Applying customer intelligence in marketing: a holistic approach
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.
Bibliographic record
Abstract
Enterprises have started to adopt and apply customer intelligence, which is acquired through the support of business analytics to capitalise on big data, to optimise marketing decisions. However, little research focuses on holistically applying customer intelligence from defining and acquiring the right type of customer intelligence to applying and evaluating it for optimal outcomes. This paper presents a comprehensive approach to value creation from customer intelligence in marketing. Adapted from Bloom's taxonomy, the proposed approach significantly contributes to identifying the six levels of applying customer intelligence in marketing, including defining relevant types of customer intelligence, building appropriate strategies, identifying customer data, understanding customer analytics, setting key performance indicators for the evaluation purpose, and creating value through business questions and the interactive dashboard.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it