Why an academic lead is not worth the same as a pharma lead

A maker of lab and bioprocess equipment sells to customers who buy in very different ways. An academic group pays with research project funds, a startup spends money from a funding round and a pharma company or CDMO goes through capex approval and QA. Value, timing and the people who decide change in each case.

If they all enter the CRM with the same score, two things happen. Students downloading datasheets for a class project look like hot leads, and the pharma company that has read three pages of GMP documentation gets buried in the list. You need a model that takes into account who each contact is.

Two scores, one for fit and another for intent

It works better to run two separate scores. Fit measures whether the contact matches what you sell, and intent measures what they are doing right now. A very active postdoc can show high intent and low buying fit, while a head of MSAT can be the ideal fit and have visited only once.

These are the signals we usually propose, drawn from forms, content, demos and trade shows. The actual weights should be agreed with your sales team, looking at which contacts from recent years ended up placing an order.

  • For fit, the type of organisation, job title, country and whether they are already a customer or distributor.
  • For fit, the scale they need and the process, such as cell culture, microbial fermentation or TFF.
  • For intent, a demo or quote request, which outweighs any download.
  • For intent, downloading validation documentation, such as an IQ and OQ table of contents.
  • For intent, a trade show conversation with a specific need logged by the sales engineer.
  • For intent, several people from the same company visiting within a few weeks.

How do we filter out students without losing researchers?

Use a job title field on the form and a low fit score for students, without deleting them. Some will go on to work in a lab or at a company in the sector. Principal investigators and lab managers should earn more fit points, even if their activity on the website is lower.

Universities and public research centres follow procurement rules

At a Spanish public university, the price of the equipment weighs heavily on how it is bought. Article 118 of Law 9/2017 on public sector contracts treats supply contracts with an estimated value below 15,000 euros as minor contracts. Additional provision 54 raises that limit to 50,000 euros for public agents of the Spanish Science, Technology and Innovation System, such as public universities and public research bodies, when the contract is not for general services and infrastructure.

Above those amounts, the purchase goes through one of the other procedures set by the law, often with a published tender. For scoring, this means a researcher with an awarded grant and a benchtop system can buy soon, while a larger system may come back as a tender months later. Note it in the CRM and keep an eye on the procurement platforms.

Want us to check which signals in your CRM separate a student from a real buyer? Leave your details and we will tell you where we would start.

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Startups and pharma companies run on different calendars

At a biotech startup, funding sets the calendar. A lead with strong intent can stall until a round closes, then come back suddenly when the company announces the investment or posts job openings in process development. These public signals are worth as much as a download, and sales should be able to add them to the CRM.

At a pharma company or CDMO, each opportunity tends to be worth more and more people are involved. Engineering, QA, procurement and the project lead visit the website at different times. That is why it makes sense to score the account too, adding up the activity of everyone from the same company, not just each contact on their own.

How often should we review the scoring model?

At first, every quarter, comparing lead scores with the opportunities won or lost. Later you can space it out, but review it when you launch a new product line, enter a new market or when sales says the top-scored leads have no real project.

How to set it up in the CRM and in Google Ads

Start with a few rules and a clear threshold for handing the lead to sales. Add decay so that old activity loses points over the months, and review the leads that crossed the threshold with sales every quarter. If the highest scores had no real project behind them, change the weights.

Scoring also helps your campaigns. Google Ads lets you import conversions from the CRM if you store the click ID for each contact. If you only import leads above the threshold as conversions, campaign optimisation is based on those contacts rather than on every download, including the ones from students.

Do we need an expensive tool for lead scoring?

Not necessarily. Many CRMs include scoring features, and if yours does not, simple fields and rules will do. The hard part is agreeing the signals and weights with sales and keeping the data clean. A simple score the sales team understands is more useful than a complex model nobody looks at.

What the GDPR says about scoring contacts

Article 4(4) of the GDPR defines profiling as automated processing of personal data to evaluate aspects of a person, such as their interests or behaviour. Automated lead scoring can fall under it. Recital 47 says processing for direct marketing purposes may be regarded as carried out for a legitimate interest.

Article 21(2) gives people the right to object at any time to that processing, including profiling related to it. Article 21(4) requires this right to be brought explicitly to their attention, clearly and separately from any other information, at the latest at the first communication.

Marketing emails have their own rule. In Spain, article 21 of the LSSI requires the recipient to have requested or expressly authorised them beforehand, unless they are already a customer and you write about products similar to those they bought. A high score does not replace that consent, and the CRM should record when and how it was given.

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