Google Search is the highest-intent advertising channel available for clinical trial recruitment. A patient searching “type 2 diabetes trial near me” is actively looking for what you offer — they just need to find you before they find a competing site. But the campaign structure that works for selling consumer products does not work for clinical trial recruitment. The difference lies in how you segment your campaigns, write your ad copy, and route traffic to pages built to convert eligible patients.
Campaign Architecture: One Campaign Per Indication
Structure separate campaigns for each trial indication rather than grouping all trials into a single campaign. A diabetes campaign and a cardiovascular campaign have different patient profiles, different search terms, different geographic concentrations, and different landing pages. Combining them into one campaign forces you to share budget across different patient populations and prevents you from optimizing settings — such as ad scheduling, bid strategy, and device targeting — independently for each indication.
Within each campaign, create separate ad groups for different keyword intents. A diabetes campaign might have three ad groups: branded searches (your site name plus diabetes), condition searches (diabetes clinical trial, diabetes research study), and eligibility searches (qualify for diabetes trial, paid diabetes study). Each ad group should have its own set of ads and its own landing page matched to the search intent — eligibility searches should land on a page that leads immediately with eligibility criteria, not a general trial description.
Keyword Strategy: Match Types and Negative Keywords
Use phrase match as your primary match type for clinical trial campaigns. Broad match sends ads to searches that are semantically related but often wildly off-target — you will pay for clicks from patients searching for diabetes diet plans, diabetes medications, and diabetes news. Exact match is too restrictive and misses the long-tail variations in patient search behavior. Phrase match captures the intent (the core condition and trial-related terms) while allowing natural language variation in how patients phrase their searches.
Negative keywords are as important as your positive keyword list. Add negatives for: competitor trial names, medication names that patients might search when researching treatment options rather than trials, terms like “results” and “outcomes” indicating a patient researching published data rather than enrolling, and any condition-adjacent terms that attract irrelevant traffic. Review your Search Terms report weekly during the first month of a new campaign and add negatives aggressively — a well-built negative keyword list can reduce wasted spend by 30 to 40 percent.
Conversion Tracking: Connecting Ad Spend to Enrolled Patients
A Google Ads campaign without conversion tracking is spending budget with no way to measure what it produces. Set up conversion actions for every meaningful patient interaction: form submission, phone call from the ad, phone call from the landing page, and pre-screening completion. Assign values to each conversion based on your historical inquiry-to-enrollment rates — if one in eight inquiries leads to an enrollment, a form submission is worth roughly 12.5 percent of your average enrollment value.
With conversion data flowing, switch your bidding strategy to Target CPA (cost per acquisition) once you have accumulated at least 30 conversions. This allows Google’s algorithm to optimize bids toward users most likely to complete a conversion based on patterns in your historical data. Manual CPC bidding is appropriate only in the early data-gathering phase before you have enough conversion history for automated bidding to perform reliably.
Google Ads for clinical trial recruitment works when the campaign is built around patient intent, indication-specific segmentation, and accurate conversion measurement. A campaign structured this way generates a measurable, optimizable cost per inquiry that falls over time — turning your ad spend from a fixed expense into a predictable patient pipeline engine.