The Data Challenge in Pricing: Why Relevance Matters More Than Volume

The Data Challenge in Pricing: Why Relevance Matters More Than Volume

Data is the fourth dimension of the pricing operating model, and it is the one most likely to be simultaneously over invested in and underutilized.

Organizations spend heavily on data infrastructure, analytics tools, and reporting platforms. They accumulate vast repositories of transaction data, cost inputs, market intelligence, and customer behavior. And yet, when pricing decisions need to be made, the data that is available is often incomplete, inconsistent, or inaccessible in the format and timeframe required.

This paradox is not about technology. It is about the gap between data availability and data usability. Having data and being able to use data for pricing decisions are fundamentally different things.

The Volume Trap

The instinct in most organizations is to collect more data. The assumption is that better pricing requires more information, and that the organization’s pricing challenges would be solved if only it had access to more competitive intelligence, more customer behavior data, or more market signals.

This assumption is usually wrong. The constraint is rarely volume. It is relevance, consistency, and timeliness.

Relevance means that the data being collected is actually connected to the pricing decisions being made. Transaction level pricing data is useful. Customer profitability data is useful. Competitive price benchmarks are useful. Large datasets that cannot be linked to specific pricing decisions or performance measures are not useful, regardless of their size.

Consistency means that data definitions are shared across the organization. When finance defines margin differently than sales, or when different regions categorize customers into segments using different criteria, the resulting data cannot be compared or aggregated meaningfully. Inconsistent data produces analysis that looks precise but is fundamentally unreliable.

Timeliness means that data is available when decisions are being made, not after. Monthly reports on pricing performance are useful for trend analysis, but they cannot inform a deal that needs to close this week. The closer data availability aligns with decision timing, the more useful it becomes.

Data for Decisions, Not Just Reporting

The most common use of pricing data in many organizations is retrospective reporting. What happened last quarter. How did margin compare to plan. What was the average discount.

This type of reporting has value, but it is not sufficient. Data should also serve a forward looking, decision enabling role.

At the point of strategy and intent, data should help the organization validate assumptions, challenge biases, and calibrate pricing direction. Historical performance by segment, customer behavior patterns, and competitive positioning data all contribute to more informed strategic choices.

At the point of design and modeling, data should support scenario analysis and trade off evaluation. What happens to margin if discount structures are tightened? What is the revenue impact of repricing a specific segment? Models that cannot be populated with actual data are exercises in theory rather than practical tools.

At the point of transaction, data should give the person making the deal the context they need. What is the approved price? What discount authority exists? What are the margin implications of different options? When this data is available in real time at the point of sale, decision quality improves.

At the point of monitoring, data should enable the organization to detect problems early rather than after the damage is done. This requires not just historical data but signal data, indicators that pricing behavior is drifting from intent before the drift becomes material.

Integration as a Data Challenge

One of the most significant data challenges in pricing is integration. Pricing data typically lives across multiple systems: ERP, CRM, CPQ, billing, contract management, and often several generations of spreadsheets that were never fully retired.

When these systems are not integrated, each one holds a partial view of pricing reality. The ERP knows what was invoiced. The CRM knows what was quoted. The CPQ knows what was configured. The contract system knows what was agreed. Assembling a complete picture of pricing performance requires pulling data from all of these sources and reconciling differences in timing, structure, and definition.

This reconciliation effort is enormous, and in many organizations it consumes the majority of analytical bandwidth. Pricing analysts spend their time assembling data rather than analyzing it. By the time a complete picture is available, the decisions it should have informed have already been made.

Solving this integration challenge does not necessarily require replacing systems. It requires establishing clear data flows, common definitions, and a single reference point for key pricing metrics. The goal is not perfect integration. It is sufficient integration to ensure that pricing decisions are informed by consistent, timely, and relevant data.

Data as a Foundation for Improvement

Ultimately, data serves the same role in pricing that it serves in any managed discipline: it enables the organization to learn.

Without structured data, pricing improvement is based on anecdote and intuition. Teams believe they know where leakage occurs, which segments are underpriced, and which exceptions cause the most damage. Sometimes they are right. Often they are wrong. And without data, there is no way to tell the difference.

With structured data, pricing improvement becomes evidence based. The organization can test hypotheses, measure results, and validate whether changes produce the expected outcomes. It can distinguish between structural issues that require systemic fixes and isolated events that do not. It can track whether the pricing capability is improving over time or stagnating.

This evidence based approach does not require perfect data. It requires good enough data, consistently defined, reasonably timely, and connected to the decisions it needs to inform. Organizations that wait for perfect data before taking action end up with neither better data nor better pricing. Organizations that work with what they have and improve it incrementally build a data foundation that compounds in value just as the pricing capability itself does.

This is the seventeenth in a series exploring how organizations can connect pricing intent to execution through disciplined operating models, clear governance, and scalable workflows.

Explore more on pricing, revenue management, and commercial program optimization at the IMA360 Learning Center:

Chris Newton is Vice President of Marketing and Sales at IMA360, where he leads brand strategy, market expansion, and customer engagement. With a background spanning commercial strategy and revenue operations, Chris works closely with enterprise teams navigating the complexities of pricing, programs, and profit optimization. Connect with him on LinkedIn: