Personalised features – hungryhouse

Hungryhouse is an UK based online food ordering platform which provides customers a great takeaway experience and a valuable partnership for restaurants.

Year 

Products

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My role

Goals

• Having observed how users interact with the content on logged-in home screen, we want to offer the users more rich and personalised contents to further streamline the ordering experience.
• To increase user engagement and satisfaction, so that users would come back to order more food.

Ideas

• To offer users personalised contents on logged-in home screen, the idea is to have a few ‘swim lanes’ with up to 6 restaurant cards on a horizontal scroll selection.
• To help customers decide what to order and to leverage the social aspect of ordering food, we want to introduce item recommendations on the menu screen. We will show items that were popular/frequently ordered within the last 7 days in a horizontal swipe lane on top of the menu page.

The home screen swim lanes has 7 lanes we arrange them according to our analytics in google and user tests to determine which recommendations are most relevant to users based on their location. From top to bottom, our arrangement as blow:

• Past orders lane  – up to 3 past orders, users can easily reorder what they had in the past.
• Taste test lane (If present) – Taste test is a promotion which restaurants offer 20% off discount.
• User’s most ordered cuisine lane – up to 6 cards with restaurants from the user’s most ordered cuisine
• Most loved lane – Each year hungryhouse would award a number of restaurants for giving hungryhouse customers fantastic all-round takeaway experience of great service and delicious food. This lane shows users a maximum of 6 restaurant cards.
• Top rated lane – up to six cards with restaurants that have highest star ratings
• Lunch / Dinner deals lane – It shows the users a maximum of 6 cards of best lunch or dinner deals based on time and locations
• Recommended lane (only of any of the above lanes is not present) – this is a fallback lane, it shows users top rated restaurants in the area.
• Cuisines lane – show up to 6 top most popular cuisine types in the area

Checkout the Framer file here

Menu recommendations

We show maximum of 8 dishes which are most frequently ordered items in the last 7 days.  (Drinks and dessert are excluded), there is also a social proof feature which shows how many time the item has been ordered.

Checkout the Framer prototype here

menu recommendation in action (Iphone)

In both features, guesstures and interactions play a very important part. Testing with realistic prototypes was essential. I was able to create something with great realistic mobile interaction in Framer. We took these prototypes to the UK to test with real customers to get feedback.

After a series of user tests and iterations. We released these features on Android mobile with a stage roll out.

The results of the home screen ‘swim lanes’ did not negatively impact the conversion rate but the results was not something we were looking for. 15% of users tap a previous order and 61% end up placing an order. 2-3% of users tap a cuisine filter and 46% end up placing an order. What we learned is that most users do not interact with these ‘swim lanes’ on home screen rather tap feedme button to look for a list of restaurants within their area. There are many reasons why users don’t spend time on Home screen, the top main reasons are:

• People are just hungry and want to order food.
• The ‘feedme button’ and the top hero image area are too prominent, driving users to tap the feedme button instead of scrolling down to discover more restaurants.
• The results of the menu dishes recommendations is fairly positive. 2-3% of users tap a recommended item, the 68% of these 2-3% of users end up placing an order.

Next steps for Home ‘swim lanes’ are:

• To improve the home screen layout so that users would interact with the contents more.
• The scrolling interaction of the restaurant cards and vertical scrolling on Home screen can be more intuitive.
• Rearrange lanes to see the impact on how users interact with contents

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