Segmentation and Profiling of Infrastructure Millennial Workers in Shopping Goods Market in Region 12, Philippines
Bibliographic record
Abstract
Millennials are branded as the most powerful consumer segment. They can even spend their entire income to purchase goods or experiences. This changed when COVID-19 hammered the world. The enthusiastic shoppers became indifferent due to uncertainties. As consumers, including the most powerful consumer segment stopped behaving the way they used to, industries around the world continue to struggle, most especially non-essential retail sectors. This study aimed to provide retailers of shopping goods information about Millennials, their spending practices, the values they have, their attitude towards pandemic protocols, and their current lifestyle. Furthermore, as this group is highly heterogeneous, this study also provided retailers newly formed segments because of COVID-19. This study is a descriptive-correlational study. The data were gathered using a hybrid survey questionnaire distributed to infrastructure Millennial workers in Region 12. The main statistical tool used was hierarchical cluster analysis. The study concluded that there are three segments: The Balanced Workers, The Independent and Efficient Workers, and The Confident Workers. These segments have different characteristics in terms of spending, values, attitude, and lifestyles. It is recommended that shopping good retailers and marketing practitioners should adjust their marketing plans and strategies to address such changes in the buying 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".