The art of storytelling: how loyalty marketers can build emotional connections to their brands
Bibliographic record
Abstract
Purpose The purpose of this paper is to highlight the importance of building emotional connections between brands and consumers. Using Canada's Air Miles Reward Program as an example, the paper aims to stress the importance of using customer insight to drive branding decisions and ensure a long‐term emotional attachment to a loyalty program. Design/methodology/approach The paper thoroughly explains Air Miles' method of reaching out to its customers to glean information that could be used to re‐brand the program. The method, used during focus groups, asked collectors to re‐tell stories that were important in their life. Common themes emerged, which Air Miles incorporated into the re‐branding of their program. Findings Through specially‐designed focus groups, Air Miles strategists learned that it isn't enough to be a well‐functioning loyalty program. In order to be distinctive in an overcrowded market, Air Miles must provide collectors with an emotionally engaging experience in the redemption process. Practical implications If your customers talk about your brand as if it's a part of who they are, you have made an emotional attachment with them. Thus, your program is on the right track. Originality/value The paper takes a fresh approach to loyalty markting research as well as analyzing and improving customer loyalty.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".