Deciding on the optimal price for a product can be a balancing act of profit and customer satisfaction. With more and more customers choosing to shop around for the best deal — such as 59% of customers surveyed by Google — staying ahead of the competitors is even more crucial. That’s where dynamic pricing — among other price optimisation strategies — comes in.
But, before we dive in, let’s distinguish between dynamic pricing and price optimisation. Technically, dynamic pricing is a pricing strategy that adjusts prices in response to changes in market conditions — such as the price difference for a plane ticket in peak travel periods compared to off-peak periods — while price optimisation can describe any given price strategy. To put it another way, we can think of dynamic pricing as a particular type of price optimisation strategy but not every price optimisation strategy will also be dynamic pricing (like how all squares are a special type of rectangle but not all rectangles are squares).
So, just who is getting into dynamic pricing (and what’s it good for)?
Where are we now?
Dynamic pricing has been around since the 1970s — originally as simple, automated price strategies used by airlines, hotels, and cruises to dynamically adjust prices — it wasn’t until the rise of the Internet and e-commerce that dynamic pricing had real impact. Since then, dynamic pricing has increased in complexity — mainly from taking into account various additional factors — including, but not limited to:
- Competitors’ prices
- Local demand
- Base costs
- The weather
- Special events (for example, a spike in demand for accommodation when there’s a huge concert in town
These can all have an impact when deciding on the best price for a product. But taking such a data driven approach generates results, so it is not surprising that more and more businesses are turning to dynamic pricing to get an edge on the competition and to attract customers.
In fact, different dynamic pricing strategies have been adopted across a variety of industries. This is partially due to the versatility of these pricing strategies, which can be applied whether your products have a long life-cycle and stable base — think grocery stores, chemists, and office supply stores — or are subject to seasonality and fashion trends — including hotels and vacation rentals (such as AirBnB), airlines, and mass specialty retailers (such as Best Buy and Steam).
For example, giant retailers such as Amazon, Walmart, and Target have incorporated dynamic pricing into their pricing strategies with great success. Companies that use dynamic pricing (such as our own clients) have seen increased profit margins, while others such — as Bed Bath and Beyond — are looking to use dynamic pricing to boost declining sales and compete with Amazon and other superstores.
In order to understand how these businesses use dynamic pricing successfully, it can be useful to take a look at some examples of dynamic pricing in action.
When it comes to successful implementation of dynamic pricing strategies, Amazon is one of the first names brought up (with good reason). As the largest e-commerce store in the US and Europe, Amazon is able to competitively price products and put pressure on retailers of varying size and popularity. On average, Amazon typically changes its prices every ten minutes — and prices can change between a customer adding a product to their cart and paying for it — which has boosted Amazon’s profits by 25%.
So, how does Amazon do this?
Mainly, by collecting lots and lots of data. From monitoring competitor’s prices and the data gained from its own transactions, Amazon uses its Machine Learning algorithms to predict product sales and adjust the price accordingly. And, even though third party sellers set their own prices, about 10% are also adopting similar strategies in order to get an edge on other sellers and get featured on Amazon’s Buy Box, which serves to give their products higher exposure.
Amazon has even taken dynamic pricing into physical brick-and-mortar stores. One of Amazon’s main ventures — Amazon Books — enables the integration of dynamic pricing by not using price tags. Instead, customers use their phones to scan products or an in-store kiosk to check prices, meaning that Amazon can offer different prices customer-to-customer (those with Amazon Prime tend to get a better price) and day-to-day. Customer information — particularly browsing habits — is also used to inform product selection in-store.
Unlike Amazon, Uber controls the prices that customers pay. When demand is high in an area — whether it’s a public holiday, a rainy day, or during a public transport strike — a multiplier is applied to fares when wait times begin to increase (aka “surge pricing”). This serves the dual purpose of:
- increasing the supply of drivers in the area by increasing the amount that they get paid
- reducing demand, as customers who do not want to pay the higher fee will be more likely to wait for the prices to lower again or take an alternative mode of transport — this helps Uber by not leaving long waiting periods
In order for surge pricing to work, Uber also uses Machine Learning in its dynamic pricing strategy to forecast market conditions using multiple factors. While this requires large amounts of real-time data — on everything from traffic and weather to local events and global news — Uber is able to adjust prices frequently (up to every five minutes when surge prices are in use) as conditions change.
Like hotels before it, AirBnb has also implemented dynamic pricing. But, since AirBnb acts solely as a platform for third party hosts to list rentals, it is up to the host to decide if they will use Airbnb’s pricing strategy ( aka ‘Smart Pricing’).
So, how does this work?
Those readers who’ve been paying attention won’t be surprised to learn that Machine Learning features once again. AirBnB’s Smart Pricing — when enabled — automatically adjusts prices within a threshold that the host can set. As demand for similar properties changes, AirBnb takes several factors into account:
- The type and location of the listing
- Whether customers search for similar listings
- Amenities available and the quality of a listing
- The number of views and reviews
- Season and demand
- Time left to book
- How often a host has decided to host (eg. whether it’s several times a week versus once a month)
When selecting the price manually (i.e. without Smart Pricing), AirBnb also uses dynamic pricing to create price recommendations. And, when hosts follow these recommendations (within 5% of the recommended price), their listings are four times as likely to be booked leading to an average increase in earnings of 13%.
Ethics & Final Thoughts
While there has been some criticism concerning the ethics of dynamic pricing — such as Uber’s price surges and Target customers seeing higher product prices when they use the Target app in-store versus other locations — most companies that use dynamic pricing do so in its looser definition (without price surges). Additionally, it’s important to note that differences in pricing (such as that in the Target app) and other misleading pricing practices are illegal in Australia and businesses such as Uber are required to include additional fees — such as surge prices — in the total advertised price.
Overall, dynamic pricing can be an incredibly useful strategy that has seen increased profits for companies that implement it as part of their price optimisation strategies. And, with the integration of dynamic pricing across industries and into brick-and-mortar stores, it may become an even more crucial determiner of whether a company succeeds or sinks.
Who are we?
Remi AI is an Artificial Intelligence Platform with offices in Sydney and San Francisco. We have delivered inventory and supply chain projects across FMCG, automotive, industrial and corporate supply and more.
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