[PRACTICE NOTE] Unlocking the Field: Sales Force Effectiveness Reconsidered
Organizations measure what they can control long before they measure what they need to know. In pharma commercial operations that habit has produced one of the best instrumented functions in the industry and one of the least understood. Sales force effectiveness is a mature discipline with a settled measurement set, refined over decades and defended with real rigor.
Reach, frequency, call plan adherence, share of voice, territory optimisation and next best action all describe something real. Each one measures the field's activity. None of them measures what the field changed. That difference is not a measurement oversight, and the discipline deserves respect as the product of an era that has now closed.
Sales force effectiveness measures activity rather than consequence
The familiar critique of field force effectiveness blames the people in the territory, and that critique is almost always wrong. Field teams are capable, often more capable than the systems asking them for weekly compliance. The design gap sits in the instrumentation, which was built to confirm that a plan happened rather than to learn what a decision ran into.
Activity measures can answer whether the intended coverage occurred. They cannot answer whether the therapy occurred. A discipline that reports the first with precision and the second not at all will tend to produce confident organizations that are structurally unable to explain their own results.
The constraint moved and the measurement set stayed behind
Those measures were designed for a field whose job was access to the prescriber, at a time when access was genuinely the scarce resource. Veeva's analysis of engagement data found that 45% of HCPs are accessible to biopharma companies, down from 60% eighteen months earlier. Narrowing access raises the value of the time the field holds, which is a different claim from saying the count of that time is what matters.
Access is no longer the binding constraint on commercial outcomes. A clinically justified decision now has to survive adjudication, affordability and persistence before it becomes therapy, and any one of those can quietly undo the decision weeks after the call that produced it. Scientific value is realized when the patient accesses, starts, continues and benefits, not when the script is written. Written prescriptions are intent rather than realized value.
The commercial funnel, in other words, ends too early. Prescribe, fulfill and adhere belong in one commercial system, and the measures that govern the field were drawn before that system was recognized. The field is the only part of the enterprise standing close enough to see where a decision stops surviving. Most organizations have no sanctioned way to collect what it sees.
Why field intelligence is the superior commercial signal
The brand team ostensibly owns the brand end to end. Its instruments, though, sit almost entirely in the Path to Prescribe, namely prescribing data, market research, message testing, share tracking and promotional response. Each of those is useful, and each is retrospective, sampled or self reported. The brand often learns that progression broke long after the moment, and without knowing which account it broke in.
The resulting confidence gap is measurable. Deloitte found that only 28% of HCPs believe pharma's customer engagement strategies meet their needs, while 82% of life sciences executives are satisfied with those same strategies. A gap of that size is rarely a matter of judgment. It is more often a matter of which instruments the organization chose to trust.
Field intelligence has four properties that no brand instrument can match. It is observed rather than recalled, account specific rather than sampled, current rather than lagged, and already attached to the moment progression broke. Those properties together make it the most unimpeachable and actionable intelligence in the commercial system, which is why the voice of the frontline belongs in the same tier of credibility as prescribing data.
The frontline remains one of pharma's most underutilized intelligence systems, encountering barriers long before dashboards detect them. The commercial consequence is not abstract. Bain's work on commercial productivity found that about 40% of a doctor's drug recommendations relate to overall experience with the company. Doctors who rate a company highly are 2.3 to 2.7 times more likely to prescribe its products. What the field observes about experience is therefore an input to prescribing itself, not a soft adjacency to it.
Empowerment is the condition that makes intelligence usable
Collecting a signal and acting on a signal are governed by different parts of the organization. The second is a leadership question, and in the book it belongs to Leadership DNA rather than to operations. Two of the empowerment principles carry most of the weight here, and both can be observed from outside without a survey.
Prioritisation means visibly putting the customer and the employee on business review and board agendas, and then allocating resources in a way that matches the stated agenda. Nothing in a company signals priority more plainly than where money and leadership attention land. When neither lands near what the field is reporting, colleagues read the signal accurately and stop reporting.
Permission means granting the institutional permission employees need to uphold brand promises. In the field that question is sharpest, because colleagues routinely see what is failing a patient and have no sanctioned route to act on it. The Ritz-Carlton practice is the clearest expression of it, where employees are pre-authorized to spend up to two thousand dollars for a guest each day to resolve an issue without seeking approval. The point of that rule was never the money so much as a standing institutional answer to the question of whether someone close to the customer is trusted to act.
FieldOS gives field intelligence an operating loop
Intelligence without a loop becomes anecdote, and anecdote loses every argument it has with a dashboard. FieldOS is the operating blueprint for field intelligence and activation, and it exists to make the loop explicit rather than ad hoc. Its closed loop runs in six stages: Capture Signal, Classify Barrier, Assign Owner, Intervene, Measure Progression, Learn and Improve.
Each stage answers a failure that practitioners will recognize. Capture without classification produces noise. Classification without an owner produces reports. Intervention without progression measurement produces activity, which returns the organization to the problem it started with. AI sits at the centre of that loop supporting every stage, and it is deliberately not a stage of its own.
The loop has a diagnostic measure attached to it, and I have found it to be the single most clarifying number a commercial leader can ask for. The Transfer Coefficient is the proportion of meaningful frontline intelligence that survives the journey from customer observation to consequential organizational action and measured effect. Many organizations are surprised by how little survives, and the surprise itself is useful, because it moves the conversation from field capability to system design. A fuller description of the loop sits in the FieldOS field impact operating system.
Progression replaces activity as the measure of field impact
Measure Progression is the stage that most often gets softened in practice, so the ladder it runs on has to be concrete. The stages are Scripts Written, Filled, Therapy Started, 90-Day and Persistence, organised under the Path to Prescribe, the Path to Fulfill and the Path to Adhere. A field organization that can show movement on those stages is making a different argument from one that can show coverage.
This ladder also connects the field to the enterprise measure that senior leadership already recognizes. The Realization Rate is realized value divided by earned value, and every stage where progression stalls is a place where that ratio falls. Friction is an economic variable, and accumulated friction becomes commercial drag. The hurdles that accumulate across a journey are familiar enough to name: administrative burden, disconnected handoffs, lack of visibility, fragmented support, poor escalation and process complexity.
Effort is the common thread running through all six. Gartner's research on service effort reported that low effort customers are 61% more likely to stay with a company. In a therapy context, staying is persistence, and persistence is where earned value is either realized or quietly lost.
This is about better listening rather than better pre-call planning
Most field AI on the market today is a talking tool. Next best action, message refinement, call prep, targeting and territory optimisation all improve what the field says and when it says it. Those capabilities have genuine value, and they leave the underlying question untouched, because a better formulated message delivered into an unchanged system still produces an unchanged journey.
The tenet that governs this is uncomfortable and worth quoting rather than paraphrasing. AI will not make a fragmented commercial model customer-centric. It makes whatever operating system exists faster and more scalable, which means a fragmented model gets faster at fragmenting. The sequence matters more than the tooling, and the operating system comes first.
There is a more encouraging reading of the same evidence. AI did not replace the field organization, it made it more valuable than ever, because the one input that automated systems cannot generate is direct observation of what happened in an account. Experiential Commerce, the concept I developed in the book to describe commerce where the experience carries the value exchange, puts a premium on exactly that kind of observation. The field is the sensing layer, and sensing is the half of the problem that models cannot solve on their own.
What this asks of commercial leadership now
Pharma does not have a sales problem. It has a systemic value realization problem, and the field is both the most exposed part of that system and the best positioned to report on it. The move I would put in front of commercial leadership this planning cycle is to reassert the field as an intelligence function rather than defend it as a cost line.
That move sits inside a larger one. Launch excellence, marketing excellence and sales excellence are mature domains in pharma, and the fourth, Customer Excellence, is the one most organizations have not built. In my experience the field is where that fourth capability either becomes real or stays on a slide, because it is where the enterprise learns whether its promises survived a patient's week.
Key Takeaways
- Sales force effectiveness measures the field's activity with precision and says almost nothing about what the field changed.
- Access was the scarce resource that justified the legacy measurement set, and it is no longer the binding constraint on commercial outcomes.
- Field intelligence is observed rather than recalled, account specific rather than sampled, current rather than lagged, and already attached to the moment progression broke.
- Prioritisation and permission are the organizational conditions that decide whether a captured signal ever becomes consequential action.
- The FieldOS closed loop runs through Capture Signal, Classify Barrier, Assign Owner, Intervene, Measure Progression and Learn and Improve, with AI supporting every stage.
- Progression measured across Scripts Written, Filled, Therapy Started, 90-Day and Persistence connects field work to the Realization Rate.
Diagnostic Questions to Consider
- Whether a field colleague who observes a barrier today has a sanctioned route to escalate it, and how long that route takes to produce a decision.
- What proportion of meaningful frontline intelligence survives from observation to consequential action and measured effect in the current operating model.
- Which instruments the brand team actually relies on, and whether any of them can locate a stalled patient journey in a named account.
- Whether field performance conversations reference progression stages at all, or terminate at coverage, frequency and share of voice.
- How visibly the customer and the employee appear on business review and board agendas, and whether resource allocation follows that appearance.
Closing Reflection
The settled measures of field effectiveness were honest answers to a question the industry no longer faces. They were built for access, and access has stopped being the thing that determines whether science reaches a patient. Keeping those measures while the constraint moves is how capable organizations end up puzzled by their own performance.
What replaces them is not a new dashboard so much as a different premise about where commercial truth lives. It lives with the colleagues standing closest to the moment a decision either becomes therapy or quietly does not. Their observations are the most current and least mediated signal the enterprise holds, and in most companies that signal still has nowhere to go.
Building the route is ordinary work. Capture the signal, classify the barrier, assign an owner, intervene, measure progression, then learn from what the intervention did. Done consistently, that loop turns the most expensive channel in pharma into the most informative one. That may be the most durable advantage available to a company which does not control the transaction between its science and a patient.
About the Author
Wayne Simmons is the founder of The Customer Excellence AGENCY and the author of The Customer Excellence Enterprise (Wiley, 2024). He is founding faculty of the MS in Customer Experience Management at Michigan State University's Broad College of Business. He led global customer excellence in Pfizer's first Chief Marketing Organization and in Bayer's Customer Powerhouse.
The Customer Excellence Agency: Advancing the Pursuit of Excellence in Service of Science.
