Dynamic pricing, a practice started by American Airlines in the 1980s, is now becoming an important tool for e-commerce retailers and other businesses. Widespread in both the air travel and hotel industry, and famously well executed at Amazon, companies are now utilising dynamic pricing to respond to changes in demand and to drive significant increases in revenue. Remi AI’s Dynamic Pricing platform for example typically delivers a 17% average increase in monthly revenue across the SKUs when introduced (This figure obviously varies across industries and products).
In the age of e-commerce, where customers can now easily compare prices between multiple suppliers in a matter of seconds, price is now considered the greatest factor in the purchasing decision. A recent US survey found that 80% of survey participants considered price the single most important factor in a purchase.
“80% of survey participants considered price the single most important factor in a purchase.”
In the last 10 years, Artificial Intelligence and Machine Learning Models have had a large impact on Dynamic Pricing methods. With greater customer segmentation and the ability to analyse hundreds of thousands of SKUs, these methods have become the best performing approaches to the challenges of pricing and dynamic changes in pricing strategies.
Although they are complex models, these Dynamic Pricing machine learning models are grounded in a very simple concept:
Deliver the right price for every customer while increasing revenue for the business.
Even a small retail business with only a few core products will have a wide range of customers with different tastes, values and budgets. For one of our recent clients we ran user bucketing and uncovered over 40 different customer types. Each of these different customer types displayed different preferences and buying behaviours.
With any artificial intelligence that is controlling dynamic prices, its primary responsibility is to uncover the fundamentals of the supply demand curve for each SKU in your product range and then price effectively against that curve to increase revenue. Supply and demand is perhaps one of the fundamental concepts of economics and it is the backbone of a market economy.
Demand refers to how much a product or service is desired by buyers. The quantity demanded is the amount of a product people are willing to buy at a certain price; the relationship between price and quantity demanded is known as the demand relationship.
As many of you will know, traditionally the higher the price of the good, the lower the quantity demanded.The lower the price, the greater the demand for the good.
For the inverse, Supply represents how much the market can offer. Quantity supplied refers to the amount of a certain good producers are willing to supply at a given price. The correlation between price and how much of a good or service is supplied to the market is known as the supply relationship. Price, therefore, is a reflection of supply and demand.
As technologies for Machine Learning (ML) and Artificial Intelligence (AI) become more advanced and the dimensions of available data expand, dynamic pricing is going beyond its traditional inventory management function and enabling companies to understand this demand relationship. Pricing is becoming intelligent and continually adjusting to changing consumer behaviour and demand preferences, while also responding to organisational inventory and marketing requirements as well as other external pricing influences.
Today, Artificial Intelligence approaches such as Remi AI’s dynamic pricing platform is enabling enterprises to marry rich data sets with sophisticated pricing models and apply our artificial intelligence techniques. The product is pricing alternatives across thousands of product stock keeping units (SKUs). These offers are also uniquely tailored to the individual consumer dynamically at the point of engagement, and thus more likely to generate a sale.
The Remi AI approach to dynamic pricing.
Across our clients, the algorithmic approach changes, but there are three fundamental steps in our dynamic pricing pipeline that are important to understand.
Stage 1. Customer Clusters
In the context of customer segmentation, cluster analysis is the use of a mathematical models to discover groups of similar customers based on finding the smallest variations among customers within each group. These homogeneous groups are known as “personas”.
Arguably the greatest advancement that machine learning has brought to dynamic pricing has been the highly accurate customer segmentation. Older approaches treated the customer base a homogeneous whole, whereas the reality is that every company has a wide range of customer personas.
Stage 2. Understanding Demand Relationship for every ‘persona’.