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The Problem with GR Measurement Isn't the Math — It's the Model

PoliTraQ

The Problem with GR Measurement Isn't the Math — It's the Model

The Question You're Actually Asking

Most GR teams don't have a measurement problem. They have a framing problem.

They measure outputs — did we get a meeting, did the minister attend, did the bill change. Then they try to convert outputs into outcomes, run into the attribution gap, and conclude that GR is fundamentally unmeasurable.

That conclusion is wrong. But it's understandable why teams land there. The standard framing of GR measurement treats it like a sales pipeline: you put effort in, you get a result, you measure the conversion rate. Except the conversion is never clean, the cycle is months longer than any CRM is designed for, and the outcome you're measuring is often determined by factors that had nothing to do with your work.

Bloomberg Government reported in September 2025 that GR measurement challenges are driving demand for more sophisticated data-driven systems — but even teams that adopt them often end up measuring activity rather than impact, because the underlying frame hasn't changed. The dashboard gets more elaborate; the insight doesn't.

So teams either abandon measurement entirely, or they produce elaborate dashboards that track activity but don't actually tell them if they're getting better.

The problem isn't the math. It's that the question being asked is the wrong question.

A peer-reviewed study published in the Journal of Public Affairs (Yonsei University, January 2026) used a three-phase Delphi method with 25 expert panelists from leading technology companies to identify core competencies for government affairs managers. The highest-consensus KSAs identified were Stakeholder Management, Ethics and Integrity, Political Acumen, and Networking and Diplomacy — all inputs and process capabilities, not outcome metrics. The study notes that this reflects the strategic nature of the role, but it also reveals something important: even experts in the field treat GR effectiveness as a function of relationship and process quality, not win/loss ratios. The measurement frame hasn't caught up with the competency frame.

The Fee-for-Result Problem (Canada-Specific)

Before going further, it's worth addressing the structural constraint that colours this entire conversation for Canadian practitioners.

Under the federal Lobbying Act, fee-for-result arrangements are prohibited for consultant lobbyists. You cannot structure an engagement where payment is contingent on a specific outcome — a meeting held, a minister convinced, a regulation changed. The prohibition exists to prevent paid influence from being undisclosed, and it means consultant GR teams operate under a different commercial pressure than their clients assume.

The interesting thing is that this constraint has bled into how in-house teams think about measurement too. "We can't prove ROI" has become received wisdom in GR circles generally, even where the regulatory prohibition on fee-for-result doesn't technically apply. The consultant world created a shorthand — GR measurement is impossible — and the in-house world adopted it without examining whether the reasoning still held.

It doesn't. The prohibition on fee-for-result tells you something about the limits of outcome-based commercial arrangements in GR. It tells you nothing about whether GR activities can be measured for diagnostic purposes. These are different questions.

What the Right Question Actually Is

The standard question in GR measurement is: did we win?

This is the wrong question.

The right question is: are we getting better at the things that determine whether we win?

These sound similar. They are not.

"Did we win?" produces binary thinking. Win/lose. Success/failure. It treats each engagement as a discrete outcome and each outcome as a verdict on your work. This is the logic that produces the attribution problem — because if you're asking "did our work cause this outcome?", you're asking a question that requires control groups and counterfactuals that GR simply doesn't have.

"Are we getting better at the things that determine whether we win?" produces diagnostic thinking. It asks: what are the inputs that produce good outcomes in GR? Are those inputs improving over time? Are we getting faster at identifying relevant consultations? Are we building relationships with the right people before we need them? Are we catching issues earlier? Are our submissions stronger?

These questions can be answered. They don't require attribution — they require tracking. And they produce a fundamentally different kind of insight: not "did we win this time?" but "are we building a system that wins more often over time?"

The Westside Conjugate Model

To understand what diagnostic measurement looks like in practice, it helps to look at an unrelated domain where measurement is similarly hard: elite strength training.

Louie Simmons developed the conjugate method at Westside Barbell in the 1970s and 80s. Westside is famous for producing some of the strongest powerlifters in the world, many of them competing in weight classes where raw strength-to-body-weight ratio is the entire sport.

Here's the relevant part: Westside doesn't measure a single workout. They don't ask "is this athlete getting stronger?" after any given session. They measure across cycles — microcycles of one week, mesocycles of four to six weeks, macrocycles of six to twelve months — and they use those measurements to drive specific training decisions.

The diagnostic logic works like this: identify the specific weak point (posterior chain? lockout? speed off the floor?), select specific exercises to address that weak point (not exercises that look impressive, exercises that fix the actual gap), track whether the weak point is getting stronger across cycles, adjust the program based on results.

The key insight is that in the conjugate method, failure is not just acceptable — it's informative. A lifter who can't lock out at 90% of their max isn't "failing." They're telling you where the program needs to change. The program is always diagnosing and adjusting. Measurement is not a scoreboard; it's a feedback loop.

GR measurement works the same way.

Why "Did We Win?" Produces Bad GR

Consider what happens when a GR team measures itself on wins and losses:

You can't control the outcome. A minister changes their vote not because of your brief but because of a conversation you didn't know happened. A regulation goes your way for reasons that have nothing to do with your work and everything to do with an election cycle. You can do everything right and lose, or do everything wrong and win. Outcome measurement rewards noise as much as signal.

You learn nothing from a loss. If you measure on outcomes and you lose, you know you lost. You don't know why. The attribution gap means the feedback is uninterpretable. Did you lose because your arguments were weak, because you engaged the wrong stakeholder, because you came in too late, because someone else had a better relationship, because the political environment shifted? Outcome measurement doesn't tell you.

You optimize for the wrong thing. If you're measured on wins, you focus on files where a win is likely. You avoid the difficult files, the long-shot files, the files where the right outcome is important but the probability of achieving it is low. A team that only works solvable problems is a team that isn't doing the hardest part of GR.

You miss the compounding. The most valuable GR work is often invisible — building a relationship with someone who will matter in two years, monitoring a file quietly enough to see it coming, developing expertise in an area that will be relevant when the issue heats up. None of this shows up in win/loss measurement. You only see it in hindsight, and often not even then.

The Diagnostic Alternative

The conjugate model asks a different set of questions:

What are our specific weak points? Not "are we good at GR?" but "where specifically are we losing ground?" Coverage gaps? Timing failures? Stakeholder blind spots? Relationship deficits? Intelligence lags?

What are we doing to address those weak points? And: are those actions actually working?

Are we getting faster or slower at the things that matter? Can we identify a relevant consultation before it closes? Can we reach the right person before the decision is made? Can we produce a strong submission faster than we could six months ago?

These questions are answerable. They require different data than win/loss measurement — more process-oriented, more qualitative in the moment, but ultimately more useful because they tell you where to put your effort.

The Canada Context

There's a specific reason this framing matters more in Canada than in some other jurisdictions.

Canadian GR operates across multiple jurisdictions simultaneously. Federal, provincial, municipal — sometimes Indigenous governance layers too. The coverage problem is real: most teams have blind spots, and those blind spots are often where issues emerge before anyone is watching. A monitoring system that tells you earlier, more often, and across more jurisdictions than your competitors is a structural advantage, not just a compliance tool.

The fee-for-result constraint reinforces the diagnostic approach. If you can't structure engagements around outcomes, you have to structure them around inputs and process. That's actually a healthier frame for building long-term GR capability — and it's the frame that the conjugate model naturally supports.

What Comes Next

The first step isn't choosing KPIs. It's identifying your weak points.

Once you know where you're losing ground, you can design the specific measurements that tell you whether your efforts to address those weak points are working. Those measurements won't look like anyone else's — they'll be specific to your organization's issue areas, your political environment, and your current capabilities.

The conjugate model doesn't produce a universal template. It produces a diagnostic logic that you apply to your own situation. The insight is that measurement, done properly, is a tool for getting better — not a scoreboard for proving you were right.

That's a more useful tool, and a more honest one.

PoliTraQ helps GR teams identify their weak points and track the inputs that matter over cycles, not just the outcomes that make headlines. Request a demo to see how GR intelligence works in practice. Request a demo

References

The Problem with GR Measurement Isn't the Math — It's the Model | PoliTraQ Blog