Real-time competitive price tracking saves time
BetterBasket helps Supermercados Econo enable quick, strategic decisions
Leon Zhang
Sep 3, 2024
About Supermercados Econo
Supermercados Econo is the largest supermarket chain in Puerto Rico, with 64 locations across the island. Founded in 1970 as a simple operation to take on international chains, Econo now employs 7000 employees with $1.6bn in annual sales.
Proven Impact with BetterBasket
7%
increase in
revenue
revenue
Econo SKUs priced with
BetterBasket’s solution
achieved 7% higher
revenue while keeping
margins steady compared
to SKUs that did not use BetterBasket.
“ Before BetterBasket, it took a lot of time and human research to track 200 items. Now, we track over 3000 items weekly and it's a much better way to analyze pricing.
– Juancarlos Rosario, Insights Director
– Juancarlos Rosario, Insights Director
“ The platform has made tracking competitor’s pricing strategies much simpler, all in one click and in one place. Now we can anticipate, react, and adjust our strategies faster.
– Alfonso Wong, Head Buyer
– Alfonso Wong, Head Buyer
Challenge
Supermercados Econo was stuck between a rock and a hard place: raise prices and risk losing their customer base, or take a margin hit? They competed with an international giant with every day low pricing (EDLP) and frequent discounts. Econo did not have the time or budget to react to each price change.
Category managers had a list of 3000 competitive SKUs that they had to monitor manually across competition - key value items like chicken breasts, eggs, fruit punch, and more that are often on promotional flyers and purchased frequently. Every day, they had to decide the price of each SKU as market prices fluctuated and competitors deployed different promotional strategies.
Category managers had a list of 3000 competitive SKUs that they had to monitor manually across competition - key value items like chicken breasts, eggs, fruit punch, and more that are often on promotional flyers and purchased frequently. Every day, they had to decide the price of each SKU as market prices fluctuated and competitors deployed different promotional strategies.
Solution
Using BetterBasket, category managers were alerted not only on how competition was changing item prices in real-time, but also how to react in a prompt but intelligent way. For example, for prominent items like rice that featured heavily on promotional material, category managers were automatically prompted that they needed to react quickly to market pricing to fix price images.
Other times, when the database showed a steep price drop in a less visible item like fruit punch that suggested a vendor-funded promo, Econo knew to first negotiate with its vendor for a matching discount.
Other times, when the database showed a steep price drop in a less visible item like fruit punch that suggested a vendor-funded promo, Econo knew to first negotiate with its vendor for a matching discount.
Results
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Having a flexible system that learns from pricing changes was key as Econo observed customer reactions.
After fine-tuning our pricing experiment, we found that items representing just 30% of revenue drove 70% of incremental sales.
Some items, like frozen chicken breasts, spiked in revenue after price decreases and brought in continued shopping trips, prompting us to continue price matching the item to sustain this success. Meanwhile, tostones (fried plantains) proved to be less elastic, with customers not responding to discounts. Accordingly, we stopped price matching this item, saving Econo valuable "promo budget."
Critically, our estimation of the elasticity of these items involved not only analyzing customer reactions to price changes but also controlling for external factors such as seasonality, competitor pricing, and cross-product interactions. While observing changes in demand is a crucial first step, a thorough understanding of price elasticity requires a model that accounts for variables like the time of year, weather conditions, and whether a product is boosting sales of complementary items or depressing the sell-through of substitutes.
After fine-tuning our pricing experiment, we found that items representing just 30% of revenue drove 70% of incremental sales.
Some items, like frozen chicken breasts, spiked in revenue after price decreases and brought in continued shopping trips, prompting us to continue price matching the item to sustain this success. Meanwhile, tostones (fried plantains) proved to be less elastic, with customers not responding to discounts. Accordingly, we stopped price matching this item, saving Econo valuable "promo budget."
Critically, our estimation of the elasticity of these items involved not only analyzing customer reactions to price changes but also controlling for external factors such as seasonality, competitor pricing, and cross-product interactions. While observing changes in demand is a crucial first step, a thorough understanding of price elasticity requires a model that accounts for variables like the time of year, weather conditions, and whether a product is boosting sales of complementary items or depressing the sell-through of substitutes.
Other benefits included:
Time Savings
Saving 20 hours per week per store
Assortment Gaps
Find missing trending items
Historical Trends
Track price history over time
Thanks to Madeline Xavier for reading a draft of this.