Leveraging influencers to tell an authentic brand story and drive return on investment: Case study of Travel Alberta and Expedia Media Solutions
Bibliographic record
Abstract
Travel Alberta, the official tourism marketing agency of the Province of Alberta, was looking to increase awareness for the region by captivating the imagination of US and Canadian travellers through unique, compelling content. Together with Expedia Media Solutions, it built a campaign that showcased the diverse experiences that Alberta has to offer, increased general awareness for the region and drove consumers to engage and interact with the brand through social media and aspirational videos. The campaign combined high-impact and tactical brand placements on Expedia points of sale in the USA and Canada, a branded Expedia blog, social media outreach, customised video content and influencer engagement. Top travel bloggers were sent to Alberta, where they captured video content around tourist destinations to serve as blog and video ad content. The campaign was a huge success, resulting in more than 81,000 room nights and 22,250 airline tickets booked to and in Alberta. It also drove more than 4.1 million social media impressions across Facebook, Twitter and Google+.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".