The Importance of Conversion Tracking in Life Science Marketing
Željislav Petrovič
You Can’t Improve What You Don’t Measure
In life science marketing, success isn’t just about driving website traffic or racking up clicks – it’s about generating real business outcomes. If you want to know whether your campaigns are doing the job, you need to track what actually matters: the actions people take that move them closer to becoming customers.
That’s where conversion tracking comes in. Without it, you are left guessing which marketing strategies are actually working, and which are wasting your budget. With it, you’ll have a clear view of which strategies drive leads and revenue, allowing you to optimize spending and prove your marketing’s ROI.
In this article, we’ll walk you through the basics of conversion tracking, highlight its benefits, explain how it helps track the customer journey, and give you a quick look at how to set it up with tools like Google Tag Manager (GTM) and Google Analytics 4 (GA4).
What is Conversion Tracking?
Simply put, conversion tracking is the practice of monitoring user actions that align with your marketing or business objectives. A “conversion” can be any meaningful interaction – from requesting a quote to downloading a brochure or signing up for a demo.
Not every conversion is equally valuable. Some directly generate revenue, while others reveal a user’s intent or signal interest in your products / services. If you only track the end-of-funnel actions, you’ll miss out on valuable insights about how prospects are engaging before they’re ready to make a purchase decision. On the other hand, if you focus only on early interactions, you won’t get a clear picture of which campaigns actually drive revenue.
That’s why conversion tracking isn’t just about collecting data. It’s also about structuring and analyzing that data in a way that can support smarter decisions. By organizing different types of conversions and understanding their role in the customer journey, you can evaluate not just whether your marketing is working, but how it’s working. This approach makes it easier to identify the touchpoints that generate interest, the ones that accelerate movement through the funnel, and the ones that actually close the deal / result in sales.
Conversion Groupings: Macro vs. Micro and Across the Funnel
To gain meaningful insights from conversion data, marketers typically divide conversions into macro and micro actions, and then align them with stages of the marketing funnel. This hierarchy ensures you can see not only which campaigns generate final sales, but also how prospects are engaging along the way.
- Macro Conversions: These are the primary, high-value actions that directly contribute to your business’s revenue or lead pipeline. For a life science company, a macro conversion is the ultimate desired action, such as a purchase or a quote request.
- Micro Conversions: These are mid- to low-value, supporting actions that indicate a user is moving down the marketing funnel and has some level of interest. While they may not have immediate monetary value, they are crucial for understanding user intent and can be used for building highly targeted audiences. Examples include downloading a product brochure, watching a product video, or adding a product to a cart.
Mapping Conversions Across the Funnel
Mapping conversions to funnel stages provides a clearer picture of their role in the customer journey. While the funnel is rarely linear in practice, this framework helps clarify which actions reflect awareness, consideration, or decision-making.
| Conversion Type | Funnel Stage | Examples | Strategic Value |
| Micro | Top of Funnel (Awareness) | Video plays, blog post views, pricing page views, product/service page views, CTA button clicks | Measures top-of-funnel reach and content engagement. Helps identify new audience segments and analyze user behavior. |
| Micro | Middle of Funnel (Consideration) | Whitepaper, case study and brochure downloads, webinar registrations, newsletter sign-ups, contact form submissions, phone calls | Indicates a user’s active interest. Provides a list of warm leads for follow-up and retargeting. |
| Macro | Bottom of Funnel (Decision) | Product/service inquiries, quote requests, demo sign-ups, e-commerce purchases | Represents the final goal. Directly ties marketing efforts to sales and ROI. |
Why You Should Use Conversion Tracking
Implementing conversion tracking provides a powerful foundation for data-driven marketing, helping life science companies focus on strategies that truly deliver results. Some of the key benefits include:
- Measure the Effectiveness of Marketing Campaigns: Understand which channels, campaigns, and even specific ads are driving meaningful results. For example, see whether paid search, email, or social campaigns are generating qualified leads for a new product line you launched.
- Understand Multi-Channel Influence – Conversion tracking reveals how different marketing channels work together. For example, you can see that a lead who filled out a quote request form first discovered your brand through a LinkedIn ad, subscribed to your email newsletter, and after some time re-visited the website through a display remarketing ad to complete the form. These insights can help you optimize your cross-channel strategies.
- Improve Marketing ROI: Identify what works and double down on it, while reducing spend on underperforming campaigns. Conversion tracking allows you to allocate budget efficiently, ensuring every dollar invested in marketing contributes to revenue or high-quality leads.
- Optimize User Journeys: Detect where prospects drop off in the funnel, from awareness to decision. Use these insights to refine landing pages, CTAs, and forms, increasing the likelihood of conversion at every stage.
- Enable Better Targeting: Use conversion data to segment audiences more effectively, personalize messaging, and retarget users based on real engagement signals. For instance, you can target users who downloaded a datasheet but haven’t yet requested a quote.
- Report With Confidence: Present accurate, actionable insights to company leadership or key stakeholders, backed by real conversion data rather than surface-level clicks or traffic. This builds credibility and clearly demonstrates marketing impact.
- Align With Sales: Track the actions that matter most to your sales team, such as quote requests or purchase requests, not just page views. This ensures marketing and sales are aligned around shared goals and driving measurable business growth.
What Actions Life Science Businesses Should Track
Here are some of the specific actions you should be tracking, organized by the user’s intent and your strategic goals:
- Awareness & Content Engagement Actions: These are actions that build general awareness and measure content resonance. This includes tracking video plays (e.g., watching a product demo video for over 30 seconds), key button clicks (e.g., “View Products”, “View Services”, or “Download Data Sheet”), as well as views of product/service pages, pricing page, and key blog posts or resource pages. These signals tell you what content is resonating with your audience at the top of the funnel.
- Lead Generation & Nurturing Actions: These actions are focused on converting website visitors into leads and providing them with the information they need to become sales-ready opportunities. This includes contact form submissions, downloads of high-value gated content like whitepapers and case studies, and webinar sign-ups.
- Opportunities & High-Intent Actions: These are a subset of most valuable macro conversions, signaling a user is ready for a direct sales conversation. This includes tracking phone number clicks and phone calls, a click on a sales representative’s calendar link to book a meeting, product/service inquiries, request for a quote, or a demo sign-up.
- E-commerce & Revenue Actions: For businesses such as an online store selling lab equipment or consumables, tracking transactional e-commerce actions is crucial. This includes actions like adding a product to the cart, starting the checkout process, adding payment info, and completing the final purchase. This data is essential for calculating Return on Ad Spend (ROAS) and optimizing your campaigns to drive revenue.
How to Set Up Conversion Tracking
Implementing conversion tracking may seem daunting, but with the right tools and a well-defined plan, it’s a straightforward process. Here we will focus on the Google tech stack (Google Tag Manager, Google Tag, and Google Analytics 4), since these are among the most common and effective tools for setting up conversion tracking. While other platforms have their own solutions, starting with Google provides a solid foundation that can be integrated with LinkedIn, Meta, or X.
- Google Tag Manager (GTM): Think of GTM as your command center for deploying and managing all tracking codes. Instead of adding snippets of code directly to your website, you can manage them all in one place. GTM uses “tags” (the code snippets you want to fire), “triggers” (the conditions that define when tags to fire), and “variables” (placeholders for information GTM needs). This approach is highly flexible and reduces the need for constant developer support.
- Google Tag (gtag.js): For simpler conversion tracking requirements or if you prefer a direct approach without GTM, you can implement conversion tracking using Google Tag. This method involves placing the global tag snippet and event-specific snippets directly on your website. While less flexible than GTM for managing multiple tags, it’s a quick and effective way to track conversion events and send conversion data to Google Analytics and Google Ads simultaneously.
- Google Analytics 4 (GA4) for Data Collection: Regardless of whether you use GTM or the Google Tag, GA4 is the warehouse that collects and processes your conversion data. In GA4, everything is an event, and you can mark specific events, such as a form_submit or purchase, as a conversion (now “key event”). This allows you to centralize your data and understand how different traffic sources, campaigns or web pages contribute to your business goals.
- Leveraging Built-in E-commerce Tracking: For platforms like Shopify, conversion tracking is often integrated directly into the system. These platforms can be configured to automatically send standard e-commerce events (like purchase, add_to_cart, and checkout_started) directly to your GA4 property without the need for manual setup of GTM tags for tracking conversions.
Sending Conversion Data to Advertising Platforms
Once your conversion events are defined and tracked, you can use them to feed and optimize your advertising efforts. A key advantage of using the Google ecosystem is that conversions defined in GA4 can be directly imported into Google Ads.
For other platforms like LinkedIn, Meta, and X you will use GTM to deploy their specific conversion tags (e.g., the LinkedIn Insight Tag or Meta Pixel). These tags can fire based on the same triggers you set up for your GA4 events, ensuring your conversion data is consistent across platforms. By feeding this data back, you enable the platforms’ machine learning algorithms to automatically optimize your campaigns to find more users who are likely to convert, ultimately maximizing your return on investment (ROI).
- A Note on Enhanced Conversions: For Google Ads, an additional step you can take is implementing enhanced conversions. This feature works by securely using hashed first-party data (like email addresses) from your conversion events to match with signed-in Google users. This provides a more accurate conversion measurement and a fuller view of your customer journey, especially in a world with increasing privacy restrictions and cookie limitations. Enhanced conversions help close the gaps that traditional tracking methods may leave, giving the Google Ads algorithm better data to optimize your campaigns effectively.
Common Conversion Tracking Mistakes To Avoid
Even for experienced marketers, conversion tracking can sometimes be really challenging. Here are some common pitfalls and tips to avoid them:
- Data Discrepancies Between Platforms: It’s normal to see differences in reported conversions between GA4 and Google Ads. These gaps often come from differences in attribution models, reporting windows, or how each platform processes data. Instead of stressing about mismatched numbers, focus on understanding why the discrepancies exist and use each platform for its strengths.
- Overlooking Micro-Conversions: Marketers often fixate only on “big” conversions, like quote and demo requests, or purchases. But smaller actions – such as downloading a brochure, watching a product video, or spending time on key pages – can reveal how prospects move through the funnel. Tracking these micro-conversions provides a fuller picture of user intent and helps you optimize earlier touchpoints.
- Inconsistent Naming Conventions: Without a clear system for naming events, tags, and triggers, your tracking setup quickly becomes messy and hard to maintain. A simple, consistent naming convention (e.g., contact_form_submit, quote_request, view_services_button_click) keeps your data clean and makes it easier for everyone to understand it.
- Double-Counting Conversions: A common but overlooked mistake is firing the same conversion tag multiple times – for example, when a form submission reloads the page and triggers the same conversion hit twice, or when both GA4 and Google Ads tags are set to fire for the same action without proper configuration. Always test your setup in GTM’s preview mode to make sure each conversion is counted properly. Note that this rule applies only to events meant to be counted once (such as form submissions, downloads, and sign-ups). For repeatable events like “add to cart” or “purchase”, it’s correct (and expected) to record a conversion each time the event occurs.
- Misaligned Conversion Goals: Not every tracked action is equally valuable. If you optimize your campaigns toward the wrong goals – say, demo video plays instead of qualified demo requests – you risk wasting budget on “easy” conversions that don’t meaningfully contribute to generating revenue. Always align your primary conversion goals with real business objectives, while keeping micro-conversions as supportive signals.
- Bot Traffic and Fake Conversions: Automated bot activity can trigger events and inflate your conversion numbers, making it seem like campaigns are performing better than they really are. A red flag is when you see sudden spikes in low-quality leads or unusual patterns of behaviour, such as forms filled with fake names, random characters, or suspicious email addresses. To deal with it, you can try enabling bot filtering in GA4 and consider using tools like ClickCease that help block fake traffic before it skews your data.
- Consent and Privacy Considerations: Depending on your market, user consent may be a regulatory requirement (e.g., GDPR in the EU). Even outside regulated regions, it’s still a good practice to be transparent about data collection and provide users with clear choices. Tools like GTM’s Consent Mode can help you manage this process smoothly while also reinforcing trust with your audience.
How Conversion Tracking Proves Marketing ROI: An Illustrative Example
Let’s imagine a life science company, “BioTech Innovations,” that wants to increase lead generation for a new line of laboratory instruments, including a high-throughput DNA sequencer, while keeping cost per lead at a reasonable level.
The Problem
BioTech Innovations is spending $5,000 per month on Google Ads to drive traffic to their website and generate leads. Their ad budget is split equally between two main campaigns:
- One targeting broad, general keywords like “genomic research tools”, and,
- Another targeting specific, high-intent keywords like “DNA sequencer for research”.
While they generate a steady flow of qualified leads, they have no clear visibility into which campaign is generating these leads and how much is the cost per lead in these campaigns.
The Solution
They implement a comprehensive conversion tracking setup using GTM and GA4.
- GTM: They use GTM to create a GA4 event tag that captures all “Demo Request” form submissions on their website, sending each submission to GA4 as a demo_request event.
- GA4: The demo_request event is marked as a key event, ensuring it’s tracked as a meaningful conversion in analytics.
- Google Ads: This conversion event is imported into Google Ads and set as the primary goal. Both campaigns are then switched to the Maximize Conversions bid strategy, allowing Google to optimize performance specifically for the demo_request goal conversions.
The Results
After three months of conversion tracking, the data tells a clear story.
- The campaign targeting broad, general keywords is generating 80% of the paid search traffic, but contributes only 15% of the demo_request conversions.
- Conversely, the campaign targeting more specific, high-intent keywords, while driving less traffic, delivers more than 80% of the demo_request conversions at a moderately lower cost.
Based on these insights, BioTech Innovations reallocates 75% of its budget to the campaign with high-intent keywords. Within a few months:
- Their overall cost per lead decreases by about 20%
- Their monthly lead volume increases by roughly 25-30%
This data-driven decision transformed their marketing from guesswork into a more efficient, ROI-focused program. While the entire example is illustrative, the numbers and patterns are realistic and reflect what many companies experience after implementing conversion tracking.
Conclusion
Understanding which marketing efforts actually drive results is critical in life science marketing and that’s why data-driven strategies aren’t just an advantage, they’re an absolute necessity. Conversion tracking gives you the clarity you need to move beyond vanity metrics like clicks and impressions, and focus on what truly matters: qualified leads, sales opportunities, and measurable ROI.
With a properly set-up conversion tracking system, you’ll gain actionable insights into which marketing channels and campaigns are driving results. This enables smarter budget allocation, better campaign optimization for real results, and more predictable growth for your business.
At BioBM, we help life science companies put these systems in place and turn marketing data into actionable insights. If you’re ready to stop guessing and start proving your marketing ROI, get in touch with us today.
Google Performance Max Campaigns Make Revenue Attribution Critical for Life Science Marketers
Carlton Hoyt
TL;DR Summary:
- Performance Max campaigns are being promoted more aggressively to new advertisers.
- While the setup of Performance Max campaigns may seem simple, life science marketers need to do the necessary groundwork to accurately track conversion value.
- Google Ads’ AI will aggressively optimize for whatever goal it is set to.
- Performance Max provide very little visibility into the audiences being targeted.
- If life science marketers aren’t optimizing for actual business results, they will waste their ad budgets on ineffective campaigns.
- We’ve seen Google’s attempts at self-optimization go wrong before …
- … but executed correctly, Performance Max campaigns can significantly improve return on advertising spend (ROAS) compared to legacy Google Ads campaign types.
Google Ads AI is smart, but it’s your job to ensure it has the data it needs.
Google Ads AI: a Blessing and a Curse?
Performance Max (also known as PMax) a newer Google Ads campaign type which heavily leverages AI to achieve specific goals, such as sales, leads, or phone calls. By using all of Google’s advertising inventory, Performance Max campaigns often achieve a lower cost per action (CPA) than traditional search ad campaigns, and frequently even outperform retargeting campaigns in CPA. Performance Max campaigns are also wider-reaching, capable of advertising to customers through search, Google shopping, display, YouTube, Maps, Gmail, and the Discover feed on Android devices. They use the data you feed it through your conversion goals and Analytics connection, combined with Google’s own proprietary audience signals, to aggressively optimize for the stated goals. It will even mix-and-match ad assets to find the best performing combinations, create new ad assets for you, and find the best performing landing pages on your website. Sounds great, right?
It can be, but life science marketers who don’t have deep experience in Google Ads need to be careful. Performance Max campaigns are “black boxes” with limited visibility and control of keywords, placements, and audience targeting. Marketers who erroneously believe they can simply set up a Performance Max campaign, add some assets, and let Google’s AI handle the rest are going to burn money and see poor campaign performance. This is further complicated for many life science companies, which often conduct transactions partially or entirely offline (unlike e-commerce) and have complex buying cycles involving numerous stakeholders. As a result, revenue can be significantly disconnected from easily traceable online actions.
Furthermore, Google has not consistently proven that it is fluent in the language of the life sciences. We have seen this before, with product ads (for which Google chooses the keywords it believes to be relevant) for laboratory equipment showing in searches for drug paraphernalia and auto-applied recommendations jumbling search ad copy through obnoxious keyword stuffing devoid of context.  I therefore do not recommend blindly trusting Google Ads without providing significant guidance – and for PMax campaigns, that guidance primarily comes from your conversion data.
Conversion Tracking vs. Revenue Tracking
Implementing comprehensive conversion tracking is much simpler than setting up revenue tracking and feeding actual data on conversion values back to Google Ads. Is conversion tracking alone sufficient for most life science organizations who want to use Performance Max campaigns? Almost never. The unusual exceptions are if you only sell through ecommerce or if all your conversions have approximately the same value. Therefore, life science marketers should do one of two things in order to property attribute sales data:
- Get actual revenue data and feed it back to Google Analytics and / or Google Ads. Since almost all life science companies have offline sales, this requires having the proper technology implemented to correctly attribute sales data to leads and then send this data back to Google Ads. Many CRMs do this, for instance. If you do not have a CRM, you can achieve this yourself using the Google Ads API, but it is non-trivial to set up properly.
- Estimate the average value for each type of conversion event whose value you can’t automatically track (i.e., all but ecommerce events) and add those conversion values to Google Ads / Google Analytics. This can be done in a number of ways, but using Google Tag Manager is often the simplest.
Failing to provide Google Ads with some measure conversion value will lead to your Performance Max campaigns optimizing solely for the number of conversions. This can lead Google Ads to inadvertently optimize for lower-value conversions, which frequently have a lower cost per acquisition.
PMax vs. Shopping Ads vs. Search Ads vs. Display Ads?
If you are curious which campaign is correct for you, there is no universal answer. You can scour the internet for data and case studies and find a lot of conflicting reports (and even more anecdotes devoid of data). Google Ads performance is so sector-specific and even business-specific that no blanket statement can be made. In our own experience, however, this is what we’ve found:
- Performance Max campaigns get far more clicks at a much lower cost per click (CPC) but have considerably lower conversion rates and conversion values than search ads. PMax traffic tends to be lower-quality, which is to be expected. It’s hard to get better traffic quality than people who are in the process of looking for what you are selling. When using Performance Max ads to replace or supplement search ads, the objective is to get decent traffic quality at a low enough cost that the higher volume of PMax traffic compensates for its lower quality. Achieving this will depend on your specific circumstances and thoroughly optimizing your campaigns.
- Since Performance Max campaigns recently added search terms reports, there is little practical difference between PMax and Google Shopping ads if you are using feed-only data. However, PMax also allows additional assets to provide more cross-platform exposure.There isn’t a definitive correct choice here, as shopping ads with Smart Bidding are extremely similar to PMax.
- Display ads are almost functionally irrelevant after the launch of PMax. Even for display retargeting, PMax is often able to achieve better conversion rates. PMax almost always generates more traffic, typically of comparable quality to display ads. Costs are usually similar, although PMax campaigns can be somewhat higher than most display ad campaigns depending on your display ad targeting.
Again, this is in our own experience, which is strictly in marketing to life scientists and closely adjacent sectors. The only way to definitively determine if Performance Max campaigns will work for you is to test them.
Tips to Get Your Performance Max Campaigns Performing
Properly Establish Campaign Priority
Campaign priority can be directly set for campaigns with shopping ads. For search ads, it’s less simple: there are automatically applied prioritization rules you can’t easily get around. The rules summarize to this: if there is an exact match, regardless of whether the keyword rule used is exact match, then the campaign with the exact match will be used (which for PMax would need to be a search theme since keywords aren’t directly set). If there are no exact matches, then Google’s AI takes its best guess. If AI can’t figure it out, then Ad Rank is used. For display ads, expect PMax to get priority.
Don’t Allow PMax to Cannibalize Organic Leads
Some things you just don’t need to bid on. For instance, if no one is bidding on your branded terms (and even potentially if they are), you don’t need to be bidding on them either. Ads for branded terms will perform extremely well, however, so Performance Max campaigns will aggressively pursue them, causing you to spend money on leads you would have acquired anyway. Stop this unproductive behavior by setting negative keywords for branded terms.
Closely Monitor Keywords & Make Use of Negative Keywords
This is crucial for all Google Ads campaigns, but particularly for Shopping and PMax campaigns where direct keyword control is limited. Be vigilant about your keywords and proactive about setting negative keywords to prevent wasted ad spend on traffic that is irrelevant or low-value.
Use Audience Signals
One of the biggest gripes about Performance Max campaigns is traffic quality. Force Google’s AI to optimize for higher-value audiences by feeding it audience segments, customer lists and remarketing lists. (In our experience, PMax campaigns are particularly effective for remarketing. We’ve largely stopped using standalone Google Ads remarketing campaigns in favor of PMax.)
Keep a Close Eye on Low-Volume Campaigns
Google Ads’ AI needs enough data and signals to be able to optimize itself, or else it may not perform well. The key question is: how much data is enough? Smarter Ecommerce ran an ROAS test for PMax campaigns with different monthly conversions to measure how they performed. The result? Less than 30 conversions per month leads to poor performance and the performance gets better the more conversion data that Google Ads has to work with within the Performance Max campaign. Even campaigns with over 1000 conversions per month performed slightly better than those with 500 to 1000 conversions per month. Their takeaway was that 150+ conversions / month was the sweet spot. Less than that and it might not perform. It is noteworthy however that in their experiment the number of campaigns with “below target” ROAS declined rapidly as conversions per month increased, but those performing above target stayed roughly flat. If their data is to be believed (and it is from 14,000 campaigns), then there is a roughly 30% chance that PMax campaigns will perform well no matter how many conversions you have. Either that or there is something uncontrolled in the data regarding how highly converting companies vs. low converting companies set their target ROAS.
Chart from Smarter Ecommerce
Use PMax as a Supplement for Search and Shopping Campaigns
It’s easy for PMax campaigns to be wasteful and burn through ad budgets. Run PMax concurrently with search and shopping campaigns until it consistently demonstrates superior ROAS
Optimize, Optimize, Optimize
This is not a recommendation which is specific to Performance Max campaigns, but it is especially important for Performance Max campaigns. Your manual optimization efforts are what will keep the guardrails on and help Google Ads’ AI better learn what works. Create diverse creative assets, replace periodically underperforming ones, A/B test, and run experiments.
Segment Your Campaigns … But Not Too Much
PMax campaigns perform best when segmentation is used intelligently to guide Google Ads into doing what you want. For campaigns without product feeds this could mean segmenting by geography, audience signals, “similar to” segments, lists, etc. For campaigns with product feeds, you may want to segregate your best performing products, segment by overarching product types, new vs. returning customers, etc. It is also recommended to have a “catch-all” segment to include offerings that fall outside of your defined segments. The best segmentation to use will depend on the nature of your business.
You’ll need to segment your asset groups as well and ensure you have appropriate ad assets for each segment. PMax is decent at figuring out what assets make sense with what products or landing pages, but ultimately you should take responsibility to ensure that you have sensible, coherent ads.
However you segment, be sure not to over segment. Remember that PMax needs enough data to optimize, and if you are hyper-segmenting to the point where many segments have relatively few conversions, Google Ads’ AI won’t be able to optimize your campaigns well.
The #1 Thing To Remember for PMax Campaigns
PMax can be a powerful addition to almost any life science Google Ads account – if done properly. The #1 thing to remember is that Google Ads’ AI can only optimize for what it has data on. Without accurate tracking of conversion values, Google may make assumptions or optimize for suboptimal actions, negatively impacting your campaigns. With proper tracking of conversion values, however, Performance Max campaigns can help unearth leads and customers that might be untargetable through other Google Ads campaign types while delivering low CPAs and high ROAS.
I’ve been getting AI generated spam for well over a year. It was immediately clear to me when it started. My spam emails became slightly more personalized than regular spam. They were all short: usually 2-4 sentences. The topics seemed to come in waves, all vaguely relevant to the owner of a small business or someone in marketing: there was the virtual assistant spam, the “do you want to sell your business” spam, and – my favorite – the AI generated spam selling AI generated spam tools. Most importantly: they were no less annoying to me than regular spam; unwanted and unsolicited interruptions in my day requiring me to manually mark them all as such.
Then, last week, something new happened. I got a very poorly targeted email from a life science company:
The notion that someone in life science marketing would want to buy genomes and metabolic pathways is ridiculous, but the real revelation was that the AI generated spam has penetrated into the life science market! This made me wonder if it’s changed people’s opinions about spam: after all, the whole point of AI generated spam is to replicate the more effective elements of one-to-one cold emailing. Perhaps improved personalization and relevance actually do make people more receptive to it.
Survey time!
The only way to answer the question is to ask. We posted a simple poll to the LabRats subreddit asking if they get AI-generated spam from scientific suppliers. I don’t think the result should be considered surprising:
A little over half the respondents report getting AI generated spam from scientific suppliers, and of those people almost all of them dislike it as much as regular spam.
What should we learn from this?
AI isn’t a magic bullet. It just makes bulk unsolicited emails a lot easier. Rented lists and low-cost bulk email service providers did too, and a lot of companies used them until deliverability plummeted and marketers realized that the costs to their brand’s reputation weren’t worth it.
Cold emails can be highly effective when executed correctly, with genuine, meaningful personalization and hyper-targeted sales pitches. It’s probable that AI sales tools will get to the point where they can do that, but the current iterations of generic AI sales tools just aren’t there. Like the bulk spam before it, we expect that AI spam will be increasingly, and preemptively, relegated to spam folders as mail servers slowly but surely learn that no one wants it.
Supercharge Your Google Ads With Every Possible Extension
Željislav Petrovič
Life science companies constantly face numerous challenges in capturing their audience’s attention on crowded search engine result pages (SERPs) with Google Ads. To make their ads stand out and attract their scientific audiences it is essential to present key information in a clear and engaging way. That’s where Google’s ad extensions (now called assets) come in as a game changer. They transform the ads from simple, inconspicuous text into rich, informative experiences by making them more noticeable, valuable, and engaging for the viewer.
By leveraging a variety of ad extensions, you can present all crucial information related to the products or services you promote (from unique product features to product variants/service packages with pricing, physical locations, and more) directly on the search results page. In this post, we’ll explore the different types of ad extensions, their benefits, and the best practices that can help you maximize the impact of your Google search marketing ads and most effectively gain the attention of your life science audience.
The Benefits of Using Ad Extensions in Google Ads
Maximizing the use of ad extensions in Google Ads has multiple benefits for life science marketers, including:
- Increased Visibility: Extensions make your ads larger and more prominent on the search results page, capturing more attention from viewers.
- Higher Click-Through Rate (CTR): By adding more hyperlinks and valuable information to your ads, extensions provide users with more reasons to click, resulting in a higher CTR.
- Enhanced Ad Rank: Ad extensions contribute positively to Google’s Ad Rank formula, leading to improved ad positions and potentially lower costs per click.
- Improved Relevance: Extensions allow you to tailor your ads to specific user searches, increasing relevance and engagement.
- Better User Experience: By providing quick and easy access to relevant information, extensions improve the user experience and encourage interaction.
Overview of Google Ads Extensions and Their Suitability for Life Science Companies
To help you prioritize which ad extensions will deliver the most impact for your campaigns, we’ve split them into two categories: All-Stars: Must-Have Extensions and Other Extensions to Consider. This will make it easier to focus your efforts on the extensions that will likely yield the best results for you, while keeping additional options in mind to experiment with as needed.
All-Stars: Must-Have Extensions
1. Sitelink Extensions
- What It Is: Sitelink extensions are additional links (clickable text assets with headline and description) that appear below your ad, helping users navigate directly to specific pages or sections they may want to browse on your site.
- Use Cases: Link to individual product or service pages, collection / category pages, a contact page, quote request page, or page with downloadable content. This allows users to navigate directly to relevant content, decreasing bounce rates while improving engagement and conversion rates.
- Who Should Use It: Companies with multiple landing pages that highlight various aspects of their offerings, showcase specific products or services, provide access to valuable resources like case studies and white papers, or allow users to take meaningful action.
2. Callout Extensions
- What It Is: Callout extensions are short, non-clickable descriptive text snippets that allow you to highlight the key product/service attributes and benefits within your ad.
- Use Cases: You can use callout extensions to emphasize distinctive qualities, such as unique product/service features, certifications, warranties, fast shipping, or the availability of expert support.
- Who Should Use It: Companies with differentiating product or service attributes that aren’t easily conveyed in the main ad text should consider using callouts to highlight these strengths.
3. Structured Snippet Extensions
- What It Is: Structured Snippet extensions allow you to showcase specific aspects of your products or services in structured text format. Unlike Callout extensions, which highlight key benefits, Structured Snippets present categorized details like product types, or service packages, to clarify the offerings in the ad.
- Use Cases: Use structured snippets to list various product types you offer (e.g., “Cell Counters,” “Microplate Readers,” “Triple Quad LC-MS”) or the exact services you provide (e.g., “Cell Line Development,” “In Situ RNA Seq” “Bespoke Oncology Models”).
- Who Should Use It: Companies with a broad product line or service catalog that want to showcase a variety of options directly in the ad, making it easy for users to see their range of offerings at a glance.
4. Call Extensions
- What It Is: Call extensions are special assets that display a clickable phone number in your ad, encouraging users to contact your sales or support team directly from the ad.
- Use Cases: Sometimes a scientist wants to get straight to the point. Call extensions facilitate direct contact, enabling outreach directly from the ad.
- Who Should Use It: Companies with dedicated sales teams who have a consultative sales process.
These are the extensions that should be prioritized in your Google Ads campaigns, as they significantly enhance the relevance, engagement, and performance of your ads.
Other Extensions to Consider
1. Location Extensions
- What It Is: Location extensions allow you to display your physical business address, a map link, and distance (if applicable) to your location from the searcher’s location in the search results, which helps potential customers to easily find and visit your physical location.
- Use Cases: When you serve customers in specific locations, you can use this type of extension to show your business locations on the map..
- Who Should Use It: Companies that have brick-and-mortar locations that clients may visit (such as company headquarters, laboratories, research facilities, or regional distributors) can use this extension to demonstrate convenience and reassurance of local availability while improving ad relevance. For companies which only deal with customers remotely, this is less relevant.
2. Image Extensions
- What It Is: Image extensions allow you to add visually compelling images to your ads, which can significantly enhance the appearance of your ads and make them more engaging.
- Use Cases: You can use image extensions to visually showcase your products, team, facilities, software/app in action, or feature scientific images related to your scientific specialty.
- Who Should Use It: Companies that have a strong collection of images related to their products / services and want to provide potential clients with a quick visual preview of their offerings in the ads.
3. Lead Form Extensions
- What It Is: Lead form extensions allow users to submit their information and sign up for something you offer directly through the ad without leaving the search results page.
- Use Cases: Capture leads directly on the search results page using Google Ads’ built-in forms, allowing users to request more information, sign up for demos, or access downloadable content like whitepapers, application notes, or brochures.
- Who Should Use It: Companies focused on lead generation, whether by providing high-value downloadable resources, offering product demos or simply making it easier for potential customers to get in touch.
4. Price Extensions
- What It Is: Price extensions allow you to showcase a list of products or services with pricing right below your ad, giving potential customers instant visibility into the price of your offerings.
- Use Cases: Provide potential customers with a quick cost estimate of your offerings by displaying the exact prices of individual featured products and product variants or starting prices of specific product types, product lines, and service packages within your ad.
- Who Should Use It: Companies with standardized product/service prices, especially those that list a wide range of products or service packages on their website and do direct sales through the site.
5. Promotion Extensions
- What It Is: Promotion extensions allow you to highlight special offers, discounts, or limited-time deals directly in your ad, making it easier for potential customers to see and take advantage of your promotions.
- Use Cases: These extensions can be used to highlight promotional offers with monetary or percentage-based discounts alongside your ad, and attract users looking for special deals.
- Who Should Use It: Companies that sell tangible products, software/app subscriptions, or service packages online and run promotions with limited-time discounts.
6. App Extensions
- What It Is: App extensions allow you to embed a direct link for downloading your mobile app into the ad, making it easy for users to install and access your app without the need to visit your site first.
- Use Cases: If you offer mobile apps (such as LIMS, ELNs, reference management apps, or other apps that support research work), you can use this extension to promote your app within the ad and drive downloads for your app.
- Who Should Use It: Companies offering iOS or Android apps designed for scientists, especially those offering subscription-based apps or free apps that provide them with downstream marketing opportunities.
Best Practices for Using Ad Extensions in Life Science Campaigns
To maximize your ad extensions’ potential, make sure to follow these best practices:
1. Choose the Extensions Based on Your Campaign Goal
Not all ad extensions will be relevant to your campaign and business type. For instance, if you’re promoting an automated cell counter and your campaign objective is lead generation, callout extensions can highlight key features like high accuracy and speed, while a lead form extension can help capture leads from researchers interested in learning more. Make sure to align extensions with your objectives, whether it is generating leads, driving sales, or increasing website visits.
2. Use Extensions That Are Concise and Compelling
Scientists often scan information quickly, so it’s crucial to use ad extensions that are direct, clear, and impactful. Instead of long, generic phrases, focus on specific, concise, benefit-driven messaging. For example, rather than “Advanced Cell Counting Technology,” try “Fast & Accurate Cell Counting”’ to immediately convey value. Keep language precise and focused on what will resonate with your audience.
3. Tailor Extensions to Your Target Audience
Different audience segments within life sciences have different intentions and respond to different messaging. For example, if you’re targeting an audience with more scientific queries, sitelink extensions could lead to a white paper showcasing your technology, while for an audience with more commercial queries, they could lead to a case study showing improved results or cost savings. By aligning extensions with keyword intent, you can ensure your ads deliver the most relevant content to each audience segment.
4. Keep Extensions Fresh and Up To Date
Outdated extensions can lead to poor user experiences, negatively impacting ad performance and overall results. Make sure to regularly review and update all extensions you use, especially price extensions that should display valid product prices, promotion extensions that should reflect current special offers and discounts, and sitelink extensions that should direct people to up-to-date pages with useful resources.
5. Monitor Performance and Measure the Success of Extensions
Just like ad copy and keywords, ad extensions should be periodically evaluated for effectiveness. Use Google Ads performance reports to track and see which extensions drive the most clicks and conversions and which extensions do not generate any results. For example, if a callout extensions about key product features have a low engagement rate, consider testing different wording or replacing it with a more relevant extension. If your performance reports show that certain price or sitelink extensions drive meaningful results, try creating more extensions like these.
6. A/B Test Different Extensions and Messaging
Not all extensions will perform equally well in every campaign. To optimize performance and get the most from your ad extensions, continuously experiment with different extension types and messaging. For instance, test variations of callout extensions to see which callouts work best for your audience, or compare lead form extensions with sitelink extensions to determine which drives more conversions. Continuous testing and refining of your extensions will help you maximize ad visibility and engagement.
Following these practices will ensure you get the most out of your ad extensions. The ad relevance will be drastically improved, and you will see better engagement rates and more conversions coming from your campaigns.
Conclusion
Ad extensions provide a powerful way for life science marketers to enhance their Google Ads, providing more value to viewers, improving engagement, and ultimately driving more qualified leads or sales. After leveraging various extension types that are suitable for the products or services you promote and aligned with campaign goals, you will create a richer, more informative and engaging ad experience that resonates with audiences from the life science industry. Ready to supercharge your Google Ads campaigns with ad extensions? Contact BioBM for a customized Google Ads strategy tailored to the unique needs of your business.
Everyone knows what search engine optimization (SEO) is, and many companies take great efforts to ensure they show up near the top of organic search results and benefit from the resulting traffic which comes at no unit cost. Traditional organic search results are slowly being replaced, however, with a lot of the focus being shifted to what Google calls a search generative experience (SGE; note that this is synonymous with AI Overview on Google Search, and the SGE is titled AI Overview on the search results page). It is widely accepted that as SGE becomes more prevalent, traffic to websites from legacy organic search results will decrease. This is due to two factors:
- Fewer people will click on organic search links – or any links – when SGE is present.
- The webpage links referenced in an SGE answer have lower clickthrough than standard organic search links.
In other words, some searchers will see the answer provided by the AI overview, accept it as accurate and sufficient, and take no further action. These searchers who would have clicked through to something else in the past may simply not click on anything. The bounce rate of SERPs likely increases markedly when SGE is present. SGE also contains its own reference links, which will inevitably cannibalize some legacy organic search traffic. Data from FirstPageSage shows that the result is not dramatic (yet), but just the first link in the AI overview is already garnering 9.4% of clicks. While this compares to 39.8% for a top search position result or 42.9% for a rich snippet result when SGE results are not present, it still has to come from somewhere, and the FirstPageSage data shows SGE is now appearing on 31% of SERPs.
In this post, we’ll address what life science marketers can do, and should be doing, to address the new search paradigm of Generative Engine Optimization (GEO).
How Generative Engine Optimization and Search Engine Optimization Overlap
Luckily for search marketers, GEO and SEO have a lot of overlap. If you are doing well at optimizing for search, you are probably doing a fair job at optimizing for generative engines. A number of key SEO principles apply to GEO:
- Perform keyword research to ensure you are addressing popular user queries and develop content targeting those keywords.
- The content you create should be helpful, reliable content that demonstrates experience, expertise, authoritativeness, and trustworthiness (what Google calls E-E-A-T).
- Ensure you are signaling the relevance of your content through optimization of on-site and on-page factors (copy, metadata, schema, etc.) for targeted keywords.
- Further signal the relevance of your website and content through off-site link building.
- Ensure all your content is getting indexed.
Increasing the quantity of content, using clear language, and using technical language when appropriate also improve performance in both generative and organic search results. Other practices to improve the authority of a page or domain such as backlinking almost certainly play a role in GEO as well, as search AIs pick up on these signals (if not directly, then through their own understanding of organic search ranks).
There is further overlap if your goal in creating content is to get it seen by the maximum number of people instead of solely driving traffic to your website. In that case, disseminate your content as much as possible. While AI Overviews are not citing Reddit and other discussion forums as much as they once did, the more places your content lives, the more of a chance you’ll have that the AI will cite one of them, especially if your website itself is not well-optimized.
How GEO and SEO Differ in Practice
Optimizing for GEO is akin to specifically optimizing for rich snippets: there is additional emphasis on the content itself vs. ancillary factors. You need to pay more attention to how you provide information.
A seminal preprint paper by Pranjal Aggarwal et al uploaded to arXiv in late 2023 which coined the term generative engine optimization investigated a number of factors which they believe might help optimize for inclusion in SGE. Note that this paper has yet to pass peer review and was subject to a lot of scrutiny by SEO professionals, most intricately by Tylor Hermanson of Sandbox SEO who gave a number of compelling reasons to believe the data may be overstated, but having read the paper and a number of critiques I still think the paper contains meaningful and actionable lessons. There are two figures in this paper which I believe summarize the most interesting and useful information:
Table 1 shows how different tactics affected results. They used a metric called position-adjusted word count to measure the performance of websites in SGE before and after various GEO methods. I am more interested in this because it is an objective determination as opposed to the subjective impression metric, which basically involves feeding results into GPT-3.5 and seeing what it thinks. We can see from the results that specific types of content addition – adding quotations, statistics, or citations – have a notable impact on the position-adjusted word count for those websites. I point those out specifically not only because they have the greatest impact (along with fluency optimization), but they are not things which would necessarily be considered important if the only consideration for content creation was SEO. All the others which they tested and found to be useful – speaking clearly, fluently, technically, and authoritatively – are things which good SEO copy already needs to do. The inclusion of quotations, statistics, and citations are simply additional content.
The other interesting lesson from this paper is that the most impactful GEO methods differ based on the topic of the content.
While I would like to see this data presented the other way around – what methods are the highest performing for each category – it still makes the point. It also suggests that scientific content may receive disproportionate benefit from fluency optimization and authoritativeness. Again, those are already things which you should be factoring into your copy.
Practical Steps Life Science Marketers Should Take for GEO
If you are looking to optimize for generative engines, first ensure you are doing everything required for good SEO, as outlined above in the section of how GEO and SEO overlap. That is 80% of the job. To reiterate:
- Perform thorough keyword research to address popular and relevant queries
- Write in a way which demonstrates experience, expertise, authoritativeness, and trustworthiness (EEAT)
- Optimize on-site and on-page factors (copy, metadata, schema, etc.) for targeted keywords to demonstrate relevance
- Further demonstrate relevance through off-site link building
- Stay on top of Google Search Console and ensure your content is getting indexed
- Write more / longer content
- Write clearly and use appropriate technical language considering the subject matter
To specifically optimize for generative search beyond normal SEO, make a note to cite your sources and include statistics and / or quotations when possible. That is the lowest-hanging fruit and where most life science marketers will be fine stopping. If you really want to deep dive into generative engine optimization, however, you can use a tool such as Market Brew’s AI Overviews Visualizer to investigate how search engines’ semantic analysis algorithms perform cluster analysis with your website content and see how content is grouped and related.
Since AI overviews decrease overall clickthrough rates, another consideration for some marketers may be getting their content into the AI overviews independent of whether the content is hosted on your website or not. In these situations, you should try to disseminate your content widely across high-reputation sources, particularly Reddit. While it is not cited in SGE as much as it used to be, having your content in multiple places still increases the probability your content will be used.
Product Companies: Don’t Forget Merchant Center Feeds
While our anecdotal data shows that shopping results aren’t yet being included much in the life sciences, they are occasionally included in other industries and it would not be surprising to see them included more frequently in the life sciences in the future. When shown, these shopping results are very prominent, so ensure your Merchant Center feeds are functioning, include as much of your product portfolio as possible, and are well optimized. (Product feed optimization is a topic for another day.)
Summary
If you want to improve the likelihood that your content will appear in AI overviews and those overviews will contain links to your website, start with SEO best practices. That will get you far in both legacy organic search, which still receives most clickthroughs, as well as in SGE. From there, ensure your content which is the target of optimization efforts cites sources and includes statistics and quotations. If you sell products, ensure you are making optimal use of product data feeds.
GEO is neither difficult nor rocket science. By taking a few relatively simple steps, you’ll improve the likelihood of being included in AI overviews.
As this is a complex and novel topic, we’ve included an FAQ below.
What are you waiting for? Work with BioBM and improve your demand generation."
FAQ
Is employing current SEO best practices sufficient for good ranking in generative search?
Helpful? Yes. Sufficient? It depends.
If your products and services are relatively niche, and the questions you seek to answer with your content are likewise niche, then current SEO best practices may be sufficient. If there is a lot of competition in your field, then you may need to incorporate GEO-specific best practices into your content creation.
You can think of this similarly to how you think about SEO. If you are optimizing for niche or longer-tail terms, you might not need to do as much as you will if competing for more major, high-traffic terms. The more competition, the more you’ll likely need to do to achieve the best results. If your terms are sufficiently competitive that you are not ranking well in organic search, you should definitely not presume that whatever you are doing for SEO will reliably land you in AI overviews.
If my website has high organic search ranks, will it perform well in SGE?
I’m not sure anyone has a clear answer to this, especially since the answer still seems to be changing rapidly. Many of the studies which exist on the topic are almost a year old (an eternity in AI time).
Taking things chronologically:
- A January 2024 study by Authoritas using 1,000 terms found that “93.8% of generative links (in this dataset at least) came from sources outside the top-ranking organic domains. With only 4.5% of generative URLs directly matching a page 1 organic URL and only 1.6% showing a different URL from the same organic ranking domain.”
- A January 2024 study from seoClarity looked at the top 3 websites suggested by SGE and compared them to just the top 3 organic results on the basis of domain only. In contrast with the Authoritas study, they found that only 31% of SGE results had no domains in common with the top 3 organic results, 44% of SGE results had 1 domain in common, 24% had two domains in common, and 1% had all three domains in common. This suggests much more overlap between generative and legacy organic results, but it should be noted that it was a much smaller study of only 66 keywords.
- A January 2024 study from Varn Media, using a similar but less informative metric to Authoritas, they found 55% of SGE results had at least one link which was the same as a top-10 organic result on a given SERP. One result in the top 10 is a low bar. They did not publish the size of their study.
- A February 2024 study from SE Ranking which looked at 100,000 keywords found that SGE included at least one link from the top 10 organic search results 85.5% of the time. I don’t like this very low-bar metric, but it’s how they measured.
- A slightly more recent Authoritas study from March 2024 using 2,900 branded keywords showed that “62% of generative links […] came from sources outside the top 10 ranking organic domains. With only 20.1% of generative URLs directly matching a page 1 organic URL and only 17.9% showing a different URL from the same organic ranking domain.” Obviously branded terms are a very different beast, and it should be no surprise that SGE still references the brand / product in question when using branded terms.
- SE Ranking repeated their 100k keyword study in June 2024 and found similar results to their February study: 84.72% of AI overviews included at least one link from the top 10 organic search results. Again, I don’t love this metric, but the fact that it was virtually unchanged five months after the original study is informative.
- Another seoClarity study published in August 2024 found far more overlap between legacy organic results and SGE results. Their analysis of 36,000 keywords found that one or more of the top 10 organic web results appeared in the AI Overview 99.5% of the time and 77% of AI overviews referenced links exclusively from the top 10 organic web results. Furthermore, they found that “80% of the AI Overview results contain a link to one or more of the top 3 ranking results. And when looking at just the top 1 position, the AI Overview contained a link to it almost 50% of the time.”
The most recent seoClarity study, suggesting a much greater deal of overlap between organic web results and SGE links, tracks with my recent experiences. While I would ordinarily discount my personal experiences as anecdotal, in the face of wildly different and rapidly evolving data I find them to be a useful basis of reference.
How much could my organic search traffic be impacted by SGE?
No one has any reliable metrics for that yet. Right now, I would trust FirstPageSage when they say the impact of SGE is not yet substantial, although I view their classification of it being “minimal” with some skepticism.
A lot of people like to point to a “study” posted in Search Engine Land which found a decline in organic search traffic between 18% and 64%, but it should be noted that this is not a study at all. It is simply a model based almost entirely on assumptions, and therefore should be taken with a huge grain of salt. (Also, 18-64% is a not a narrow enough range to be particularly informative regardless.)
Is SEO still worth doing?
Absolutely, hands down, SEO is still worthwhile. Legacy organic search results still receive the majority of clickthroughs on SERPs. However, as AI continues to improve, you should expect diminishing returns, as more people get their answer from AI and take no further action. It is therefore important that whatever you need to get across is being fetched by AI and displayed in SGE – regardless of whether it leads to a click or not.
I heard there is a hack to get your products cited by generative AI more often. What’s up with that?
A paper by a pair of Harvard researchers originally posted to arXiv in April 2024 titled “Manipulating Large Language Models to Increase Product Visibility” generated a lot of interest by both AI researchers and marketers looking for a cheat code to easily generate demand without any unit cost for that demand. As the paper suggests, they did find that LLMs can be manipulated to inserting specific products when the LLM is providing product recommendations. It is unrealistic that this is going to be applicable by life science marketers, however. It is a trial-and-error method involving high-volume testing of random, nonsensical text sequences added to your product’s metadata. This means that it would be nearly impossible to test on anything other than an open-source LLM which you are running an instance of yourself (and therefore able to force the re-indexing of your own content with extremely high frequency).
Another paper submitted to arXiv in June 2024 by a team of researchers from ETH Zurich titled “Adversarial Search Engine Optimization for Large Language Models” found that LLMs are vulnerable to preference manipulation through:
- Prompt injection (literally telling the LLM what to do within the content)
- Discreditation (i.e. badmouthing the competition)
- Plugin optimization (similar to the above, but guiding the LLMs to connect to a desired API from which it will then obtain information)
While preference manipulation is simpler and feasible to implement, the problem with any overtly black-hat optimization technique remains: by the time the method is found and published, LLM developers are well on their way to fixing it, making it a game of whack-a-mole which could potentially end up in your website finding itself on a blacklist. Remember when Google took action against unnatural link building and had marketers disavow links to their sites? That was not fun for many black-hat search marketers out there. BioBM never recommends black-hat tactics for both their impermanence, likelihood of backfiring, and ethical reasons. There’s plenty of good things you can focus on to enhance your search optimization (and generative engine optimization) while providing a better experience for all internet users.
Do Scientists Use AI / LLMs for Product Discovery?
Carlton Hoyt
There has been a lot of talk about AI optimization in the marketing world, much of which was spurred by the release of a preprint article published to arXiv (pdf) in September which demonstrated that LLMs could be manipulated to increase product visibility. There is even a term for optimizing for search engines: Generative Engine Optimization, or GEO. Of course, we are immediately interested in whether any of this is meaningful to marketers in the life sciences.
Our friends at Laboratory Supply Network recently beat us to the punch and asked Reddit’s Labrats community if they use LLMs to help them find scientific products. Good question! Apparently it is also one with a clear answer.
This is a relatively small poll, but the results are so skewed that it is likely that the result is telling. In this poll, 80% of scientists responded that they never use AI for product discovery: literally zero percent of the time! Another 14% barely ever use it. Only two respondents said they use it roughly 10% of the time or more, with one saying they use it more than half the time.
Some of the comments indicate that scientists simply don’t see any relative value in AI for scientific product discovery, or see much better value from other means of product discovery.
Comment
byu/LabSupNet from discussion
inlabrats
Comment
byu/LabSupNet from discussion
inlabrats
Another indicated that AI simply might not be helpful specifically within the scientific context.
Comment
byu/LabSupNet from discussion
inlabrats
Here is the full conversation in r/labrats:
Do you use LLMs / AI to get recommendations on lab products?
byu/LabSupNet inlabrats
Maybe there will be a day where scientists adopt AI for product discovery in meaningful numbers, but it seems we aren’t there yet.
Don’t Stress About “Nofollow” Backlinks
Carlton Hoyt
TL;DR Summary
- Sites can use the HTML tag rel=”nofollow” to instruct search engines not to credit a link with any importance for the purposes of SEO
- These instructions don’t carry authority: they are merely suggestions
- Search engines, including Google, choose whether to listen to the nofollow suggestion or not
- They generally do not listen to the suggestion
- If you can generate contextually relevant backlinks from sites which use nofollow tags, go for it! You’ll likely get value from them regardless. Just don’t be spammy.
The History of HTML Link Relationship Tags
As the name implies, a link relationship tag provides context to search engines and other automated crawlers on the nature of the relationship between the source page and the destination page. Some very common ones which marketers may run into are rel=”sponsored”, which denotes links in sponsored content, rel=”ugc” which denotes links in user-generated content, and rel=”nofollow”, which is supposed to tell search engines to completely ignore a link. There are over 100 link relations recognized by the Internet Assigned Numbers Authority, however, most of which are somewhat arcane and not used by search engines in any way which would be meaningful to marketers.
Link relationship tags, AKA rel tags, came into being in 2005, largely in response to the need for a nofollow tag to combat the excessive blog, comment, and forum spam which was extremely prevalent through the 2000s. Nofollow was proposed by Google’s Matt Cutts and Blogger’s Jason Shellen. For a long time, because they didn’t have a better option, Google and other search engines treated nofollow tags as law. Not only would they give no SEO benefit to nofollow links, but for a long time Google wouldn’t even index them.
The Evolution of Nofollow
As blog and comment spam became less of an issue, and as search engines became much more powerful and able to understand context, nofollow and similar relationship tags became less important to the search engines. Google effectively said as much in an announcement on their Search Central Blog on September 10, 2019:
When nofollow was introduced, Google would not count any link marked this way as a signal to use within our search algorithms. This has now changed. All the link attributes—sponsored, ugc, and nofollow—are treated as hints about which links to consider or exclude within Search. We’ll use these hints—along with other signals—as a way to better understand how to appropriately analyze and use links within our systems.
Why not completely ignore such links, as had been the case with nofollow? Links contain valuable information that can help us improve search, such as how the words within links describe content they point at. Looking at all the links we encounter can also help us better understand unnatural linking patterns. By shifting to a hint model, we no longer lose this important information, while still allowing site owners to indicate that some links shouldn’t be given the weight of a first-party endorsement.
As stated in the post, as of March 1, 2020 Google changed the role of link relationship tags, making them suggestions (or, in Google’s words, “hints”) rather than rules.
Context Is Key
As search engines continue to become more intelligent and human-like in their understanding of context within content, life science SEO professionals need to pay greater attention to context. A nofollow backlink with just one or two sentences in a comment on a relevant Reddit post may be worth more than an entire guest post on a site with little other content relevant to your field. Focus on doing all the things which you should be doing anyway, regardless of whether the link is nofollow or not:
- Post links only in relevant places
- Contribute meaningfully to the conversation
- Don’t be spammy
- Keep your use of links to a minimum
- Write naturally and use links naturally. Don’t force it.
Case: Laboratory Supply Network
Laboratory Supply Network started a backlinking campaign with BioBM in August 2023 which relied almost entirely on backlinks in comments from highly reputable websites (including Reddit, ResearchGate, and Quora), all of which use nofollow tags on their links. At the start of the campaign, their key rank statistics were:
- Average rank: 26.08
- Median rank: 14
- % of terms in the top 10: 45.00% (63 out of 140)
- % of terms in the top 3: 21.43% (30 out of 140)
Less than 8 months later, in March 2024, we had improve their search rank statistics massively:
- Average rank: 17.54
- Median rank: 7
- % of terms in the top 10: 61.11% (88 out of 144)
- % of terms in the top 3: 39.58% (57 out of 144)
Backlinking was not the only thing that Laboratory Supply Network was doing to improve its SEO – it has a longstanding and relatively consistent content generation program, for instance – but the big difference before and after was the backlink campaign (which, again, relied almost entirely on nofollow backlinks!) In the previous year, LSN’s search statistics didn’t improve nearly as much.
Conclusions
Backlinking has long been a key component of a holistic SEO strategy, and it remains just as important as ever. Links are an important signal telling Google and other search engines what content is relevant and important with regards to any particular topic. While many highly reputable sites use rel=”nofollow” to try to discourage link spam, most link spam is more effectively dealt with in other ways, such as manual, automated, or community-driven moderation. Google knows these other moderation tools have become more effective, and therefore allows itself to treat the nofollow tag as more of a hint than a rule. If you are performing SEO for your life science company, don’t avoid sites just because they use nofollow. You can achieve good results in spite of it.
Don’t Optimize for Quality Score in Google Ads
Carlton Hoyt
Sometimes you just have to let Google be Google.
Large, complex algorithms which pump out high volumes of decisions based in part on non-quantifiable inputs are almost inherently going to get things wrong sometimes. We see this as users of Google Search all the time: even when you provide detailed search queries, the top result might not be the best and not all of the top results might be highly relevant. It happens. We move on. That doesn’t mean the system is bad; it’s just imperfect.
Quality score in Google Ads has similar problems. It’s constantly making an incredibly high volume of decisions, and somewhere in the secret sauce of its algos it makes some questionable decisions.
Yes, Google Ads decided that a CTR of almost 50% was “below average”. This is not surprising.
If your quality score is low, there may be things you can do about it. Perhaps your ads aren’t as relevant to the search terms as they could be. Check the search terms that your ads are showing for. Does you ad copy closely align with those terms? Perhaps your landing page isn’t providing the experience Google wants. Is it quick to load? Mobile friendly? Relevant? Check PageSpeed Insights to see if there are things you can do to improve your landing page. Maybe your CTR actually isn’t all that high. Are you making good use of all the ad extensions?
But sometimes, as we see above, Google just thinks something is wrong when to our subjective, albeit professional, human experience everything seems just fine. That’s okay. Don’t worry about it. Ultimately, you shouldn’t be optimizing for quality score. It is a metric, not a KPI. You should be optimizing for things like conversions, cost per action (CPA), and return on ad spend (ROAS), all of which you should be able to optimize effectively even if your quality score seems sub-optimal.
Avoid CPM Run of Site Ads
Carlton Hoyt
Not all impressions are created equal.
We don’t think about run of site (ROS) ads frequently as we don’t often use them. We try to be very intentional with our targeting. However, we recently had an engagement where we were asked to design ads for a display campaign on a popular industry website. The goal of the campaign was brand awareness (also something to avoid, but that’s for another post). The client was engaging with the publisher directly. We recommended the placement, designed the ads, and provided them to the client, figuring that was a done job. The client later returned to us to ask for more ad sizes because the publisher came back to them suggesting run of site ads because the desired placement was not available.
Some background for those less familiar with display advertising
If you are familiar with placement-based display advertising, you can skip this whole section. For the relative advertising novices, I’ll explain a little about various ad placements, their nomenclature, and how ads are priced.
An ad which is much wider than it is tall is generally referred to as a billboard, leaderboard, or banner ad. These are referred to as such because their placement on webpages is often near the top, although that is far from universally true, and even where it is true they often appear lower on the page as well. In our example on the right, which is a zoomed-out screenshot of the Lab Manager website, we see a large billboard banner at the top of the website (outlined in yellow), multiple interstitial banners of various sizes (in orange) and a small footer banner (green) which was snapped to the bottom of the page while I viewed it.
An ad which is much taller than it is wide is known as a skyscraper, although ones which are particularly large and a bit thicker may be called portraits, and large ads with 1:2 aspect ratios (most commonly 300 x 600 pixels) are referred to as half page ads. Lab Manager didn’t have those when I looked.
The last category of ad sizes is the square or rectangle ads. These are ads which do not have a high aspect ratio; generally less than 2:1. We can see one of those highlighted in purple. There is also some confusing nomenclature here: a very common ad of size 300 x 250 pixels is called a medium rectangle but you’ll also sometimes see it referred to as an MPU, and no one actually knows the original meaning of that acronym. You can think of it as mid-page unit or multi-purpose unit.
As you see, there are many different placements and ad sizes and it stands to reason that all of these will perform differently! If we were paying for these on a performance basis, say with cost-per-click, the variability in performance between the different placements would be self-correcting. If I am interested in a website’s audience and I’m paying per click, then I [generally] don’t care where on the page the click is coming from. However, publishers don’t like to charge on a per-click basis! If you are a publisher, this makes a lot of sense. You think of yourself as being in the business of attracting eyeballs. Even though to some extent they are, publishers do not want to be in the business of getting people to click on ads. They simply want to publish content which attracts their target market. Furthermore, they definitely don’t want their revenues to be at the whims of the quality of ads which their advertisers post, nor do they want to have to obtain and operate complex advertising technology to optimize for cost per view (generally expressed as cost per 1000 views, or CPM) when their advertisers are bidding based on cost per click (CPC).
What are Run Of Site Ads and why should you be cautious of them?
You may have noticed that the above discussion of ad sizes didn’t mention run of site ads. That is because run of site ads are not a particular placement nor a particular size. What “run of site” means is essentially that your ad can appear anywhere on the publisher’s website. You don’t get to pick.
Think about that. If your ads can appear anywhere, then where are they appearing in reality? They are appearing in the ad inventory which no one else wanted to buy. Your ads can’t appear in the placements which were sold. They can only appear in the placements which were not sold. If your insertion order specifies run of site ads, you are getting the other advertisers’ leftovers.
That’s not to say that ROS ads are bad in all circumstances, nor that publisher-side ad salespeople who try to sell them are trying to trick you in any way. There is nothing malicious going on. In order to get value from ROS ads, you need to do your homework and negotiate accordingly.
How to get good value from ROS ads
Any worthwhile publisher will be able to provide averaged metrics for their various ad placements. If you look at their pricing and stats you may find something like this:
| Ad Format | CTR | CPM |
| Multi-unit ROS | 0.05% | $40 |
| Billboard Banner | 0.35% | $95 |
| Medium Rectangle | 0.15% | $50 |
| Half Page | 0.10% | $50 |
| Leaderboard | 0.10% | $45 |
One good assumption is that if people aren’t clicking the ad, it means they’re not paying attention to it. There is no other reason why people would click one ad at a much higher rate than others. Averaged out over time, we cannot assume that the ads in those positions were simply better. Likewise, there would be no logical reason why the position of an ad alone would cause a person to be less likely to click on it aside from it not getting the person’s attention in the first place. This is why billboard banners have very high clickthrough rates (CTR): it’s the first thing you see at the top of the page. Publishers like to price large ads higher than smaller ads, but it’s not always the case that the larger ads have a higher CTR.
With that assumption, take the inventory offered and convert the CPM to CPC using the CTR. The math is simple: CPC = CPM / (1000 * CTR).
| Ad Format | CTR | CPM | Effective CPC |
| Multi-unit ROS | 0.05% | $40 | $80 |
| Billboard Banner | 0.35% | $95 | $27 |
| Medium Rectangle | 0.15% | $50 | $33 |
| Half Page | 0.10% | $50 | $50 |
| Leaderboard | 0.10% | $45 | $45 |
Here, we see those really “cheap” run of site ads are actually the most expensive on a per click basis, and the billboard banner is the cheapest! Again, even for more nebulous goals like brand awareness, we can only assume that CTR is a proxy for audience attentiveness. Without eye tracking or mouse pointer tracking data, which publishers are highly unlikely to provide, CTR is the best attentiveness proxy we have.
With this information, you can make the case to the publisher to drop the price of their ROS ads. They might do it. They might not. Most likely, they’ll meet you somewhere in the middle. By making a metrics-driven case to them, however, you’ll be more likely to get the best deal they are willing to offer. (ProTip: If you’re not picky when your ads run, go to a few publishers with a low-ball offer a week or so until end of the month. Most publishers sell ads on a monthly basis, and if they haven’t sold all their inventory, you’ll likely be able to pick it up at a cut rate. They get $0 for any inventory they don’t sell. Just be ready to move quickly.)
The other situation in which ROS ads are useful and can be a good value are when you want to buy up all the ad inventory. Perhaps a highly relevant publisher has a highly relevant feature and that all ads up to an audience you want to saturate. You can pitch a huge buy of ROS ads which will soak up the remaining inventory for the period of time when that feature is running, and potentially get good placements at the ROS price. Just make sure you know what you’re buying and the publisher isn’t trying to sell their best placements on the side.
Lessons
- Run of site ads aren’t all bad, but novice advertisers can end up blowing a bunch of money if they’re not careful.
- Regardless of placement, always be mindful of the metrics of the ads you’re buying.
- Even if your campaign goals are more attention-oriented than action-oriented, CPC is a good proxy for attentiveness.
Can DALL-E 3 Generate Passable Life Science Images?
Carlton Hoyt
For those uninitiated to our blog, a few months ago I ran a fairly extensive, structured experiment to compare DALL-E 2, Midjourney 5, and Stable Diffusion 2 to see if any of them could potentially replace generic life science stock imagery. It ended up being both informative and accidentally hilarious, and you can see the whole thing here. But that was back in the far-gone yesteryear of July, it is currently December, and we live in the early era of AI which means that months are now years and whatever happened 5 months ago is surely obsolete. Since Dall-E 3 came out in October, it’s worth finding out if it will do better than it did in the previous round, where DALL-E 2 was notably inferior to Midjourney for 9 of the 10 queries.
Perhaps I’ll do a more comprehensive comparison again later, but for now I’m just going to run some similar queries to the ones used last time to get a reasonable side-by-side comparison. Bing Image Creator was used to generate the images since labs.openai.com, which was used last time, is still plugged in to DALL-E 2.
Test 1: A female scientist performing cell culture at a biosafety cabinet.
The last time we tried this, DALL-E 2 gave us images that looked 75% like a picture and 25% like claymation, but even if that problem wasn’t there it was still somewhat far off. Let’s see if DALL-E 3 can do better.
I tried to be a little bit descriptive with these prompts, as supposedly DALL-E 3 uses GPT4 and better understands written requests. Supposedly. Here’s what it gave me for “A photograph of a female scientist in a laboratory sitting at a biosafety cabinet holding a serological pipette performing cell culture. Her cell culture flasks have yellow caps and her cell culture media is red.” It definitely got the yellow caps and red media. As for the rest…
It’s immediately clear that DALL-E 3, just like all its ilk, was primarily trained from large repositories of generic stock images, because all these labs look like what you would imagine a lab would look like if you didn’t know what a lab actually looked like. There are plenty of generic microscopes close at hand, although it didn’t even get those right. There are no biosafety cabinets to be found. Those vessels are essentially test tubes, not cell culture flasks. To top it off, all the female scientists look like porcelain dolls modeling for the camera. I tried to fix at least one of those things and appended “She is attentive to her work.” to the subsequent query. Surprisingly, this time it seemed to make some subtle attempts at things which might be construed as biosafety cabinets, but only to a completely naive audience (and, of course, it put a microscope in one of them).
Since DALL-E 2 arguably provided more realistic looking people in our previous test, I reverted to the simplicity of the previously used query: “A photograph of a female scientist performing cell culture at a biosafety cabinet.”
We’re not getting any closer. I have to call this an improvement because it doesn’t look like the image is melting, but it’s still very far from usable for a multitude of reasons: the plasticware is wrong, the pipettes are wrong, the people still look like dolls, the biosafety cabinets aren’t right, some of the media seems to be growing alien contamination, the background environment isn’t realistic, etc.
Today’s comic relief is brought to you by my attempt to get it to stop drawing people as porcelain dolls. I Googled around a bit and found that queries structured differently sometimes are better at generating realistic looking people so I gave this prompt a go: “2023, professional photograph. a female scientist performing cell culture at a biosafety cabinet.” What a gift it gave me.
Test 2: Liquid dripping from pipette tips on a high-throughput automated liquid handling system.
I’m choosing this one because it was the only query that DALL-E 2 was almost good at in our previous comparison. Out of 10 tests in that experiment, Midjourney produced the best output 9 times and DALL-E once. This was that one. However, stock imagery was still better. DALL-E 2’s image didn’t capture any of the liquid handler and the look of the image was still a bit melty. Let’s see if it’s improved!
Prompt: “A close up photograph of liquid dripping from pipette tips on a high-throughput automated liquid handling system.”
DALL-E 3 seems to have eschewed realism entirely and instead picked up Midjourney’s propensity for movie stills and sci-fi. Perhaps more specificity will solve this.
Prompt 2: “A close up photograph of liquid being dispensed from pipette tips into a 96-well microplate in a high-throughput automated liquid handling system.”
DALL-E clearly only has a vague idea of what a 96-well plate looks like and also cannot count; none of these “plates” actually have 96 wells. Regardless, these are no more realistic, clearly unusable, and DALL-E 2’s output would likely have a far greater probability of passing as real.
So nope, we’re still not there yet, and Midjourney is probably still the best option for realistic looking life science images based on what I’ve seen so far.
… but what about micrographs and illustrations?
All the previous posts dealt with recreations of real-world images. What about images which a microscope would take or scientific illustrations? To test that out, I quickly tested out four prompts I had used last time:
- A high-magnification fluorescent micrograph of neural tissues
- A colored scanning electron micrograph of carcinoma cells
- A ribbon diagram of a large protein showing quaternary structure
- A 3D illustration of plasmacytes releasing antibodies
Here is the best it provided for each, in clockwise order from top left:
DALL-E 3’s neurons were actually worse than DALL-E 2’s, with nothing even being remotely close. It’s carcinomas were more in line with what Midjourney provided last time, but look slightly more cartoonish. The ribbon diagram is the better than any from the last test, although the structure is blatantly unrealistic. It’s plasmacytes could make for a passable graphic illustration, if only they contained anything that looks like antibodies.
Conclusion
DALL-E 3 is a clear improvement from DALL-E 2. While it may be two steps forward and one step back, overall it did provide outputs which were closer to being usable than in our last test. It still has a way to go, and I don’t think it will peel us away from defaulting to Midjourney, but if it continues to improve at this rate, DALL-E 4 could provide a breakthrough for the generation of life science stock images.