Scaling revenue is currently the primary focus of most growth strategies in the e-commerce sector. Companies frequently highlight impressive gains in traffic, conversion rates, and gross merchandise value (GMV). However, experienced retailers understand that substantial revenue is meaningless if it rests on extremely narrow margins.

Too often, e-commerce brands find their margins quietly evaporating because they are still relying on outdated pricing strategies, namely, simple rule-based pricing or intuitive “gut feelings.”

In a recent episode of the Ecommerce Coffee Break podcast, Felix Hoffmann, Co-Founder and CEO of 7Learnings, sat down to discuss why traditional pricing models are breaking down and how AI-driven predictive pricing is leveling the playing field for brands looking to scale profitably.

Why rule-based pricing fails at modern e-commerce

For years, rule-based pricing was the standard automation tool for mid-market and large e-commerce retailers. A brand would simply program rules like: “If Competitor X lowers their price, undercut them by 2%,” or “Set the price to achieve a flat 30% gross margin.”

While this approach might feel like automation, Felix points out that it introduces massive, unmanaged operational complexity. “The world is becoming more complicated,” Felix explains. “If you’re using pricing rules, you probably have quite a lot of them… you are going crazy with complexity.”

This complexity is amplified in a multi-channel environment. When managing a direct-to-consumer (DTC) store alongside presence on marketplaces like Amazon, eBay, and Zalando, a rigid web of rules quickly contradicts itself, leaving retailers blind to the overall impact on profitability.

Competitor price matching is a race to the bottom

The most dangerous manifestation of rule-based pricing is automatic competitor matching. Many brands fall into the trap of obsessively tracking the market and matching the lowest available price.

Quoting Frank Berstein, podcast host Claus Lauter noted that this approach often means “the blind follows the blind.” Felix agrees, calling it a definitive “race to the bottom” that permanently destroys market value.

“Nobody can afford to match the lowest price in the market forever, not even Amazon or Zalando,” says Felix. “They don’t blindly follow. Big players use predictive pricing to understand: ‘If I am not following the lowest price, what would it mean for that specific product?’ Maybe you don’t even have enough inventory to justify a discount, so keeping the price higher is actually the optimal move.”

Predictive Pricing is the modern alternative to rule-based pricing

If rule-based pricing is failing, what is the alternative? The answer lies in Predictive Pricing, powered by modern AI and virtually unlimited cloud computing power.

Unlike reactive rules, predictive pricing works proactively. It uses advanced machine learning algorithms to simulate scenarios on a massive scale; generating billions of daily predictions. The AI calculates exactly what will happen to demand if a product is matched to a competitor, if it is undercut, or if it is priced 5%, 10%, or 20% higher.

By analyzing thousands of data points; including historical demand, competitor movements, seasonal trends, product attributes, and even weather patterns, the AI identifies the unique price elasticity of every item across different sales channels. This allows brands to find the exact “sweet spot” that maximizes either margin or volume, depending on their current business objectives.

Profitable decisions require transaction-level cost analysis

True profitability requires moving past high-level averages and looking directly at transaction-level costs. To feed a predictive pricing engine effectively, retailers must understand the total cost structure attached to every single sale. These costs include:

  • Varying marketplace commission fees
  • Logistics and fulfillment expenses
  • Return rates (and their associated operational costs)
  • Performance marketing spend per transaction

“Every marketplace charges differently and has a different cost structure,” Felix notes. “The better you understand that, the better you can predict it and make decisions in the right direction.”

With a precise view of transaction costs, predictive models can execute highly strategic trade-offs. For instance, if a brand wants to grow its overall business by 10%, the AI can identify precisely where to invest its margins. Instead of slashing prices across the board, it will determine whether a discount will yield the best return on your DTC site, on Amazon, or on Zalando, protecting bottom-line health while capturing market share.

Data quality is the gatekeeper of successful pricing

Transitioning to an AI-driven pricing model yields staggering results. Rigorous A/B testing has proven that implementing predictive pricing can trigger profit uplifts of over 100 percent for certain product categories, while simultaneously liquidating overstock efficiently.

However, unlocking these results requires a baseline of high-quality data. Felix emphasizes that while most CEOs believe they have their data fully sorted, the true challenge lies in attribution maturity.

“Most people know their outbound costs generally, but do they know them exactly on each transaction?” asks Felix. Connecting performance marketing campaigns directly to the specific conversions and transactions they generated is often the hardest homework an e-commerce brand has to do, but it is essential for the AI to make accurate predictions.

Predictive Pricing is necessary for market survival

A decade ago, dynamic, AI-optimized pricing was a luxury weapon reserved exclusively for corporate titans like Amazon and Zalando. Today, the technology has democratized.

As consumers increasingly rely on automated tools and AI assistants to guide their own buying decisions, retailers must fight tech with tech. Implementing AI-driven pricing is no longer an optional “nice-to-have” innovation for the future; it is an active requirement for market survival.

By moving past rigid rules and gut instincts, predictive pricing models level the playing field. They give mid-market and growing brands the exact same analytical capabilities as the industry giants, ensuring that every product on every channel is always priced perfectly for profit.

Want to see how predictive pricing can transform your e-commerce margins? Book a demo with the 7Learnings team today or connect with Felix Hoffmann on LinkedIn.