Dynamic pricing can fit into a company’s strategy in a number of distinct ways, not limited to hitting budget goals and enhancing revenue. For example, an attraction might be interested in increasing its attendance, managing visitor congestion or increasing its membership conversion rate, while a retailer needs to increase its turnover rate while maintaining a reasonable margin. At Digonex, we have used dynamic pricing algorithms to address all these concerns and more.
Some business objectives we have been given by our clients include:
In most implementations, there are multiple business goals in play, requiring combination into a single, well-defined economic problem for dynamic pricing to solve. Therefore, it’s easy to see that a generic pricing algorithm cannot serve every client without customization. The objective should be defined carefully, and a pricing algorithm should be designed to pursue the specific objective. It is also critical to define the objective carefully to avoid misalignment of incentives between the dynamic pricing provider and its client.
Let’s use a simple and stylized example to illustrate the importance of defining the objective properly. Consider a production that is to be played on Saturday in a 5000-seat venue with a five-day sale period (i.e., tickets go on-sale on Tuesday). The demand on each day for the show is D(p,t) = 1800 – 30p – 150t + 30t2, where p is ticket price and t is proximity to Saturday. Hence, the number of tickets demanded depends on not only ticket price but also purchase date.
Further, let’s compare two different objectives: revenue maximization (without having to sell out) and sell out at highest possible revenue. The optimal prices for these objectives are illustrated below (Figure 1). Prices are lower, as one would expect, when the objective is to sell out the venue.
Figure 1
With these two sets of prices, obtained by solving for two different objectives, the number of tickets sold and the total revenue generated from the show are illustrated in the following charts. In the revenue maximization case, 4.2K tickets are sold (Figure 2) and about $118K is generated (Figure 3). In the sellout case, 5K tickets are sold and about $114K is generated.
Figure 2
Figure 3
At Digonex, we design and customize our pricing algorithms to the unique needs of our clients. Before we begin our algorithm development, our team has multiple conversations with our clients to fully understand their objectives, so that our solutions for them will be designed properly. Therefore, not only are attractions not served with pricing algorithms that were intended for sports teams, but also two different attractions are treated with the customization needed to serve their unique objectives.