1 A BLUEPRINT FOR HELPING CUSTOMER INSIGHT PROFESSIONALS DRIVE BUSINESS GROWTH
Bibliographic record
Abstract
This paper was prompted by the arrival at AIA of a new Chief Customer Officer keen to ensure that the organisation had in place a blueprint to guarantee that its customer insight capabilities were fit to purpose and would enable AIA, the largest Pan-Asian life insurance group, to truly listen to the voice of customers around the world. It has been over 50 years since Marshall McLuhan, the Canadian visionary, first articulated the phrase ‘Global Village’. Over this period, the debate about the role of ‘globalism ’ in the context of the tenacity of ‘localism ’ has continued, including considerable contributions being made by the market research industry. Today, this continues to be a fascinatingly complex issue. Just what frameworks do we have in place to make sense of Asian professionals who now consider themselves to be citizens of the world, crave a Rolex watch or an Aston Martin DB9, drink Smirnoff, but who may still consider an arranged marriage or seek permission from their parents before travelling abroad? The way powerful global forces interact with embedded local tradition remains an intriguing challenge. AIA recognises that in both the East and West, customer insight – truly understanding customer motivations – will become an even greater driver of business success than it has been over the last
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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 | 0.019 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.033 | 0.018 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.021 | 0.014 |
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".