are clarified including product-aware intelligence, customer DNA intelligence, customer experience intelligence, and customer value intelligence. The applications of customer intelligence are also elucidated with relevant marketing decisions to maximize value creation. To illustrate the framework, an example is presented. The importance and originality of this study are that it responds to changes in customer intelligence in the age of massive data and covers multifaced aspects of marketing decisions.
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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".