The Retailer Who Must Not Be Named Case Study

[Case study] The Retailer Who Must Not Be Named Case Study

There are only a few large competitors this retailer has on Lithuanian market. That was a primary reason to restrict any mentions of the brand or industry in this case study: to keep that advantage Competera provides against its rivals.

Yet, the retailer’s management shared their successful-with-Competera story anyway.



We changed the format everybody used to see case studies packed, and interviewed the retailer on how they’re using Competera Platform products.

Here are their answers:


“Tell us your story. When and why did you start using Competera?”

“Our business is 10 years old now, and permanently holds its position among the TOP-5 retailers in the country.

We’ve been working with Competera since 2017, from the first moment we found ourselves keen for qualitative and quickly received data about our competitors’ pricing and comparing this data to our inventory. The goal was to set prices lower than competitors did and then to push them up to establish margins.”


“How did you use the data that Competera delivers? Are there any new usage scenarios you didn’t think of initially, but found later?”

“We use Competera Competitive Data to compare our prices to competitors. After analysis has been done we use custom pricing rules made in Excel to apply new pricing to our inventory and upload data enriched prices to our ERP-system.”


“How has the work changed with Competera involved?”

“Competera was the first tool we used, yet one thing was clear since the very beginning: It helps to compare many prices at once.”


“Did your business performance change with Competera?”

“As we arrange a lot of different sales each day, our managers have noticed, that after starting to use Competera, customers started to buy products that previously were not so popular.

We think it’s because Competera helped to adapt the pricing of those products.“

About Competera Competitive Data

Competera Competitive Data is based on accurate, clean and close to real-time data scraping, processing, configuration, and delivery.

There are three pillars the product is built upon:

Full Stack Coverage helps to collect product data from any brand within major international markets. Data we collect for our clients include pricing, promotion, markdown and stock data, tracked weekly, daily, and hourly.

Errorless Architecture makes data reliable for pricing decisions. The product has self-learning algorithms for data collection, 360-degree view on crawling errors, and 98% SLA.

Flexible Integration is crucial so we create different options for all our clients. They can use our insightful dashboard with custom reports or access through your own platform with API.

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