The Baker’s Choice: Adding a pinch of magic on the app homepage

The world offers up a platter of incredible experiences to savor, and it’s been Headout’s mission since day one to serve this platter to every one of our unique and diverse guests.

The first step forward? Customize these experiences with a major overhaul to our home page and city pages. 

Using a new insight driven recipe for the home page, our guests will be presented with meaningful recommendations based on their prior bookings with us. And as they explore these recommendations, we’ll help them find their next adventure!

Recognition joy: Why customization works

The delight of interacting with something customized for you!

Don’t you love it when you walk into your favorite bakery, and your usual server hands you a bag of your favorite pastry (still warm!) that’s been set aside just for you? And maybe it’s raining, and they throw in a cup of hot chocolate for you, because you always get that at the slightest drizzle!

Pretty much the same way, recommendations are offered by most online platforms as a way to suggest relevant products to you, leveraging search or order history. These recommendations, if they hit their mark, will increase the likelihood of you coming back for more, simply because of the joy of being recognized.

The initial forays into customization, some time in the mid 90s, were rudimentary, employing basic algorithms to generate recommendations. But today, so much more can be done to make an app experience truly, uniquely yours.

What our existing homepage and city pages lacked

Be it solo, with family, with friends, maybe budget, indulgent in luxury, no two itineraries look the same. And so it is the need of the hour to give our guests the right kind of suggestions based on their unique travel styles.

All it will take is a cursory look at our existing homepage and city pages on the app to tell you that the same vanilla information architecture for all our users exists, regardless of their booking dates, experience locations or personal preferences. 

Before and after we worked our magic

Identifying touchpoints for recommendations

We brainstormed, centering our ideas around trying to identify who would be browsing for experiences on the Headout App. And we came up with two broad categories to start with, the basics:

  1. New users
  2. Existing users with bookings.

Then we went deeper to create customization levers for both of these broader buckets of users.

New Users

Crafting the best possible experience for new users

For the users who have just downloaded the app and are browsing for the first time, we gave them a bird’s eye view of what we could offer, and where our supply of experiences really shines. Since the users aren’t new, they’re obviously not converted, so the user intent is not really clear at this point. And so, we served them recommendations at a global level to drum up that dormant wanderlust to start planning their very first adventure with us!

Existing Users With Bookings

Customizing and simplifying journeys for existing users

As for users who already have trusted us with at least one booking, we simply upped the game! We gave them curated recommendations at different stages of their browsing journey and booking dates.

Based on proximity to booking date

How we differentiated between user personas based on booking dates

We decided to progressively reveal recommendations for a user who has an existing booking with us. These recommendations would be relevant to the experience previously booked by the user. 

Until one day before the booking

During this time, we cannot be sure of the user’s location unless they’ve enabled the location settings. The user could very well be in their home country while pre booking an experience in their destination country. So we decided that their recommendations should be based on the number of days left from their booked experience. We enable a ‘Recommendations’ tab that categorizes recommendations around their booked experience into different categories such as Luxury and Leisure, Adventure and Thrill, Gastronomic Delights etc. A ‘Nearby’ tab would show experiences in close proximity to the booked experience.

On the day before and day of booking

A day before or on the day of their booking, we can safely assume our guest is in their destination country. Finally, an opportunity to make the ‘Nearby’ recommendations all the more meaningful. ‘Nearby’ won’t be a tab anymore, but a carousel of the top 10 attractions near their booked experience, pushed right to the top. The other ‘Recommendations’ stay the same, but below the ‘Nearby’ carousel.

Until 7 days after booking

We decided to keep a leeway of 7 more days after the booked experience is completed, hypothesizing that the user would still be in or near the destination country of their booked experience. At this point, we remove ‘Nearby’ recommendations but their experience is still customized according to the city of their booking, if only through our own curation.

After 7 Days once booking is completed

After the guest has completed their existing booking with us and has no further bookings made, we work with the assumption that the guest has either headed on to a different destination, or their trip has ended and they’re headed back home. Here, we remove all layers of customization for them, and display the new user homepage once again, featuring experiences from a global lens for them to get inspired for their next adventure.

Based on browsing intent

As the guest scrolls through the homepage and city page, their propensity to interact with certain recommendations increases. The more the user scrolls without clicking on a particular recommendation, the less likely it is they’ve found anything worth their interest. Our goal here was to anticipate this browsing journey, and strategically place recommendations where they are most likely to be interacted with. It’s a safe bet to assume the further down a user scrolls in the homepage or city page, they’d need more help refining their search for a new experience.

Leveraging rankings and social proofing for gentle nudges

Adding social proofing was key to enable better decision making for our users

While considering booking an experience, it’s a common behavioral pattern to rely on others’ experience for the same and make a decision accordingly. This is why we decided to display ‘Top Attractions’ near the booked experience in a ranked format from 1 to 10 based on 30 day volume of orders, and highlighted the ratings for each collection to nudge the user to make an informed decision while choosing their next adventure.

Rankings and reviews helped us leverage social proofing

Why the abyss of the “endless scroll” can work sometimes

We all know the perils of doom scrolling. Endless scrolling can be quite an unpleasant experience, but you miss all the shots you don’t take. 

Sometimes, a user reaches the end of a city page, having scrolled through all the available filtering options. This means the user is yet to convert at this point, so with nothing left to lose, we open our floodgates! We show every available experience in the city, a massive list along with filter options based on Experience Categories. This also showcases our vast supply of experiences at a glance, and gives the user a chance to check out the unmatchable variety.

And we hope they find something that is handpicked just for them!

The newly added endless scroll section

Dessert for thought: Takeaway

This was just the very first step in a thousand mile journey to provide a deeply meaningful browsing experience for our guests. When our guests make even one booking, their trust is with us, even if a little, and we want to do everything in our power to make sure we provide them with only select, handpicked experiences that they could derive the most joy from.We’ve come a long way, from showing an experience to all users regardless of their preferences and history with us, to curating experiences for them based on their bookings. Bigger swings coming up ahead, as we foray into making their journey even more magical!

This blog wouldn't have been possible without the amazing team behind it. A special thank you to Ajith Shaji for his incredible illustrations that truly enhanced the value of my words. I also want to give a big shoutout to Amulya Chintaluri for her proofreading expertise, which greatly improved the clarity in my content. Finally, a big, heartfelt thanks to Ramakrishna V for his constant support and guidance.

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