Setting the optimal price is (and may always be) a challenge. While deciding whether (and by how much) your prices change during spikes in demand, when sales plateau, or during unexpected events might seem trivial, it makes a significant difference in the long run. And, if you’re in the business of setting prices, dynamic pricing and price optimisation are probably terms that you’ve heard being tossed around.
So, what’s the difference and why does it matter?
Price Optimisation Versus Dynamic Pricing
While these terms are used a lot (and often interchangeably), distinguishing between them can lead to more useful conversations when it comes to deciding which pricing strategy you adopt. Generally speaking, price optimisation describes any pricing strategy that considers different factors when suggesting an optimal price or price range for a given scenario. These factors tend to include:
- Competitor prices
- Base costs and inventory levels
- Seasonality and demand
The optimal price will often maximise some measure of customer value - such as value-for-money - without sacrificing a company’s bottom-line, and can be tailored to suit the objectives a company wants to achieve.
On the other hand, dynamic pricing can be thought of as a specific pricing strategy that comes under the umbrella of price optimisation. At its core, dynamic pricing strategies suggest small price changes in a temporal manner (and often in real-time) in response to changes in demand, inventory levels, or other market conditions. Generally, this means that prices will increase when demand increases and will decrease or return to the original price when demand has dropped, with Uber’s famous “surge pricing” strategy as just one example.
Why does this matter?
While it might feel easier to think of dynamic pricing and price optimisation as one and the same, they can differ in the information required to implement them and the situations where they work best.
A dynamic pricing strategy works especially well in:
- Industries where total market supply is restricted
- Industries with short selling horizons
- Perishable capacity
- Stochastic and price-sensitive demand customers choose products based on their willingness to pay
To start with, let’s consider an online retailer. The inventory restricts the number of products that can be sold and website traffic data provides information on how demand changes over time. With this information in hand, you can adjust prices so that they increase when traffic is high or in the leadup to particular holidays or events that trigger an increase in demand, and are reduced when traffic decreases or the event has passed. While this means you may sell fewer items, the higher price means that you still make a profit.
This works by assuming that:
- Not every customer is willing to pay a particular price for a product,
- When there is a large number of visitors to your site, a higher proportion of customers who are willing to pay higher prices than when there are fewer visitors.
This can also be applied to brick-and-mortar stores too. Take restaurants as a similar example. The number of units - in this case, tables - available to sell is restricted by the capacity of the venue, and by the need to sell before the seating, since any profits from empty tables is lost. Using dynamic pricing, restaurants can offer prices that vary depending on the day and time that a customer is looking to dine - such as discounts during the week or a surcharge on bookings made for busier nights.
This variation in price uses price elasticity to achieve two things:
a) demand will decrease on busier nights as customers with a lower willingness to pay choose to book a table on a quieter or ‘off-peak’ night, and
b) customers that are willing to pay the higher price to dine on a busier night, resulting in a spread of revenue across the week.
There are plenty of ways to tailor dynamic pricing to suit brick-and-mortar stores, which you can read more about here.
For industries where products have relatively long life cycles and demand changes relatively slowly, simpler price optimisation techniques may be more suitable, especially in cases where changing prices frequently is more difficult or costly. Instead, you might choose to set an initial price and a discount or promotional price depending on what products or services you offer, which can then be optimised:
- Initial price optimisation - suited for stable products with long life cycles, and should match baseline demand
- Promotional price optimisation - can boost sales of long-life-cycle products, new products, or product bundles
- Discount price optimisation - suited for products with short life cycles and can be used to attract new customers
Since dynamic pricing is a form of price optimisation, it makes sense that they have plenty of common features. Whether the pricing strategy you decide to implement dynamic pricing or another price optimisation strategy, your best choice will enable you to achieve your company’s objectives and set the optimal price for your product.
Want to know more? Head over to our blog for more interesting AI reads (including our monthly collection of recommended reading). Or, check out a case study (or two) detailing how our dynamic pricing software is being used to improve forecasting accuracy and increase revenue.