46%
Higher Revenue
51%
Higher Units Sold
"How would I know that, for example, if I dropped prices by 20 cents, profits would increase by 50%? It’s very hard to reach such a conclusion with manual experimentation. With catalan you can get this and other insights after only a few weeks."
Saul Holding
Founder
The Problem
Time-consuming experimentation and pricing decisions
Saul Holding, Founder of Etched, described their pricing journey as a two-stage evolution before integrating catalan into their operations. Initially, they benchmarked against competitors to decide prices based on their desired positioning – a more luxurious brand that charged premium prices. In the second stage, they ventured into A/B testing trying new prices they thought would boost sales. A/B tests lasted about two weeks, or until they found a statistically significant result. They initially attempted to do them manually but eventually adopted A/B testing software from the Shopify app store. Still, the process of analyzing results and making final pricing decisions was time-consuming and took time away from other tasks that were critical for the business.
"Etched didn't have a well-defined pricing strategy, and I was not confident at all that we were pricing correctly," reflected Saul. "Were we losing potential sales due to high prices that tried to signal a premium product? Determining prices consumed excessive time – internal discussions, meetings, and A/B tests all contributed to prolonged decision-making."
The Solution
The third stage of Etched’s pricing journey featuring catalan
To address these challenges, Etched turned to catalan, leveraging the platform's dynamic pricing capabilities during a two-month pilot phase for 42 selected SKUs. Their primary objective was to enhance revenue. Employing a dynamic split-testing approach, Etched alternated between catalan-Priced Days and Etched-Priced Days, assigning each the responsibility for pricing during specific periods. This method aimed to uphold price stability, avoiding extreme fluctuations through the establishment of minimum and maximum price boundaries for individual SKUs. The choice of days for each pricing approach was randomized to eliminate potential biases.
Over the course of the pilot, catalan executed approximately 1,260 dynamic price changes. These adjustments, on average, resulted in price decreases across all 42 SKUs, as catalan detected that the products were slightly overpriced. Throughout the pilot, revenue increased for 27 SKUs and decreased for the remaining 15.
