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Why Most Channel Incentive Programs Can’t Prove ROI. And What Motivation Science Reveals

Chris Dornfeld Chris Dornfeld | December 12, 2025

Team Collaborating at a Computer While Reviewing Data and Discussing Project Insights.

When the global manufacturing arm of a Fortune 500 company launched an ambitious channel incentive program, expectations were high. The target: a 20% sales lift across 12 markets, a higher share of wallet among their master resellers.

But six months in as the dashboard lit up green and revenue was improving – the VP of Channel Sales pressed his team with a simple question, “Which partner behaviors are driving that lift?”. The question was met with blank stares. The team could tell what the result was, but not why.

It turned out that while they had paid out millions in rewards, they lacked visibility into partner engagement, learning completion, deal-registration behavior, even response to communication outreach.

This illustrates a critical blind spot we see in many manufacturing companies: you cannot optimize what you cannot measure. If your channel incentive program only shows “payouts by region” or “sales by partner tier,” you’re flying blind. And in today’s fast-moving manufacturing channel, that means missed opportunities at best, and at worst potential risk of losing market share.

In this post we’ll explore why visibility into ROI and sales-behavior is so limited in many large organizations, what motivation science adds to the equation, and what channel leaders should do right now to measure, influence, and prove the behaviors that lead to growth.

 

Why Visibility Is So Limited Today

Legacy Systems Focus on Transactions Not Motivation

Most channel programs still run on legacy tools, spreadsheets, or custom-built systems designed ten or twenty years ago. They capture invoices, rebate claims, payouts – all the outcomes, but almost none of the behaviors that created those outcomes. Did the partner attend training? Did the seller register the deal early? Did they complete the micro-challenge? Did they even open the communication? These “leading indicators” disappear into the gaps between systems.

And when all you measure is outcomes, you’re always late. By the time a number shows up on a payout report, the opportunity to influence it is already gone.

A few years ago, a multi-billion-dollar construction software company ran into a similar problem – but with safety, not sales. They were trying to understand which job-site behaviors actually reduced accidents. Like many leaders, they assumed the most important drivers would be safety compliance activities: toolbox talks, safety documentation, or required learning modules.

But when they analyzed thousands of projects across hundreds of clients, they discovered something completely unexpected: The strongest predictor of safe outcomes wasn’t safety paperwork — it was how complete the construction documentation was before the project even started.

The issue wasn’t training. It was sequencing.

When documentation was incomplete, work activities started out of order or without the right context. Crews filled in the missing information on the fly, often under pressure. That meant delays, interference between trades, rushed corrections and ultimately, significantly higher safety risk.

The lesson was profound: The most important behaviors are often the ones you’re not measuring. And if you’re only measuring end results, you’ll never uncover them.

Channel programs face the same challenge. If your system can only tell you “who earned what,” you miss the early behaviors – the ones that predict momentum, the ones that signal disengagement, and the ones that drive ROI. Without visibility into those leading indicators, you’re not managing motivation. You’re just reporting history.

Data Silos Across Products, Regions, Distributors

In many manufacturing firms, channel data lives in separate systems: CRM, MDF/Co-op portals, learning management systems, distributor POS feeds, spreadsheets. The channel leader receives a monthly consolidated report of “top partners, payouts, spend.” It’s like driving with the rear-view mirror.

No unified “pane of glass” means you can’t answer which partner segments are lagging in engagement, which sellers are losing momentum, which campaigns are resonating.

Low Signal‐to‐Noise Ratio

Even when data exists, actionable insight often don’t. Dashboards show volumes and payouts, but rarely engagement patterns, partner micro-behaviors, or correlations between behaviors and ROI. Leaders spend time reconciling data rather than shaping strategy.

Incentive industry partners contribute to this noise because the incentive industry makes most of its profit from the throughput of incentives, not the impact. They are motivated to keep attention focused on the incentive activity because in many cases companies over-incentivize to drive results.

 

Why This Matters — The Business Impact

You Can’t Link Incentives to the Behaviors That Drive Margin

Some of the highest-value channel behaviors in manufacturing are early deal registration, new product training, cross-selling, engagement or driving new SKU adoption. If you don’t measure these, you reward only the transaction and ignore the behavior.

Loss of visibility into behavior means you can’t answer: which partner activities grew share, which dropped off, which are at risk of moving to a competitor?

Incentive Spend Becomes a Cost Centre, Not a Growth Engine

Without behavioral proof, your incentive budget looks like a marketing expense rather than a strategic growth lever. CFOs ask “What return are we getting?” but you answer only “Here’s how much we paid out.” That weakens influence and threatens budget cuts.

Slow, Uncertain Decision Cycles

If visibility is delayed, you can’t pivot quickly. A campaign underperforms, a product launch lags, a partner tier disengages — and by the time you act, it’s too late. Competitors who can measure partner behavior faster win mindshare and deal flow.

 

What Motivation Science Tells Us About Visibility

Behavior Science: You Must Measure Leading Indicators

Motivation science teaches that humans need immediate, clear feedback to engage. Confidence and momentum build when people see progress. In your channel program, those leading indicators (e.g., micro-learning completion, portal logins, challenge participation) matter far more than just the sale.
If your partner sees their progress, gets recognition early, they’re far more likely to stay active. Without behavior visibility, you’re driving results only by hope, not by design.

Data Science: Patterns Predict Performance

Modern analytics can help you answer: which behaviors correlate with high-value outcomes? Which partner cohorts are at risk? Which incentive designs are truly motivating?

By combining behavior-level data (engagement, activity) with outcome data (sales, margin), you build predictive models that tell you not just what happened, but what will happen – and what to change.

Combined: Motivation Science = Behavior Science + Data Science

Here’s the differentiated point: Data science tells you what is happening. Behavior science tells you why it’s happening and how to influence it. Together, they give you the ability to move from reactive to proactive, from payouts based on history to behavior-based strategies for the future. The integration of these areas we call Motivation Science.

For a large technology client, we developed predictive tools that can forecast sales results six months in advance with about a 90% degree of accuracy. Think for a minute how your decisions could change and how much it would benefit your business to have that kind of information, and the understanding of the behaviors that you can affect to change those outcomes.

 

What “Good Visibility” Looks Like

Centralized, Real-Time Data Across the Channel Ecosystem

Imagine a dashboard that pulls CRM deal registrations, learning completions, partner portal log-ins, claim submissions, POS data, all in near real-time. That gives you the ability to spot trending behaviors, not just outcomes.

Behavior-Level Analytics (Not Just Payout Reports)

Good analytics might show: partner X logged in 3× this week; partner Y completed the new SKU certification; partner Z dropped logins by 40%. You can then link those behaviors to outcomes: Which partners who completed certification sold more? Which didn’t?

This data gives you actionable insight: move or intervene early.

Automated Insights & Optimization Recommendations

If you can build a system that flags: “Partner cohort A is under-engaged; consider a micro-challenge”; or “Region North has high logins but low claim submissions; check portal UX”, then you shift from passive reporting to active management.

 

Channel Visibility Maturity Model

From Payout Reporting to Predictive, Behavior-Driven ROI

 

Stage What It Looks Like Risks / Outcomes
(1) Transaction-Only Reporting (Reactive)
  • Only invoices, claims, and payouts are captured.
  • Reporting monthly/quarterly.
  • No behavioral or engagement data.
  • Leaders see what happened, not why.
High risk: No ability to influence outcomes; programs run on history and hope.
(2) Fragmented Visibility (Siloed)
  • Engagement data exists but lives in different systems (CRM, LMS, email, portal).
  • Heavy reliance on spreadsheets & manual reconciliation.
  • Anecdotal feedback fills data gaps.
Medium risk: Decisions slow, inconsistent, and based on partial information.
(3) Consolidated Outcome Data (Organized, but Backward-Looking)
  • Dashboards consolidate sales, payouts, segments.
  • You can see performance by region/tier/product.
  • Behavioral data still missing or lightly used.
Risk: Optimization focuses on cost efficiency, not behavior change or real ROI.
(4) Behavioral Insight Layer (Proactive)
  • Leading indicators tracked (training, portal use, rule views, deal-registration timing).
  • Behaviors correlated with outcomes (“certified partners sell 3.5× more”).
  • Early warnings for disengagement or momentum drop.
  • Programs designed around behavior, not just sales.
Opportunity: Leaders can shape performance and intervene early.
(5) Predictive, AI-Supported Optimization (Advanced)
  • Fully centralized data across CRM, LMS, POS, incentives, communications.
  • Machine learning identifies patterns and forecasts outcomes.
  • Program rules and rewards adjust dynamically.
  • Real-time visibility into partner motivation, momentum, and ROI.
Outcome: Incentive spend becomes a strategic growth engine — targeted, efficient, self-optimizing.

Most manufacturing companies are somewhere on this continuum. The key isn’t to jump immediately to Level 5, it’s to understand where your program is today and build the capabilities step by step.

 

Consider Your Next Steps

  1. Start measuring leading indicators today. Define what you want to influence (e.g., training, deal-registration, cross-sell behaviors) and track it.
  2. Audit your current data flows. Where does your channel sales data live? How long to get reports? Where are silos?
  3. Shift your team’s mindset from transactional → behavioral. Ask: What behavior are we trying to change? Not just What volume?
  4. Pick one pilot program to test behavioral insights. Example: 90-day certification challenge + reward + track subsequent sales growth.
  5. Build the habit of frequent feedback loops. Communicate behaviors regularly, highlight progress, sustain momentum.

 

Conclusion: The New Era of Channel Incentives

Channel success now depends on seeing why performance happens, not just tracking what happened. If you continue to run your incentive program purely on payout reports, you’re flying blind. But by focusing on motivation science (behavior and data science) channel leaders can understand the deeply human mechanisms behind partner performance.

Those who master motivation science visibility will spend smarter, pivot faster, and win more partner mindshare in an increasingly competitive manufacturing ecosystem.

Learn more about how One10 can help elevate your channel incentive programs.

Chris Dornfeld

Chris Dornfeld

As One10’s Executive Vice President of Product Strategy, Chris is turning motivation science into measurable business impact through innovative incentive and recognition solutions. He has over two decades of experience building high performing organizations at the intersection of technology, design and the human experience. With a background spanning start-up companies, global corporations, higher education, architecture and as the CIO for the City of St. Louis – Chris has a unique vantage point to understand how technology and culture shape our ever-changing work experience.