Episode resources

• Connect with Kenny on LinkedIn

• Book recommendations: Christopher Goffs' books on sales compensation transparency, and Logic, the theory of inquiry by John Dewey

On designing incentives balancing sales performance and predictability

At Red Hat, Kenny brings a rigorous yet adaptive framework to plan design — one that he shares often begins with a clean slate each year to stay grounded in business strategy.

But building plans for a company with both 20-year legacy products and brand-new offerings demands nuance, not just structure:

“We tend to start from scratch every year. Somebody always has some kind of feedback...Once you have your measures sorted out, you're looking at — are any of those interdependent? That can be a party, right? Because you make things interdependent and they become less predictable, really.”

Kenny Smith, Sales Incnetive Plan Design Lead

Red Hat

Kenny highlights a subtle but critical point here: not all parts of the business can be treated equally when it comes to predictability. Mature product lines with consistent patterns allow for more reliable performance forecasting. But newer launches — or shifts in channel strategy — often lack clean historical data, making plan design a more probabilistic exercise.

“If we have a sample of 500 people covering a similar territory, we can make good predictions about performance and expense. But if it’s something new, you have to think about those metrics differently.”

Here's how he approaches things:

In other words, the framework Kenny applies to comp isn’t fixed — it flexes depending on data availability, GTM complexity, and customer behavior. Kenny also stresses the importance of starting with the decision you need to make, and then backing into the data required to support it — not the other way around.

“You have to get practical about your framework. What decisions do I need to make? And therefore, what data do I need for those?”

This approach reinforces two key takeaways for comp professionals:

1. There’s no “set it and forget it” in enterprise comp. Red Hat redesigns the plan annually, using fresh feedback and a realistic lens on how different buyers and sellers behave — from steady-state consumption clients to those who appear every five years with a bundled procurement ask.
2. Over-optimization creates unpredictability. Interdependent metrics may seem sophisticated, but they can confuse reps and reduce clarity. Before introducing dependencies (e.g. margin + bookings, or channel + product mix), model real scenarios — especially edge cases. Ask: will reps understand how to win under this model? Will it reward the behaviors we need consistently?

Incentive design is as much about managing uncertainty as it is about driving results. Kenny’s method shows how he thinks to balance flexibility with fairness — and how to design for the reality of sales, not just the spreadsheet.

Better plans are backed by a chorus of data not a solo source

Incentive design is rarely as simple as plugging in a perfect dataset. At Red Hat, Kenny has learned to be skeptical of overly clean, singular sources of truth — especially when real compensation dollars are on the line.

Rather than betting the plan on just one “high quality” dataset, Kenny prefers to cross-reference multiple sources, even if they’re messy, fragmented, or pulled from different systems. If five imperfect datasets all point in the same direction, he’s more confident the insights will hold up in reality.

“I get more cautious the fewer measures I have available to me, even if it appears like it’s really high quality data... If I get five mediocre sources… and they all kind of tell a similar story, then I'm more confident saying I know something about how to design a plan.”

Kenny Smith, Sales Incnetive Plan Design Lead

Red Hat

This mindset is especially critical in complex GTM models — where comp plans span different offerings, partner channels, and regional nuances. What looks airtight in a spreadsheet can quickly unravel in practice.

Kenny warns that single-source data often fails in edge cases. A dataset might support strategic priorities for most of the field, but without broader context, it risks excluding segments of the salesforce that simply don’t have the opportunity to succeed under narrow or overly specialized metrics.

The real risk? Designing a plan that appears viable in theory — but lacks the depth to support a fair, scalable rollout across thousands of payees.

The takeaway: Instead of aiming for perfect data, aim for a complete picture. Prioritize directional alignment across your sources, and always consider the context in which that data will drive behavior.

Try this:

• Cross-check your proposed performance metrics against 3–5 other datasets: historical attainment, pipeline, quota history, customer segments, win rates, etc.

• Look for consistency, not perfection — the goal is a composite picture.

When activity data becomes comp-ready

Despite all the talk of incentivizing behavior, activity-level data rarely makes it into the plan design conversation — often because it’s fragmented, self-reported, or hard to verify. While the allure of activity-based incentives is well understood, incorporating activity-based incentives (ABIs) is not without challenges.    

But Kenny has a workaround: use “handshakes” across teams to validate the data’s integrity.

“Where it becomes a great measure is where there's some kind of handoff… An SDR sets a meeting, an AE has to agree to take it, the client agrees to attend. Maybe an opportunity gets logged,” he explains. “You gain some confidence with an activity measure because it's not just the rep saying, ‘I did this.’ There’s agreement across functions.”

Kenny Smith, Sales Incnetive Plan Design Lead

Red Hat

Overall, Kenny sees activity data can become comp-ready when multiple people with different incentives agree it happened — not when it’s self-reported in isolation.

To apply this:

• Prioritize metrics where at least two functions (e.g., SDRs and AEs, or sales and customer success) must confirm the activity occurred.

• Look for downstream validation — did the meeting result in an opp? Did a handoff lead to progress?

• Avoid using activity data as a comp driver unless it’s crowd-verified and tied to outcomes.

Sales comp as jazz: It’s not about the note — it’s what comes next

Some of Kenny’s most resonant insights stem from his background in music — but not in the way you might expect. He doesn’t romanticize compensation as pure creativity. Instead, he sees comp as a form of structured responsiveness: decisions that only make sense in context, and whose value depends on what follows.

"There’s a Miles Davis quote: ‘There’s no such thing as a wrong note — only an interesting decision...
That really applies in comp. A decision might look off, but if it serves what the organization needs, it might be the right one."

In Kenny’s view, plan design isn’t about finding a perfect formula and sticking to it. It’s about reacting in real time — to shifting priorities, edge cases, stakeholder feedback, and new data — and adjusting your next move accordingly.

That’s where compensation professionals earn their influence: not by chasing textbook best practices, but by applying judgment in moments where the business requires trade-offs. As he shares:

“That's the last 5–10% of plan design: really getting to something that motivates the sales force versus something that [only] looks good on paper.”

Using data to overcome stakeholder bias

Finally, when it comes to aligning executives on plan changes, Kenny’s approach is part educator, part facilitator. He doesn’t expect data alone to win the argument — especially when stakeholders bring their own histories and biases into the room.

“Nothing is more dangerous than somebody who saw something work really well at the previous organization,” he laughs.

Kenny’s advice? Pilot the change. Show results. And always return to the guiding principles the team agreed on at the outset.

Play your next note with intention

While there's no perfect formula for comp design — that’s exactly the point. As Kenny puts it, even the most “optimized” plan is subordinate to the needs of the business and the value delivered to the customer.

As Kenny put it, a decision isn’t right or wrong in isolation — it depends on what you do next.

Instead of searching for the flawless equation, make it your goal to focus on clarity, calibration, and context — and like a great jazz solo, always play the next note with intention.