Esteemed Online Beauty Store Increased Gross Profit By 30%
And Recouped The Cost Of Pricing Automation During The Pilot Test


Online beauty store.

Official distributor of skincare and haircare premium brands: Vichy, La Roche-Posay, Innéov.

15,000 SKUs.

300 brands.

Eastern Europe.

Before the pilot test launch, the retailer used a high-low pricing strategy:

The store set regular, relatively high prices, utilizing a price markup matrix — a set of markup tiers for various segments of goods.

Endless constantly changing promotions helped drive sales and maintain high customer loyalty. The company rarely changed regular prices, as its pricing strategy did not require it and sales grew.

In 2022, the company decided to test a more innovative pricing approach, considering it as a potential point of additional growth. In July 2022, the retailer launched a pilot test of dynamic pricing with Imprice.
Dynamic Pricing As A New Growth Opportunity
Description Of The Pilot

To estimate the potential effect of dynamic pricing implementation.


AI-powered Analytics
KVI pricing, competitive pricing
Competitors' price monitoring automation
AI-driven price optimization

/ AI - artificial intelligence /
A common way of pilot launching for eCommerce companies is taking 2 zones (2 cities, 2 regions)
as "test" and "control" groups.
In the test region, the retailer implements dynamic pricing; in the control region, the company utilizes its "old" pricing approach.

The online beauty store had to adopt an alternate approach for its pilot.
As the retailer charged the same prices for all zones, its team ran the pilot test within one product category, which was in the top-two of the store in terms of revenue. The category had around 2,000 items in its assortment range. Pricing experts divided it into two parts, 1,000 SKUs each.

During the pilot, the Imprice platform was automatically calculating prices of the "pilot half" of the category.
For the "control half" of the category, the retailer continued to use its current high-low pricing strategy.
Pricing experts monitored and compared the performance dynamics of the test and control groups of items to evaluate the effect of dynamic pricing.
The company ran the pilot at the end of July 2022 and passed the following steps.

Step 1. AI-powered Analytics.

Pricing experts ran the Imprice AI-powered analytics algorithms to identify the role of each SKU in the test group. The result was the following segments of goods:
hard KVIs,
soft KVIs,
profit drivers,
complementary goods,
substitutes, and others.

Step 2. Competitors' prices gathering.

For each KVI, Imprice started automatically collecting prices on the three largest online stores offering pharmacy cosmetics, and on the four largest marketplaces in the country.
Competitors' prices were updated several times a week.

Step 3. Dynamic pricing launch.

The platform set prices of KVIs with a competitive rule-based pricing module. The module automatically maintained KVIs' prices at the optimal position compared to competitors. When the price of competitors changed, the module automatically adjusted the online beauty store's price, considering business constraints of the retailer.

Imprice AI-driven algorithms automatically optimized prices of other items at the "portfolio" level. That is, the algorithms calculated sets of prices which ensured the maximum gross profit from sales of the entire test group range, considering how each SKU could affect revenue and sales of other SKUs.

The platform recalculated prices every day, automatically adjusting prices when competitors' prices or internal factors (such as cost prices) changed, and for the demand sensing.

1 - AI-powered Analytics.

2 - Competitors' prices gathering.

3 - Dynamic pricing launch.
Essential details of the third step.

1 - The retailer operated with MRP (minimum retail price) and RRP (recommended retail price) of its suppliers. Imprice automatically uploaded current sets of MRP and RRP, utilizing these values as permitted price range limits in pricing rules; MRP was a price limit from below, and RRP was a price limit from above.
During the pilot, pricing experts tested a hypothesis about the willingness of consumers to buy some items with relatively inelastic demand at a price higher than the RRP. For such items, pricing experts set higher upper limits of price range permitted for price optimization.

2 - In this case, AI-driven price optimization algorithms were learning and sensing the demand under higher complexity conditions.
The reason was the ongoing "noise", caused by varying promotions, like "discount for skincare brand XXX". The store's consumers were accustomed to these promotions, and actively responded to such ad activities. Sometimes the price of a SKU had raised, and then its sales increased sharply. Such an increase in sales was caused by promotions of other items, not by the SKU's price changing. In reality, consumers bought a lot of items with promo markdowns, and also they bought the SKU with raised price as a complementary good.
Thus, during demand sensing, price optimization algorithms had to correctly handle such "price paradoxes".

3 - Since the company rarely changed regular prices, there was no history of price changes for many items.
For such SKUs, the algorithms performed an expanded demand sensing, increasing the number of price experiments.
Key results

+ 70% Sales volume (in pieces) growth

+ 30% Gross profit growth

+ 40% Revenue growth
At the beginning, the pilot was planned to be completed by the end of October. However, the company managed to gain a significant increase in sales and profit performance much faster.
At the end of August, the retailer's experts compared the sales of the "test" and the "control" groups of SKUs. The results were as follows:
Sales volume
(in pieces)
Gross profit
The performance of the "pilot-control" groups was as follows:
As can be seen, the % of the margin in revenue in the pilot group decreased more than in the control group.

However, in the pilot group, revenue, sales volume in pieces and gross profit grew by tens of percent; there was no such increase in the control group.

Could the growth be the result of cannibalization? Is that possible, the sales of the test group "ate" the sales of SKUs from the control group, and the whole category's sales did not grow?

To answer these questions, we should compare the sales of the pilot category to that of the entire assortment range of this online beauty store.
Sales volume in pieces growth in the pilot category is 1.5 times higher than that in the entire assortment range.
The growth in revenue and gross profit in the pilot category is several times higher than that in the other part of the assortment.

Therefore, the entire pilot category had grown in terms of sales and profits, and cannibalization was not the cause of growth in the test group of SKUs.

+ 70% Sales volume (in pieces) growth

+ 30% Gross profit growth

+ 40% Revenue growth
The retailer's team rated the pilot test as successful. During the pilot test, the company recouped the cost of pricing automation.
In September 2022, they started the rollout proceeding to complete the Imprice implementation across their entire product assortment.
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