In a previous article, we wrote about the Pricing Activation Framework — the journey from diagnostics to AI-powered pricing guidance and measurable business impact. The core idea was simple: Diagnostics explain what happened. Pricing intelligence helps identify what can happen next. Strategy and execution help achieve real business targets. From Pricing Diagnostics to Price Uplift Most companies already have pricing data, dashboards, reports, and customer information, but man

From Diagnostics to AI-Powered Guidance and True Business Impact Pricing has always been one of the most powerful levers for business performance. A small pricing improvement can have a disproportionate effect on profit, often more than equivalent improvements in volume or cost. Yet in many organizations, pricing remains underused, underanalyzed, or treated as a periodic commercial exercise rather than a strategic capability. The problem is rarely a lack of data. Most compani

A practical guide to turning complexity into competitive advantage Price optimization has always been challenging — but AI has raised both the ceiling and the stakes. Done right, it becomes a powerful growth engine. Done poorly, it amplifies bad decisions at scale. Today, most companies are still stuck in the “NOW” state — fragmented data, unclear signals, limited internal alignment, and reactive pricing decisions. The opportunity is to move toward the “AFTER” state — structu

Beyond Elasticity: Understanding Price Sensitivity in Real-World Systems Many teams believe they’ve solved pricing once they estimate price elasticity. The logic seems straightforward: “If I know how demand reacts to price changes, I can predict volume, simulate scenarios, and find the optimal price.” Clean. Quantitative. Convincing. In reality? It rarely works like that. 🤔 Let’s unpack why. Elasticity Is Not Stable — It’s a Moving Target Price elasticity is often treated as

A Simple Framework to Understand Revenue Changes - the key role of AI In a recent post , we introduced a simple framework to analyze how price, cost, and volume impact profit. In this post, we apply the same thinking to revenue — because before margins, costs, or efficiency, everything starts with how revenue moves. Revenue changes over time — but too often, we don’t fully understand why. 🤔 Was it: higher prices? more volume? or a different mix of products, channels, or cust

A Simple Framework to Find Out Profit changes year-over-year — but too often, we don’t fully understand why. 🤔 Was it a shift in costs? A change in volume? Or was it pricing — often considered the most powerful profit lever? Without a structured way to break this down, businesses risk reacting to symptoms rather than addressing the real cause. A Simple Way to Decompose Profit Impact To truly understand what’s driving profit movements, you can break the change into three impa

Despite its direct impact on profitability, pricing remains one of the most underdeveloped capabilities in modern organizations. The core issue is simple: pricing is still not treated as the strategic priority it should be. But why does this happen? Below are some of the most common root causes. Not all apply everywhere, but you’ll likely recognize at least a few behind most pricing failures or weak pricing management. Pricing Teams Are Too Small to Drive Impact Compared with

How to maximize revenue and profit Finding the optimal price for profit and /or revenue is the holy grail for most businesses. But it’s far less straightforward than it seems. Let’s break it down. Revenue is (relatively) simple We know the basics: If a product is inelastic (e > -1), increasing the price increases revenue If it’s elastic (e < -1), increasing the price decreases revenue So, in theory, if you know elasticity, you can estimate the price change that maximizes reve

Most Companies Track Price. Very Few Track Pricing Performance. Most companies know their: List price Discounts Margins But very few companies track pricing performance . Pricing performance answers a different question: How effectively does pricing drive revenue, profit, and market outcomes? To answer that question, companies need a system of pricing metrics , not just a few isolated KPIs. Below is a simple framework for thinking about pricing metrics. Price Level Metrics Th

They collapse quietly, and long before you notice! 😲 Many industries are under serious margin pressure. The reasons are familiar: ⚠️ Intense competition ⚠️ Price pressure ⚠️ Limited customer budgets ⚠️ Rising energy and raw-material costs ⚠️ Macro uncertainty Even highly differentiated markets like AI are feeling it. For example, in a recent post, Gary Bailey highlighted that OpenAI may be operating at margins as low as -70%. So here’s the real question: 👉 How easy is it t

AI is no longer about demos and hype — it’s about who can turn usage into profit! Almost a year ago, I wrote about the coming AI monetization war! Since then, two shifts are impossible to ignore: 1️⃣ AI buyers now care more about business outcomes than the technology 2️⃣ AI vendors face pressure to monetize amid exploding compute costs 👉 OpenAI’s pricing experiments made this visible: The $200 Pro plan looked expensive. Yet even that tier struggles with profitability due to

Data is everywhere. It’s pouring in from every corner of your business - sales figures, customer behaviour, market trends, pricing fluctuations. But here’s the kicker: data alone is useless unless you can turn it into something that drives real decisions. That’s where data-driven insights come in. They’re the secret sauce that transforms raw numbers into strategic gold. Let’s dive into how you can harness the power of data to not just survive but thrive in today’s competitiv

🤓 Everyone loves quoting McKinsey’s rule : 1% price increase → 10% profit increase! 🚀 It’s quoted everywhere. And sounds universal. It isn't. 🤔 👉 There is no 1→10 rule. The real relationship is simple: % profit change = % price change ÷ % profit margin That’s it. No magic. No mystery. When the McKinsey rule actually works: Margins around 10% Typical for manufacturing and industrial firms Now look at tech 👇 SaaS companies: Contribution margin: ~80% 1% price increase → ~

Price cuts rarely help you hit revenue goals — and they almost never protect profit. 😲 In a previous post (Part 1), we showed how the volume needed to keep profit steady is often unrealistic. Here’s Part 2 — and it reveals the next mistake: 👉 Skipping the elasticity check. Watch the video above to see the elasticity needed to hit profit targets for: 🔸 Margins from 5% → 80% 🔸 Discounts from 1% → 20% 🔸 Profit goals from –10% → +10% Here’s what the data makes clear: 1️⃣ N

The latest Gen4I Summit in Athens made one thing clear: AI is reshaping businesses right now! Here are the top insights driving the new AI reality: 1️⃣ AI is Everywhere Use cases are growing fast across operations, customer experience, product, and strategy. 2️⃣ Proof Over Promises Real deployments now lead the way. They deliver clear, measurable wins. 3️⃣ AI Must Impact the P&L The question has shifted from “Can AI do this?” to “Does AI improve revenue, margin, or speed?” 4

Price cuts don’t fail by chance — they fail by math. Here’s the checkpoint almost everyone forgets. 😯 Two checkpoints In a previous post (Part 1), we looked at the first checkpoint: ➡️ Required volume growth: 15%+ is challenging, 25%+ extremely hard But there is also a second checkpoint: ➡️ Price elasticity A simple guide 🟩 Low elasticity (1.0–1.5) Buyers care less about price Stronger differentiation e.g., branded clothing, specialty coffee, toiletries 🟨 Medium elastic

Price cuts are easy to implement and welcomed by customers, but rarely deliver the results we expect. In a previous post , we examined why price cuts typically fail to generate the required revenue growth. Now, let’s extend the same logic to profit — and here’s where things become even more challenging. When it comes to profit, one additional parameter changes the entire story: 👉 Your current margin. How Margin Shapes profit growth when discounting A single formula links m

Most people think yes — reality says otherwise 👇 Many teams believe they need perfect data before using AI in pricing (or anywhere else). More data helps — but waiting for perfection delays results. You can start now and still create impact. Here’s where AI fails due to data — and where it shines. 👇 ❌ Areas where AI is unlikely to work ➡️ Limited accessibility: Data is scattered across systems (ERP, CRM, etc.) with restricted or no access to key information. ➡️ Corrupted d

Most discounts demand scary volume jumps. Read on to see what to check before you enter the discount zone👇 Discounting strains margins and cash flow 💸 And “cut price = sell more” is often weaker than it looks. Why? It’s just math. 🧮 One formula links revenue, price, and volume changes: r = p + q + p*q (r,p,q: % change of revenue, price, quantity) You can use this formula to estimate the real volume uplift needed to hit revenue targets at a given discount. In most cases, yo

Too many companies rush to “plug in” AI — and end up optimizing for the wrong goals. ✔️ Yes, there are great examples — Amazon, Uber, and airlines that master dynamic pricing daily. ❌ But there are painful lessons — from Wendy’s surge-pricing flop to the Oasis concert ticket fiasco in the UK. So… how do you make sure your AI pricing initiative lands on the success side of history? Based on experience, check these boxes before you launch 👇 1️⃣ Clarity Define what success mea