In this episode of the Sunny Ray Show, host Sunny Ray talks with Vanya Rivero, founder of Price Lab, about why retail sits on mountains of data and still guesses at pricing. Vanya recounts setting around 3,000 prices at a retailer, spending about four hours twice a week in Excel, and seeing the same structural gap at two different companies. That experience led her to build a pricing optimization platform for online and offline retailers. She explains why software has been slow to reach retail pricing, how COVID-era digital transformation budgets opened the door, and where retailers lose money by treating every product in a category the same way. The conversation covers how to pitch automation to pricing teams without fear, how enterprise clients found Price Lab before the category was widely known, the shift toward vertical specialization in pharmacies, electronics and home goods, and who else must approve a purchase inside a large retailer. Vanya also previews a pricing consultant in a box launching in mid-December and shares her view that pricing teams will become smaller, more strategic and AI-powered within five years.
A former retail pricer who set 3,000 prices in Excel explains why data-rich retailers still guess, and how AI can fix it.
What did one round of price changes actually cost you in hours when you worked inside a retailer?
I was responsible for setting around 3,000 prices, and it took about four hours twice a week. I used inventory, margins and monthly sales, then leaned on intuition. After roughly the first 100 products in Excel, the analysis basically stopped. I saw the same thing at two retailers, so I realized it was structural.
What did you keep hearing from retailers that turned one frustration into a market thesis?
Every retailer had a huge amount of data but no real way to turn it into better pricing decisions. I kept hearing that they have the data but still do pricing in Excel and rely on their pricing managers' experience. Across different retailers and countries, the problem wasn't the people, it was the lack of technology.
Why has software failed to reach retail pricing until now?
COVID was a major catalyst for digital transformation, and many retailers created innovation teams with real budgets. Pricing has always been one of the most important and most overlooked levers, but retailers lacked the resources to tackle it. There is also a network effect: once a large retailer proves the ROI, others make pricing a priority.
What is the most expensive pricing mistake you've seen a retailer make?
It usually comes down to margins and markup. Retailers assume one category means one pricing strategy, but every product plays a different role. Some are hooks that should carry big discounts, while exclusive products can support a bigger markup. Retailers skip that homework, and that's where they lose a lot of opportunity.
How do you sell automation to a team without creating fear?
I position automation as removing the work nobody wants to do, not the people who do it. Pricing teams spend hours cleaning data and updating Excel files. When a pricing manager sees the technology gives better information and frees up hours, the conversation changes. We're not replacing them, we're giving them a superpower.
How would you explain Price Lab to a merchandising director, and what changes on their Monday?
Price Lab is an AI-powered pricing platform that helps teams decide what price and discount to put on each product to maximize sales and profitability. Instead of spending hours in Excel, you start the week with clear recommendations: what to raise, what to discount, by how much, and why. Your team still decides.
What are you launching in the next 90 days?
By mid-December we're launching a new product I'm really excited about, essentially a pricing consultant in a box. It analyzes a retailer's current pricing, evaluates what works, and recommends a new strategy and business rules. It can serve smaller retailers who can't afford a consultant, or complement large consulting firms.
What does a retail pricing team look like five years from now?
I think pricing teams become smaller, more strategic and AI-powered. They will spend less time calculating prices and more time making decisions.
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