Machine Learning primarily based totally dynamic pricing at scale
Dynamic pricing is a pricing strategy where the price of a product or service changes flexibly in response to current market demands. This has long been the norm in the hospitality and airline industries, but more and more industries are turning to these algorithmic approaches to increase their profits. This is a complex problem that requires that a business be able to anticipate and adjust for all factors affecting the price of a product in a short time frame, making Machine learning and artificial intelligence-based approaches ideal. Furthermore, because there may be multiple products and/or multiple locations where customer buying behavior may change, the optimal approach may involve a large set of models adapted to the product or location.
In this article, we take a look at some of the challenges of dynamic pricing from the perspective of ML deployment.
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Dynamic pricing as a multi-modal problem
To determine the best price, pricing teams develop models to estimate price sensitivity: how changes in price change the rate at which potential customers buy a product or subscribe to a service. In general, higher prices return higher per-unit profit, but lower total sales volume. The goal is to find a price that balances per-unit profit and sales volume to yield optimal revenue.
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Customer price sensitivity can be related to several factors, such as:
customer segment
Product Type
Lack/availability of product or service
competitive landscape
And more.
Sales volume is also driven by how many people view the product; May be affected by:
seasonal demand
environmental factors that help drive or drive demand
Promotion and marketing campaign
and other factors.
In dynamic pricing, the goal is to estimate all the factors that affect supply and demand and to optimize price accordingly – in a relatively short time frame, sometimes even at pre-purchase levels. Estimating each one of those factors can be complex, and to determine a pricing strategy even for a single product, a pricing team may need to develop several models:
Elasticity, or Price Sensitivity Model
supply and demand forecasting model
customer segmentation model
website traffic model
even more.
In a dynamic pricing scenario, all these models, working together, algorithmically determine the prices that consumers will see.
Each type of model can be the responsibility of different team members; Models can have varied retraining schedules. Since one model may depend on other models in the pricing stack, changes to one model can potentially affect others.
For example, a change in the customer segmentation model, even if it is an improvement, can change the prices that a repeat customer sees. If that price is high, there's a risk of alienating or driving that customer away. The impact of the new customer segmentation model must therefore be evaluated in the context of the entire pricing process. This may result in changes to other models as well; For example, the price sensitivity model may have to be retrained using a new customer segmentation model.
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All these changes may also require A/B testing.
To further complicate things, customer behavior and price sensitivity – and therefore pricing strategy – can vary from store to store, not just across products, but across sales territories, or in the case of physical commerce. This means that, depending on the domain, pricing may require models adapted to different levels of granularity: regional, seasonal, local, or even at the product level.
All of this leads to a plethora of models, which work together to determine prices. When a business has to consider dozens of regions, hundreds of stores, and hundreds or even thousands of products, it can quickly create thousands of models. The resulting complexity can overwhelm data scientists and the pricing team, who have to coordinate the evaluation and deployment of these models and monitor their performance in production.
For example, a nationwide real-estate company came to Nearlearn looking for a way to improve its pricing processes. They wanted to optimize their pricing based on region, location, product type and sales channel. This required multiple models per region and channel in the hundreds of thousands. These models need to be refreshed on a weekly basis. The complexity of developing, managing, evaluating, and ultimately implementing these pricing models threatened to overwhelm their relatively small data science team.
Using Nearlearn, the team was able to reduce their prototype model to less than a quarter of the time before, with significantly fewer computation resources. With the ability to quickly refresh and redeploy their pricing models, as well as monitor their performance on an ongoing basis, the data science team no longer has access to their models and overall pricing.
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