Structure of the Smart Pricing System
The SPS comprises three blocks: statistics, prediction, and recommendations
Statistics
- Reports of any level of detail or degree of aggregation
- Interactive and demonstrative dashboards showing sales dynamics, customer activity, and price level
Prediction
- Predicting the situation for the next month with any level of detail, for the entire market, for each commodity, object, or service
- Identification of potential hidden patterns in existing data
Recommendations
- Recommendation on price policy and rates are set up and dynamically modified subject to customers’ needs.
- Manual or automatic adjustment of the strategy for indexing prices
Solution Capabilities
The SPS comprises algorithms that self-train based on accumulated data and works in real-time.
- Decreasing labor costs on pricing of goods and services
- Automating the process of price list creation
- Simple and faster calculation of discounts
- Tracking sales dynamics; sales targets analysis
- Analysis of competitors' pricing policies
- Refining the frequency of indexing for automated goods categories or groups
Advantages of SPS Implementation
- Getting additional profit due to dynamic analytics and prediction
- Testing the expediency and elasticity of demand in real time
- Higher marketability due to market analysis with adjustable level of detail
- Lower labor costs and automated routine tasks
- Prompt decision-making due to availability of reliable information about the business
- Efficient marketing campaigns and prediction of future sales
Get a preliminary calculation and estimate for the project
Implemented projects
- Automating the price indexing for real estate property
- Automating the initial price determination
- Analysis of plan fulfillment and pace of sales
- Support for decision-making in real time
- Predicting the sale of each apartment at an optimal price
- Keeping track of historical sales data
- Recognition of all necessary parameters (increase in prices, per square meter price, house section, number of rooms, total apartments sold or apartments sold by set criteria, etc.)
- Decreasing the labor costs for pricing
- 80% of transactions were fulfilled in the period predicted by the system
- Keeping track of frequency, recentness, and amount of adjustment of fares for a direction
- Determining competitors and market shares by directions
- Assessing information on sales and revenues
- Checking whether competitors’ fares according to information from aggregators are available for sale
- Determining the minimal fare and fares of all the groups in the minimal booking class
- Determining the fare level for higher booking classes (maintaining the so-called ‘fare ladder’)
- Rounding the recommended fare level according to the customer’s rules
- Calculating journey time for recommended fare levels
- Up to 3% of additional revenue (revenue is forecasted daily)
- Relevant competitors’ fares, option to obtain data according to set indicators or periods
- Recommendations on fare modifications
Our skills
1
Proprietary methodology of data management implemented in TOP-40 companies
2
Hands-on experience of implementation in finance and transportation industries and B2C companies
3
5 years of implementing Enterprise and Open Source products
4
Implementing turnkey projects, from the need and task identification to solution implementation and subsequent support
5
Certified Data Architect - Big Data specialists in the field of data management (Integration, Quality & Governance)
