Optimizing Digital Marketing Performance in Pharma and Biopharma: case Alfa Laval
Bibliographic record
Abstract
The subject of this thesis is to leverage customer-centric content to improve Alfa Lava´s AI Search discoverability and digital marketing outcomes in B2B pharma and biopharma sectors. The thesis was commissioned by Alfa Laval. The main purpose of the thesis is to identify more customer centric content and how to boost the AI Search visibility for Alfa Laval. The aim is to create suggestions for Alfa Laval´s digital content for pharma/ biopharma industries that will eventually result in increased sales. The results are in form of FAQ (frequently asked question) page, list of upcoming trends that can be used as content topics and suggestions for more customer centric content. The theoretical framework consists of three main sections: what is effective digital marketing overall and what does it mean in these industries, what is customer centric content and how the AI Search works and visibility can be increased. Two data collection methods were used in this project. Benchmarking was used to determine the current position of Alfa Laval in comparison to three competitors when considering digital marketing. The second data collection method was an interview of Alfa Laval´s members and distributor. Interviews examined the FAQ from customers and anticipated upcoming trends specifically in the pharma and biopharma industries. As a final suggestion FAQ list, specific for pharma/biopharma industries, that was created based on interviews, list of customer centric content in the form of upcoming trends and other relevant suggestions in creating customer centric content and boosting AI Search visibility. Further studies could focus on AI technology development and implementation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".