Price optimization is all about finding the right balance between profit for your business and value for your customer. Intelligent and dynamic price optimization helps in identifying what the customer is willing to pay while ensuring business profitability and competitiveness. Using Artificial Intelligence for price optimization in real time can help retailers put effective pricing automation solutions.
Understanding and adapting to market changes can be difficult in determining the optimal pricing for a product or service. Retailers often face two questions: First, What’s the best price that we can sell a product or service in a given period of time; Second What is the fair price of the product given the state of the market.
The pricing strategies used in the retail world have some peculiarities. Some retailers determine the prices of the product suggest by the manufacturer while others use keystone strategy which basically doubles the determines the price of the product. Some retailers use dynamic pricing strategies which change according to the the number of visitors on a webpage, season, day of the week and these prices tend to have huge variation and often drive customers away because they feel they are at an unadvantage.
Our team will first learn more about your business processes and your reasoning behind current and historical prices. We will also take a look at what data is available and what additional data from external sources can be used for building AI based price optimization system tailored to specific customer needs and for specific products and services.
Our AI system enable retailers to develop intelligent and efficient pricing strategies that work much better than existing rule based pricing strategies. Our algorithms and models can use data from different channels for training and inference. This can include transactional data, product data, reviews, inventory and supply data, geographic data and customer behaviour data. Our models can even be trained when no specific behaviour data is available in case of brick and mortar stores.
Once the ML system is trained on custom data and business needs and once deployed will work with realtime data from different stream to dynamically optimize prices in real time.
We train a combination of different machine learning models such as supervised models, generalized linear models, deep learning models and ensemble models. Reinforcement learning research has shown great results in price optimization problems. Reinforcement learning (RL) allows humans to stay in the loop and give feedback to the system for price strategies. This allows the ML models to learn directly from human feedback.
RL systems are hard to train and are bounds ahead in terms of complexity even when compared to deep learning models. That’s what we are here for. Our team specializes in Reinforcement Learning and have published research papers in this space as well. Here is a research paper that shows application of this technology for price optimization.
We also use clustering algorithms to groups products that fall in similar categories or share similar features. This helps our system jointly predict prices and demand of products that never sold or are highly likely to sell.
Our algorithms take data from different channels within your business and we also use competition data from price comparison websites via APIs for training our models if any such data is available.
We also take into account the different KPIs that are important to the business and use this in the objective functions when training the models.
Using vast amounts of different data available such as transaction data, customer data, product data, supply chain data etc, our AI system can track changes in demand and markets in real time and gives optimal pricing strategies.
The system continuously tracks changing markets, demands and adjusts the pricing for your products and services accordingly. The KPIs used in the objective function help the business achieve their targets for sales and profitability and ultimately deliver exceptional value for the customer.