Are Mature Female Consumers Well Served by the Fashion Retail Sector
Bibliographic record
Abstract
The fashion retail sector is going through extremely challenging times with continuing globalisation and the ongoing impact of the recent global recession in many markets. Markets are highly competitive and companies must strive to craft strategies which will deliver competitive advantage. Across the developed and the developing world, populations are ageing. The trend is so marked as to have been termed an ‘Agequake’ by A.T. Kearney, (2011, 1) in terms of its predicted impact on economies, companies and most particularly, retailers. Women aged 50 and over are now one of the most powerful consumer groups in the UK, spending more than £2.5bn per season on fashion (Kantar Worldpanel 2014). Furthermore, 90% of British retailers are seeing most growth come from the 50 plus sector (Smithers, 2014). Yet research commissioned by retailer JD Williams in 2014 found that over 60% of mature women (defined in the study as 50 plus) felt ‘underserved’ and ‘forgotten’ by the fashion industry. Given the macro, socio-demographic trends of a growing segment of mature consumers with high disposable incomes, the question must be asked why mature women feel the fashion industry has forgotten them and whether fashion retailers are overlooking the opportunity for competitive advantage which would accrue from targeting this potentially lucrative segment? This paper will review the key literature in the areas of segmentation and maturity to gain an insight into the segmentation strategies of the fashion retail sector and their perception of the mature female consumer. Furthermore, the possibility of an academic / practitioner divide will be reviewed to establish whether this exists and if so to establish its impact on the segmentation strategies of fashion retailers.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".