Editor’s Note: This is the fourth in a five-part series.

Overcoming Skepticism, Building Trust and Identifying Your Analytics Champions

“This sounds like a Theranos scam.” – The moment that almost killed a billion-dollar success story.

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Overcoming Skepticism

Skepticism
During the final stages of the system configuration described in our case study, the pricing leader joined a project review. This person had decades of experience. They were familiar with every pricing tool on the market. Despite the progress, they remained skeptical of what the system was capable of delivering.

Two Conversations, One Turning Point
Two conversations ultimately shaped the trajectory of this project. The first took place when I was at dinner in Belgrade while visiting members of our development team. We had expected this to be a routine update. The second, more intense conversation, unfolded a week later as I walked the streets of Zurich.

In both conversations, two people played central roles:

• The pricing leader, who was skeptical and strongly considering shutting the project down^[1]
• The project manager, who was familiar with all aspects of the project and the beneficiary of the strawman model we built that allowed for transparent validation

During the meeting while I was in Belgrade, the pricing leader raised serious concerns, though their tone remained measured. After I presented our approach and shared the rationale behind our framework, they turned to the project manager and asked a simple, pointed question:

“Will this work?”

The answer came with calm, steady confidence:

“Yes. And if there are issues, I’m confident we can quickly course correct.”

That moment of support kept the project moving forward.

But the tension didn’t go away. A week later, while I was in Zurich, the conversation picked up again, this time with more urgency, more doubt and more pressure. After reviewing our progress and evaluating what the solution was designed to deliver, the team leader paused and said something I’ll never forget:

“This sounds like a Theranos scam.”

Those words hit hard. I’ll never forget the feeling of that moment. I had given everything to this project. Yet at that moment, it seemed as though the client believed I had intentionally misled them. I was devastated.

At the end of this discussion, once again, the pricing leader turned to the project manager and asked the same question they had asked in Belgrade:

“Will this work?”

And once again, the project manager gave the same grounded response:

“Yes. And if there are issues, I’m confident we can quickly course correct.” ^[2]

That quiet moment of trust became the turning point. The project didn’t just survive. It accelerated.

Looking back, I now recognize where the pricing leader’s skepticism came from. It wasn’t personal. It was shaped by years of disappointment with similar projects and broken promises.

What helped rebuild trust was not just the technical architecture, but the clarity of what the system was designed to do. We didn’t just promise automation; we demonstrated a model capable of simulating outcomes under varying cost and tariff scenarios. We showed how financial impact could be forecast across millions of customer-SKU combinations, quickly, repeatably and with traceable logic. For the first time, the pricing strategy wasn’t reactive. It was data-driven and scenario-ready.

There are a few important lessons from that experience.

In many ways, we are all like that pricing leader when faced with something that claims to be revolutionary. Most of us have been burned before. New ideas come at us constantly. Skepticism is understandable. As leaders, the real challenge is not just identifying what or when to trust. The better question is: who do we trust?

In every successful analytics initiative I’ve observed, the difference has been the people behind it. Success is consistently tied to the capabilities of those architecting the effort. The most valuable of those capabilities is sound judgment, the ability to assess and understand, imagine and solve problems.

When a high-judgment individual presents an idea, I pay attention. Even if I’m initially skeptical, I pay more attention when that person frames their idea like this:

• I recommend we do X (the idea)
• Here’s the evidence this approach will work… (a POC)
• The cost will be K. The expected return is R^[3]
• We will measure progress through these metrics…
• The risk is Y, and we will mitigate it through Z
• The next step is A

Even if I do not fully understand the details of X, if the reasoning is sound, the return is meaningful, and I trust the person presenting the idea – I’m willing to place a bet.

Scenario modeling builds trust. A framework that enables ‘what if’ analysis across best case, worst case and expected scenarios, and quantifies margin impact before decisions are made, shifts the conversation from opinion to insight. That’s when skepticism starts to fade.

As leaders, we may not fully understand every detail of an idea. We can compensate for this deficiency by trusting and empowering high-judgment individuals, especially those who bring ideas with structure as noted above.

Identifying and Inspiring the Right People

One theme runs through every successful analytics project:

Technology doesn’t build the system. People do.

Even the best-designed model will fail without the right people behind it. The key is building a team of individuals who are problem solvers, creative thinkers, understand technology’s capabilities, and communicate clearly.

Three Questions

To identify whether you have the right people to build an advanced purpose-built analytics framework as described in this document, start with three essential questions:

1. Tell me about a complex analytics problem you’ve solved.
   Look for someone who can immediately identify an example and walk through the entire process, with intricate details. This is the determining factor as to whether or not they are the right person to work on a strategic analytics project. If they can’t explain the details, they are not the right person for the job.
2. Can you explain how a left join, right join, and inner join work — and when you’d use each?
   This question measures their technical fluency in working with data. Anyone working in the field of analytics must be comfortable with (and get excited about!) connecting and joining data. The qualified analyst should be able to quickly provide 3-4 examples in a way that anyone would understand the value.
3. Why is data attribution important?
   The only acceptable answer is some version of:

“Discrete data attribution allows us to create formulas and are the foundation to build scalable models.”

If your team cannot confidently answer these questions, they are not yet ready to lead your purpose-built tariff analytics solution.

Traits of High Judgment Pricing Team Members

High-judgment team members bring the traits needed to build and scale the type of pricing system described in this paper. These traits include:

• Problem solvers (They’ve proven themselves by building high-value solutions.)
• Domain expertise (They know the business and customers from multiple perspectives)^[4]
• Curious (they are always learning and experimenting with new technologies)
• Obsessed with details (but don’t let details get in the way of progress)
• Comfortable with ambiguity—and skilled at turning it into clarity through logic

As a leader, you must position high judgment team members for success. Here’s the best way to do this:

Trust

Trust is essential to success. Nothing motivates high-judgment individuals more than knowing their leaders support them. One of the best ways to demonstrate trust is to protect them when they take smart risks. I know of one leader (now retired) who did just that. On several occasions, one of his judgment individuals presented an idea that was controversial. Each time this leader said, “Go ahead. I will stand behind you.” The cumulative benefit of these ideas to this company was over hundreds of millions of dollars in company valuation.

In summary, your foundation of success for a purpose-built analytics platform must:

• Position the right people
• Trust and empower those people

Addressing a Self-Imposed Constraint

Throughout this paper, we’ve covered several key constraints to building an effective pricing framework:

• Skepticism
• Lack of data attribution
• The wrong people
• Not trusting the right people

One of the most significant, and often self-imposed, constraints is how organizations manage access to their data.

Here’s the issue: many companies index on data instead of indexing data. That is, many companies over-focus on protecting data rather than enabling its use.

What do we mean?

Indexing on data means prioritizing data protection to the point where every action is guarded, gated, or delayed.[5]

In other words, too many companies overprotect data. The result is slower decisions and stalled innovation. Of course, protecting sensitive information is critical. When taken too far, this mindset can slow or even stop progress. It introduces friction into every request, burdens internal teams and can even drive away the very people capable of unlocking value from the data.

A better approach is to structure and deliver data in ways that accelerate its use and increase decision-making speed (“indexing data”). It’s about making data available in the right format, with the right context, and at the speed to empower trusted teams to act.

The companies that succeed at building scalable pricing frameworks don’t compromise security. But they also don’t treat access as a threat. They establish secure, well-defined data flows that enable modeling, analysis and iteration to occur within days instead of months.

The result is faster decisions, more accurate modeling and stronger retention of high-judgment individuals who thrive when trusted to use data effectively. The principle is simple: When the flow of data is slowed, the pace of innovation is also reduced. If you empower teams with access to data with minimal friction, the results follow — more iterations, lower cost, high quality outcomes.

Contrarian Views and Common Pitfalls

Any strong framework must be able to withstand healthy skepticism. And while this paper outlines what’s possible, it’s equally important to acknowledge what often gets in the way.

1. Waiting Isn’t a Strategy
   Some leaders still believe tariffs are temporary and assume they can wait them out instead of investing in systems to manage them effectively. That may have been true a decade ago, but current trends show tariff volatility is now a structural feature of global trade, not a temporary disruption. According to Gartner, over 60% of supply chain leaders expect ongoing trade policy changes to materially impact pricing strategy in the next five years. Inaction carries a measurable cost both in lost margin and in reputational risk with customers.
2. The Belief that “Our Reps Will Figure It Out”
   Too often, companies assume that sales teams will adjust pricing based on new cost inputs. Our experience shows the primary bottleneck is not customer resistance, but sales team hesitation caused by a lack of clarity and context. Without a system that explains the rationale, magnitude and impact of pricing recommendations, reps hesitate. Or worse, they revert to outdated pricing. Enablement, transparency and confidence are essential to successful high-stakes pricing execution.
3. Why Most Internal Tools Are Not Scalable Pricing Systems Capable of Effectively Managing Volatile Tariffs
   Some organizations default to building pricing tools in-house (usually Excel-based or partially automated within ERP platforms). While these tools may work in limited scenarios, most lack audit trails, rule traceability and the collaborative features needed for scalable execution. When the system becomes opaque or breaks during high-volume events, the credibility of pricing operations suffers. Structured systems must be designed to withstand volume, velocity and variance.
4. Confusing “AI” with “Autopilot”
   As mentioned in the AI section, it’s a mistake to view artificial intelligence as a substitute for good process and human oversight. AI can accelerate outcomes, but only when applied through structured data attribution, clearly defined logic and strong governance. Without structured thinking, AI at best creates noise, and at worst, creates chaos. 
5. Not Aligning Pricing with Sourcing Strategy
   Pricing cannot operate in a vacuum. In organizations where pricing and sourcing teams don’t collaborate, margin erosion is often self-inflicted. For example, pricing may assume tariff costs are rising while sourcing negotiates those same costs downward. When pricing and sourcing strategies are misaligned, customer messaging becomes inconsistent and internal confusion grows. Cross-functional alignment is essential.

The purpose-built framework described in this paper is not a quick fix. It is a highly customized analytics system. It requires the right people, and the right leaders. When designed, with imaginative minds, it will be a force multiplier to help you manage through disruption.

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The Distributors’ Analytics Imperative Series

• Part 1: When Survival Demands an Analytics Revolution
• Part 2: Attribution Breakthrough – Building on Bedrock
• Part 3: The Analytics Prescriptive Engine — From Static Reports to Strategic Recommendations
• Part 4: The Human Algorithm: Why Great Analytics Fail Without Great People
• Part 5: How to Turn Analytics into an Organizational Muscle

 

Footnotes

[1] One of the reasons for this that should be noted is that there was great pressure to perform. Inflation was occurring during this time. We had to balance getting the day-to-day activities done, while working on the new. That was one of the main reasons for the desire to shut our process down, because the same resources that we relied on needed to execute on current business demands. Fortunately, we were able to minimize the involvement of the client’s team by taking on most of the burden of validation and other time-consuming activities.

[2]The project manager was confident because, despite mistakes caused by our hyper-accelerated schedule, we were able to quickly course correct.

[3] I once learned an important lesson from the CEO of a large distributor: “Invest heavily in ideas with high potential returns, even if the chances of success are low.” Jeff Bezos follows a similar mindset. He understands that while many ideas may fail, it only takes one to create a breakthrough — delivering returns not just in double, but triple or even quadruple digits.

[4] Of all the traits listed, this is the least important. This can be learned.

[5] Another way of stating this: Focus on the ability to transact vs. protecting your transaction data.