The competition in construction industry is very strong today and, as a result, the sales cycle is long so significant efforts should be made to maintain the market value of real estate. In an unstable economic situation, it is very important to forecast demand correctly. Performing this process manually is not the best solution due to a high probability of making an error in pricing. State-of-the-art research is needed, which requires large investments and labor costs. Sales efficiency can only be ensured if a considerable number of factors are taken into account and calculations are error-free.
Due to the high probability of making errors in manual forecasting of supply and demand, the pricing process gets increasingly complex and involves long-term, time-consuming and expensive research. To ensure effective sales, numerous of factors should be taken into account in setting the best price. The innovative Intelligent Pricing System (abbreviated as IPS) offered by Innodata will help do this in quickly and efficiently.
What is the Intelligent Pricing System?
Innodata’s intelligent pricing system is designed for automated accurate forecasting and balancing of prices and tariffs. The solution is widely used by construction developers, retail, transport, and logistics companies and large service businesses with a fixed range of services.
The system is a technology for developing a model and determining the cost of goods and services. It is designed for accurate automated forecasting and balancing of prices and tariffs. It enables the management of the dynamics and timely modification of price offers due to market events, increase in sales without increasing costs, and also mitigation of the impact of human errors on the situation. Innodata specialists used the Big Data technology and neural networks to develop a qualitatively new approach to the post-processing of collected data, which makes it possible to apply the built mathematical models with the highest efficiency, mitigate errors, and enhance the interpretability of the results. This system:
It should be stressed that the process of generating recommendations and comments on pricing occurs automatically.
Operation principles of the Intelligent Pricing System
The Intelligent Pricing System model is balanced and designed to process about three hundred variables. The solution takes into account seasons of the year 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’s operation. This process, which runs on a daily basis, includes the compilation of a statistics block, a forecast block, and a recommendations block. Let us now consider these blocks 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 of the sale of the real estate asset 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 of sales of a particular apartment. Recommendations block. This block provides parameters that show the recommended change in prices for real estate objects, apartment types, etc. Recommendations are generated based on pre-configured strategies, while the strategies themselves can be configured both by a 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 (for example, using XGBoost). The analytical model is developed using several methods. The model takes historical data into account and uses them efficiently. 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.
There are three main tasks that the Intelligent Pricing System can solve. Each of them is considered below in more detail. Growth in income without increasing expenses. This issue can include both maximization of revenue and growth in sales under a constraint that the costs remain unchanged. The task directly entails the next one.
Enhanced competitiveness of the construction company. This indicator implies that the use of the innovative pricing system enables the organization to react more rapidly to significant events on the market and flexibly change product prices accordingly. The IPS is necessary for forecasting the pricing dynamics with consideration for all affecting factors and, at the same time, minimizing humans involvement. 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 period for price changes. All processes are executed in real time, and, consequently, adjustments are made promptly, which stimulates 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 in the asset.
The main business goals of the Intelligent Pricing System are to:
The system makes it possible to forecast sales and the best time period for price changes, reduce human labor efforts for cost determination by optimizing the business process and provides support in real time. A list of the unique solution capacities includes the assessment of transaction probability; generation of a daily forecast for each transaction, grouping of results, and price management based on the actual demand for the object - if the forecast for actual demand exceeds the planned one, there is an opportunity for more frequent increases in price.
The benefits of using Innodata’s Intelligent Pricing System cannot be overestimated: enhancement of competitiveness level, stimulation of demand, an increase in revenues, fine tuning of price fluctuations by predicting future transactions, verification of the advisability of recommendations and elasticity of demand in real time, and increased additional profit due to a flexible approach to data. The main effect provided by the System in the company’s business landscape is that the main goal – maximization of revenue without increasing costs – is achieved. Application of the solution enables an increase in revenues due to the correct stimulation of sales based on real demand. A nice bonus is the optimization of labor costs, including the analytics department’s operations where all operations were previously conducted manually. Decision-making used to take a day or more previously, whereas now the process only requires a few minutes. The combination of all these factors provide a powerful impetus for business development.