What is the Intelligent Pricing System?
This solution is a technology for compiling a model and determining the cost of goods and services. It enables the management of the dynamics and timely changes of price offers due to market events, boosting of sales without increasing costs, and also mitigation of the impact of human errors on the situation.
The operation of the Intelligent Pricing System (IPS) is based on Big Data and neural network technologies. The solution developed by Innodata specialists implements a qualitatively new approach to post-processing of collected data, which makes it possible to apply the built mathematical models with the maximum efficiency, mitigate errors, and enhance the interpretability of results. The IPS creates a baseline model for predicting the dynamics of pricing, while identifying the main factors, both explicit and implicit, that affect the process. Then the business model is built, optimized and monitored, all the parameters involved in its operation are fine-tuned, and the model is enriched with additional information. The entire procedure for generating pricing instructions is performed automatically.
Tasks handled by the Intelligent Pricing System
There are three main tasks that can be fulfilled by the IPS, each of which is considered below in more detail.
An income growth without increasing expenses. This issue can include both revenue maximization and sales growth under the constraint that the costs do not change. The task directly entails the next one.
Enhanced competitiveness of a construction company. This indicator implies that the use of the innovative pricing system enables the organization to react more promptly to significant events on the market and flexibly change the product prices accordingly. The IPS is necessary for forecasting the pricing dynamics with consideration for all affecting factors and, at the same time, minimizing the involvement of humans. Both aspects positively affect the company’s competitiveness.
Qualitative stimulation of demand. As mentioned above, the intelligent pricing system makes it possible to forecast sales and determine the ideal time for price changes. All processes are executed in real time, hence adjustments are made promptly, which facilitates the conclusion of transactions with potential customers. A daily forecast is generated for each transaction; the results are aggregated, and the price is controlled based on the actual demand for the asset.
Intelligent Pricing System principles of operation
The Intelligent Pricing System model is balanced and designed to process about three hundred variables. The solution takes into account seasonality and all internal and external factors of significance, including currency exchange rate fluctuations.
Three data blocks are formed at the initial stage of the System operation. This process, which runs on a daily basis, includes the formation of the statistics block, the forecast block, and the recommendation block. They are considered below in more detail.
Statistics block. The block provides an interactive report that contains sales dynamics parameters, price levels, consumer activity, etc. The report aggregation level is configurable, including the organization’s overall indicators and the parameters of a particular real estate asset.
Forecast block. This block yields the likelihood that a real estate asset will be sold in the near future. This block is updated on a daily basis, and the results that it includes can be drilled down to the lowest level, up to the likelihood that a particular apartment will be sold. Recommendation block. This block provides parameters that show the recommended change in prices for real estate objects, apartment types, etc. Recommendations are generated on the basis of pre-configured strategies, while the strategies themselves can be configured both by the user and automatically by the system, based on the results obtained.
The results are generated by the system using state-of-the-art algorithms for self-learning of mathematical models. The analytical model is developed using several methods. The model takes historical data into account and uses them effectively. As the final step, the model is trained in real time. Ninety percent of the transaction accuracy fall exactly in the period reflected in the model. If the completeness of data is 85% or more, the model correctly predicts the statistics of expected transactions.
According to the authors of the intelligent pricing system, its main advantage is that it boosts revenues by correctly stimulating sales based on the actual demand. It is expected that the product will be especially in demand in the construction business since it provides both an enhancement of competitiveness, stimulation of demand, an increase in profits, and all that with maximum accuracy in determining prices and forecasting transactions. This is possible owing to a flexible approach to data and real-time analysis of large volumes of incoming information. In addition, labor costs are optimized, including analytics department operations all of which were performed manually before. Decision-making used to take a day or more previously, and now the process only requires a few minutes.