Bibliographic record
Abstract
Millennials will grow to be 42% of the population within 5 years, and represent one trillion dollars in spending power. There is little information available from millennials themselves and no definitive voice on how they can be reached by marketers. Marketing to Millennials offers insights to marketing professionals about how to market to these 14‐29 year‐olds most effectively. As a millennial herself, the researcher demystifies which trends relating to consumption habits are actually relevant to millennials and how they can be successfully applied to marketing strategies. Specifically, a combination of internet research, personal experience, observation, and discussions with peers was used to create recommendations. The findings show that trends such as infolust, mobile, cause marketing, frugality and convenience do indeed apply to millennials and can be used to market to this group effectively. Millennials have a need to check and track what is happening in their world. They want to do this on the go, and thus have a desire to always be switched on, and receive information on their hand‐held devices. Cause marketing has a striking, widespread impact on them, and can be used effectively to encourage brand‐switching behaviour. This age group represents the most frugal consumer segment, yet it is obsessed with convenience. The implication of these trends applying to millennials is the ability to create strong marketing programs that satisfy the needs identified within the trends. Companies are starting to realize that the best way to find out how to market to millennials is to ask one.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.048 | 0.007 |
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".