Next Generation Pricing Software. Explained

Discover how Competera helps retailers to solve the most complex pricing use cases in order to maximize their bottom-line metrics.

At the core of Competera lies a synergetic technological approach. It allows us to offer the most appropriate solutions for each client’s individual needs.

Manageability with any

business rules and constraints

Optimal blend of technologies

for every stage of the SKU journey

Industry agnostic approach

able to cover any retailer’s pricing needs

Interpretable price recommendations

Case 1

First price recommendation

The ability to predict the attractiveness of a future price is the cornerstone of this case. To do this, we use an AI algorithm powered with computer vision, tabular data, and natural language processing. It correlates the new product with similar ones and offers the first price based on previous years data, including prices, turnover, elasticity, etc. with regard to the current market situation.

First price recommendation

Data input

  • 2 years historical sales
  • Historical price changes
  • Product cost
  • Product descriptions, features

Approach

  • Similar products classification
  • Processing of factors affecting price
  • Search for the best selling price
Case 2

Market-driven pricing

To meet this case, we combine machine learning with rule-based pricing. An auxiliary ML algorithm using Graph Theory identifies a retailer’s competitors and potential KVIs. Next, the second deep-learning algorithm calculates elasticity coefficients and helps to sustain optimal price positioning. All the repricing processes are done with a rule-based engine that lies at the core of customizable decision trees of any complexity.

Market-driven pricing

Data input

  • Historical sales data (min 2 years)
  • Historical competitive data
  • Any third-party data

Approach

  • True competitors and KVIs identification for optimal price positioning
  • Pricing logic built on any business constraints and variables
  • Tailor-crafted pricing decision trees
Case 3

Demand-based pricing

Competera's demand-driven pricing is powered with bespoke deep-learning algorithms measuring product elasticity and its cross-elasticity to ensure that goals on both the category and portfolio level are achieved. The accuracy of every recommendation is gained through an analysis of a range of external non-pricing factors impacting sales.

Demand-based pricing

Data input

  • Historical sales (min 2 years)
  • Historical promo (min 2 years)
  • Historical price changes
  • Product stock availability
  • Product description

Approach

  • Measuring the response of demand to price changes
  • Processing historical data to evaluate the impact of pricing and non-pricing factors
  • Selecting optimal prices for the whole shelf or product category
  • Aligning goals on category and portfolio levels
Case 4

Promo optimization

At the heart of this technology lies the Neural Network capable of predicting the effect of a given promo scenario. Based on cannibalization effect evaluation and comprehensive scenario testing, Competera helps to plan optimal promos with maximum effectiveness for the retailer.

Promo optimization

Data input

  • Historical sales (min 2 years)
  • Historical promo (min 2 years)
  • Promo calendar
  • Product description

Approach

  • Selection of products that suit promo strategy best
  • Calculation of incremental volume and sales cannibalization
  • Determining the optimal promo depth for chosen products
  • Suggesting optimal promo periods
Case 5

Markdown optimization

Competera’s algorithms analyze retailer’s historical sales data to recommend an optimal discount at an SKU-level so the targeted stock level is reached with a maximum margin rate. Based on set parameters (max. promo depth, markdown’s time frames, expected stock level), the platform’s time-series algorithm forecasts the hit for the stock level and margin.

Data input

  • Historical sales (min 2 years)
  • Historical promo (min 2 years)
  • Promo calendar
  • Product description
  • Product stock availability

Approach

  • Suggesting sequential discount periods
  • Calculating cross elasticities and sales cannibalization effect
  • Differentiated approach instead of blanket discounts
  • Preventing profit margin from drop

Trusted Security Solution

Competera Platform is built in Google Cloud Platform™. This means all data processed in our service is protected by the Google infrastructure security standards

  • Customer data never leaves Google Cloud™ and Google Bigquery™ perimeter. A complete history of the operations on customers data is always available
  • All your data processed in Competera Platform is stored in separate per-customer projects in Google BigQuery™, an analytics data storage. Google BigQuery™ provides the tools for data usage audit and access control
  • Each customer can pick a region for storing data where Google Cloud Platform™ is available
  • Separate per-customer Google Cloud Project used for computing and data-processing purposes

Get empowered with
the next-generation technologies

Enhance your pricing using advanced software provided by Competera

Best Analytics / BI Solutions

E-commerce Germany award

Price Optimization Solutions

Constellation

#1 eCommerce Solutions Software 2019

Crozdesk

Top 3 startups at the AI Summit

London Tech Week

Great User Experience

Finance Online

High Performer 2020

G2 Crowd