One of Europe’s leading tourist destinations faced a conundrum: young and old alike dream of visiting the destination, but not all make the step from intending to visit to actually buying a ticket.
The reasons for this conversion gap are numerous – not least geographical distance – but the main obstacle remains ticket price.
Strategic Research was selected to optimize ticket pricing, by identifying the optimum price offer that maximizes visits, without lowering margins.
The challenge: How to boost visitor numbers without reducing profitability?
We put in place a conjoint analysis methodology (Choice-based Conjoint) that broke down the product – entry ticket to the destination – into its key characteristics. The aim: to analyze how customers make decisions in this category.
A ticket’s characteristics include its price, but also the length of validity, the sales channel and time of purchase and restrictions on use.
Using consumer choices to set prices
Conjoint analysis represents a reliable solution to this type of problem because the way the consumer makes decisions is deduced from analysis of choices similar to those experienced in real life, rather than simple declarations by consumers. By analyzing this type of data, we can understand how the customer thinks, and predict purchase decisions in any given situation. Similarly, we can predict the demand associated to different possible price levels.
Conjoint analysis represented the best solution for the problem. We recreated the existing offer as options for customers, but also simulated new offer scenarii, in which we adjusted the price of existing tickets or modified the characteristics of certain tickets, testing different restrictions and lengths of validity, for example. Each of these new scenarii generated a level of consumer demand and corresponding revenue for the group. We then compared each of these with the current level of demand and revenue.
This simulation exercise enabled us to determine the optimal offer from the point of view of the market. By integrating costs associated with these new offers into the model, we were able to select the offers that maximized consumer demand at an acceptable cost to the group.
As a result, the group was able to thoroughly review its pricing according to how customers bought tickets. But the benefits of our work extended beyond the scope of the project. The exercise also enabled the marketing and pricing teams to predict more accurately the decision-making process of potential visitors, particularly in terms of variables unrelated to price, such as seasonality and flexibility.