Innodata: DPrice Dynamic Pricing System

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• Defining Business Intelligence
• Big Data

2022: Registration of dynamic pricing system DPrice for developers

On May 11, 2022, the Russian developer Innodata announced that it had registered its own dynamic pricing product in the Unified Register of the Ministry of Communications of Russian programs for electronic computers and databases. DPrice is a system for ensuring transparency in setting prices for real estate objects in development. The solution has pre-built templates, flexible settings and castomization capabilities.

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DPrice includes a set of services for automating labor-intensive business processes, such as indexing real estate prices, determining the starting price, analyzing plan execution, and selling rates.

The solution operates on the basis of a mathematical algorithm that predicts the probability of selling real estate. The platform offers the possibility of adjusting the parameters for calculating the increase in prices for them depending on the requirements of the developer and the market situation. The system is easily tailored to the customer's individual needs.

The DPrice platform was created on the basis of technologies of artificial intelligence (Artificial Intelligence), including methods of optimization and machine learning (Machine Learning). The solution is applicable to mitigate risks when the company does not have automated market assessment tools. It also helps to improve decision-making.

"DPrice is a specialized tool for developers, taking into account the specifics and many factors necessary for sales and maintaining positions in the real estate market. We have been implementing projects in the field of demand forecasting and pricing management for a long time and this allowed us to include relevant practices in the product functionality. We already have several ongoing projects, and we see that the demand for DPrice is only growing. Several releases and scaling of the system to other industries are planned for the near future, taking into account the specifics of the business of each of them, "- comments the executive director of Innodata company Alexander Sergienko.

2018

Opportunities. Principle of operation. Tasks to be solved

According to information for February 2018, the intelligent pricing system is designed for an automated accurate forecast and balancing of prices and tariffs.

Principle of operation

The system works as follows: three blocks of information are formed on a daily basis for system users.

The statistics Block provides for an interactive report, including indicators related to sales dynamics, price level, customer activity, etc. Reports of different degrees of aggregation are provided, starting from the total company figures to the level of a specific real estate object.

The forecast block provides for a daily updated probability of selling a real estate in the next month. Forecast results can be aggregated up to the level of the type of apartments and even to the level of risers of a particular section in the project.

The recommendation block includes daily updated values for the value of changes in prices for real estate objects, apartment types, and parking lots. At the same time, the recommendations can be customized by the user, change dynamically depending on the possibilities for changing prices that are created for a particular real estate object based on the analysis of the input components of the system.

Opportunities

• developing a basic model for predicting the dynamics of pricing, identifying the main visible and hidden factors affecting the dynamics of development;
• implementation of business model construction, optimization and monitoring;
• high-precision adjustment of parameters and variables affecting its operation;
• enrichment of the model with additional data.
• assessment of the probability of the transaction;
• calculation of daily forecast for each transaction,
• grouping of results, as well as price control based on actual demand for an object: if the forecast for actual demand exceeds the planned one, that is, the possibility for more frequent value increases.

At the same time, the process of forming recommendations and comments on pricing is automated.

Tasks to be solved

The main business tasks that the intelligent pricing system solves:

• maximizing revenue
• increase sales without increasing costs
• rapid response to developments affecting pricing in a highly competitive market
• forecasting pricing trends
• consideration of the number of factors of influence,
• minimizing the influence of the "human factor."

Announcement of intelligent pricing system

"Innodata" On February 14, 2018, the company introduced an intelligent pricing system. According to the developers, the solution will be in demand among developers in, in particular construction, transport and retail logistics companies and large service organizations with a permanent line of services.

The high probability of error due to the "human factor" in manual forecasting of demand and supply makes the pricing process increasingly complex, implying long-term, time-consuming and expensive research. Many factors need to be taken into account in order to ensure effective sales. The intellectual pricing system from Innodata will help to do this relatively quickly.

Using Big data technologies and neural networks, the company's specialists developed a post-processing approach to the obtained data, which allows you to achieve high efficiency from built mathematical models, reduce error and increase the interpretability of the result.

The system allows you to predict sales and the best period for price changes, reduce human labor costs for the cost formation process by optimizing the business process, and provides real-time support.

The solution model is balanced and provides for about 200 variables, taking into account seasonality factors, both internal and external determining factors are used, such as, for example, fluctuations in currency quotes.

The results obtained by the system are achieved using the self-learning algorithms of the mathematical model (for example, using XGBoost). The analytical model is built on the basis of several developed methods. The model takes historical data into account. To complete the training, the model is trained in real time. 90% of the accuracy of the transaction is due to the period that is reflected in the model. Provided that 85% or more of the data is provided, the model correctly predicts the statistics of expected transactions.

The main effect of using an intelligent pricing system in the company's business architecture is to achieve the main goal - maximizing revenue without increasing costs, "said Maxim Sytnikov, Product Owner solutions, Innodata company. - The business effect of using the system is difficult to overestimate: first of all, this is maximizing revenue without increasing costs, increasing competitiveness, stimulating demand, increasing revenue, accurately adjusting value fluctuations by forecasting a future transaction, checking the appropriateness of recommendations and elasticity of demand in real time, increasing additional profit due to a flexible approach to data. And as a pleasant bonus - optimization of labor costs, for example, the analytical department that supports manual pricing, as well as an increase in the speed of decision-making from day to several minutes.