Bibliographic record
Abstract
The article presents the concept and subject of web analytics, its main goals and tasks, such as data gathering, data analysis, calculation of KPI, forming of online strategies, describes the main metrics used by specialists when analyzing a website – Bounce Rate, Hit, Visitor/Session, Activity Time, Click, First Visit/Session. After careful consideration the most effective web analytics tool was chosen – Google Analytics. It’s working principles were described and his advantages and disadvantages were analyzed. Working with UTM was described in case with Google Analytics and in common. A website was also selected for analysis using Google Analytics – Google Merch Shop, which is powered by Google and contains different types of Google merchandise. Google Analytics was used to gather different statistics regarding users and visitors activity on the web site. After this, such metrics as Visitors number, visitors’ geographical data and visitor’s conversion number were calculated. Based on the data obtained, weak points of the website were found, due to which the flow of users from some countries was too low, and the percentage of users who made a purchase was significantly lower than the percentage of users who viewed the product. These weak points are limited localization of the site, unclear shipping methods for customers that are outside of USA or Canada, outdated UI/UX design of items page and lack of description with photos on this page. As a result, four recommendations for the further development of the website were formed based on the research data and weak points of the site. It was recommended to add more localizations for people all over the world, increase number of photos, which could make potential customers more interested in making a purchase, and add a detailed description with characteristics of every item, available on the site
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 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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
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".