Foodhub Delivers a Personalised Experience Using MoEngage Segmentation | MoEngage

Foodhub Delivers a Personalised Experience Using MoEngage Segmentation

Read Case Study
Watch Video
case-study-banner-image
About Foodhub case-study-brand-logo

Foodhub, an online food ordering company founded in 2017, is the third-largest online food portal in the United Kingdom. They offer a wide selection of restaurants and takeaways for customers to order from. Their mission is to provide food to their customers at no additional cost so they don’t charge for service. The brand recently expanded its services to countries such as Ireland, Australia, New Zealand, Canada, and the United States.

case-study-brand-image
Business Need

Foodhub understands that its customer base varies with diverse food cravings and preferences. They aimed to segment their customer base using data insights. To do this, they needed to understand customers on a deeper level and segment them based on their needs, preferences, and affinity so they could curate a list of relevant local restaurants. This deeper insight-based segmentation will help them connect with their customers using hyper-personalised messaging, resulting in strong brand recall and loyalty. To do this efficiently, they wanted to onboard an insights-led customer engagement platform.

Business Need

Ours is a customer-centric brand, and we believe in building a product for and by them. When we were evaluating MoEngage, we found the same quality in them. The team has been fast and attentive with their onboarding process. They ensured that they provided us with the features our brand and our current engagement strategy needed. MoEngage accepted us not as a client but as a partner, and that’s the most important part of this collaboration.

Nick Bottai
Marketing Director, Foodhub
Products Used
case-study-product-used-image
Custom Segments
Create easy-to-use cohorts based on behavioral, funnel, and RFM analysis
case-study-product-used-image
RFM Segmentation
Create nuanced segments based on recency, frequency, and monetary value of customer transactions