Developing Machine Learning Models for Dynamic Pricing
We previously talked about price optimization and dynamic pricing. Today, we are going to look at using machine learning (Ml) in dynamic pricing models.
With artificial intelligence (AI) technology now going mainstream, dynamic pricing is something that even small retailers and e-commerce players can now use to compete in the retail market.
E-commerce activity generates too much data for a team of humans to handle. ML solves this issue because it can process data faster and without stopping. A well-designed ML algorithm even learns and makes pricing suggestions in real-time. This allows retailers to set product prices based on supply and demand, also known as dynamic pricing. To put it plainly, ML is valuable because it automates a task that is almost impossible for humans to do manually.
But before that, the retailer needs to not only know his inventory but also what data is incoming. ML-powered software gets information from data to throw up dynamic pricing solutions.
ML works on a simple philosophy – the larger the data sets, the better the learning process, and the better the outcome. Over time, ML-based software only improves in performance.
Analysts can take other factors into account for dynamic pricing. For example, an analyst could choose weather, demand, operating costs of the company, competition, the minimum price, and the best price, etc.
In the end, for a competitive pricing strategy, ML solutions can repeatedly scrape the web to collect important information about prices set up by competitors for similar products, what consumers’ opinions were about products, as well as the pricing history over the last number of days/weeks.
So, when ML is used, what difference does it make in dynamic pricing? AI and ML permit wider data analysis, which results in better-off resolution functionality.
How to develop a general dynamic pricing model:
The most important considerable aspect is the level of granularity you are aiming for. For example, are you looking at a single customer level or an entire segment?
Another crucial factor is defining strategic goals that align with business goals. Profit maximizing is obvious, right? But you could also choose goals for getting new customers or satisfying old customer satisfaction metrics.
The ML-based dynamic pricing model can then be developed once the answers to the above points come in. The model will predict whether someone will make a purchase at a price best optimized at that moment in time.
The models can be used either using the Generalized Linear Models (GLMs), or the Deep Learning methods.
Benefits of Dynamic Pricing Model
- Aiding product bundling and discount creation
- The clustering algorithm can quickly associate a new product/service with similar products to obtain a probable price segment.
- Predict demands for items that don’t have transaction data
- To anticipate early trends
Businesses that do deploy ML-based models for dynamic pricing tend to increase their margins by 10% and more, and sales also increasing by up to 10%. Adjusting prices in real-time through dynamic pricing does that for your business.
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