Website Analytics of a Google Ads Campaign for a Men’s Mental Health Website: Comparative Analysis
Bibliographic record
Abstract
BACKGROUND: Men with mental health and addictions problems seek information and help from health service providers and community support less often than women with such problems. Online health resources offer men rapid access to self-care recommendations and resources and anonymity; however, only a few websites are specifically developed for men. Headstrong - Taking Things Head-On was a community pharmacy and online health promotion initiative for men living with mental health and addictions problems. The Headstrong website was developed to offer a curated collection of print and online recommended resources (primarily self-help oriented) for depression, anxiety, insomnia, tobacco and alcohol use problems, and suicide. To increase awareness of the initiative and use of the website's content and resource recommendations, a Google Ads campaign was developed. OBJECTIVE: This study aimed to compare user acquisition and behavior on the Headstrong website during and after a Google Ads campaign. METHODS: The Google Ads campaign was launched on December 21, 2017, and run until February 28, 2018. Website analytics (acquisition of new users, behavior in terms of at-website actions and duration, devices used, and conversions [link-outs to recommended resources]) in a 30-day period during the campaign (January 26, 2018 to February 24, 2018) were compared to a similar 30-day period after the campaign (March 23, 2018 to April 21, 2018). A cost analysis of the ad campaign was also performed. RESULTS: The ad campaign generated 3011 clicks and 4.5 million impressions in total. In addition, the campaign received 1311 website users during the 30-day period of the ad campaign as compared to 241 users during the 30-day period after the ad campaign (P<.001). Return visitor (17.7% vs 27.8%) and nonbounce (19.5% vs 39.8%) user rates as well as session duration (42 vs 102 seconds) and page views per session (1.4 vs 2.1) were lower during the ad campaign than after the campaign (P<.01 for all). The 30-day period of the ad campaign included 9 sessions with conversions initiated by an ad click. Paid and display ads accounted for 63% of the site traffic during the ad campaign, most of which came from mobile phone users. Desktops were the most-common device used after the ad campaign acquired the website via direct and organic searches primarily (92%). The estimated cost per session with one or more conversions was Can $54.69 and cost per conversion was Can $32.81. CONCLUSIONS: A Google Ads campaign designed to direct men to the Headstrong website increased the number of user visits by more than five-fold. However, engagement by users responding to the ad campaign was substantially lower than that by users who visited the website via other acquisition methods, possibly reflecting the nonspecific online targeting of men by the ad campaign. General targeting of men online to promote men's mental health appears to have limited value.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".