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Your company sells premium tea online. ₹499 for a 250g box. Decent sales. Decent margins.
Then the Head of Finance walks into the weekly meeting and says: "Our raw material costs went up. We need to raise prices to ₹549."
Everyone nods. The price goes up. Two months later, the Head of Sales is in the same meeting looking panicked: "Orders are down 12%. What happened?"
Everyone looks at you. The analyst.
This is Price Sensitivity Analysis. It is one of the most important things you will ever do as an analyst, and I have never seen a bootcamp teach it. It sits at the exact intersection of data, business strategy, and basic economics.
It is the kind of work that gets analysts invited to leadership meetings.
The Core Idea (Without the Economics Jargon)
When you raise the price of a product, some customers will still buy it. Some won't.
The only question that matters is: Did the extra money you made from the customers who stayed make up for the revenue you lost from the customers who left?
If you raise prices 10% and lose only 2% of your customers, you are making more money. If you raise prices 10% and lose 25% of your customers, you just destroyed your business.
Economists call this "Price Elasticity of Demand." You don't need to memorize that term. You just need to understand the math.
Let's Work Through a Real Example
Before the price increase:
Price: ₹499
Monthly orders: 10,000
Monthly revenue: ₹49,90,000
Cost per unit: ₹300
Monthly profit: (499 - 300) × 10,000 = ₹19,90,000
After the price increase:
Price: ₹549
Monthly orders: 8,800 (Orders dropped 12%)
Monthly revenue: ₹48,31,200
Cost per unit: ₹300 (Unchanged)
Monthly profit: (549 - 300) × 8,800 = ₹21,91,200
Wait. Look at those numbers again. Revenue dropped by ₹1.6 Lakh. But Profit INCREASED by ₹2 Lakh. Why? Because each unit you sell now makes ₹249 in profit instead of ₹199, and the 12% drop in volume wasn't large enough to offset that massive margin gain.
This is why price sensitivity analysis matters. The obvious answer ("Orders are down, the price increase failed!") is wrong. You need to look at both revenue AND profit. And usually, profit is what the CEO actually cares about.
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The Chart Your Manager Actually Wants to See
Not a pie chart. Not a messy bar chart with 15 categories.
They want a simple table forecasting the scenarios:
Price Point | Est. Monthly Orders | Revenue | Profit |
₹449 | 11,500 | ₹51,63,500 | ₹17,13,500 |
₹499 | 10,000 | ₹49,90,000 | ₹19,90,000 |
₹549 | 8,800 | ₹48,31,200 | ₹21,91,200 🏆 |
₹599 | 7,000 | ₹41,93,000 | ₹20,93,000 |
₹649 | 5,200 | ₹33,74,800 | ₹18,14,800 |
Look at the Profit column. It peaks perfectly at ₹549. Go higher, and you lose too many customers. Go lower, and you don't make enough margin per unit.
This table, presented simply, is the kind of analysis that dictates a company's entire pricing strategy. And you built it with SQL and basic arithmetic. No machine learning required.
The Nuances Most Analysts Miss
Not all customers are equally sensitive. Corporate buyers expensing their tea don't care about a ₹50 increase. A college student buying it with pocket money absolutely does. Segment your analysis by customer type.
Time delays. Right after a price increase, you see a sharp drop. Some of that recovers as customers accept the "new normal." Wait 6-8 weeks before drawing final conclusions.
Competitors matter. If you raised prices and your competitor didn't, you aren't just measuring price sensitivity—you are measuring relative value.
Discounts ruin the data. If you raised the sticker price from ₹499 to ₹549 but then aggressively ran a "10% off" coupon, your effective price is ₹494. Your data is garbage. Always analyze the actual paid price, not the listed price.
One thing to do this week
Pick any product you personally buy regularly (a grocery item, a software subscription, Swiggy deliveries). Check if the price has changed in the last year. Ask yourself: Did I buy less of it? Did I switch brands? Did I even notice?
You just did a qualitative price sensitivity analysis on yourself. Now imagine doing it with 100,000 customers and a SQL database. That's the job.
Next week: How to Present Bad News to Your Manager — because finding the insight is only half the battle. Communicating it without getting fired is the other half.
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