How The Chefz serves the perfect meal with Amazon Personalize

This is a guest post by Ramzi Alqrainy, Chief Technology Officer, The Chefz.

The Chefz is a Saudi-based online food delivery startup, founded in 2016. At the core of The Chefz’s business model is enabling its customers to order food and sweets from top elite restaurants, bakeries, and chocolate shops. In this post, we explain how The Chefz uses Amazon Personalize filters to apply business rules on recommendations to end-users, increasing revenue by 35%.

Food delivery is a growing industry but at the same time is extremely competitive. The biggest challenge in the industry is maintaining customer loyalty. This requires a comprehensive understanding of the customer’s preferences, the ability to provide excellent response time in terms of on-time delivery, and good food quality. These three factors determine the most important metric for The Chefz’s customer satisfaction. The Chefz’s demands fluctuate, especially with spikes in order volumes at lunch and dinner times. Demand also fluctuates during special days such as Mother’s Day, the football final, Ramadan dusk (Suhoor) and sundown (Iftaar) times, or Eid festive holidays. During these times, the demand can increase by up to 300%, adding one more critical challenge to recommend the perfect meal based on time of the day, especially in Ramadan.

The perfect meal at the right time

To make the ordering process more deterministic and to cater to peak demand times, The Chefz team decided to divide the day into different periods. For example, during Ramadan season, days are divided into Iftar and Suhoor. On regular days, days consist of four periods: breakfast, lunch, dinner, and dessert. The technology that underpins this deterministic ordering process is Amazon Personalize, a powerful recommendation engine. Amazon Personalize takes these grouped periods along with the location of the customer to provide a perfect recommendation.

This ensures the customer receives restaurant and meal recommendations based on their preference and from a nearby location so that it arrives quickly at their doorstep.

This recommendation engine based on Amazon Personalize is the key ingredient in how The Chefz’s customers enjoy personalized restaurant meal recommendations, rather than random recommendations for categories of favorites.

The personalization journey

The Chefz started its personalization journey by offering restaurant recommendations for customers using Amazon Personalize based on previous interactions, user metadata (such as age, nationality, and diet), restaurant metadata like category and food types offered, along with live tracking for customer interactions on the Chefz mobile application and web portal. The initial deployment phases of Amazon Personalize led to a 10% increase in customer interactions with the portal.

Although that was a milestone step, delivery time was still a problem that many customers encountered. One of the main difficulties customers had was delivery time during rush hour. To address this, the data scientist team added location as an additional feature to user metadata so recommendations would take into consideration both user preference and location for improved delivery time.

The next step in the recommendation journey was to consider annual timing, especially Ramadan, and the time of day. These considerations ensured The Chefz could recommend heavy meals or restaurants that provide Iftaar meals during Ramadan sundown, and lighter meals in the late evening. To solve this challenge, the data scientist team used Amazon Personalize filters updated by AWS Lambda functions, which were triggered by an Amazon CloudWatch cron job.

The following architecture shows the automated process for applying the filters:

1. A CloudWatch event uses a cron expression to schedule when a Lambda function is invoked.
2. When the Lambda function is triggered, it attaches the filter to the recommendation engine to apply business rules.
3. Recommended meals and restaurants are delivered to end-users on the application.

Conclusion

Amazon Personalize enabled The Chefz to apply context about individual customers and their circumstances, and deliver customized recommendations based on business rules such as special deals and offers through our mobile application. This increased revenue by 35% per month and doubled customer orders at recommended restaurants.

“The customer is at the heart of everything we do at The Chefz, and we’re working tirelessly to improve and enhance their experience. With Amazon Personalize, we are able to achieve personalization at scale across our entire customer base, which was previously impossible.”

-Ramzi Algrainy, CTO at The Chefz.

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About the authors

Ramzi Alqrainy is Chief Technology Officer at The Chefz. Ramzi is a contributor to Apache Solr and Slack and technical reviewer, and has published many papers in IEEE focusing on search and data functions.

Mohamed Ezzat is a Senior Solutions Architect at AWS with a focus in machine learning. He works with customers to address their business challenges using cloud technologies. Outside of work, he enjoys playing table tennis.