Selling Wellness: An Analysis Of Goop And Poosh’s Visual Marketing On Instagram
Bibliographic record
Abstract
In conjunction with the rising “self-care” culture, wellness has become a key consumer trend in Western societies. The multi-trillion-dollar industry can, perhaps, thank social media for its significant growth in recent years. With proven benefits pertaining to brand awareness, consumer engagement, and purchase intention, social media has become an integral element of the twenty-first century business, particularly within the wellness industry. However, social media scholars have yet to sufficiently address the corporate approach to selling wellness. Engaging with everything from mindfulness and nutrition to fitness and beauty, how do wellness companies market themselves in such a multidimensional industry? This Major Research Paper explores the Instagram marketing strategies of two globally renowned wellness companies, goop and Poosh. A qualitative content analysis, guided by a visual social semiotic framework, was conducted on 100 of the companies’ most-liked image-based posts from January to May 2021. The findings of this study suggest that goop and Poosh’s content generates more likes on Instagram when it prioritizes the physical wellness domain, emphasizes celebrity affiliation, utilizes high modality, people-centric imagery, incorporates an aesthetic of concealment/fragmentation, and references a “link in bio.”
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 | 0.001 | 0.003 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".