Why Your Dynamic Pricing Algorithm Might Be Turning Off Customers 🐛

Most businesses today see AI as a smart investment. It promises precision, efficiency, and a better customer experience. With more data than ever before, many believe that using a dynamic pricing algorithm will deliver faster growth and higher margins. But recent evidence tells a different story.

New research from Carnegie Mellon University reveals a concerning pattern: AI personalised pricing and ranking systems can quietly reduce customer value. Even when there’s no obvious price discrimination, prices can rise, and trust can fall. The culprit? Over-reliance on personalised data and ranking algorithms that prioritise revenue over relevance.

This isn’t just a tech issue. It’s a pricing issue. And it’s time we addressed it.

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The Data-Value Paradox

We often assume that more data leads to better decisions. But in pricing, that assumption doesn’t always hold up.

Many businesses feed their AI systems every possible detail about their customers—from browsing habits to past purchases—hoping to improve targeting and conversion. But without clear intent and quality controls, that data can become noise. Worse, it can distort how products are ranked and priced.

The Carnegie Mellon study shows that when a dynamic pricing algorithm relies on personalised price ranking systems, it often reduces exposure to cheaper alternatives. Customers searching online tend to view products one at a time. If high-margin items appear first, that’s what gets bought. The result? Higher prices, fewer choices, and a shift away from value-based decisions.

In these cases, data-driven pricing doesn’t enhance value—it limits it.

How AI-Driven Dynamic Pricing Algorithm Can Undermine Trust

Let’s say a customer visits an online store. A dynamic pricing algorithm recognises their preferences and ranks products based on what it predicts they’ll like most. But that top-ranked product also happens to be one of the priciest. Cheaper alternatives are buried further down. The customer clicks, buys, and leaves—none the wiser.

Now repeat that thousands of times across a platform.

The algorithm appears to be working. Conversions are steady. Average order values go up. But underneath, trust is eroding. Customers begin to feel they’re being steered—not served. Some stop coming back. Others complain about fairness. What started as data-driven pricing ends up as silent churn.

This is how AI-driven dynamic pricing without proper oversight becomes a brand risk. And in Australia, where consumer trust is hard-earned and the ACCC closely monitors digital platform behaviour, it could also become a compliance issue.

When Data and Price Ranking Systems Limit Customer Value

More data should mean smarter decisions. But only if it’s the right data, used correctly.

Businesses often collect and apply more data than necessary, assuming volume equals insight. But when a dynamic pricing algorithm uses unfiltered or poorly structured inputs, it can mislead. It may reinforce existing biases, distort price ranking, and lead to decisions that aren’t aligned with customer value.

In contrast, the study found that unpersonalised rankings—those based on general trends rather than individual profiles—resulted in lower prices and stronger consumer outcomes. These approaches preserved competition and gave customers a broader view of their options.

It’s not the quantity of data that matters; it’s how thoughtfully and ethically you apply it.

How to Build a Customer-Based AI-Driven Dynamic Pricing Algorithm

If your business uses AI for pricing or price ranking, this is the time to take a step back. Ask whether your dynamic pricing algorithm is truly serving your customers—or just your margins.

1. Audit your ranking systems. See what products are consistently shown first. Are they always the most profitable?

2. Check for price sensitivity loss. Track whether customers are less responsive to price differences over time.

3. Run A/B tests with generic rankings. Compare outcomes when everyone sees the same options. What changes?

4. Set fairness guardrails. Prevent low-cost or high-value items from being buried. Encourage visibility balance.

5. Add a human layer to oversight. Don’t let the algorithm run without regular review. Ask tough questions.

6. Be open with your customers. Let them know how recommendations are made. Transparency builds trust.

7. Get ahead of regulation. Start tracking and documenting pricing fairness now. Don’t wait for a regulatory push.

Pricing teams need to think beyond optimisation and start embedding fairness, transparency, and intent into every data-driven pricing decision.

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Rethinking What “Good” Data-Driven Pricing Looks Like

AI has changed how we price, but not what pricing should stand for.

Smart pricing is still about delivering value, earning trust, and building long-term customer relationships. The moment we rely on a dynamic pricing algorithm only to maximise margin, we risk losing the bigger picture.

Customer-based pricing and personalisation can work—but only if designed for both performance and fairness. That means better data standards. It means asking, “Is this helping our customer?” just as often as “Is this growing our revenue?”

Using AI-driven dynamic pricing presents both real opportunities and hidden risks. If you’re unsure where to start or want to check if your current setup serves both your business and your customers, reach out. We’re here to help you make pricing smarter, fairer, and more effective.

For a comprehensive view of maximising growth in your company, Download a complimentary whitepaper on Digital Transformation.

Are you a business in need of help aligning your pricing strategy, people and operations to deliver an immediate impact on profit?

If so, please call (+61) 2 9000 1115.

You can also email us at team@taylorwells.com.au if you have any further questions.

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