Marketing leaders and marketing automation providers, you can call off the lynching parties… I’m not actually coming after marketing qualified leads (MQLs), just the way we think about them right now. MQLs are not dead, but the way they are quantified by the tools science marketers are using, needs adjustment.
Universal tools, unique problems: the Life Science conundrum
The advent of marketing automation tools like Adobe’s Marketo, Hubspot, Active Campaign alongside other account intent tools like 6Sense, DemandBase and others have lifted a whole lot of grunt work out of the funnel across all industries. They have allowed marketers to focus on more quantifiable metrics and allowed sales professionals a wealth of opportunity to understand their customers better. To say otherwise would be an affront to the efforts of all those involved and in all of those tools. On the face of it, it’s everything that we all need in order to get better at understanding our customers, but does it actually hit the mark when it comes to Life Sciences?
The very purpose of these tools is to watch interactions and engagement, to generate interest and to present them to sales – who get customers to a point of purchase. The very nature of Life Science sales says otherwise. Our sales cycles are extremely long (for larger capital products), but even down to seemingly smaller, less expensive tools, testing and retesting of products is part of the cycle. This means reading more content, application notes, articles, and webinars on the product’s use. All great signals of interest, but do they mean purchasing power, or an interest in actually getting the product?
Aside from this, there are also many stakeholders involved in the process, and with every role there is nuance. For example, a Senior Scientist is a title, but does it tell you what area they’re working in? Is it RNA, Gene Editing, Vaccine Development etc?
The illusion of intent: Scientific curiosity as a metric killer
Then there’s the bane of intent data: scientific curiosity. Countless times you can see recipients of content having absolutely nothing to do with that area of research; the title of the article or application note ‘was just interesting’ to them, so they read it. Scientific curiosity is the greatest blessing for the development of novel treatments but the bane of those marketers sifting through the ‘intent’ confusion that creates in terms of data analytics. It’s like loving that your kid plays with Lego but hating the mess that it creates.
Many of these B2B marketing tools focus on interaction and engagement, something that would make sense in a consumer setting. As an example, searching across content and site interaction for ‘running shoes’ will almost be a total certainty of purchase intent. In Life Science, equating the same principle is what leads to a mass volume of ‘false positive’ MQLs. They’re doing all the right things: they are presumably the right ICP (Ideal Customer Profile) but they’re not actually the right person, or worse, they’re not actually interested.
On top of this, marketing automation tools score quick, repeated short-term engagements as priority and as signs of high quality, but penalize those that may consume your marketing content pieces over a long stretch of time.
So how does this happen within these tools?
Flaw A: The Irrelevant Engagement Trap
The system identifies a surge of activity at a target account, but due to the complex, specialized roles within an R&D lab or facility, the activity is attributed to the wrong person. The individual who is consuming highly- technical content is often an early-stage pre-target discovery researcher, an academic collaborator, or a PhD candidate. Their engagement with your eduactional content is driven by genuine scientific curiosity, grant research, or troubleshooting a known issue – not a puchase request. Yet, the marketing automation (MA) system scores this high-frequency, yet low-authority activity as a clear signal of purchase intent, creating a massive volume of “false positive” MQLs over time. These can either get picked up and disqualified by marketing in some cases, or are pushed down in priority order if the MA tech tools you are using allow you to do that. More often than not, due to the sheer workload most marketing teams are under, these false positve signals are routed through to Sales who end up with another admin task to work on.
Flaw B: The Granularity Gap
The ICP for a highly technical product might require targeting a Process Development Scientist specializing in RNA signalling. Most intent platforms and CRM systems simplify this to the generic title of “Senior Scientist” or “Process Engineer.” This causes the system to miss the essential nuance required to qualify a lead; possibly not being caught by the exclusion criteria in the system. If the intent data can’t distinguish between a Lab Manager with budget authority and a Research Associate in an unrelated department or identify the nuanced role, the signal is useless, and the costly sales outreach that follows is destined to fail.
The ‘pointless’ funnel and the cost of admin
What’s the result? Well… as marketers we can’t neglect somebody who is interacting heavily with our content. They might be the right person (at least the system is indicating so). They might want to buy something. This time. Plus, you need to measure the success of all your marketing campaigns, right?
So you recruit sales to go through all these ‘high intent’ opportunities. Sure, they can look through account-level interaction data from an account based marketing (ABM) platform to get insight into the account’s possible interest, but with hundreds of records marked ‘high intent’ to go through and generic titles that don’t highlight the nuance of their actual roles, there begins the tug of war between marketing and sales.
Marketing wants to validate metrics so they can either change their marketing tack or measure the success of assets. Sales just want to hit their targets. Instead, we get broken sales confidence in marketing qualified leads, a long-term follow-up process by sales that leaves marketing waiting months to get campaign feedback, and, for the customer experience, a wealth of prospects being sent content that isn’t actually relevant to their day-to-day work. The very antithesis of agility.
Commercial and operational pain
This isn’t a swipe decrying sales as lazy. In fact, sales are doing what they are supposed to be doing: finding the most agile way to get to the right people and sell products. Ultimately though, what that is, is low hanging fruit and ideally we want more from the tree.
Businesses are potentially leaving thousands worth of revenue on the table both in terms of missed opportunities and all that will have been wasted in terms of marketing time, asset subscriptions, and campaign development because the follow-up isn’t as agile and nuanced as it could be.
The fact is that the sales person’s time is precious whether that is a Business Development Manager (BDM) or a Sales Development Representative (SDR), for example. The BDM should be spending their time delivering demos, engaging stakeholders, pulling in FAS (Field Application Scientists) and other SMEs (Subject Matter Experts), driving the needle forward into their accounts. Their SDRs should be supporting the BDMs to map out accounts, find other stakeholders to engage with, and strategically work with their BDM. When they are instead tasked with MQL validation, the system forces specialized, high-cost personnel into low-value administrative work. People who are likely bored by what feels like meaningless work that gets them no closer to their bonus.
The final fatal flaw: Inability to manage time
The ultimate fatal flaw of generic marketing automation is its inability to manage time. At the heart of it all, marketing needs to be agile, but the metrics in these systems are often built for a high-volume, transactional environment which fundamentally misaligns with Life Science sales cycles.
Aside from just penalizing the quality of engagement, this misalignment creates content management overwhelm for marketers, who have to spend excessive amounts of time manually auditing and updating long nurture paths for leads so they aren’t receiving old information, protocols, or other data that could be the difference between a sale and an unsubscribe.
The generic MQL as we know it, in its flawed intent and simplification of titles and job roles, is both inefficient and expensive.
Marketing in Life Science is different. The primary function isn’t to generate high volume leads but to accelerate the sales process for technical and sales experts and provide customers with a journey that actually works for them.
Quantifying scientific velocity
So, if we move beyond vanity and look at this scientifically, what we now need to do is put systems in place to quantify scientific velocity:
- Score depth, not volume: Define and prioritize the most valuable assets like application notes or technical data over generic clicks or downloads.
- Segment by expertise, not job title: This one is trickier and may require specialized AI scraping tools to verify roles/job titles that are actually relevant to your buyer journey, as well as aggregating high-quality intent from the technical user and verifying intent from the economic buyers (Principal Investigators, procurement).
- Change the incentives: Give marketing a focus that makes sense – Sales Accepted Leads (SALs) rather than MQLs – to foster collaboration and alignment between marketing and sales on what is actually working for the business.
Like good science, lead management needs specificity. We might not have the FDA dropping cold stares at our every move but the validity and the impact of the work that we (marketing and sales) do on the bottom line is vitally important – not just financially, but for patients. In order to do that, we need to ensure that the tools and mechanisms we put in place for lead management actually reflect the buyer’s journey and don’t have both teams searching through smoke and mirrors to get results.
