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
Increased total GM
Increased total revenue
Promotion
Increased promo gross margin
Increased promotional revenue
Seasonal Sales
Increased Seasonal Sales GM
Increased Seasonal Sales revenue
* KPMG (2017) The potential of pricing. 

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