Event Essentials Strategic Marketing Plan: : Marketing Options for Event Management Services & Software Providers in the COVID Digital Age
Bibliographic record
Abstract
Our objective was to detect feasible and effective strategic marketing options for our community partner Event Essentials (EE), an event management software provider operating out of California, U.S.A. These strategic marketing options were chosen based on market research, internal and external analyses, and the client’s needs that were presented to us. Along with the implementation of the strategic options, our team curated targeting, positioning, and communications strategies that would fit within the strategic marketing plan for optimal results. The market research we conducted initially consisted of both secondary and primary data sources. After our initial proposal, our team decided to weigh the value of our primary data more heavily when evaluating the proposed strategic options. This data was richer and better suited the client’s issue(s). We obtained the primary data in interviews with a convenience sample of potential clients in the target industries. We believe that with the successful implementation of our strategic marketing plan and recommendations, Event Essentials will increase their brand awareness, reach, brand recognition, and new business development; Resulting in the sustained growth and success of their business throughout the COVID-19 era and beyond. Department: Business Faculty Mentor: Dr. Fernando Angulo-Ruiz
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".