Experts’ Perspective on the Development of Experiential Marketing Strategy: Implementation Steps, Benefits, and Challenges
Bibliographic record
Abstract
Consumer needs change over time as a result of the fast-paced advancement in technology and the induction of the Internet, expansion that leads to a difficulty for brands to adapt their marketing promotion strategy and trying to remain innovative and effective at meeting their consumers’ expectations. This research investigates what effect experiential marketing campaigns have on both customers’ perception and business outcomes, aiming to develop a deeper understanding of experiential marketing, its challenges, and benefits, to understand customers’ reactions to experiential touchpoints, to explore what type of technology increases experiential perceived value, and to envisage the evolution of experiential marketing strategy. To capture all the important facets of the research objectives, an exploratory survey based on the voices of 31 marketing experts from all around the world was applied. By identifying the key drivers of experiential marketing campaigns in a hybrid setting, the present study highlighted the important role that experiential marketing has as a communication strategy, offering additional insights to marketing specialists on the experiential marketing implementation steps. A theoretical framework of the steps needed to put into practice an experiential marketing strategy was proposed.
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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 | 0.025 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".