The Problem

A manual pricing experimentation process with limited variables

At Farmu, pricing strategy is a delicate balance between staying competitive and optimizing profits. Before incorporating catalan into their operations, Farmu faced significant challenges with their manual and fragmented pricing approach. Martin, Farmu’s Head of Growth, explains, "our pricing methodology was centered around Key Value Items (KVIs) and long-tail products. We used KVIs to signal affordability and increased prices in long-tail products to optimize profits. However, benchmarking market prices for our extensive SKU range proved to be a manual and inefficient process, requiring the work of a full-time person."

Pablo, Farmu’s Revenue Analyst, adds, "We lacked the necessary inputs for pricing and struggled to execute our KVIs and long-tail strategies due to data scarcity and manual efforts. Our team spent significant time on the process, resulting in inefficiency and limited competitiveness." Pricing optimization is all about data quantity and quality, and Farmu was not confident that they had enough external market data. Further, the data they were able to access was not always 100% reliable.

Manual price adjustments were carried out monthly, and while experimentation was part of their approach, it was ad hoc and manual, primarily through Google Sheets. They relied heavily on A/B testing to experiment with prices. The team changed the price of a product and analyzed the impact on demand and profit in a few days. After a few iterations, if the net impact was positive, they permanently changed the price of the product. This process lacked standard criteria for choosing which products to test and often led to unexpected changes in demand and profit that left Pablo and his team guessing.

Farmu found catalan through one of its revenue leaders who heard about the company and they decided to run a pilot. They knew they wouldn’t be able to scale using these manual pricing strategies. This became particularly important as Farmu was getting ready to raise a round of financing to continue scaling in Latin America.

The Solution

Dynamic pricing iterations to find the optimal price for every product each day with catalan

The urgency for pricing optimization became evident as other revenue-boosting strategies plateaued. In some months, pricing is the critical factor affecting sales results.

Farmu conducted a 60-day pilot for 70 SKUs across two cities in Colombia with catalan. The platform made 1890 dynamic price changes during the pilot, redistributing prices across Farmu’s product portfolio.

Through a dynamic split-test strategy, pricing strategies alternated between Catalan-Priced Days and Farmu-Priced Days, with each managing pricing for a specific number of days. The approach maintained stability and prevented extreme price fluctuations by establishing minimum and maximum price limits for each SKU. The selection of days for each pricing approach was randomized, mitigating any potential bias.

During the pilot, catalan, on average, decreased prices for 20 SKUs and increased them for 50 SKUs. Both strategies resulted in increased revenue and margins: 16 SKUs saw an increase in revenue with price decreases and 37 SKUs had revenue increase with higher prices. All results were measured after the pilot ended.