Improving Email Marketing Campaigns: Bridging the gap Between Business and Consumers
Bibliographic record
Abstract
Email marketing campaigns are a system of advertising used widely by most business organizations today. Although not as flashy or captivating as contemporary forms of advertisements, email marketing remains one of the most useful regarding return on investment and consumer exposure. There are several issues surrounding creating effective email marketing campaigns, most prevalent is increasing customer open rates and converting consumer exposure. Most organizations find problems with email frequency and individuals believing messages received from businesses are nothing more than spam or advertisements. This research seeks to bridge the gaps between business to consumer communication strategies and explores the several potential underlying causes affecting customer beliefs about email marketing campaigns, specifically in the automobile industry. Facets of email marketing such as implementing segmentation strategy, preferences regarding content of emails (subject lines, body content, key words, images), and email frequency. Evidence is collected based on target market and demographic characteristics through secondary research of scholarly literature/academic journals and primary research with communication experts, industry executives, and general consumers. The current participant pool (n=4) of in-depth interviews provided insights into consumer behavior, strategies in increasing consumer involvement, and personal preferences regarding email content. A survey has also been administered current responses equaling n=144. Some major findings of the research performed include importance of segmentation strategies and increasing personal relevance of email content, improving subject lines through personalization and decreasing consumer spam beliefs, and creating marketing campaigns designed specifically for certain platforms/devices used most by recipients. Faculty Mentor: Fernando Angulo-Ruiz Department: Marketing
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 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 | 0.019 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".