In life sciences, the buying journey for high-value equipment rarely follows a neat, linear path. Decisions involve multiple stakeholders, long periods of research, and procurement processes that don’t resemble traditional ecommerce. This makes accurate tracking a real challenge, but it’s not impossible if you approach it in the right way.
Understanding the buying trigger
Take the example of a research scientist working in an industrial manufacturing company. To complete a specific project, they need a specialist piece of equipment worth around £5,000. Their first step isn’t to head to Google Shopping or an online marketplace. Instead, they consult academic papers, scientific case studies, and the insights shared at conferences to validate whether the purchase is truly necessary. Once the requirement becomes clear, the decision passes to the lab technicians and managers who oversee day-to-day operations. They are the ones who confirm the feasibility of acquiring the equipment and begin moving the process forward.
How the research phase unfolds
From there, the journey widens. Multiple people are now involved, each with their own priorities. Lab teams will share thoughts internally, colleagues may put forward preferred suppliers based on prior experience, and procurement departments may be constrained by pre-approved vendor lists. Where no obvious supplier exists, the group will research online, moving between search engines, vendor websites and specialist forums. The route to purchase is collective, not individual, and heavily influenced by both internal processes and external validation.
What the customer journey looks like
Because of the cost and complexity of the purchase, very few researchers buy equipment directly. Only those with budget authority and a company card can complete the transaction themselves. For most, the likely outcome is that product information is shared internally, either as a URL or through a generated quote, before procurement takes over. Some vendor websites even allow a “share basket” function or downloadable quote that formalises this handover. At this point, offline channels such as email or phone calls with suppliers often dominate, making digital attribution even harder to maintain.
Reconciling revenue back to ads
This fragmented process makes it extremely difficult to tie revenue back to ad activity with any degree of accuracy. Achieving 75 per cent or more attribution is unrealistic, and even 50 per cent is rarely possible. For most scientific businesses, reconciling between 10 and 20 per cent of revenue to digital clicks is about as good as it gets. The exact figure depends on how many stakeholders are involved, how long the process runs, and how much of the procurement takes place outside of digital platforms.
Why tracking is so difficult
Several factors contribute to this lack of visibility. Different stakeholders use different devices and browsers, which breaks the journey into unconnected fragments. The research and validation process itself can stretch out for months. Quotes often serve as an interim step between discovery and purchase, creating a long lag time. Many procurement teams bypass websites entirely once they’ve identified a vendor, using established relationships via email or phone. And, in many cases, final purchases are handled through purchase order systems that don’t resemble ecommerce at all.
A more suitable approach
Traditional ecommerce tracking simply doesn’t work in this environment. Instead, success depends on adapting methods to fit the reality of the scientific buying journey. Targeting needs to be highly specific, focusing on detailed product searches rather than broad, generic terms. Advertising spend should be directed towards the products and geographies proven to generate revenue, not spread thinly across the full catalogue. On-site engagement data is particularly valuable, interactions with technical specifications, detailed product pages and customer-service touchpoints can all act as meaningful signals of intent.
Rather than trying to force full attribution, businesses should use the data they do capture to build predictive revenue models. Feeding product-level revenue and engagement data into these models gives a much clearer picture of likely outcomes. Deploying them via server-side Google Tag Manager provides control over how this data is shared across platforms, ensuring both human account managers and algorithmic bidding systems are working with the most relevant information available.
Why it matters
With life sciences purchases, the human decision-making process is long and collaborative. Giving account managers better quality data enables them to allocate budget more effectively. At the same time, algorithms that drive bidding and targeting need structured signals to optimise towards the most profitable customers. Without this, they’re left to guess – and in a sector where purchases are rare and expensive, that’s a costly risk.
By moving beyond traditional ecommerce tracking and adapting to the realities of the life sciences buying journey, businesses can reduce wasted spend, improve cost per acquisition and increase the long-term value of their customers. It won’t give you perfect attribution, but it will deliver more reliable performance in one of the most complex markets to measure.
