Optimizing Digital Marketing for Generation Y: An Investigation of Developing Online Market in Bangladesh
Bibliographic record
Abstract
This market research aims to investigate the digital information scanning and the internet using habit of Generation Y in Bangladesh, a rapidly developing e-commerce market. It also examines the key variables to optimize digital advertisement for the target group. The study segments Gen Y into two age groups, 18-25 who are students and between 26-35 years who are working, in order to explore the transformation in online consumer behaviour. Most data have been presented according to the segments and gender. The analysis in this study is based on 305 face to face surveys during April-June 2015 using structured questionnaires. This research found that Gen Y with internet access living in the capital of Bangladesh has similar traits as found in many researches focused on developed online market. Facebook is their primary source of information and they have moved away from watching TV. But they are still exposed to the traditional advertisements through billboards and other non-digital media. They are mostly active online on Facebook, Google and YouTube from 5PM to 2AM. This information, along with other findings in this research, provides rich data for advanced targeting variable in Google AdWords and Facebook Advertising. Pop-up ad is the least liked feature by Gen Y. They prefer innovative and interactive ads displaying new product information. They also tend to click on online ads showing discount coupons and similar offers. Ads representing social benefits motivate Gen Y. This study sets a benchmark as it is the first of its kind in the context of Bangladesh. Thus it can be used as the foundation of a longitudinal study on the topic.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".