Toothpaste
Managers used demand-based price and promo recommendations
The health&beauty retailer Kosmo used Competera's platform to optimize pricing
Kosmo is a leading Eastern European retailer with over 100 brick-and-mortar stores across 41 regions. The health&beauty company has been operating since 1994 and has over a million members in its shopping club. In addition to dozens of global brands, the retailer also sells a range of private labels.
Regular demand-driven recommendations for price and promo decisions
Managers used demand-based price and promo recommendations
Managers used traditional manual pricing methods
The two groups (160 products) have similar seasonality, as well as profit and revenue patterns. What is more, these groups are unlikely to be in the same basket, so changes in the test category could not have a significant impact on the control category, and vice versa.
Before launching the project, the retailer provided the necessary data to Competera, which included but was not limited to historical data, competitive data, and data regarding business goals and restrictions.
Kosmo has managed to maximize revenue, gain more flexibility in the creation of pricing and promo strategies, has softened the effect of significant promo pressure, and has become less dependant on competitors' pricing moves.
Revenue
Profit margin saving
Despite significant promo pressureSales items
Gross profit
FrontRetailers and vendors use promos to stimulate financial performance. However, this leads to cutting prices non-stop. Everything has its limits, though. We are faced with a question of how to satisfy the customer while keeping the prices beneficial for the business. To do so, I believe, we need to shift from price wars to predictive pricing.
The company used this pricing approach for some 2,000 KVI products (out of 10,000 items in the portfolio) across over 100 points of sale and different price zones.
The in-house ERP and Excel-based pricing systems had technical limitations to store and process pricing data.
Pricing managers lacked time to process all the necessary data and consider all the pricing and non-pricing factors to set optimal prices with necessary speed.
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