Most industrial mid-market businesses think they need pricing software to get pricing intelligence. The opposite is true. Nine out of ten already have the data — hidden in their ERP, made inaccessible by reporting habits that have never been refreshed.

In a first pricing diagnostic, the most frequently asked question is: "What additional data do you need?" The answer is almost always the same: "Nothing extra. What you already have is enough — if we read it properly."

This article describes the seven datasets that sit in nearly every B2B ERP and that, together, deliver 80% of the pricing intelligence a mid-market business needs. You do not need new software to start here. You need an Excel export and a few hours of analytical time — for the first iteration.

Key takeaways

• The ERP contains seven core datasets that together deliver 80% of pricing intelligence — usually all present, rarely combined.
• The problem is not missing data, but fragmented reporting. Sales reporting, finance reporting, and operations reporting rarely sit alongside each other.
• Four analyses are immediately possible: pocket price waterfall, whale curve, discount drift over time, and realized vs. list price tracking.
• Pricing software is no replacement for data discipline. Anyone installing software without discipline automates chaos. Anyone reading data with discipline often finds that software selection comes much later.
• Always start with what you already have. Truth in Pricetainability™ asks for no new systems — it asks for honest reading of existing systems.

The data you already have today

In a typical industrial mid-market ERP (SAP, Microsoft Dynamics, Odoo, Exact, Navision), seven core datasets sit that together deliver enough intelligence for a first pricing diagnostic:

1. Sales transactions Per line: customer ID, product ID, date, quantity, list price, discount, invoice price. This is your base layer. Available at most mid-market businesses for at least 3 to 5 years.

2. Customer master Customer name, sector, region, segment, contract ID, payment term, any customer classification. This connects transactions to segmentation.

3. Product master Product name, product line, category, cost price (standard COGS), unit. Indispensable for margin analysis at transaction level.

4. Discounts, rebates, and credits (off-invoice) Booked at customer or invoice level, often as separate memos or annual rebate payouts. Crucial for the pocket price waterfall.

5. Payment data Actual payment terms, early-payment discounts, outstanding items. This connects pricing to cash flow.

6. Cost-to-serve indicators Number of order lines, number of credits, number of special deliveries, number of support contacts. Not always directly in ERP, often in CRM or service system. Sufficient for allocation analyses.

7. Contract data Term, indexation clause, volume commitments, tier structure. At mid-market businesses often in a separate folder or contract management system.

None of these datasets is exotic. All of them are booked for financial, fiscal, or operational reasons. What is missing is not data — what is missing is integration and interpretation.

Why that data is invisible anyway

Three structural reasons why mid-market businesses do not see their own data as pricing intelligence:

1. Data sits in different reporting streams that never meet. Sales reports at transaction level (revenue per customer per product). Finance reports per period (P&L per quarter). Operations reports per process (order lead time, delivery times). Those three never speak as one whole — while pricing intelligence sits exactly at the intersection.

2. ERP reporting is built for financial reporting, not for commercial analytics. Standard reports from SAP or Dynamics show revenue and COGS. They rarely show customer profitability at pocket level, and almost never show gross-to-net erosion per segment. Anyone who reads only standard reports does not see the pricing portion of reality.

3. The assumption "we need data" overrides "we need analysis." When an organization realizes it has no pricing intelligence, the first reflex is: a software project. That project gathers new data or integrates existing data — typically 6 to 12 months of work. But the fundamental intelligence could often have been won in an Excel workbook within a week.

"In more than half of pricing diagnostics, the client had more pricing intelligence within three weeks than they had generated in five years of operations. Not through magic — through asking questions internally that had never been asked."

Four analyses you can do this week

With the seven datasets and an experienced analyst, you can produce these four core analyses within 1 to 2 weeks:

Analysis 1 — Pocket price waterfall per segment For your top 5 customer segments: from list price to pocket price, with all on- and off-invoice deductions. Output: one visual bar chart per segment, plus a comparison across them. Immediately reveals which segments have the most leakage.

→ See also: Net Revenue thinking

Analysis 2 — Whale curve at customer level Top 50 customers ranked by net profitability (after cost-to-serve allocation). Output: one line chart, one accompanying table with top-10 profit makers and top-10 loss positions.

→ See also: The whale curve

Analysis 3 — Discount drift over time Realized discount rate per customer per year over 5 years. Output: scatter plot or heatmap showing which customers display upward drift and which are stable.

→ See also: Discount drift

Analysis 4 — Realized vs. list price tracking Per product line: average selling price vs. list price as percentage, quarterly over 8 quarters. Output: line chart per product line. Reveals which product lines are gradually selling out relative to the list.

None of these analyses requires new software. All are achievable with SQL queries on the ERP, exported to Excel or Power BI. What is required: an analyst who recognizes the pattern and asks the right questions.

When is software actually needed?

The previous section may suggest pricing software is unnecessary. It is not. Software becomes valuable the moment your organization:

• Wants to repeat four or more pricing analyses regularly rather than have them executed one-off.
• Needs real-time access to margin data at the moment of quoting — not in retrospect.
• Has installed margin governance and wants to connect data to formal approval flows.
• Has a transaction volume so large that manual analysis is no longer an option (typically above €100 million revenue or more than 100,000 transactions per year).

For mid-market below that threshold, software is usually too early. The value comes from discipline, not from tooling. An Excel workbook with well-defined formulas and a quarterly update often delivers more than a software package half the organization ignores.

Yggra Sentinel is intentionally designed to bridge that gap: a light-touch analytics layer that runs on your existing ERP, requires no migration or integration, and delivers the four analyses above as standard views. It works for mid-market — but only for mid-market that has completed the Truth phase. Putting software on an undisciplined organization is burning money.

The sequence: data first, tools later

In the Pricetainability™ framework, this is a core rule: Truth → Policy → Execution → Governance. Tools sit in Execution. Not in Truth.

Anyone who starts at Execution (software) without Truth (reading data) does three things wrong:

1. They buy software based on assumed needs rather than actual needs. The selection is then rarely the right one.
2. They automate existing inefficiencies rather than solving them first. Software amplifies what is already there — for or against you.
3. They lose the political moment. When an organization suddenly starts a long software project, resistance forms. A diagnose-first approach first builds conviction based on its own data, and only then installs tooling.

The sequence principle is therefore non-negotiable: no Sentinel installation without preceding diagnostic. Not because the revenue is unwelcome — because a tool on an undisciplined system only automates leakage.

Bottom line

Anyone who wants to improve pricing does not start with "what software do we need?" but with "what data do we already have, and what does it say?" The answer to the second question is, for most mid-market businesses, surprising: they have far more than they think, and they use far less of it than they realize.

The bridge between data and intelligence is not a project. It is a reading posture. Anyone who reads their ERP data with the right questions — pocket price, whale curve, drift, realized rates — gets a diagnostic within weeks that businesses otherwise reach only after a year-long software project.

Start there. Software comes later, when the discipline is in place. Otherwise, software is expensive theater.