As Bill Waid, GM at FICO Decision Management, “Dynamic pricing, a practice started by American Airlines in the 1980s, has now become a common marketing technique for many companies. From hotels, airlines and entertainment events to perhaps the most well-known e-retailer, Amazon, these companies have been using dynamic pricing to improve profitability relative to rapid changes in supply and demand. However, most dynamic pricing models assume that a firm sells identical products to its customer base overtime. Even the models that do allow for product differentiation, generally assume that the seller offers a manageable number of distinct products.” (source)
Don Fluckinger, Executive Editor at TechTarget, said “ What’s new is AI-assisted dynamic pricing algorithms hard-wired into CRM platforms, giving users the ability to automate real-time pricing according to market demand, local events, customer data, etc.” (source)
This growth in this field is primarily linked to the growth of online commerce. Indeed, in an article published on Kantify.com, we realize that “Online retail fulfills two of the fundamental needs of algorithmic pricing:
1. A huge quantity of data, to allow pricing specialists to create robust statistical models of customer behavior.
2. The ability to change prices quickly and efficiently.
As the prevalence of online retail grows, so too does algorithmic pricing. In light of this, many of the major retailers and marketplaces have acquired companies that specialize in algorithmic pricing, to allow them to gain an edge over their competitors.” (source)
What is Algorithmic pricing
A pricing algorithm is a mathematical formula, where a given price is calculated based on several variables. A simplified example of a pricing algorithm could be this: the price of an apple in an online grocery store could be a function of the price of a banana in the same store (a substitute), the price of an apple in a competing store, and whether there is a lot of demand for apples (by looking at the number of clicks this product has received, relative to other products). The formulas usually attempt to maximize or minimize an outcome: the revenue of the company, the number of unsold products, etc.
How does it work?
Ted Gaubert, Co-Founder & CTO at Noodle Analytics, helps us understand how data is constantly being collected about your customer behavior such as:
(The following words are extracted from this article)
- What type of items did you look at?
- How long did you spend on each web page?
- What items did you purchase?
- What items did you put in your basket??
- Where are you from?
All this data and more gets fed into an AI engine that translates your behavior into a persona and tries to predict things about you, one of them being estimating the ‘maximum price’ you are willing to pay.
However, ‘willingness to pay’ can be used to determine how likely you will purchase an item at the current market price. This likelihood gets incorporated into demand predictions by micro-segment and, ultimately, the price. Consequently, the AI engine can control sales by knowing how much to sell at what price. Moreover, an AI can gather data from other traditional sources. For instance, an AI can learn about local events that are happening in real time on a global basis far more economically than what could ever be achieved by a group of humans. This enables a company leveraging AI to gather asymmetric information which enables better demand predictions and strategic pricing decisions.
Don Fluckinger reminds us that “Today, CRM software running on cloud platforms, coupled with AI tools, takes dynamic pricing algorithms and couples it with state-of-the-art sales automation. AI dynamic pricing has become fully integrated in a more complex sales strategy and can use more and more data available.”
Prices can vary daily or even every few minutes.
The case of Uber
Ivan Didur ,CTO & Co-Founder at DataRoot Labs, said “A ride-hailing service like Uber for instance, might decide to charge you more for a trip between one wealthy part of the city and another. Or, you might be expected to pay more for a journey to a poorer neighborhood, where the company’s drivers might be reluctant to go. Uber relies extensively on machine learning to establish a robust and reliable dynamic pricing system. With the help of Machine Learning, Uber is able to create a future-aware forecast of multiple conditions of the market combined with a system that is very sensitive to external elements, such as the global news events, weather, historical data, holidays, local events, time, traffic, etc.”
They can then adjust their pricing strategies accordingly. So if, for example, your online history behaviour indicates that you don’t like spending time in certain parts of the city, they might charge you more when you decide to go there on rare occasions. Similarly, online retailers are starting to use the data they’ve collected to determine how responsive people are to special offers. This situation is a real game-changer for the industry.
Gone are the day where one will need to sit in front of a screen for hours, determining prices based on the info from a market research.
Ability to Adjust to Competition Pricing & Costs
An article published by Crealytics said that “According to a survey by Ask Your Target Market, 79% of consumers stated that they consider themselves to be bargain shoppers. 78% said that they compare prices from multiple sources before making their purchase. Adjusting your prices to stay competitive is crucial in the online environment, where competition research takes the customer only a few seconds/clicks.
This is especially true in feed based advertising where all the products are presented to the shopper side by side. In fact, Google Shopping actually penalizes products that are not competitively priced by giving them a lower position in the ad listing regardless of CPC.
Dynamic pricing has become critical in e-commerce and marketplaces, mostly due to automation. Whereas in a store, employees would have to physically change the pricing on thousands of items, online the price of an item can be dynamically adjusted without much cost to the business.” (source)
AI Dynamic pricing maximises profit, conversion, market share or any other desired business KPIs. Instant pricing recommendations cut the long lead time from manual, bespoke pricing decisions and enable employees to instead focus on customer-facing high value activities. According to Ted Gaubert, “The result of all the complex algorithmic interplay determines the price we are quoted, the advertisements we are shown and the product mix we find when shopping.” (source)
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