Personalisation models make use of various signals and behaviors to build an understanding of user preferences while making sure business criteria are also met. Let us explore what entails such an exploration.

In the previous blog, we discussed key components that make up a personalisation setup with the sole purpose of improving customer experience and helping our business bottomline. Customers are at the very center of any business. At Delivery Hero they are our heroes and no personalisation journey can work without understanding the customers. Let’s dive deeper into this topic and how it shaped our personalisation journey.

This article was featured on Delivery Hero’s Tech Blog.

A Team of Heroes

It is very important to understand and answer questions related to users’ behavior, motivations, preferences and requirements. Exploratory analysis is a key step which powers not just the initial set of tasks but is an on-going activity which helps throughout the journey. This step generates a number of key insights, ideas and helps identify potential challenges which we try to handle in subsequent phases as the system matures.

To understand how this helps, let us first talk about how the exploratory analysis works out. The most widely used and obvious method is to leverage all the data we have about our customers. This can be in the form of transactions, the kind of items they order, their restaurant and price point preferences and more. This form of analysis makes use of implicit signals which we leverage as feedback to understand different customer behaviors. This also helps in developing personas and categorization of usage patterns.

Customers in Singapore and Thailand Love 💖 Indian vendors “ for late-night dinners

The second way of analyzing customer behavior is through explicit feedback. When customers use our apps, they are presented with the opportunity to rate the packaging, delivery, rider, restaurant/vendor and more such characteristics. They can also provide textual feedback in the form of reviews. These explicit signals are even more impactful towards building an understanding of the customer base. Most modern recommendation systems make use of both implicit and explicit feedback to improve overall experience.