explōrātiō reads your price and sales history and returns, by SKU and by category, how much demand shifts when price changes. With ready-made scenarios from −20% to +20% so you can simulate before touching the price list, and executive recommendations on priority SKUs — ready to defend the next promotion, price increase, or margin adjustment decision without relying on gut feel.
The study arrives ready to use — you get the portfolio map (where you can raise price, where you can discount, where any change only destroys revenue) and the recommendation by SKU. For the team that sets prices, for whoever defends the plan in committee, and for SMBs that want to test before applying across the full portfolio.
When the conversation is only aggregate revenue or target margin, without knowing whether the product you are changing is elastic or inelastic in the analyzed interval. The study classifies each item and shows the trade-off between price, volume, and revenue before you touch the price table — so you do not cut margin where you did not need to, or raise price where demand was already at the limit.
Marketing, finance, and operations on the same read about discount, list price, and promotion. You receive an executive report and committee slides — in business language, with the calculation workbook open for the finance team to audit the numbers if they want.
Test the method on one category or up to 10 SKUs before taking the study to the full portfolio. Diagnostic package with a short timeline and one-time delivery — no open-ended data science project, no need to hire a pricing team.
Six analytical blocks in the PDF report, each answering a concrete question — from portfolio distribution to scenarios by category. Below, real delivery screenshots (with reference data) — click to enlarge. In your study, each chart comes with the read translated into an executive recommendation.
Real delivery screenshots · reference data · your study uses only your base
How sensitive your customer is to the price of each product and category — calculated on your own history, not estimated from market benchmarks. You leave with the numerical base to decide where to change price without destroying revenue, where you can raise margin without losing volume, and where demand simply does not tolerate variation in the analyzed interval.
Six pieces in the report. Each one exists to answer a concrete question that will come up in the meeting with finance, the commercial team, or leadership — not to fill slides.
Elasticity histogram by sign, count of SKUs with negative/positive elasticity, and boxplots of |E| by category (log scale), with |E| = 1 threshold. Shows where the portfolio is elastic or inelastic before prioritizing items.
Average E by category and top 15 most and least price-sensitive SKUs. Aligns commercial and category teams on who deserves discount, price increase, or list stability.
Scatter of price change (%) vs quantity (%) by category, elastic/inelastic shape, and average revenue change by department. So finance, commercial, and operations arrive at the meeting with the same numerical base.
Demand curve, revenue vs price, simple elasticity, midpoint and combined, suggested optimal price, and executive recommendation box. Closes the decision on highest-impact items.
Dual-axis chart (quantity and revenue vs price) plus scenario table from −20% to +20% in 5% steps. Simulate list price, promotion, or price increase with auditable numbers.
Revenue and demand aggregated by category at each uniform price change (−20% to +20%). Shows which department gains or loses revenue when price moves up or down in block.
Six steps, from receiving the base to the readout meeting. Everything agreed upfront — methods, intervals, timeline. Nothing is adjusted afterward to "find" a prettier result.
From briefing to presentable report
Revision per package — up to 30 days after formal delivery, in one consolidated round within the signed scope. Requests outside that window are treated as new scope, with a separate proposal. If the base needs treatment before the study, we add the Data Clean timeline and say so plainly.
Portfolio diagnosis, read by category, price scenarios, SKU deep-dive, and number audit in the CFO meeting. Each scenario describes a concrete decision — and how the report arrives to resolve it. Illustrative scenarios, based on typical market cases.
Illustrative scenarios · based on typical market cases
Three study sizes, one-time delivery. Price varies by how many SKUs and categories enter the analysis. Before closing, we look at a sample of your price history in the initial conversation — if your base does not yet allow a reliable calculation, we say so, without signing scope in the dark.
R$ 4.957
R$ 12.847
R$ 19.957
For agencies that want to deliver the study under their brand, and for data teams that want the study in the technical standard they integrate, specific formats by conversation. Quarterly recurrence models (price review cycle) and pilot packages with a discount on the first study — just ask.
An elasticity study is about portfolio scope, and sometimes the scope changes — a new SKU enters, an additional category appears, an extra scenario is requested. Instead of hiding the rule, we leave the table in plain sight: what triggers an add-on, what becomes a new study, and what each case costs on top of the contracted package.
+35%
Change up to 25% of signed scopeEx.: Diagnostic (R$ 4.957) → R$ 1.735.
+50%
Change above 25% of scopeEx.: Diagnostic (R$ 4.957) → R$ 2.479.
25%
Threshold by added SKUsEx.: Diagnostic — 10 SKUs. +2 (20%) → targeted. 3rd extra SKU (≥30%) → broad.
25%
Threshold by added scenarios or cutsEx.: Full Study — 4 scenarios. +1 (25%) targeted; 2nd extra (50%) → broad. Applies to channel/region.
30d
One round after formal deliveryOne round after formal delivery (Full Study and Continuous Pricing). Diagnostic: initial briefing only.
New
Outside add-on — separate quoteAnother category/sub-portfolio, granularity (SKU→EAN), period outside briefing, unplanned cross/promotional elasticity, new quarter.
Criteria A and B measure % of scope changed · higher index prevails · targeted add-on +35% or broad +50% on package · all within signed scope
In general, 6 to 24 months of price and volume by SKU or category — enough to observe real price variation in the period. Shorter series may enter the Diagnostic package if there are clear list changes; the report states when the read is fragile.
Yes, at the aggregate level (SKU, category, national region). Segmentation by store or channel enters when the data exists — mainly in Full Study and Continuous Pricing packages. Without that cut, the study still answers "how much demand responds to price" in the window submitted.
The Diagnostic package exists to test the method on up to 10 SKUs or one category. If the series does not allow a reliable calculation, we say so in the briefing — before closing — and indicate what is missing (more price points, base cleanup, or a smaller cut).
Transfer via secure channel; use only to run your study. Nothing is trained for other clients or reused. After delivery and the revision window, we agree on retention and deletion — documentation aligned with LGPD when legal or DPO needs it.
Spreadsheet or ERP/BI export with date, SKU (or category), price, and quantity sold — CSV or XLSX, separator and decimal agreed in scope. There is no platform upload: you send via secure link or agreed email; we return PDF report (six analytical blocks), CSV workbook, and, in larger packages, slides.
Six blocks, per scope: portfolio distribution and sign; elasticity by category and rankings; comparative price × quantity × revenue; deep-dive on priority SKUs (curves, simple E, midpoint and combined, optimal price, and recommendation); scenarios −20% to +20% with table; and portfolio aggregated by category. All mirrored in the CSV workbook for audit.
No. It is a delivered study with fixed timeline and scope — one-time purchase per package. You receive static PDF and CSV, not dashboard access or login. A new round (another quarter, another category) is contracted separately; the Continuous Pricing package covers a larger portfolio for those who review price frequently.
The technical terms that appear in the report, the CSV, and the readout meeting. It is here because whoever operates data (BI, finance, pricing) will want to open and audit — whoever only receives the recommendation does not need to read it.
Measures how much quantity sold responds when price changes, in the analyzed interval. It is the core of the study: turns price and volume history into diagnosis and scenarios for list price decisions.
Formula: Ep = (ΔQ/Q) / (ΔP/P). Compares two moments using starting price and quantity as reference. Useful when the base is stable; sensitive to the starting point — which is why we cross-check with the arc method.
Uses the midpoint of price and quantity between the two moments. More appropriate when comparing ranges or distinct scenarios in the same interval — usually the main reference when both methods converge.
Arithmetic mean between simple and midpoint elasticity when both are finite. It is the reference for demand curves, optimal price, and scenarios −20% to +20% in the report — consensus between methods, in the spirit of MMM Combined.
By |E| module: |E| < 1 inelastic (volume barely sensitive), |E| = 1 unitary, |E| > 1 elastic (price weighs more). In the report it appears in text, not only as a standalone coefficient.
Simulation of how revenue (price × quantity) responds to price changes within the analyzed interval. It is the bridge between elasticity and the conversation with finance — "does a 5% increase destroy or increase revenue?"
Price that maximizes expected revenue R = P × Q in the analyzed range, using combined elasticity and reference price/quantity from the period. In the PDF it appears on the curves (optimum marker) and in the recommendation box of the SKU deep-dive.
Auditable spreadsheet with assumptions, formulas, and results by SKU or category — for the BI or finance team to reproduce the reasoning without relying on oral memory.
Study scope per package: from one pilot category (up to 10 SKUs) to dozens of SKUs with segmentation by channel or region, when data allows.
Short history, little price variation, high noise, or insufficient granularity. In those cases the report states the caveat — we do not deliver a pretty number without a base.
Elasticity study delivered within signed scope. Ready for the next pricing meeting, for the commercial team to defend discount policy, and for finance to audit the numbers — no "almost ready", no endless revision round, no half delivery.
The initial conversation is to look at a sample of your price and sales history and say plainly: there is an elasticity study here that resolves the question, or it is still too early (low volume, insufficient price variation, series too short). If yes, we outline scope, SKUs, categories, and timeline on the spot. If not, you still leave with the diagnosis.