Numbers drive business decisions, but pricing strategy often relies more on gut feel than data. That’s something Daniela León Cornejo wants to change. After years designing pricing strategies across industries, she’s seen how the right approach can help companies outperform competitors even in tough markets. Her three-part methodology shows how combining traditional pricing wisdom with modern AI can uncover hidden revenue opportunities.
Designing Profitability-Based Pricing
Most companies start with a simple approach: calculate costs, add desired profit, and there’s your price. But Daniela says that’s just the beginning. “The common mistake that usually companies do is that they establish a flat profitability target,” she explains. That one-size-fits-all approach leaves money on the table.
Instead, Daniela advocates for a more nuanced strategy. “You should differentiate your clients by segments and take into consideration the strategy you want to implement,” she says. Different markets might need different approaches – some focused on growth, others on profitability. Customer lifetime value matters too, but Daniela warns against relying on gut feelings. “Any type of information around that about the expectation of future revenues from a client has to be incorporated in the model of profitability based pricing,” she notes. “If that’s only an override because I feel like I should do that… that’s not enough.”
Understanding Customer Willingness to Pay
Looking inward at costs only tells half the story. “Willingness to pay as a methodology allows you to look at the client, understand its characteristics and the relationship with your product or your service,” Daniela explains. The signs are everywhere if you know where to look. Customer behavior – like switching between companies or showing strong brand loyalty – reveals how much they value your offering. Even timing matters. “The moment in which that client is going to purchase your product or service is something very important,” Daniela notes. Urgent needs often mean higher willingness to pay.
But data has limits. “Data can talk a lot… but data can only tell you what it knows,” Daniela cautions. If you’ve only ever charged high prices, data won’t show what happens at lower price points. That’s why she emphasizes experimentation: “Experimentation is key… because willingness to pay models are built on data.”
Leveraging AI for Smarter Pricing
The most sophisticated approach combines traditional methods with modern technology. “AI for pricing approach… that’s the reinforcement learning in particular,” Daniela explains. This isn’t just automation – it’s about learning from market responses in real time. This works especially well in retail, where quick feedback loops provide rich data. “You get this price into the market, you get the results in terms of conversion in terms of volumes profits etc, and then from that response you learn or the machine learns,” she says. The technical side matters too. “Python has provide a complete technological transformation,” Daniela notes. “It has changed processes that took like one week to around 10 minutes.”
When these three approaches work together – profitability based pricing, willingness to pay analysis, and AI-driven learning – companies can spot opportunities they might have missed. As Daniela puts it, these methods help “uncover revenue opportunities that you never know existed and lead you to outperform your competition.”
Success in pricing isn’t just about having the right tools – it’s about using them together effectively. Daniela’s approach shows how combining traditional business wisdom with modern technology creates a powerful edge. Companies can start small, implementing one piece at a time, but the real magic happens when all three methodologies work in harmony. That’s when pricing becomes more than just a number – it becomes a strategic advantage that drives sustainable growth.
To learn more about Daniela León Cornejo and her approach, check out her LinkedIn Profile.
