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I'm a Data Practitioner with limited experience working in the CPG (Consumer Packaged Goods) domain. Just when I was getting comfortable solving business problems, I hit upon a new one : "I needed to build a Machine Learning model for optimizing Tide detergent pricing."
My first reaction? Panic.
I had heard buzzwords like "dynamic pricing" and "price optimization" thrown around in meetings, but I had no real depth of understanding. How do you even begin to price a product dynamically? What factors matter? How does ML fit into this?
That's when I decided to take a step back and build a strong foundation. This blog is my learning journey - the real-world examples that convinced me this matters, the scenarios that made concepts click, and the hands-on calculations that transformed theory into intuition.
Before diving into concepts, I needed motivation. Why does dynamic pricing matter?
Turns out, it's happening everywhere:
I discovered that in 2024:
Coca-Cola increased prices by 9% using dynamic pricing strategies
PepsiCo raised prices by 5% based on real-time demand and supply chain costs
Unilever (which owns multiple CPG brands like ours) is using AI-powered dynamic pricing to stay competitive
But it gets more interesting:
Amazon changes prices 2.5 million times per day
Uber uses surge pricing that can 3x your fare during peak hours
Airlines have been doing this for 40+ years - same seat, same flight, 10 different prices
In October 2024, US policymakers actually called out Coca-Cola, PepsiCo, and General Mills for "aggressive pricing practices." This isn't just theory - it's a live battlefield with real consequences.
My realization: If I don't understand dynamic pricing, I'm bringing a knife to a gunfight.
Abstract concepts don't stick with me. I need scenarios. So here's the first scenario that made dynamic pricing click:
Imagine owning a coffee shop near an office area. Currently, you charge $3 for coffee all day:
8-10 AM: Long queues, could sell 200 cups but can only make 150 (demand exceeds supply)
2-4 PM: Moderate traffic, sell 80 cups
6-8 PM: Almost empty, sell only 20 cups
The question: Are you leaving money on the table?
My first thought was: "If I increase morning prices, won't customers just shift to evening when it's cheaper?"
This led to one of the most important concepts I learned:
Elastic vs Inelastic Demand
Inelastic demand: Office workers NEED coffee at 8 AM before work. They can't shift to 7 PM - it doesn't solve their problem. They'll pay more.
Elastic demand: Students or freelancers might say "I'll skip the expensive morning coffee and come at 6 PM for a discount."
The insight: Different customers have different willingness to pay and different flexibility. Dynamic pricing works because of this heterogeneity.
Theory is good, but I needed to work through numbers myself. Here's the scenario I worked on:
I'm pricing Tide 1kg pack across 3 stores with a current fixed price of $10:
Store | Location Type | Weekly Demand | Competitor Price | Customer Income |
|---|---|---|---|---|
A | Premium Mall | 150 units | $12 | High |
B | Suburban Market | 200 units | $9 | Medium |
C | Budget Area | 100 units | $8 | Low |
Current total revenue: $4,500/week
What if I adjusted prices based on customer segments:
Store A: Increase to $11 (premium customers are less price-sensitive)
Store B: Keep at $10 (competitive zone)
Store C: Reduce to $9 (match competitor, attract price-sensitive buyers)
I initially calculated revenue as:
Store A: 150 × $11 = $1,650
Store C: 100 × $9 = $900
Wrong! I forgot that price changes affect demand.
Assumptions:
Store A: 10% demand drop (price-sensitive customers leave)
Store C: 20% demand increase (attracted by lower price)
Store A (Premium Mall):
Demand drop: 150 × 10% = 15 units
New demand: 150 - 15 = 135 units
New revenue: 135 × $11 = $1,485 (vs old $1,500)
Store B (Suburban Market):
No change: 200 × $10 = $2,000
Store C (Budget Area):
Demand increase: 100 × 20% = 20 units
New demand: 100 + 20 = 120 units
New revenue: 120 × $9 = $1,080 (vs old $1,000)
Total new revenue: $4,565 (vs old $4,500)
Net gain: $65/week
$65 out of $4,500 is just 1.44%. Seemed tiny.
But then I learned about scale effect:
For P&G (Tide's parent), 1.44% on $5 billion = $72 million annually
Small percentages × large scale = massive impact
Looking deeper, Store A actually lost revenue. Why?
The rule: When you increase price, the percentage price gain must exceed the percentage demand loss.
Store A: +10% price, -10% demand = slight loss
Store C: -10% price, +20% demand = net gain
In reality, premium customers (Store A) are more inelastic. A 5% demand drop would've been more realistic, making Store A profitable too.
Once I understood the core concept, I learned that dynamic pricing isn't one strategy - it's a toolkit:
Movie tickets: Matinee ($8) vs Evening ($15)
For Tide: Monsoon season (high detergent demand) vs Winter
Fashion retail: End-of-season clearance
For Tide: Discounting old packaging when launching new design
Student discounts, loyalty programs
For Tide: Bulk buyers ($9/kg) vs retail customers ($10/kg)
Uber surge, hotel rates during conferences
For Tide: Premium locations vs budget areas (what we calculated!)
Dynamic pricing isn't a silver bullet. Here are the landmines:
If someone buys Tide at $11 in Store A, then sees their friend bought it for $9 in Store C, they feel cheated. This can lead to:
Brand switching
Negative reviews
Loss of trust
Solution: Transparency. Label it as "Premium location pricing" or "Bulk discount."
I drop Tide to $9
Competitor drops Ariel to $8
I go to $7.50
Competitor goes to $7
Result: Race to the bottom, everyone loses
Solution: Don't always match competitors. Focus on brand value and differentiation.
Managing dynamic prices across:
500 stores
50 products
Daily changes
Online and offline channels
Requires robust IT systems, staff training, and can lead to errors.
Can't discriminate based on race, religion, etc.
Geographic/time-based pricing is usually legal
But consumer protection laws vary by country
If Tide is always on sale, customers think: "Why would I ever pay full price?"
Premium brands (Apple, Nike) rarely discount to protect perceived value.
After this deep dive, here are the mental models I now carry:
Elastic vs Inelastic Demand: Know your customers' flexibility and willingness to pay
Scale Effect: Small percentage improvements = huge dollar impact
Price-Demand Trade-off: Price increase only works if demand doesn't drop proportionally
Context Matters: What works for Amazon (millions of daily changes) differs from CPG (weekly/monthly adjustments)
It's Not Just Math: Psychology, ethics, competition, and operations all matter
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