By the time a customer sends a request for proposal, they’ve already decided who they want to work with. Everyone else is filing paperwork for second place.
That’s the reality most commercial teams in scientific development are still refusing to organize around. They wait for the demand signal, the email, the quote request, the inbound, and then wonder why their close rates against the incumbent are so brutal. Sadaf Z. Malik, a commercial leader with over ten years in life sciences sales and business development, has built her approach around one governing insight: the teams winning right now stopped waiting for demand signals and started reading program signals instead.
What Analytics Beats Intuition On
A good rep can hold maybe fifteen accounts in their head with real depth. That’s the ceiling. Analytics does not have that ceiling. It holds the whole list, reads the signal across hundreds of accounts, and surfaces trends a human would need years to see. Reps are no longer running their territory off the accounts they happen to remember. They are running it off the whole market.
But analytics loses where it always loses, in the room. It will not tell a rep why a champion went quiet, or that a program died inside a company before the org chart caught up. “No matter how far these analytics go, it’s not going to be the device that tells you this person trusts you,” Sadaf says. “You’ve got to go there and do that in person.” Analytics arms the rep. The rep still has to show up.
Program Signals, Not Demand Signals
Traditional selling waits for the demand signal. By the time it lands in an inbox, the framing battle is already lost. Someone else helped write the spec.
The smarter motion tracks how programs are actually developing at target accounts before procurement gets involved: hiring into specific research areas, publication activity, conference presence, funding events. Tools like Scileads aggregate those signals continuously, allowing reps to map program progression and stay in conversation with customers long before a formal buying process begins. When an RFP does come out, the team reading program signals is already the preferred conversation partner, not scrambling to get introduced. That upstream position is the difference between being chosen and being compared.
The same discipline pays off further down the pipeline, at the moment a deal starts to stall. Patterns surface in the data. A customer has not responded in thirty days. A priority shifted. A champion moved teams. Instead of hounding that customer for a quote, reps can walk in with a smarter question. What changed? Where does this fit into your longer-term vision? “The analytics are helping our reps ask better questions instead of asking basic ones,” Sadaf says. “When you ask more comprehensive questions about really understanding their why, that’s when you become a better partner.”
What You Would Hand An Agent, And What You Would Not
Everyone is demoing AI agents. Where they earn their keep is the craft work around the deal. Sharper messaging. Execution plans tuned to where a company’s programs actually are. First-pass outreach that sounds like a human did the homework. That work used to get done only for the top five accounts. Now it gets done across the whole list.
What Sadaf would not hand over is the relationship. “Nobody wants to buy from a robot,” she says. Customers want the human on the other end, the person who understands their science and can sit with them when a program hits trouble. That runs on trust, and trust is not a workflow. “You can’t automate a partnership,” she says. Automate the prep. Never automate the partnership.
What Analytics Lets You Skip
Walking into a new territory used to mean a list of accounts and a year of manual grinding to figure out which ones were real. Reps Googled every company one by one and were not fully productive until year two. That was the tuition. That year is gone. An account list gets dropped into an analytics tool that maps out who the accounts are, who the contacts are, who is funded, who is hiring, which programs are moving. What used to be a six-to-nine-month ramp is now sixty to ninety days. “The ramp-up period of a year being normal is not normal anymore,” Sadaf says. Anyone still running the old ramp is paying tuition they do not need to pay.
The same shift shows up in outreach. Two years ago, personalization was a luxury item reserved for the top handful of accounts. Everybody else got a version of the same email and a one percent hit rate. Now the account list gets uploaded, program trackers pulled in, and messaging generated that hits each customer’s pain points. What used to be one percent is now ten or fifteen percent, because the outreach stopped sounding like a fishing expedition and started sounding like someone did the work. Specificity used to be reserved for the top of the funnel. Now it is the floor.
The Tool Is Not The Moat
“Anyone telling you their tool is the moat is selling you the tool,” Sadaf says. Analytics is table stakes. Everyone will have access to the same registries, the same enrichment, the same models. That layer commoditizes fast. The moat is what a team builds on top of it, the proprietary models, the scientific depth, the domain expertise that cannot be purchased from a vendor. Teams that mistake having the tool for having the moat are one procurement cycle away from parity. That is the compounding advantage, the one that does not come in the tool box, and the one competitors cannot buy their way to.
Follow Sadaf Z. Malik on LinkedIn for more on AI-enabled commercial strategy and life sciences sales.
