Conjoint Analysis
Uncovering consumer preferences with conjoint analysis
Conjoint analysis is a helpful tool when one needs to understand how customers will find specific features attractive in a product or service.
What is conjoint analysis?
Conjoint analysis is a survey-based study to assess how customers value different attributes or features of a given product or service. It has the advantage of allowing researchers control over the experimental variables used to generate the data and creating a more realistic decision model for the population because it forces product or service evaluation as a whole, which is similar to their actual purchasing situation.
It works by comparing a combination or subset of features by placing the product side by side with the competition’s or fictitious versions and asking the customers which one they would buy. The results are then analyzed through statistical methods, which reveal the implicit valuation customers put on certain features that make up the whole product/service.
Analysing Banking Services
For the banking sector, one might be interested in studying the bank’s offer as a whole compared to the competition’s. This would be implemented by doing several surveys to study different hypotheses, such as preference towards:
Online facilities;
Branches closer to home;
Lower waiting time on branches;
More extended branch working schedule;
Faster transactions;
Covering all products and services;
A monthly fee for a package of services;
More options from stock markets for investors;
The result of these studies will help the bank in prioritizing the services that are perceived as most important to customers and where the bank’s offer is currently lacking, thus obtaining the greatest expected return on investment.
Other similar analysis can be made on specific products or services, comparing the different features of the current offer and the competitors or of an idealized improved version before launching it.
Want to learn more?
Miguel Cabrita
Senior Data Scientist, Co-founder
Miguel has helped various companies in banking and finance implement lead scoring and AI solutions.
Having a strong technological background and understanding of business processes and the banking industry helps him detect specific needs and offer the necessary AI solutions for each of them.
