Applying Customer Journey Mapping in Social Marketing to Understand Salt-Related Behaviors in Cooking. A Case Study
Bibliographic record
Abstract
Worldwide, salt consumption exceeds the World Health Organization's recommendation of a daily intake of 5 g. Customer journey mapping is a research method used in market research to understand customer behaviors and experiences and could be useful in social marketing as well. This study aimed to explore the potential of customer journey mapping to better understand salt-related behaviors performed during the preparation of household cooking. We tracked the journey of four women in their kitchens for approximately two hours to observe the preparation of lunch. Individual journey maps were created, one for each woman, that were composited into a single journey map. We found that customer journey mapping was a suitable research method to understand how food preparers made decisions around adding salt and artificial seasonings at each stage of the journey. In contrast to the interviewee' responses, it was observed that the four women added salt and artificial seasonings consistently and incrementally with little control and without any standard measure. In this study, we demonstrate the utility of customer journey mapping in a novel context and nudge social marketers to include this tool in their repertory of research methods to understand human behavior.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".