"Catalan’s model thrives on a consistent stream of data, and the revenue impact correlates with sales volume. Recognizing this, I made the decision to allow catalan to price some of our best-selling SKUs, and that's when we truly witnessed the transformative potential of catalan at Verbena. I highly recommend that companies consider implementing catalan for at least some of their high-selling products right from the start”
Founder & CEO
Blossoming challenges in the process of pricing flowers
At Verbena, pricing flowers was more of an art than a science. Alejandro Zorrilla, founder & CEO, described their manual pricing approach as a weekly trial-and-error process. They experimented with mark-up prices, closely monitoring weekly sales to gauge the immediate impact on revenue. "Sometimes, revenue remained stable or even increased, but occasionally it plummeted, revealing the elasticity of flower prices," Alejandro recalls. "Although this weekly process was done relatively quickly in spreadsheets, it carried risks for Verbena."
Challenges abounded in the floral business. Flowers don't have a fixed price; they demand dynamic pricing. Seasonal spikes during holidays like Mother's Day and Valentine's Day added complexity. Geographic factors mattered too; in warmer regions, flowers had shorter lifespans, necessitating higher prices. The type of flower and its longevity were also pricing factors. With so many variables, frequent price adjustments were necessary, making the world of flowers intricate. Alejandro explains, “Price changes correlate significantly with conversion rates on our website. This emphasizes the critical role of optimal pricing in driving top-line revenue.”
Alejandro believed that there was room for improvement in their weekly manual pricing process and heard about catalan through a close friend, who introduced him to one of catalan’s co-founders.
Transitioning from a trial-and-error approach to an AI-powered technology
After a few meetings, catalan and Verbena scoped out a 60-day pilot covering 27 SKUs in Queretaro, one of Verbena’s main markets in Mexico. Using a dynamic split-testing method, Verbena switched between catalan-Priced Days and Verbena-Priced Days, each taking charge of pricing for specific periods. This strategy helped maintain stable prices and prevented wild swings by setting minimum and maximum price limits for each SKU. The selection of days for each pricing approach was random, ensuring fairness and avoiding any potential bias.
catalan executed a total of 540 price changes during the pilot. This involved reducing prices for 17 SKUs while increasing prices for 10 SKUs. catalan sought to maximize overall profit for the portfolio of 27 SKUs, increasing and decreasing prices, revenue and profits for different products.