SmartPricing.myRetailStrategy

for a more predictable and profitable retail business

Intelligent Retail Pricing Solution to increase sales and gross margins

We work with retail companies to find profitable pricing solutions for standard pricing, promotions, competitive pricing, Private Label (PL). eCommerce and pricing new products. The pricing algorithms and approaches have been developed by international experts in the field of retail. The solution operates on SaaS technology, and uses the latest methods of machine learning and big data processing.

Target benefits

Using Intelligent Retail Pricing Solutions, such as SmartPricing — based on dynamic price elasticity and purchase transaction data, and machine learning — recent research indicates that retailers can achieve the following outcomes:

Sales and Gross Margin

Promotion

Seasonal Sales

Increased total GM

Promotion

Seasonal Sales

2-6%

10-20%

5-10%

Increased total revenue

Increased promotional revenue

Increased Seasonal Sales revenue

1-5%

5-10%

11-20%

Our solution for business-critical retail pricing
challenges

Dynamic retail pricing

Optimal pricing, taking into account elasticity and optimization strategies
KVI pricing
Cross-elasticity
Optimization of product range categories by price segments
Psychological pricing

Advanced features pricing

Pricing based on a three-level retail business model
Competitive pricing
Pricing for product family
New product entry pricing
Private Label (PL) price gap optimization

Retail price promotions

SKUs recommendations for promotion
Machine learning algorithms for various promotion types
Optimization of discount depth and length
Markdown sales strategies for seasonal goods

Dynamic pricing for eCommerce

Standard price calculations based upon search data
Multi-category competitive pricing
Dynamic bidding based on behavioral analyses
Dynamic personalized discount offers

Key retail industry sectors

Grocery retail

Drugstores

Fashion retail

HoReCa

eCommerce

Three-level retail business model

Statistical data calculations of price elasticity for product categories can be performed according to three typical levels of a retail business model: store format, geography and store clusters. You might have supermarkets in different geographic areas — for example, in both Boston and New York City; and stores at different locations within Boston. Nonetheless, it is highly probable that there can be differences in demand and elasticity for a product category — say, dairy products — even within stores of the same format.

Machine learning and empirical rules

Our experts have developed machine learning algorithms that are resistant to statistical data errors and take into account the principles of behavioral economics based on established empirical rules. Thanks to this, you can use SmartPricing in “autopilot” mode, set certain pricing strategies and fine-tune through many parameters. This includes setting constraints such as, for example, the limits of price increases and gross margin profitability both at all category levels and at the level of an individual SKU.

Related services